diff --git a/.gitattributes b/.gitattributes index e62434402296af40554d164c785bfdba0d8408e1..cf8825a2c1870920511680891eb22272cf1cb91e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,46 +1,4 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text *.ckpt filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text *.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -*.safetensors filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tar filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text -core.o filter=lfs diff=lfs merge=lfs -text -*.bin.* filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text *.bin filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tar.gz filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.wav filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 68bc17f9ff2104a9d7b6777058bb4c343ca72609..0000000000000000000000000000000000000000 --- a/.gitignore +++ /dev/null @@ -1,160 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; 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used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ diff --git a/AR/data/bucket_sampler.py b/AR/data/bucket_sampler.py deleted file mode 100644 index 647491f7d71ec697a3fa492163c3c5e1a145025f..0000000000000000000000000000000000000000 --- a/AR/data/bucket_sampler.py +++ /dev/null @@ -1,162 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/bucketsampler.py -import itertools -import math -import random -from random import shuffle -from typing import Iterator -from typing import Optional -from typing import TypeVar - -import torch -import torch.distributed as dist -from torch.utils.data import Dataset -from torch.utils.data import Sampler - -__all__ = [ - "DistributedBucketSampler", -] - -T_co = TypeVar("T_co", covariant=True) - - -class DistributedBucketSampler(Sampler[T_co]): - r""" - sort the dataset wrt. input length - divide samples into buckets - sort within buckets - divide buckets into batches - sort batches - """ - - def __init__( - self, - dataset: Dataset, - num_replicas: Optional[int] = None, - rank: Optional[int] = None, - shuffle: bool = True, - seed: int = 0, - drop_last: bool = False, - batch_size: int = 32, - ) -> None: - if num_replicas is None: - if not dist.is_available(): - raise RuntimeError("Requires distributed package to be available") - num_replicas = dist.get_world_size() if torch.cuda.is_available() else 1 - if rank is None: - if not dist.is_available(): - raise RuntimeError("Requires distributed package to be available") - rank = dist.get_rank() if torch.cuda.is_available() else 0 - if torch.cuda.is_available(): - torch.cuda.set_device(rank) - if rank >= num_replicas or rank < 0: - raise ValueError( - "Invalid rank {}, rank should be in the interval" - " [0, {}]".format(rank, num_replicas - 1) - ) - self.dataset = dataset - self.num_replicas = num_replicas - self.rank = rank - self.epoch = 0 - self.drop_last = drop_last - # If the dataset length is evenly divisible by # of replicas, then there - # is no need to drop any data, since the dataset will be split equally. - if ( - self.drop_last and len(self.dataset) % self.num_replicas != 0 - ): # type: ignore[arg-type] - # Split to nearest available length that is evenly divisible. - # This is to ensure each rank receives the same amount of data when - # using this Sampler. - self.num_samples = math.ceil( - (len(self.dataset) - self.num_replicas) - / self.num_replicas # type: ignore[arg-type] - ) - else: - self.num_samples = math.ceil( - len(self.dataset) / self.num_replicas - ) # type: ignore[arg-type] - self.total_size = self.num_samples * self.num_replicas - self.shuffle = shuffle - self.seed = seed - self.batch_size = batch_size - self.id_with_length = self._get_sample_lengths() - self.id_buckets = self.make_buckets(bucket_width=2.0) - - def _get_sample_lengths(self): - id_with_lengths = [] - for i in range(len(self.dataset)): - id_with_lengths.append((i, self.dataset.get_sample_length(i))) - id_with_lengths.sort(key=lambda x: x[1]) - return id_with_lengths - - def make_buckets(self, bucket_width: float = 2.0): - buckets = [] - cur = [] - max_sec = bucket_width - for id, sec in self.id_with_length: - if sec < max_sec: - cur.append(id) - else: - buckets.append(cur) - cur = [id] - max_sec += bucket_width - if len(cur) > 0: - buckets.append(cur) - return buckets - - def __iter__(self) -> Iterator[T_co]: - if self.shuffle: - # deterministically shuffle based on epoch and seed - g = torch.Generator() - g.manual_seed(self.seed + self.epoch) - random.seed(self.epoch + self.seed) - shuffled_bucket = [] - for buc in self.id_buckets: - buc_copy = buc.copy() - shuffle(buc_copy) - shuffled_bucket.append(buc_copy) - grouped_batch_size = self.batch_size * self.num_replicas - shuffled_bucket = list(itertools.chain(*shuffled_bucket)) - n_batch = int(math.ceil(len(shuffled_bucket) / grouped_batch_size)) - batches = [ - shuffled_bucket[b * grouped_batch_size : (b + 1) * grouped_batch_size] - for b in range(n_batch) - ] - shuffle(batches) - indices = list(itertools.chain(*batches)) - else: - # type: ignore[arg-type] - indices = list(range(len(self.dataset))) - - if not self.drop_last: - # add extra samples to make it evenly divisible - padding_size = self.total_size - len(indices) - if padding_size <= len(indices): - indices += indices[:padding_size] - else: - indices += (indices * math.ceil(padding_size / len(indices)))[ - :padding_size - ] - else: - # remove tail of data to make it evenly divisible. - indices = indices[: self.total_size] - assert len(indices) == self.total_size - - # subsample - indices = indices[self.rank : self.total_size : self.num_replicas] - assert len(indices) == self.num_samples - - return iter(indices) - - def __len__(self) -> int: - return self.num_samples - - def set_epoch(self, epoch: int) -> None: - r""" - Sets the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas - use a different random ordering for each epoch. Otherwise, the next iteration of this - sampler will yield the same ordering. - - Args: - epoch (int): Epoch number. - """ - self.epoch = epoch diff --git a/AR/data/data_module.py b/AR/data/data_module.py deleted file mode 100644 index 037484a9b49145b04fdb3a0b9664013913df606b..0000000000000000000000000000000000000000 --- a/AR/data/data_module.py +++ /dev/null @@ -1,74 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/data_module.py -from pytorch_lightning import LightningDataModule -from AR.data.bucket_sampler import DistributedBucketSampler -from AR.data.dataset import Text2SemanticDataset -from torch.utils.data import DataLoader - - -class Text2SemanticDataModule(LightningDataModule): - def __init__( - self, - config, - train_semantic_path, - train_phoneme_path, - dev_semantic_path=None, - dev_phoneme_path=None, - ): - super().__init__() - self.config = config - self.train_semantic_path = train_semantic_path - self.train_phoneme_path = train_phoneme_path - self.dev_semantic_path = dev_semantic_path - self.dev_phoneme_path = dev_phoneme_path - self.num_workers = self.config["data"]["num_workers"] - - def prepare_data(self): - pass - - def setup(self, stage=None, output_logs=False): - self._train_dataset = Text2SemanticDataset( - phoneme_path=self.train_phoneme_path, - semantic_path=self.train_semantic_path, - max_sec=self.config["data"]["max_sec"], - pad_val=self.config["data"]["pad_val"], - ) - self._dev_dataset = self._train_dataset - # self._dev_dataset = Text2SemanticDataset( - # phoneme_path=self.dev_phoneme_path, - # semantic_path=self.dev_semantic_path, - # max_sample=self.config['data']['max_eval_sample'], - # max_sec=self.config['data']['max_sec'], - # pad_val=self.config['data']['pad_val']) - - def train_dataloader(self): - batch_size = max(min(self.config["train"]["batch_size"],len(self._train_dataset)//4),1)#防止不保存 - sampler = DistributedBucketSampler(self._train_dataset, batch_size=batch_size) - return DataLoader( - self._train_dataset, - batch_size=batch_size, - sampler=sampler, - collate_fn=self._train_dataset.collate, - num_workers=self.num_workers, - persistent_workers=True, - prefetch_factor=16, - ) - - def val_dataloader(self): - return DataLoader( - self._dev_dataset, - batch_size=1, - shuffle=False, - collate_fn=self._train_dataset.collate, - num_workers=max(self.num_workers, 12), - persistent_workers=True, - prefetch_factor=16, - ) - - # 这个会使用到嘛？ - def test_dataloader(self): - return DataLoader( - self._dev_dataset, - batch_size=1, - shuffle=False, - collate_fn=self._train_dataset.collate, - ) diff --git a/AR/data/dataset.py b/AR/data/dataset.py deleted file mode 100644 index b1ea69e600414f71c9d208b4bea5a8b3e467f8bb..0000000000000000000000000000000000000000 --- a/AR/data/dataset.py +++ /dev/null @@ -1,320 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/t2s_dataset.py -import pdb -import sys - -# sys.path.append("/data/docker/liujing04/gpt-vits/mq-vits-s1bert_no_bert") -import traceback, os -from typing import Dict -from typing import List - -import numpy as np -import pandas as pd -import torch, json -from torch.utils.data import DataLoader -from torch.utils.data import Dataset -from transformers import AutoTokenizer - -from text import cleaned_text_to_sequence - -# from config import exp_dir - - -def batch_sequences(sequences: List[np.array], axis: int = 0, pad_value: int = 0): - seq = sequences[0] - ndim = seq.ndim - if axis < 0: - axis += ndim - dtype = seq.dtype - pad_value = dtype.type(pad_value) - seq_lengths = [seq.shape[axis] for seq in sequences] - max_length = np.max(seq_lengths) - - padded_sequences = [] - for seq, length in zip(sequences, seq_lengths): - padding = ( - [(0, 0)] * axis + [(0, max_length - length)] + [(0, 0)] * (ndim - axis - 1) - ) - padded_seq = np.pad(seq, padding, mode="constant", constant_values=pad_value) - padded_sequences.append(padded_seq) - batch = np.stack(padded_sequences) - return batch - - -class Text2SemanticDataset(Dataset): - """dataset class for text tokens to semantic model training.""" - - def __init__( - self, - phoneme_path: str, - semantic_path: str, - max_sample: int = None, - max_sec: int = 100, - pad_val: int = 1024, - # min value of phoneme/sec - min_ps_ratio: int = 3, - # max value of phoneme/sec - max_ps_ratio: int = 25, - ) -> None: - super().__init__() - - self.semantic_data = pd.read_csv( - semantic_path, delimiter="\t", encoding="utf-8" - ) - # get dict - self.path2 = phoneme_path # "%s/2-name2text.txt"%exp_dir#phoneme_path - self.path3 = "%s/3-bert" % ( - os.path.basename(phoneme_path) - ) # "%s/3-bert"%exp_dir#bert_dir - self.path6 = semantic_path # "%s/6-name2semantic.tsv"%exp_dir#semantic_path - assert os.path.exists(self.path2) - assert os.path.exists(self.path6) - self.phoneme_data = {} - with open(self.path2, "r", encoding="utf8") as f: - lines = f.read().strip("\n").split("\n") - - for line in lines: - tmp = line.split("\t") - if len(tmp) != 4: - continue - self.phoneme_data[tmp[0]] = [tmp[1], tmp[2], tmp[3]] - - # self.phoneme_data = np.load(phoneme_path, allow_pickle=True).item() - # pad for semantic tokens - self.PAD: int = pad_val - # self.hz = 25 - # with open("/data/docker/liujing04/gpt-vits/mq-vits-s1bert_no_bert/configs/s2.json", "r") as f:data = f.read() - # data=json.loads(data)["model"]["semantic_frame_rate"]#50hz - # self.hz=int(data[:-2])# - self.hz = int(os.environ.get("hz", "25hz")[:-2]) - - # max seconds of semantic token - self.max_sec = max_sec - self.min_ps_ratio = min_ps_ratio - self.max_ps_ratio = max_ps_ratio - - if max_sample is not None: - self.semantic_data = self.semantic_data[:max_sample] - - # {idx: (semantic, phoneme)} - # semantic list, phoneme list - self.semantic_phoneme = [] - self.item_names = [] - - self.inited = False - - if not self.inited: - # 调用初始化函数 - self.init_batch() - self.inited = True - del self.semantic_data - del self.phoneme_data - # self.tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext-large") - # self.tokenizer = AutoTokenizer.from_pretrained("/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large") - - def init_batch(self): - semantic_data_len = len(self.semantic_data) - phoneme_data_len = len(self.phoneme_data.keys()) - print("semantic_data_len:", semantic_data_len) - print("phoneme_data_len:", phoneme_data_len) - print(self.semantic_data) - idx = 0 - num_not_in = 0 - num_deleted_bigger = 0 - num_deleted_ps = 0 - for i in range(semantic_data_len): - # 先依次遍历 - # get str - item_name = self.semantic_data.iloc[i,0] - # print(self.phoneme_data) - try: - phoneme, word2ph, text = self.phoneme_data[item_name] - except Exception: - traceback.print_exc() - # print(f"{item_name} not in self.phoneme_data !") - num_not_in += 1 - continue - - semantic_str = self.semantic_data.iloc[i,1] - # get token list - semantic_ids = [int(idx) for idx in semantic_str.split(" ")] - # (T), 是否需要变成 (1, T) -> 不需要，因为需要求 len - # 过滤掉太长的样本 - if ( - len(semantic_ids) > self.max_sec * self.hz - ): #########1###根据token个数推测总时长过滤时长60s（config里）#40*25=1k - num_deleted_bigger += 1 - continue - # (T, ), 这个速度不会很慢，所以可以在一开始就处理，无需在 __getitem__ 里面单个处理#### - phoneme = phoneme.split(" ") - - try: - phoneme_ids = cleaned_text_to_sequence(phoneme) - except: - traceback.print_exc() - # print(f"{item_name} not in self.phoneme_data !") - num_not_in += 1 - continue - # if len(phoneme_ids) >400:###########2：改为恒定限制为semantic/2.5就行 - if ( - len(phoneme_ids) > self.max_sec * self.hz / 2.5 - ): ###########2：改为恒定限制为semantic/2.5就行 - num_deleted_ps += 1 - continue - # if len(semantic_ids) > 1000:###########3 - # num_deleted_bigger += 1 - # continue - - ps_ratio = len(phoneme_ids) / (len(semantic_ids) / self.hz) - - if ( - ps_ratio > self.max_ps_ratio or ps_ratio < self.min_ps_ratio - ): ##########4#3~25#每秒多少个phone - num_deleted_ps += 1 - # print(item_name) - continue - - self.semantic_phoneme.append((semantic_ids, phoneme_ids)) - idx += 1 - self.item_names.append(item_name) - - min_num = 100 # 20直接不补#30补了也不存ckpt - leng = len(self.semantic_phoneme) - if leng < min_num: - tmp1 = self.semantic_phoneme - tmp2 = self.item_names - self.semantic_phoneme = [] - self.item_names = [] - for _ in range(max(2, int(min_num / leng))): - self.semantic_phoneme += tmp1 - self.item_names += tmp2 - if num_not_in > 0: - print(f"there are {num_not_in} semantic datas not in phoneme datas") - if num_deleted_bigger > 0: - print( - f"deleted {num_deleted_bigger} audios who's duration are bigger than {self.max_sec} seconds" - ) - if num_deleted_ps > 0: - # 4702 for LibriTTS, LirbriTTS 是标注数据, 是否需要筛？=> 需要，有值为 100 的极端值 - print( - f"deleted {num_deleted_ps} audios who's phoneme/sec are bigger than {self.max_ps_ratio} or smaller than {self.min_ps_ratio}" - ) - """ - there are 31 semantic datas not in phoneme datas - deleted 34 audios who's duration are bigger than 54 seconds - deleted 3190 audios who's phoneme/sec are bigger than 25 or smaller than 3 - dataset.__len__(): 366463 - - """ - # 345410 for LibriTTS - print("dataset.__len__():", self.__len__()) - - def __get_item_names__(self) -> List[str]: - return self.item_names - - def __len__(self) -> int: - return len(self.semantic_phoneme) - - def __getitem__(self, idx: int) -> Dict: - semantic_ids, phoneme_ids = self.semantic_phoneme[idx] - item_name = self.item_names[idx] - phoneme_ids_len = len(phoneme_ids) - # semantic tokens target - semantic_ids_len = len(semantic_ids) - - flag = 0 - path_bert = "%s/%s.pt" % (self.path3, item_name) - if os.path.exists(path_bert) == True: - bert_feature = torch.load(path_bert, map_location="cpu") - else: - flag = 1 - if flag == 1: - # bert_feature=torch.zeros_like(phoneme_ids,dtype=torch.float32) - bert_feature = None - else: - assert bert_feature.shape[-1] == len(phoneme_ids) - return { - "idx": idx, - "phoneme_ids": phoneme_ids, - "phoneme_ids_len": phoneme_ids_len, - "semantic_ids": semantic_ids, - "semantic_ids_len": semantic_ids_len, - "bert_feature": bert_feature, - } - - def get_sample_length(self, idx: int): - semantic_ids = self.semantic_phoneme[idx][0] - sec = 1.0 * len(semantic_ids) / self.hz - return sec - - def collate(self, examples: List[Dict]) -> Dict: - sample_index: List[int] = [] - phoneme_ids: List[torch.Tensor] = [] - phoneme_ids_lens: List[int] = [] - semantic_ids: List[torch.Tensor] = [] - semantic_ids_lens: List[int] = [] - # return - - for item in examples: - sample_index.append(item["idx"]) - phoneme_ids.append(np.array(item["phoneme_ids"], dtype=np.int64)) - semantic_ids.append(np.array(item["semantic_ids"], dtype=np.int64)) - phoneme_ids_lens.append(item["phoneme_ids_len"]) - semantic_ids_lens.append(item["semantic_ids_len"]) - - # pad 0 - phoneme_ids = batch_sequences(phoneme_ids) - semantic_ids = batch_sequences(semantic_ids, pad_value=self.PAD) - - # # convert each batch to torch.tensor - phoneme_ids = torch.tensor(phoneme_ids) - semantic_ids = torch.tensor(semantic_ids) - phoneme_ids_lens = torch.tensor(phoneme_ids_lens) - semantic_ids_lens = torch.tensor(semantic_ids_lens) - bert_padded = torch.FloatTensor(len(examples), 1024, max(phoneme_ids_lens)) - bert_padded.zero_() - - for idx, item in enumerate(examples): - bert = item["bert_feature"] - if bert != None: - bert_padded[idx, :, : bert.shape[-1]] = bert - - return { - # List[int] - "ids": sample_index, - # torch.Tensor (B, max_phoneme_length) - "phoneme_ids": phoneme_ids, - # torch.Tensor (B) - "phoneme_ids_len": phoneme_ids_lens, - # torch.Tensor (B, max_semantic_ids_length) - "semantic_ids": semantic_ids, - # torch.Tensor (B) - "semantic_ids_len": semantic_ids_lens, - # torch.Tensor (B, 1024, max_phoneme_length) - "bert_feature": bert_padded, - } - - -if __name__ == "__main__": - root_dir = "/data/docker/liujing04/gpt-vits/prepare/dump_mix/" - dataset = Text2SemanticDataset( - phoneme_path=root_dir + "phoneme_train.npy", - semantic_path=root_dir + "semantic_train.tsv", - ) - - batch_size = 12 - dataloader = DataLoader( - dataset, batch_size=batch_size, collate_fn=dataset.collate, shuffle=False - ) - for i, batch in enumerate(dataloader): - if i % 1000 == 0: - print(i) - # if i == 0: - # print('batch["ids"]:', batch["ids"]) - # print('batch["phoneme_ids"]:', batch["phoneme_ids"], - # batch["phoneme_ids"].shape) - # print('batch["phoneme_ids_len"]:', batch["phoneme_ids_len"], - # batch["phoneme_ids_len"].shape) - # print('batch["semantic_ids"]:', batch["semantic_ids"], - # batch["semantic_ids"].shape) - # print('batch["semantic_ids_len"]:', batch["semantic_ids_len"], - # batch["semantic_ids_len"].shape) diff --git a/AR/models/__init__.py b/AR/models/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/AR/models/t2s_lightning_module.py b/AR/models/t2s_lightning_module.py deleted file mode 100644 index f9dfc648e06e05ba04a45069525e13381e0bd1f1..0000000000000000000000000000000000000000 --- a/AR/models/t2s_lightning_module.py +++ /dev/null @@ -1,140 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_lightning_module.py -import os, sys - -now_dir = os.getcwd() -sys.path.append(now_dir) -from typing import Dict - -import torch -from pytorch_lightning import LightningModule -from AR.models.t2s_model import Text2SemanticDecoder -from AR.modules.lr_schedulers import WarmupCosineLRSchedule -from AR.modules.optim import ScaledAdam - - -class Text2SemanticLightningModule(LightningModule): - def __init__(self, config, output_dir, is_train=True): - super().__init__() - self.config = config - self.top_k = 3 - self.model = Text2SemanticDecoder(config=config, top_k=self.top_k) - pretrained_s1 = config.get("pretrained_s1") - if pretrained_s1 and is_train: - # print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"])) - print( - self.load_state_dict( - torch.load(pretrained_s1, map_location="cpu")["weight"] - ) - ) - if is_train: - self.automatic_optimization = False - self.save_hyperparameters() - self.eval_dir = output_dir / "eval" - self.eval_dir.mkdir(parents=True, exist_ok=True) - - def training_step(self, batch: Dict, batch_idx: int): - opt = self.optimizers() - scheduler = self.lr_schedulers() - loss, acc = self.model.forward( - batch["phoneme_ids"], - batch["phoneme_ids_len"], - batch["semantic_ids"], - batch["semantic_ids_len"], - batch["bert_feature"], - ) - self.manual_backward(loss) - if batch_idx > 0 and batch_idx % 4 == 0: - opt.step() - opt.zero_grad() - scheduler.step() - - self.log( - "total_loss", - loss, - on_step=True, - on_epoch=True, - prog_bar=True, - sync_dist=True, - ) - self.log( - "lr", - scheduler.get_last_lr()[0], - on_epoch=True, - prog_bar=True, - sync_dist=True, - ) - self.log( - f"top_{self.top_k}_acc", - acc, - on_step=True, - on_epoch=True, - prog_bar=True, - sync_dist=True, - ) - - def validation_step(self, batch: Dict, batch_idx: int): - return - - # # get loss - # loss, acc = self.model.forward( - # batch['phoneme_ids'], batch['phoneme_ids_len'], - # batch['semantic_ids'], batch['semantic_ids_len'], - # batch['bert_feature'] - # ) - # - # self.log( - # "val_total_loss", - # loss, - # on_step=True, - # on_epoch=True, - # prog_bar=True, - # sync_dist=True) - # self.log( - # f"val_top_{self.top_k}_acc", - # acc, - # on_step=True, - # on_epoch=True, - # prog_bar=True, - # sync_dist=True) - # - # # get infer output - # semantic_len = batch['semantic_ids'].size(1) - # prompt_len = min(int(semantic_len * 0.5), 150) - # prompt = batch['semantic_ids'][:, :prompt_len] - # pred_semantic = self.model.infer(batch['phoneme_ids'], - # batch['phoneme_ids_len'], prompt, - # batch['bert_feature'] - # ) - # save_name = f'semantic_toks_{batch_idx}.pt' - # save_path = os.path.join(self.eval_dir, save_name) - # torch.save(pred_semantic.detach().cpu(), save_path) - - def configure_optimizers(self): - model_parameters = self.model.parameters() - parameters_names = [] - parameters_names.append( - [name_param_pair[0] for name_param_pair in self.model.named_parameters()] - ) - lm_opt = ScaledAdam( - model_parameters, - lr=0.01, - betas=(0.9, 0.95), - clipping_scale=2.0, - parameters_names=parameters_names, - show_dominant_parameters=False, - clipping_update_period=1000, - ) - - return { - "optimizer": lm_opt, - "lr_scheduler": { - "scheduler": WarmupCosineLRSchedule( - lm_opt, - init_lr=self.config["optimizer"]["lr_init"], - peak_lr=self.config["optimizer"]["lr"], - end_lr=self.config["optimizer"]["lr_end"], - warmup_steps=self.config["optimizer"]["warmup_steps"], - total_steps=self.config["optimizer"]["decay_steps"], - ) - }, - } diff --git a/AR/models/t2s_lightning_module_onnx.py b/AR/models/t2s_lightning_module_onnx.py deleted file mode 100644 index bb9e30b9f914d86256bf98a28c8f2e3b37012762..0000000000000000000000000000000000000000 --- a/AR/models/t2s_lightning_module_onnx.py +++ /dev/null @@ -1,106 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_lightning_module.py -import os, sys - -now_dir = os.getcwd() -sys.path.append(now_dir) -from typing import Dict - -import torch -from pytorch_lightning import LightningModule -from AR.models.t2s_model_onnx import Text2SemanticDecoder -from AR.modules.lr_schedulers import WarmupCosineLRSchedule -from AR.modules.optim import ScaledAdam - - -class Text2SemanticLightningModule(LightningModule): - def __init__(self, config, output_dir, is_train=True): - super().__init__() - self.config = config - self.top_k = 3 - self.model = Text2SemanticDecoder(config=config, top_k=self.top_k) - pretrained_s1 = config.get("pretrained_s1") - if pretrained_s1 and is_train: - # print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"])) - print( - self.load_state_dict( - torch.load(pretrained_s1, map_location="cpu")["weight"] - ) - ) - if is_train: - self.automatic_optimization = False - self.save_hyperparameters() - self.eval_dir = output_dir / "eval" - self.eval_dir.mkdir(parents=True, exist_ok=True) - - def training_step(self, batch: Dict, batch_idx: int): - opt = self.optimizers() - scheduler = self.lr_schedulers() - loss, acc = self.model.forward( - batch["phoneme_ids"], - batch["phoneme_ids_len"], - batch["semantic_ids"], - batch["semantic_ids_len"], - batch["bert_feature"], - ) - self.manual_backward(loss) - if batch_idx > 0 and batch_idx % 4 == 0: - opt.step() - opt.zero_grad() - scheduler.step() - - self.log( - "total_loss", - loss, - on_step=True, - on_epoch=True, - prog_bar=True, - sync_dist=True, - ) - self.log( - "lr", - scheduler.get_last_lr()[0], - on_epoch=True, - prog_bar=True, - sync_dist=True, - ) - self.log( - f"top_{self.top_k}_acc", - acc, - on_step=True, - on_epoch=True, - prog_bar=True, - sync_dist=True, - ) - - def validation_step(self, batch: Dict, batch_idx: int): - return - - def configure_optimizers(self): - model_parameters = self.model.parameters() - parameters_names = [] - parameters_names.append( - [name_param_pair[0] for name_param_pair in self.model.named_parameters()] - ) - lm_opt = ScaledAdam( - model_parameters, - lr=0.01, - betas=(0.9, 0.95), - clipping_scale=2.0, - parameters_names=parameters_names, - show_dominant_parameters=False, - clipping_update_period=1000, - ) - - return { - "optimizer": lm_opt, - "lr_scheduler": { - "scheduler": WarmupCosineLRSchedule( - lm_opt, - init_lr=self.config["optimizer"]["lr_init"], - peak_lr=self.config["optimizer"]["lr"], - end_lr=self.config["optimizer"]["lr_end"], - warmup_steps=self.config["optimizer"]["warmup_steps"], - total_steps=self.config["optimizer"]["decay_steps"], - ) - }, - } diff --git a/AR/models/t2s_model.py b/AR/models/t2s_model.py deleted file mode 100644 index 083dc0998e227d08beeb347b552964b3f2969671..0000000000000000000000000000000000000000 --- a/AR/models/t2s_model.py +++ /dev/null @@ -1,327 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py -import torch -from tqdm import tqdm - -from AR.models.utils import make_pad_mask -from AR.models.utils import ( - topk_sampling, - sample, - logits_to_probs, - multinomial_sample_one_no_sync, -) -from AR.modules.embedding import SinePositionalEmbedding -from AR.modules.embedding import TokenEmbedding -from AR.modules.transformer import LayerNorm -from AR.modules.transformer import TransformerEncoder -from AR.modules.transformer import TransformerEncoderLayer -from torch import nn -from torch.nn import functional as F -from torchmetrics.classification import MulticlassAccuracy - -default_config = { - "embedding_dim": 512, - "hidden_dim": 512, - "num_head": 8, - "num_layers": 12, - "num_codebook": 8, - "p_dropout": 0.0, - "vocab_size": 1024 + 1, - "phoneme_vocab_size": 512, - "EOS": 1024, -} - - -class Text2SemanticDecoder(nn.Module): - def __init__(self, config, norm_first=False, top_k=3): - super(Text2SemanticDecoder, self).__init__() - self.model_dim = config["model"]["hidden_dim"] - self.embedding_dim = config["model"]["embedding_dim"] - self.num_head = config["model"]["head"] - self.num_layers = config["model"]["n_layer"] - self.norm_first = norm_first - self.vocab_size = config["model"]["vocab_size"] - self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"] - self.p_dropout = config["model"]["dropout"] - self.EOS = config["model"]["EOS"] - self.norm_first = norm_first - assert self.EOS == self.vocab_size - 1 - # should be same as num of kmeans bin - # assert self.EOS == 1024 - self.bert_proj = nn.Linear(1024, self.embedding_dim) - self.ar_text_embedding = TokenEmbedding( - self.embedding_dim, self.phoneme_vocab_size, self.p_dropout - ) - self.ar_text_position = SinePositionalEmbedding( - self.embedding_dim, dropout=0.1, scale=False, alpha=True - ) - self.ar_audio_embedding = TokenEmbedding( - self.embedding_dim, self.vocab_size, self.p_dropout - ) - self.ar_audio_position = SinePositionalEmbedding( - self.embedding_dim, dropout=0.1, scale=False, alpha=True - ) - - self.h = TransformerEncoder( - TransformerEncoderLayer( - d_model=self.model_dim, - nhead=self.num_head, - dim_feedforward=self.model_dim * 4, - dropout=0.1, - batch_first=True, - norm_first=norm_first, - ), - num_layers=self.num_layers, - norm=LayerNorm(self.model_dim) if norm_first else None, - ) - - self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False) - self.loss_fct = nn.CrossEntropyLoss(reduction="sum") - - self.ar_accuracy_metric = MulticlassAccuracy( - self.vocab_size, - top_k=top_k, - average="micro", - multidim_average="global", - ignore_index=self.EOS, - ) - - def forward(self, x, x_lens, y, y_lens, bert_feature): - """ - x: phoneme_ids - y: semantic_ids - """ - x = self.ar_text_embedding(x) - x = x + self.bert_proj(bert_feature.transpose(1, 2)) - x = self.ar_text_position(x) - x_mask = make_pad_mask(x_lens) - - y_mask = make_pad_mask(y_lens) - y_mask_int = y_mask.type(torch.int64) - codes = y.type(torch.int64) * (1 - y_mask_int) - - # Training - # AR Decoder - y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS) - x_len = x_lens.max() - y_len = y_lens.max() - y_emb = self.ar_audio_embedding(y) - y_pos = self.ar_audio_position(y_emb) - - xy_padding_mask = torch.concat([x_mask, y_mask], dim=1) - ar_xy_padding_mask = xy_padding_mask - - x_attn_mask = F.pad( - torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device), - (0, y_len), - value=True, - ) - y_attn_mask = F.pad( - torch.triu( - torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), - diagonal=1, - ), - (x_len, 0), - value=False, - ) - xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0) - bsz, src_len = x.shape[0], x_len + y_len - _xy_padding_mask = ( - ar_xy_padding_mask.view(bsz, 1, 1, src_len) - .expand(-1, self.num_head, -1, -1) - .reshape(bsz * self.num_head, 1, src_len) - ) - xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask) - new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype) - new_attn_mask.masked_fill_(xy_attn_mask, float("-inf")) - xy_attn_mask = new_attn_mask - # x 和完整的 y 一次性输入模型 - xy_pos = torch.concat([x, y_pos], dim=1) - xy_dec, _ = self.h( - (xy_pos, None), - mask=xy_attn_mask, - ) - logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1) - # loss - # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum - loss = F.cross_entropy(logits, targets, reduction="sum") - acc = self.ar_accuracy_metric(logits.detach(), targets).item() - return loss, acc - - # 需要看下这个函数和 forward 的区别以及没有 semantic 的时候 prompts 输入什么 - def infer( - self, - x, - x_lens, - prompts, - bert_feature, - top_k: int = -100, - early_stop_num: int = -1, - temperature: float = 1.0, - ): - x = self.ar_text_embedding(x) - x = x + self.bert_proj(bert_feature.transpose(1, 2)) - x = self.ar_text_position(x) - - # AR Decoder - y = prompts - prefix_len = y.shape[1] - x_len = x.shape[1] - x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool) - stop = False - for _ in tqdm(range(1500)): - y_emb = self.ar_audio_embedding(y) - y_pos = self.ar_audio_position(y_emb) - # x 和逐渐增长的 y 一起输入给模型 - xy_pos = torch.concat([x, y_pos], dim=1) - y_len = y.shape[1] - x_attn_mask_pad = F.pad( - x_attn_mask, - (0, y_len), - value=True, - ) - y_attn_mask = F.pad( - torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1), - (x_len, 0), - value=False, - ) - xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to( - y.device - ) - - xy_dec, _ = self.h( - (xy_pos, None), - mask=xy_attn_mask, - ) - logits = self.ar_predict_layer(xy_dec[:, -1]) - samples = topk_sampling( - logits, top_k=top_k, top_p=1.0, temperature=temperature - ) - - if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num: - print("use early stop num:", early_stop_num) - stop = True - - if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS: - # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS) - stop = True - if stop: - if prompts.shape[1] == y.shape[1]: - y = torch.concat([y, torch.zeros_like(samples)], dim=1) - print("bad zero prediction") - print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]") - break - # 本次生成的 semantic_ids 和之前的 y 构成新的 y - # print(samples.shape)#[1,1]#第一个1是bs - # import os - # os._exit(2333) - y = torch.concat([y, samples], dim=1) - return y - - def pad_y_eos(self, y, y_mask_int, eos_id): - targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad( - y_mask_int, (0, 1), value=1 - ) - # 错位 - return targets[:, :-1], targets[:, 1:] - - def infer_panel( - self, - x, #####全部文本token - x_lens, - prompts, ####参考音频token - bert_feature, - top_k: int = -100, - early_stop_num: int = -1, - temperature: float = 1.0, - ): - x = self.ar_text_embedding(x) - x = x + self.bert_proj(bert_feature.transpose(1, 2)) - x = self.ar_text_position(x) - - # AR Decoder - y = prompts - prefix_len = y.shape[1] - x_len = x.shape[1] - x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool) - stop = False - # print(1111111,self.num_layers) - cache = { - "all_stage": self.num_layers, - "k": [None] * self.num_layers, ###根据配置自己手写 - "v": [None] * self.num_layers, - # "xy_pos":None,##y_pos位置编码每次都不一样的没法缓存，每次都要重新拼xy_pos.主要还是写法原因，其实是可以历史统一一样的，但也没啥计算量就不管了 - "y_emb": None, ##只需要对最新的samples求emb，再拼历史的就行 - # "logits":None,###原版就已经只对结尾求再拼接了，不用管 - # "xy_dec":None,###不需要，本来只需要最后一个做logits - "first_infer": 1, - "stage": 0, - } - for idx in tqdm(range(1500)): - if cache["first_infer"] == 1: - y_emb = self.ar_audio_embedding(y) - else: - y_emb = torch.cat( - [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1 - ) - cache["y_emb"] = y_emb - y_pos = self.ar_audio_position(y_emb) - # x 和逐渐增长的 y 一起输入给模型 - if cache["first_infer"] == 1: - xy_pos = torch.concat([x, y_pos], dim=1) - else: - xy_pos = y_pos[:, -1:] - y_len = y_pos.shape[1] - ###以下3个不做缓存 - if cache["first_infer"] == 1: - x_attn_mask_pad = F.pad( - x_attn_mask, - (0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y) - value=True, - ) - y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y) - torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1), - (x_len, 0), - value=False, - ) - xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to( - y.device - ) - else: - ###最右边一列（是错的） - # xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device) - # xy_attn_mask[:,-1]=False - ###最下面一行（是对的） - xy_attn_mask = torch.zeros( - (1, x_len + y_len), dtype=torch.bool, device=xy_pos.device - ) - # pdb.set_trace() - ###缓存重头戏 - # print(1111,xy_pos.shape,xy_attn_mask.shape,x_len,y_len) - xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache) - logits = self.ar_predict_layer( - xy_dec[:, -1] - ) ##不用改，如果用了cache的默认就是只有一帧，取最后一帧一样的 - # samples = topk_sampling(logits, top_k=top_k, top_p=1.0, temperature=temperature) - if(idx==0):###第一次跑不能EOS否则没有了 - logits = logits[:, :-1] ###刨除1024终止符号的概率 - samples = sample( - logits[0], y, top_k=top_k, top_p=1.0, repetition_penalty=1.35 - )[0].unsqueeze(0) - if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num: - print("use early stop num:", early_stop_num) - stop = True - - if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS: - # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS) - stop = True - if stop: - if prompts.shape[1] == y.shape[1]: - y = torch.concat([y, torch.zeros_like(samples)], dim=1) - print("bad zero prediction") - print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]") - break - # 本次生成的 semantic_ids 和之前的 y 构成新的 y - # print(samples.shape)#[1,1]#第一个1是bs - y = torch.concat([y, samples], dim=1) - cache["first_infer"] = 0 - return y, idx diff --git a/AR/models/t2s_model_onnx.py b/AR/models/t2s_model_onnx.py deleted file mode 100644 index 92f2d745af3a9a2eba012d59e45ae6d2af19c5d8..0000000000000000000000000000000000000000 --- a/AR/models/t2s_model_onnx.py +++ /dev/null @@ -1,337 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py -import torch -from tqdm import tqdm - -from AR.modules.embedding_onnx import SinePositionalEmbedding -from AR.modules.embedding_onnx import TokenEmbedding -from AR.modules.transformer_onnx import LayerNorm -from AR.modules.transformer_onnx import TransformerEncoder -from AR.modules.transformer_onnx import TransformerEncoderLayer -from torch import nn -from torch.nn import functional as F -from torchmetrics.classification import MulticlassAccuracy - -default_config = { - "embedding_dim": 512, - "hidden_dim": 512, - "num_head": 8, - "num_layers": 12, - "num_codebook": 8, - "p_dropout": 0.0, - "vocab_size": 1024 + 1, - "phoneme_vocab_size": 512, - "EOS": 1024, -} - -inf_tensor_value = torch.FloatTensor([-float("Inf")]).float() - -def logits_to_probs( - logits, - previous_tokens = None, - temperature: float = 1.0, - top_k = None, - top_p = None, - repetition_penalty: float = 1.0, -): - previous_tokens = previous_tokens.squeeze() - if previous_tokens is not None and repetition_penalty != 1.0: - previous_tokens = previous_tokens.long() - score = torch.gather(logits, dim=0, index=previous_tokens) - score = torch.where( - score < 0, score * repetition_penalty, score / repetition_penalty - ) - logits.scatter_(dim=0, index=previous_tokens, src=score) - - if top_p is not None and top_p < 1.0: - sorted_logits, sorted_indices = torch.sort(logits, descending=True) - cum_probs = torch.cumsum( - torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1 - ) - sorted_indices_to_remove = cum_probs > top_p - sorted_indices_to_remove[0] = False # keep at least one option - indices_to_remove = sorted_indices_to_remove.scatter( - dim=0, index=sorted_indices, src=sorted_indices_to_remove - ) - logits = logits.masked_fill(indices_to_remove, -float("Inf")) - - logits = logits / max(temperature, 1e-5) - - if top_k is not None: - v, _ = torch.topk(logits, top_k) - pivot = v.select(-1, -1).unsqueeze(-1) - logits = torch.where(logits < pivot, inf_tensor_value, logits) - - probs = torch.nn.functional.softmax(logits, dim=-1) - return probs - - -def multinomial_sample_one_no_sync( - probs_sort -): # Does multinomial sampling without a cuda synchronization - q = torch.randn_like(probs_sort) - return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int) - - -def sample( - logits, - previous_tokens, - **sampling_kwargs, -): - probs = logits_to_probs( - logits=logits, previous_tokens=previous_tokens, **sampling_kwargs - ) - idx_next = multinomial_sample_one_no_sync(probs) - return idx_next, probs - - -class OnnxEncoder(nn.Module): - def __init__(self, ar_text_embedding, bert_proj, ar_text_position): - super().__init__() - self.ar_text_embedding = ar_text_embedding - self.bert_proj = bert_proj - self.ar_text_position = ar_text_position - - def forward(self, x, bert_feature): - x = self.ar_text_embedding(x) - x = x + self.bert_proj(bert_feature.transpose(1, 2)) - return self.ar_text_position(x) - - -class T2SFirstStageDecoder(nn.Module): - def __init__(self, ar_audio_embedding, ar_audio_position, h, ar_predict_layer, loss_fct, ar_accuracy_metric, - top_k, early_stop_num, num_layers): - super().__init__() - self.ar_audio_embedding = ar_audio_embedding - self.ar_audio_position = ar_audio_position - self.h = h - self.ar_predict_layer = ar_predict_layer - self.loss_fct = loss_fct - self.ar_accuracy_metric = ar_accuracy_metric - self.top_k = top_k - self.early_stop_num = early_stop_num - self.num_layers = num_layers - - def forward(self, x, prompt): - y = prompt - x_example = x[:,:,0] * 0.0 - #N, 1, 512 - cache = { - "all_stage": self.num_layers, - "k": None, - "v": None, - "y_emb": None, - "first_infer": 1, - "stage": 0, - } - - y_emb = self.ar_audio_embedding(y) - - cache["y_emb"] = y_emb - y_pos = self.ar_audio_position(y_emb) - - xy_pos = torch.concat([x, y_pos], dim=1) - - y_example = y_pos[:,:,0] * 0.0 - x_attn_mask = torch.matmul(x_example.transpose(0, 1) , x_example).bool() - y_attn_mask = torch.ones_like(torch.matmul(y_example.transpose(0, 1), y_example), dtype=torch.int64) - y_attn_mask = torch.cumsum(y_attn_mask, dim=1) - torch.cumsum( - torch.ones_like(y_example.transpose(0, 1), dtype=torch.int64), dim=0 - ) - y_attn_mask = y_attn_mask > 0 - - x_y_pad = torch.matmul(x_example.transpose(0, 1), y_example).bool() - y_x_pad = torch.matmul(y_example.transpose(0, 1), x_example).bool() - x_attn_mask_pad = torch.cat([x_attn_mask, torch.ones_like(x_y_pad)], dim=1) - y_attn_mask = torch.cat([y_x_pad, y_attn_mask], dim=1) - xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0) - cache["k"] = torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))\ - .unsqueeze(1).repeat(self.num_layers, 1, 1, 1) - cache["v"] = torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))\ - .unsqueeze(1).repeat(self.num_layers, 1, 1, 1) - - xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache) - logits = self.ar_predict_layer(xy_dec[:, -1]) - samples = sample(logits[0], y, top_k=self.top_k, top_p=1.0, repetition_penalty=1.35)[0].unsqueeze(0) - - y = torch.concat([y, samples], dim=1) - - return y, cache["k"], cache["v"], cache["y_emb"], x_example - - -class T2SStageDecoder(nn.Module): - def __init__(self, ar_audio_embedding, ar_audio_position, h, ar_predict_layer, loss_fct, ar_accuracy_metric, - top_k, early_stop_num, num_layers): - super().__init__() - self.ar_audio_embedding = ar_audio_embedding - self.ar_audio_position = ar_audio_position - self.h = h - self.ar_predict_layer = ar_predict_layer - self.loss_fct = loss_fct - self.ar_accuracy_metric = ar_accuracy_metric - self.top_k = top_k - self.early_stop_num = early_stop_num - self.num_layers = num_layers - - def forward(self, y, k, v, y_emb, x_example): - cache = { - "all_stage": self.num_layers, - "k": torch.nn.functional.pad(k, (0, 0, 0, 0, 0, 1)), - "v": torch.nn.functional.pad(v, (0, 0, 0, 0, 0, 1)), - "y_emb": y_emb, - "first_infer": 0, - "stage": 0, - } - - y_emb = torch.cat( - [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1 - ) - cache["y_emb"] = y_emb - y_pos = self.ar_audio_position(y_emb) - - xy_pos = y_pos[:, -1:] - - y_example = y_pos[:,:,0] * 0.0 - - xy_attn_mask = torch.cat([x_example, y_example], dim=1) - xy_attn_mask = torch.zeros_like(xy_attn_mask, dtype=torch.bool) - - xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache) - logits = self.ar_predict_layer(xy_dec[:, -1]) - samples = sample(logits[0], y, top_k=self.top_k, top_p=1.0, repetition_penalty=1.35)[0].unsqueeze(0) - - y = torch.concat([y, samples], dim=1) - - return y, cache["k"], cache["v"], cache["y_emb"], logits, samples - - -class Text2SemanticDecoder(nn.Module): - def __init__(self, config, norm_first=False, top_k=3): - super(Text2SemanticDecoder, self).__init__() - self.model_dim = config["model"]["hidden_dim"] - self.embedding_dim = config["model"]["embedding_dim"] - self.num_head = config["model"]["head"] - self.num_layers = config["model"]["n_layer"] - self.norm_first = norm_first - self.vocab_size = config["model"]["vocab_size"] - self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"] - self.p_dropout = float(config["model"]["dropout"]) - self.EOS = config["model"]["EOS"] - self.norm_first = norm_first - assert self.EOS == self.vocab_size - 1 - self.bert_proj = nn.Linear(1024, self.embedding_dim) - self.ar_text_embedding = TokenEmbedding(self.embedding_dim, self.phoneme_vocab_size, self.p_dropout) - self.ar_text_position = SinePositionalEmbedding(self.embedding_dim, dropout=0.1, scale=False, alpha=True) - self.ar_audio_embedding = TokenEmbedding(self.embedding_dim, self.vocab_size, self.p_dropout) - self.ar_audio_position = SinePositionalEmbedding(self.embedding_dim, dropout=0.1, scale=False, alpha=True) - self.h = TransformerEncoder( - TransformerEncoderLayer( - d_model=self.model_dim, - nhead=self.num_head, - dim_feedforward=self.model_dim * 4, - dropout=0.1, - batch_first=True, - norm_first=norm_first, - ), - num_layers=self.num_layers, - norm=LayerNorm(self.model_dim) if norm_first else None, - ) - self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False) - self.loss_fct = nn.CrossEntropyLoss(reduction="sum") - self.ar_accuracy_metric = MulticlassAccuracy( - self.vocab_size, - top_k=top_k, - average="micro", - multidim_average="global", - ignore_index=self.EOS, - ) - self.top_k = torch.LongTensor([1]) - self.early_stop_num = torch.LongTensor([-1]) - - def init_onnx(self): - self.onnx_encoder = OnnxEncoder(self.ar_text_embedding, self.bert_proj, self.ar_text_position) - self.first_stage_decoder = T2SFirstStageDecoder(self.ar_audio_embedding, self.ar_audio_position, self.h, - self.ar_predict_layer, self.loss_fct, self.ar_accuracy_metric, self.top_k, self.early_stop_num, - self.num_layers) - self.stage_decoder = T2SStageDecoder(self.ar_audio_embedding, self.ar_audio_position, self.h, - self.ar_predict_layer, self.loss_fct, self.ar_accuracy_metric, self.top_k, self.early_stop_num, - self.num_layers) - - def forward(self, x, prompts, bert_feature): - early_stop_num = self.early_stop_num - prefix_len = prompts.shape[1] - - x = self.onnx_encoder(x, bert_feature) - y, k, v, y_emb, stage, x_example = self.first_stage_decoder(x, prompts) - - stop = False - for idx in range(1, 1500): - enco = self.stage_decoder(y, k, v, y_emb, stage, x_example) - y, k, v, y_emb, stage, logits, samples = enco - if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num: - stop = True - if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS: - stop = True - if stop: - break - y[0, -1] = 0 - return y, idx - - def infer(self, x, prompts, bert_feature): - top_k = self.top_k - early_stop_num = self.early_stop_num - - x = self.onnx_encoder(x, bert_feature) - - y = prompts - prefix_len = y.shape[1] - x_len = x.shape[1] - x_example = x[:,:,0] * 0.0 - x_attn_mask = torch.matmul(x_example.transpose(0, 1), x_example) - x_attn_mask = torch.zeros_like(x_attn_mask, dtype=torch.bool) - - stop = False - cache = { - "all_stage": self.num_layers, - "k": [None] * self.num_layers, - "v": [None] * self.num_layers, - "y_emb": None, - "first_infer": 1, - "stage": 0, - } - for idx in range(1500): - if cache["first_infer"] == 1: - y_emb = self.ar_audio_embedding(y) - else: - y_emb = torch.cat( - [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1 - ) - cache["y_emb"] = y_emb - y_pos = self.ar_audio_position(y_emb) - if cache["first_infer"] == 1: - xy_pos = torch.concat([x, y_pos], dim=1) - else: - xy_pos = y_pos[:, -1:] - y_len = y_pos.shape[1] - if cache["first_infer"] == 1: - x_attn_mask_pad = F.pad(x_attn_mask, (0, y_len), value=True) - y_attn_mask = F.pad( - torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1), - (x_len, 0), value=False - ) - xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0) - else: - xy_attn_mask = torch.zeros((1, x_len + y_len), dtype=torch.bool) - xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache) - logits = self.ar_predict_layer(xy_dec[:, -1]) - samples = sample(logits[0], y, top_k=top_k, top_p=1.0, repetition_penalty=1.35)[0].unsqueeze(0) - if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num: - stop = True - if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS: - stop = True - if stop: - if prompts.shape[1] == y.shape[1]: - y = torch.concat([y, torch.zeros_like(samples)], dim=1) - break - y = torch.concat([y, samples], dim=1) - cache["first_infer"] = 0 - return y, idx \ No newline at end of file diff --git a/AR/models/utils.py b/AR/models/utils.py deleted file mode 100644 index 25fe44636d9126030b34c734a4bd5d0205c98e58..0000000000000000000000000000000000000000 --- a/AR/models/utils.py +++ /dev/null @@ -1,160 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/utils.py\ -import torch -import torch.nn.functional as F - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: - """ - Args: - lengths: - A 1-D tensor containing sentence lengths. - max_len: - The length of masks. - Returns: - Return a 2-D bool tensor, where masked positions - are filled with `True` and non-masked positions are - filled with `False`. - - #>>> lengths = torch.tensor([1, 3, 2, 5]) - #>>> make_pad_mask(lengths) - tensor([[False, True, True, True, True], - [False, False, False, True, True], - [False, False, True, True, True], - [False, False, False, False, False]]) - """ - assert lengths.ndim == 1, lengths.ndim - max_len = max(max_len, lengths.max()) - n = lengths.size(0) - seq_range = torch.arange(0, max_len, device=lengths.device) - expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len) - - return expaned_lengths >= lengths.unsqueeze(-1) - - -# https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py -def top_k_top_p_filtering( - logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1 -): - """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering - Args: - logits: logits distribution shape (batch size, vocabulary size) - if top_k > 0: keep only top k tokens with highest probability (top-k filtering). - if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). - Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) - Make sure we keep at least min_tokens_to_keep per batch example in the output - From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 - """ - if top_k > 0: - top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) # Safety check - # Remove all tokens with a probability less than the last token of the top-k - indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] - logits[indices_to_remove] = filter_value - - if top_p < 1.0: - sorted_logits, sorted_indices = torch.sort(logits, descending=True) - cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) - - # Remove tokens with cumulative probability above the threshold (token with 0 are kept) - sorted_indices_to_remove = cumulative_probs > top_p - if min_tokens_to_keep > 1: - # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below) - sorted_indices_to_remove[..., :min_tokens_to_keep] = 0 - # Shift the indices to the right to keep also the first token above the threshold - sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() - sorted_indices_to_remove[..., 0] = 0 - - # scatter sorted tensors to original indexing - indices_to_remove = sorted_indices_to_remove.scatter( - 1, sorted_indices, sorted_indices_to_remove - ) - logits[indices_to_remove] = filter_value - return logits - - -def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0): - # temperature: (`optional`) float - # The value used to module the next token probabilities. Must be strictly positive. Default to 1.0. - # top_k: (`optional`) int - # The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50. - # top_p: (`optional`) float - # The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1. - - # Temperature (higher temperature => more likely to sample low probability tokens) - if temperature != 1.0: - logits = logits / temperature - # Top-p/top-k filtering - logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p) - # Sample - token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) - return token - - -from typing import Optional, Tuple - - -def multinomial_sample_one_no_sync( - probs_sort, -): # Does multinomial sampling without a cuda synchronization - q = torch.empty_like(probs_sort).exponential_(1) - return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int) - - -def logits_to_probs( - logits, - previous_tokens: Optional[torch.Tensor] = None, - temperature: float = 1.0, - top_k: Optional[int] = None, - top_p: Optional[int] = None, - repetition_penalty: float = 1.0, -): - previous_tokens = previous_tokens.squeeze() - # print(logits.shape,previous_tokens.shape) - # pdb.set_trace() - if previous_tokens is not None and repetition_penalty != 1.0: - previous_tokens = previous_tokens.long() - score = torch.gather(logits, dim=0, index=previous_tokens) - score = torch.where( - score < 0, score * repetition_penalty, score / repetition_penalty - ) - logits.scatter_(dim=0, index=previous_tokens, src=score) - - if top_p is not None and top_p < 1.0: - sorted_logits, sorted_indices = torch.sort(logits, descending=True) - cum_probs = torch.cumsum( - torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1 - ) - sorted_indices_to_remove = cum_probs > top_p - sorted_indices_to_remove[0] = False # keep at least one option - indices_to_remove = sorted_indices_to_remove.scatter( - dim=0, index=sorted_indices, src=sorted_indices_to_remove - ) - logits = logits.masked_fill(indices_to_remove, -float("Inf")) - - logits = logits / max(temperature, 1e-5) - - if top_k is not None: - v, _ = torch.topk(logits, min(top_k, logits.size(-1))) - pivot = v.select(-1, -1).unsqueeze(-1) - logits = torch.where(logits < pivot, -float("Inf"), logits) - - probs = torch.nn.functional.softmax(logits, dim=-1) - return probs - - -def sample( - logits, - previous_tokens: Optional[torch.Tensor] = None, - **sampling_kwargs, -) -> Tuple[torch.Tensor, torch.Tensor]: - probs = logits_to_probs( - logits=logits, previous_tokens=previous_tokens, **sampling_kwargs - ) - idx_next = multinomial_sample_one_no_sync(probs) - return idx_next, probs diff --git a/AR/modules/__init__.py b/AR/modules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/AR/modules/activation.py b/AR/modules/activation.py deleted file mode 100644 index 5ca888b5e557777be5a09482a84ce00967f67236..0000000000000000000000000000000000000000 --- a/AR/modules/activation.py +++ /dev/null @@ -1,428 +0,0 @@ -# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/activation.py -from typing import Optional -from typing import Tuple -import torch -from torch import Tensor -from torch.nn import Linear -from torch.nn import Module -from torch.nn.init import constant_ -from torch.nn.init import xavier_normal_ -from torch.nn.init import xavier_uniform_ -from torch.nn.modules.linear import NonDynamicallyQuantizableLinear -from torch.nn.parameter import Parameter - -from torch.nn import functional as F -from AR.modules.patched_mha_with_cache import multi_head_attention_forward_patched - -F.multi_head_attention_forward = multi_head_attention_forward_patched - - -class MultiheadAttention(Module): - r"""Allows the model to jointly attend to information - from different representation subspaces as described in the paper: - `Attention Is All You Need `_. - - Multi-Head Attention is defined as: - - .. math:: - \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O - - where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`. - - ``forward()`` will use a special optimized implementation if all of the following - conditions are met: - - - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This - restriction will be loosened in the future.) - - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad`` - - training is disabled (using ``.eval()``) - - dropout is 0 - - ``add_bias_kv`` is ``False`` - - ``add_zero_attn`` is ``False`` - - ``batch_first`` is ``True`` and the input is batched - - ``kdim`` and ``vdim`` are equal to ``embed_dim`` - - at most one of ``key_padding_mask`` or ``attn_mask`` is passed - - if a `NestedTensor `_ is passed, neither ``key_padding_mask`` - nor ``attn_mask`` is passed - - If the optimized implementation is in use, a - `NestedTensor `_ can be passed for - ``query``/``key``/``value`` to represent padding more efficiently than using a - padding mask. In this case, a `NestedTensor `_ - will be returned, and an additional speedup proportional to the fraction of the input - that is padding can be expected. - - Args: - embed_dim: Total dimension of the model. - num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split - across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``). - dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout). - bias: If specified, adds bias to input / output projection layers. Default: ``True``. - add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``. - add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1. - Default: ``False``. - kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``). - vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``). - batch_first: If ``True``, then the input and output tensors are provided - as (batch, seq, feature). Default: ``False`` (seq, batch, feature). - - Examples:: - - >>> # xdoctest: +SKIP - >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads) - >>> attn_output, attn_output_weights = multihead_attn(query, key, value) - - """ - __constants__ = ["batch_first"] - bias_k: Optional[torch.Tensor] - bias_v: Optional[torch.Tensor] - - def __init__( - self, - embed_dim, - num_heads, - dropout=0.0, - bias=True, - add_bias_kv=False, - add_zero_attn=False, - kdim=None, - vdim=None, - batch_first=False, - linear1_cls=Linear, - linear2_cls=Linear, - device=None, - dtype=None, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(MultiheadAttention, self).__init__() - self.embed_dim = embed_dim - self.kdim = kdim if kdim is not None else embed_dim - self.vdim = vdim if vdim is not None else embed_dim - self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim - - self.num_heads = num_heads - self.dropout = dropout - self.batch_first = batch_first - self.head_dim = embed_dim // num_heads - assert ( - self.head_dim * num_heads == self.embed_dim - ), "embed_dim must be divisible by num_heads" - - if add_bias_kv: - self.bias_k = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs)) - self.bias_v = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs)) - else: - self.bias_k = self.bias_v = None - - if linear1_cls == Linear: - if not self._qkv_same_embed_dim: - self.q_proj_weight = Parameter( - torch.empty((embed_dim, embed_dim), **factory_kwargs) - ) - self.k_proj_weight = Parameter( - torch.empty((embed_dim, self.kdim), **factory_kwargs) - ) - self.v_proj_weight = Parameter( - torch.empty((embed_dim, self.vdim), **factory_kwargs) - ) - self.register_parameter("in_proj_weight", None) - else: - self.in_proj_weight = Parameter( - torch.empty((3 * embed_dim, embed_dim), **factory_kwargs) - ) - self.register_parameter("q_proj_weight", None) - self.register_parameter("k_proj_weight", None) - self.register_parameter("v_proj_weight", None) - - if bias: - self.in_proj_bias = Parameter( - torch.empty(3 * embed_dim, **factory_kwargs) - ) - else: - self.register_parameter("in_proj_bias", None) - self.out_proj = NonDynamicallyQuantizableLinear( - embed_dim, embed_dim, bias=bias, **factory_kwargs - ) - - self._reset_parameters() - else: - if not self._qkv_same_embed_dim: - raise NotImplementedError - else: - self.in_proj_linear = linear1_cls( - embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs - ) - self.in_proj_weight = self.in_proj_linear.weight - - self.register_parameter("q_proj_weight", None) - self.register_parameter("k_proj_weight", None) - self.register_parameter("v_proj_weight", None) - - if bias: - self.in_proj_bias = self.in_proj_linear.bias - else: - self.register_parameter("in_proj_bias", None) - - self.out_proj = linear2_cls( - embed_dim, embed_dim, bias=bias, **factory_kwargs - ) - - if self.bias_k is not None: - xavier_normal_(self.bias_k) - if self.bias_v is not None: - xavier_normal_(self.bias_v) - - self.add_zero_attn = add_zero_attn - - def _reset_parameters(self): - if self._qkv_same_embed_dim: - xavier_uniform_(self.in_proj_weight) - else: - xavier_uniform_(self.q_proj_weight) - xavier_uniform_(self.k_proj_weight) - xavier_uniform_(self.v_proj_weight) - - if self.in_proj_bias is not None: - constant_(self.in_proj_bias, 0.0) - constant_(self.out_proj.bias, 0.0) - - if self.bias_k is not None: - xavier_normal_(self.bias_k) - if self.bias_v is not None: - xavier_normal_(self.bias_v) - - def __setstate__(self, state): - # Support loading old MultiheadAttention checkpoints generated by v1.1.0 - if "_qkv_same_embed_dim" not in state: - state["_qkv_same_embed_dim"] = True - - super(MultiheadAttention, self).__setstate__(state) - - def forward( - self, - query: Tensor, - key: Tensor, - value: Tensor, - key_padding_mask: Optional[Tensor] = None, - need_weights: bool = True, - attn_mask: Optional[Tensor] = None, - average_attn_weights: bool = True, - cache=None, - ) -> Tuple[Tensor, Optional[Tensor]]: - r""" - Args: - query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False`` - or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length, - :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``. - Queries are compared against key-value pairs to produce the output. - See "Attention Is All You Need" for more details. - key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False`` - or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length, - :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``. - See "Attention Is All You Need" for more details. - value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when - ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source - sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``. - See "Attention Is All You Need" for more details. - key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key`` - to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`. - Binary and byte masks are supported. - For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for - the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value. - need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``. - Default: ``True``. - attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape - :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size, - :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be - broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch. - Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the - corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the - corresponding position is not allowed to attend. For a float mask, the mask values will be added to - the attention weight. - average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across - heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an - effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads) - - Outputs: - - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched, - :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``, - where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the - embedding dimension ``embed_dim``. - - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``, - returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or - :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and - :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per - head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`. - - .. note:: - `batch_first` argument is ignored for unbatched inputs. - """ - is_batched = query.dim() == 3 - if key_padding_mask is not None: - _kpm_dtype = key_padding_mask.dtype - if _kpm_dtype != torch.bool and not torch.is_floating_point( - key_padding_mask - ): - raise AssertionError( - "only bool and floating types of key_padding_mask are supported" - ) - why_not_fast_path = "" - if not is_batched: - why_not_fast_path = ( - f"input not batched; expected query.dim() of 3 but got {query.dim()}" - ) - elif query is not key or key is not value: - # When lifting this restriction, don't forget to either - # enforce that the dtypes all match or test cases where - # they don't! - why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)" - elif self.in_proj_bias is not None and query.dtype != self.in_proj_bias.dtype: - why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match" - elif ( - self.in_proj_weight is not None and query.dtype != self.in_proj_weight.dtype - ): - # this case will fail anyway, but at least they'll get a useful error message. - why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match" - elif self.training: - why_not_fast_path = "training is enabled" - elif not self.batch_first: - why_not_fast_path = "batch_first was not True" - elif self.bias_k is not None: - why_not_fast_path = "self.bias_k was not None" - elif self.bias_v is not None: - why_not_fast_path = "self.bias_v was not None" - elif self.dropout: - why_not_fast_path = f"dropout was {self.dropout}, required zero" - elif self.add_zero_attn: - why_not_fast_path = "add_zero_attn was enabled" - elif not self._qkv_same_embed_dim: - why_not_fast_path = "_qkv_same_embed_dim was not True" - elif attn_mask is not None: - why_not_fast_path = "attn_mask was not None" - elif query.is_nested and key_padding_mask is not None: - why_not_fast_path = ( - "key_padding_mask is not supported with NestedTensor input" - ) - elif self.num_heads % 2 == 1: - why_not_fast_path = "num_heads is odd" - elif torch.is_autocast_enabled(): - why_not_fast_path = "autocast is enabled" - - if not why_not_fast_path: - tensor_args = ( - query, - key, - value, - self.in_proj_weight, - self.in_proj_bias, - self.out_proj.weight, - self.out_proj.bias, - ) - # We have to use list comprehensions below because TorchScript does not support - # generator expressions. - if torch.overrides.has_torch_function(tensor_args): - why_not_fast_path = "some Tensor argument has_torch_function" - elif not all( - [ - (x is None or x.is_cuda or "cpu" in str(x.device)) - for x in tensor_args - ] - ): - why_not_fast_path = "some Tensor argument is neither CUDA nor CPU" - elif torch.is_grad_enabled() and any( - [x is not None and x.requires_grad for x in tensor_args] - ): - why_not_fast_path = ( - "grad is enabled and at least one of query or the " - "input/output projection weights or biases requires_grad" - ) - if not why_not_fast_path: - return torch._native_multi_head_attention( - query, - key, - value, - self.embed_dim, - self.num_heads, - self.in_proj_weight, - self.in_proj_bias, - self.out_proj.weight, - self.out_proj.bias, - key_padding_mask if key_padding_mask is not None else attn_mask, - need_weights, - average_attn_weights, - 1 - if key_padding_mask is not None - else 0 - if attn_mask is not None - else None, - ) - - any_nested = query.is_nested or key.is_nested or value.is_nested - assert not any_nested, ( - "MultiheadAttention does not support NestedTensor outside of its fast path. " - + f"The fast path was not hit because {why_not_fast_path}" - ) - - if self.batch_first and is_batched: - # make sure that the transpose op does not affect the "is" property - if key is value: - if query is key: - query = key = value = query.transpose(1, 0) - else: - query, key = [x.transpose(1, 0) for x in (query, key)] - value = key - else: - query, key, value = [x.transpose(1, 0) for x in (query, key, value)] - - if not self._qkv_same_embed_dim: - attn_output, attn_output_weights = F.multi_head_attention_forward( - query, - key, - value, - self.embed_dim, - self.num_heads, - self.in_proj_weight, - self.in_proj_bias, - self.bias_k, - self.bias_v, - self.add_zero_attn, - self.dropout, - self.out_proj.weight, - self.out_proj.bias, - training=self.training, - key_padding_mask=key_padding_mask, - need_weights=need_weights, - attn_mask=attn_mask, - use_separate_proj_weight=True, - q_proj_weight=self.q_proj_weight, - k_proj_weight=self.k_proj_weight, - v_proj_weight=self.v_proj_weight, - average_attn_weights=average_attn_weights, - cache=cache, - ) - else: - attn_output, attn_output_weights = F.multi_head_attention_forward( - query, - key, - value, - self.embed_dim, - self.num_heads, - self.in_proj_weight, - self.in_proj_bias, - self.bias_k, - self.bias_v, - self.add_zero_attn, - self.dropout, - self.out_proj.weight, - self.out_proj.bias, - training=self.training, - key_padding_mask=key_padding_mask, - need_weights=need_weights, - attn_mask=attn_mask, - average_attn_weights=average_attn_weights, - cache=cache, - ) - if self.batch_first and is_batched: - return attn_output.transpose(1, 0), attn_output_weights - else: - return attn_output, attn_output_weights diff --git a/AR/modules/activation_onnx.py b/AR/modules/activation_onnx.py deleted file mode 100644 index b54acd999f165cb7d47a9388461e3f31164cd380..0000000000000000000000000000000000000000 --- a/AR/modules/activation_onnx.py +++ /dev/null @@ -1,178 +0,0 @@ -# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/activation.py -from typing import Optional -from typing import Tuple -import torch -from torch import Tensor -from torch.nn import Linear -from torch.nn import Module -from torch.nn.init import constant_ -from torch.nn.init import xavier_normal_ -from torch.nn.init import xavier_uniform_ -from torch.nn.modules.linear import NonDynamicallyQuantizableLinear -from torch.nn.parameter import Parameter - -from torch.nn import functional as F -from AR.modules.patched_mha_with_cache_onnx import multi_head_attention_forward_patched - - -class MultiheadAttention(Module): - __constants__ = ["batch_first"] - bias_k: Optional[torch.Tensor] - bias_v: Optional[torch.Tensor] - - def __init__( - self, - embed_dim, - num_heads, - dropout=0.0, - bias=True, - add_bias_kv=False, - add_zero_attn=False, - kdim=None, - vdim=None, - batch_first=False, - linear1_cls=Linear, - linear2_cls=Linear, - device=None, - dtype=None, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(MultiheadAttention, self).__init__() - self.embed_dim = embed_dim - self.kdim = kdim if kdim is not None else embed_dim - self.vdim = vdim if vdim is not None else embed_dim - self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim - - self.num_heads = num_heads - self.dropout = dropout - self.batch_first = batch_first - self.head_dim = embed_dim // num_heads - assert ( - self.head_dim * num_heads == self.embed_dim - ), "embed_dim must be divisible by num_heads" - - if add_bias_kv: - self.bias_k = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs)) - self.bias_v = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs)) - else: - self.bias_k = self.bias_v = None - - if linear1_cls == Linear: - if not self._qkv_same_embed_dim: - self.q_proj_weight = Parameter( - torch.empty((embed_dim, embed_dim), **factory_kwargs) - ) - self.k_proj_weight = Parameter( - torch.empty((embed_dim, self.kdim), **factory_kwargs) - ) - self.v_proj_weight = Parameter( - torch.empty((embed_dim, self.vdim), **factory_kwargs) - ) - self.register_parameter("in_proj_weight", None) - else: - self.in_proj_weight = Parameter( - torch.empty((3 * embed_dim, embed_dim), **factory_kwargs) - ) - self.register_parameter("q_proj_weight", None) - self.register_parameter("k_proj_weight", None) - self.register_parameter("v_proj_weight", None) - - if bias: - self.in_proj_bias = Parameter( - torch.empty(3 * embed_dim, **factory_kwargs) - ) - else: - self.register_parameter("in_proj_bias", None) - self.out_proj = NonDynamicallyQuantizableLinear( - embed_dim, embed_dim, bias=bias, **factory_kwargs - ) - - self._reset_parameters() - else: - if not self._qkv_same_embed_dim: - raise NotImplementedError - else: - self.in_proj_linear = linear1_cls( - embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs - ) - self.in_proj_weight = self.in_proj_linear.weight - - self.register_parameter("q_proj_weight", None) - self.register_parameter("k_proj_weight", None) - self.register_parameter("v_proj_weight", None) - - if bias: - self.in_proj_bias = self.in_proj_linear.bias - else: - self.register_parameter("in_proj_bias", None) - - self.out_proj = linear2_cls( - embed_dim, embed_dim, bias=bias, **factory_kwargs - ) - - if self.bias_k is not None: - xavier_normal_(self.bias_k) - if self.bias_v is not None: - xavier_normal_(self.bias_v) - - self.add_zero_attn = add_zero_attn - - def _reset_parameters(self): - if self._qkv_same_embed_dim: - xavier_uniform_(self.in_proj_weight) - else: - xavier_uniform_(self.q_proj_weight) - xavier_uniform_(self.k_proj_weight) - xavier_uniform_(self.v_proj_weight) - - if self.in_proj_bias is not None: - constant_(self.in_proj_bias, 0.0) - constant_(self.out_proj.bias, 0.0) - - if self.bias_k is not None: - xavier_normal_(self.bias_k) - if self.bias_v is not None: - xavier_normal_(self.bias_v) - - def __setstate__(self, state): - # Support loading old MultiheadAttention checkpoints generated by v1.1.0 - if "_qkv_same_embed_dim" not in state: - state["_qkv_same_embed_dim"] = True - - super(MultiheadAttention, self).__setstate__(state) - - def forward( - self, - query: Tensor, - key: Tensor, - value: Tensor, - key_padding_mask: Optional[Tensor] = None, - need_weights: bool = True, - attn_mask: Optional[Tensor] = None, - average_attn_weights: bool = True, - cache=None, - ) -> Tuple[Tensor, Optional[Tensor]]: - any_nested = query.is_nested or key.is_nested or value.is_nested - query = key = value = query.transpose(1, 0) - attn_output = multi_head_attention_forward_patched( - query, - key, - value, - self.embed_dim, - self.num_heads, - self.in_proj_weight, - self.in_proj_bias, - self.bias_k, - self.bias_v, - self.add_zero_attn, - self.dropout, - self.out_proj.weight, - self.out_proj.bias, - training=self.training, - key_padding_mask=key_padding_mask, - need_weights=need_weights, - attn_mask=attn_mask, - average_attn_weights=average_attn_weights, - cache=cache, - ) - return attn_output.transpose(1, 0) diff --git a/AR/modules/embedding.py b/AR/modules/embedding.py deleted file mode 100644 index 3a382f93d8e5c87d6d52354937494703dc81523b..0000000000000000000000000000000000000000 --- a/AR/modules/embedding.py +++ /dev/null @@ -1,81 +0,0 @@ -# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/embedding.py -import math - -import torch -from torch import nn - - -class TokenEmbedding(nn.Module): - def __init__( - self, - embedding_dim: int, - vocab_size: int, - dropout: float = 0.0, - ): - super().__init__() - - self.vocab_size = vocab_size - self.embedding_dim = embedding_dim - - self.dropout = torch.nn.Dropout(p=dropout) - self.word_embeddings = nn.Embedding(self.vocab_size, self.embedding_dim) - - @property - def weight(self) -> torch.Tensor: - return self.word_embeddings.weight - - def embedding(self, index: int) -> torch.Tensor: - return self.word_embeddings.weight[index : index + 1] - - def forward(self, x: torch.Tensor): - x = self.word_embeddings(x) - x = self.dropout(x) - return x - - -class SinePositionalEmbedding(nn.Module): - def __init__( - self, - embedding_dim: int, - dropout: float = 0.0, - scale: bool = False, - alpha: bool = False, - ): - super().__init__() - self.embedding_dim = embedding_dim - self.x_scale = math.sqrt(embedding_dim) if scale else 1.0 - self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha) - self.dropout = torch.nn.Dropout(p=dropout) - - self.reverse = False - self.pe = None - self.extend_pe(torch.tensor(0.0).expand(1, 4000)) - - def extend_pe(self, x): - """Reset the positional encodings.""" - if self.pe is not None: - if self.pe.size(1) >= x.size(1): - if self.pe.dtype != x.dtype or self.pe.device != x.device: - self.pe = self.pe.to(dtype=x.dtype, device=x.device) - return - pe = torch.zeros(x.size(1), self.embedding_dim) - if self.reverse: - position = torch.arange( - x.size(1) - 1, -1, -1.0, dtype=torch.float32 - ).unsqueeze(1) - else: - position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) - div_term = torch.exp( - torch.arange(0, self.embedding_dim, 2, dtype=torch.float32) - * -(math.log(10000.0) / self.embedding_dim) - ) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0) - self.pe = pe.to(device=x.device, dtype=x.dtype).detach() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - self.extend_pe(x) - output = x.unsqueeze(-1) if x.ndim == 2 else x - output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)] - return self.dropout(output) diff --git a/AR/modules/embedding_onnx.py b/AR/modules/embedding_onnx.py deleted file mode 100644 index b93405b45005d6101da1a3947d3d188fc460ae4e..0000000000000000000000000000000000000000 --- a/AR/modules/embedding_onnx.py +++ /dev/null @@ -1,63 +0,0 @@ -# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/embedding.py -import math - -import torch -from torch import nn - - -class TokenEmbedding(nn.Module): - def __init__( - self, - embedding_dim: int, - vocab_size: int, - dropout: float = 0.0, - ): - super().__init__() - - self.vocab_size = vocab_size - self.embedding_dim = embedding_dim - - self.dropout = torch.nn.Dropout(p=dropout) - self.word_embeddings = nn.Embedding(self.vocab_size, self.embedding_dim) - - @property - def weight(self) -> torch.Tensor: - return self.word_embeddings.weight - - def embedding(self, index: int) -> torch.Tensor: - return self.word_embeddings.weight[index : index + 1] - - def forward(self, x: torch.Tensor): - x = self.word_embeddings(x) - x = self.dropout(x) - return x - - -class SinePositionalEmbedding(nn.Module): - def __init__( - self, - embedding_dim: int, - dropout: float = 0.0, - scale: bool = False, - alpha: bool = False, - ): - super().__init__() - self.embedding_dim = embedding_dim - self.x_scale = math.sqrt(embedding_dim) if scale else 1.0 - self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha) - self.dropout = torch.nn.Dropout(p=dropout) - self.reverse = False - self.div_term = torch.exp(torch.arange(0, self.embedding_dim, 2) * -(math.log(10000.0) / self.embedding_dim)) - - def extend_pe(self, x): - position = torch.cumsum(torch.ones_like(x[:,:,0]), dim=1).transpose(0, 1) - scpe = (position * self.div_term).unsqueeze(0) - pe = torch.cat([torch.sin(scpe), torch.cos(scpe)]).permute(1, 2, 0) - pe = pe.contiguous().view(1, -1, self.embedding_dim) - return pe - - def forward(self, x: torch.Tensor) -> torch.Tensor: - pe = self.extend_pe(x) - output = x.unsqueeze(-1) if x.ndim == 2 else x - output = output * self.x_scale + self.alpha * pe - return self.dropout(output) diff --git a/AR/modules/lr_schedulers.py b/AR/modules/lr_schedulers.py deleted file mode 100644 index 7dec462b7b825c991b5925858b42056091a98379..0000000000000000000000000000000000000000 --- a/AR/modules/lr_schedulers.py +++ /dev/null @@ -1,82 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/lr_schedulers.py -import math - -import torch -from matplotlib import pyplot as plt -from torch import nn -from torch.optim import Adam - - -class WarmupCosineLRSchedule(torch.optim.lr_scheduler._LRScheduler): - """ - Implements Warmup learning rate schedule until 'warmup_steps', going from 'init_lr' to 'peak_lr' for multiple optimizers. - """ - - def __init__( - self, - optimizer, - init_lr, - peak_lr, - end_lr, - warmup_steps=10000, - total_steps=400000, - current_step=0, - ): - self.init_lr = init_lr - self.peak_lr = peak_lr - self.end_lr = end_lr - self.optimizer = optimizer - self._warmup_rate = (peak_lr - init_lr) / warmup_steps - self._decay_rate = (end_lr - peak_lr) / (total_steps - warmup_steps) - self._current_step = current_step - self.lr = init_lr - self.warmup_steps = warmup_steps - self.total_steps = total_steps - self._last_lr = [self.lr] - - def set_lr(self, lr): - self._last_lr = [g["lr"] for g in self.optimizer.param_groups] - for g in self.optimizer.param_groups: - # g['lr'] = lr - g["lr"] = self.end_lr ###锁定用线性 - - def step(self): - if self._current_step < self.warmup_steps: - lr = self.init_lr + self._warmup_rate * self._current_step - - elif self._current_step > self.total_steps: - lr = self.end_lr - - else: - decay_ratio = (self._current_step - self.warmup_steps) / ( - self.total_steps - self.warmup_steps - ) - if decay_ratio < 0.0 or decay_ratio > 1.0: - raise RuntimeError( - "Decay ratio must be in [0.0, 1.0]. Fix LR scheduler settings." - ) - coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) - lr = self.end_lr + coeff * (self.peak_lr - self.end_lr) - - self.lr = lr = self.end_lr = 0.002 ###锁定用线性###不听话，直接锁定！ - self.set_lr(lr) - self.lr = lr - self._current_step += 1 - return self.lr - - -if __name__ == "__main__": - m = nn.Linear(10, 10) - opt = Adam(m.parameters(), lr=1e-4) - s = WarmupCosineLRSchedule( - opt, 1e-6, 2e-4, 1e-6, warmup_steps=2000, total_steps=20000, current_step=0 - ) - lrs = [] - for i in range(25000): - s.step() - lrs.append(s.lr) - print(s.lr) - - plt.plot(lrs) - plt.plot(range(0, 25000), lrs) - plt.show() diff --git a/AR/modules/optim.py b/AR/modules/optim.py deleted file mode 100644 index 98785f05b2e3aa123ae22955d677b9f68efec256..0000000000000000000000000000000000000000 --- a/AR/modules/optim.py +++ /dev/null @@ -1,622 +0,0 @@ -# Copyright 2022 Xiaomi Corp. (authors: Daniel Povey) -# -# See ../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import contextlib -import logging -from collections import defaultdict -from typing import List -from typing import Tuple - -import torch -from torch import Tensor -from torch.optim import Optimizer - - -class BatchedOptimizer(Optimizer): - """ - This class adds to class Optimizer the capability to optimize parameters in batches: - it will stack the parameters and their grads for you so the optimizer can work - on tensors with an extra leading dimension. This is intended for speed with GPUs, - as it reduces the number of kernels launched in the optimizer. - - Args: - params: - """ - - def __init__(self, params, defaults): - super(BatchedOptimizer, self).__init__(params, defaults) - - @contextlib.contextmanager - def batched_params(self, param_group, group_params_names): - """ - This function returns (technically, yields) a list of - of tuples (p, state), where - p is a `fake` parameter that is stacked (over axis 0) from real parameters - that share the same shape, and its gradient is also stacked; - `state` is the state corresponding to this batch of parameters - (it will be physically located in the "state" for one of the real - parameters, the last one that has any particular shape and dtype). - - This function is decorated as a context manager so that it can - write parameters back to their "real" locations. - - The idea is, instead of doing: - - for p in group["params"]: - state = self.state[p] - ... - - you can do: - - with self.batched_params(group["params"]) as batches: - for p, state, p_names in batches: - ... - - - Args: - group: a parameter group, which is a list of parameters; should be - one of self.param_groups. - group_params_names: name for each parameter in group, - which is List[str]. - """ - batches = defaultdict( - list - ) # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter - batches_names = defaultdict( - list - ) # `batches` maps from tuple (dtype_as_str,*shape) to list of str - - assert len(param_group) == len(group_params_names) - for p, named_p in zip(param_group, group_params_names): - key = (str(p.dtype), *p.shape) - batches[key].append(p) - batches_names[key].append(named_p) - - batches_names_keys = list(batches_names.keys()) - sorted_idx = sorted( - range(len(batches_names)), key=lambda i: batches_names_keys[i]) - batches_names = [ - batches_names[batches_names_keys[idx]] for idx in sorted_idx - ] - batches = [batches[batches_names_keys[idx]] for idx in sorted_idx] - - stacked_params_dict = dict() - - # turn batches into a list, in deterministic order. - # tuples will contain tuples of (stacked_param, state, stacked_params_names), - # one for each batch in `batches`. - tuples = [] - - for batch, batch_names in zip(batches, batches_names): - p = batch[0] - # we arbitrarily store the state in the - # state corresponding to the 1st parameter in the - # group. class Optimizer will take care of saving/loading state. - state = self.state[p] - p_stacked = torch.stack(batch) - grad = torch.stack([ - torch.zeros_like(p) if p.grad is None else p.grad for p in batch - ]) - p_stacked.grad = grad - stacked_params_dict[key] = p_stacked - tuples.append((p_stacked, state, batch_names)) - - yield tuples # <-- calling code will do the actual optimization here! - - for ((stacked_params, _state, _names), batch) in zip(tuples, batches): - for i, p in enumerate(batch): # batch is list of Parameter - p.copy_(stacked_params[i]) - - -class ScaledAdam(BatchedOptimizer): - """ - Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update - proportional to the norm of that parameter; and also learn the scale of the parameter, - in log space, subject to upper and lower limits (as if we had factored each parameter as - param = underlying_param * log_scale.exp()) - - - Args: - params: The parameters or param_groups to optimize (like other Optimizer subclasses) - lr: The learning rate. We will typically use a learning rate schedule that starts - at 0.03 and decreases over time, i.e. much higher than other common - optimizers. - clipping_scale: (e.g. 2.0) - A scale for gradient-clipping: if specified, the normalized gradients - over the whole model will be clipped to have 2-norm equal to - `clipping_scale` times the median 2-norm over the most recent period - of `clipping_update_period` minibatches. By "normalized gradients", - we mean after multiplying by the rms parameter value for this tensor - [for non-scalars]; this is appropriate because our update is scaled - by this quantity. - betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad. - Must satisfy 0 < beta <= beta2 < 1. - scalar_lr_scale: A scaling factor on the learning rate, that we use to update the - scale of each parameter tensor and scalar parameters of the mode.. - If each parameter were decomposed - as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale - would be a the scaling factor on the learning rate of p_scale. - eps: A general-purpose epsilon to prevent division by zero - param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of - learning the scale on the parameters (we'll constrain the rms of each non-scalar - parameter tensor to be >= this value) - param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of - learning the scale on the parameters (we'll constrain the rms of each non-scalar - parameter tensor to be <= this value) - scalar_max: Maximum absolute value for scalar parameters (applicable if your - model has any parameters with numel() == 1). - size_update_period: The periodicity, in steps, with which we update the size (scale) - of the parameter tensor. This is provided to save a little time - in the update. - clipping_update_period: if clipping_scale is specified, this is the period - """ - - def __init__( - self, - params, - lr=3e-02, - clipping_scale=None, - betas=(0.9, 0.98), - scalar_lr_scale=0.1, - eps=1.0e-08, - param_min_rms=1.0e-05, - param_max_rms=3.0, - scalar_max=10.0, - size_update_period=4, - clipping_update_period=100, - parameters_names=None, - show_dominant_parameters=True, ): - - assert parameters_names is not None, ( - "Please prepare parameters_names," - "which is a List[List[str]]. Each List[str] is for a group" - "and each str is for a parameter") - defaults = dict( - lr=lr, - clipping_scale=clipping_scale, - betas=betas, - scalar_lr_scale=scalar_lr_scale, - eps=eps, - param_min_rms=param_min_rms, - param_max_rms=param_max_rms, - scalar_max=scalar_max, - size_update_period=size_update_period, - clipping_update_period=clipping_update_period, ) - - super(ScaledAdam, self).__init__(params, defaults) - assert len(self.param_groups) == len(parameters_names) - self.parameters_names = parameters_names - self.show_dominant_parameters = show_dominant_parameters - - def __setstate__(self, state): - super(ScaledAdam, self).__setstate__(state) - - @torch.no_grad() - def step(self, closure=None): - """Performs a single optimization step. - - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - batch = True - - for group, group_params_names in zip(self.param_groups, - self.parameters_names): - - with self.batched_params(group["params"], - group_params_names) as batches: - - # batches is list of pairs (stacked_param, state). stacked_param is like - # a regular parameter, and will have a .grad, but the 1st dim corresponds to - # a stacking dim, it is not a real dim. - - if (len(batches[0][1]) == - 0): # if len(first state) == 0: not yet initialized - clipping_scale = 1 - else: - clipping_scale = self._get_clipping_scale(group, batches) - - for p, state, _ in batches: - # Perform optimization step. - # grad is not going to be None, we handled that when creating the batches. - grad = p.grad - if grad.is_sparse: - raise RuntimeError( - "ScaledAdam optimizer does not support sparse gradients" - ) - # State initialization - if len(state) == 0: - self._init_state(group, p, state) - - self._step_one_batch(group, p, state, clipping_scale) - - return loss - - def _init_state(self, group: dict, p: Tensor, state: dict): - """ - Initializes state dict for parameter 'p'. Assumes that dim 0 of tensor p - is actually the batch dimension, corresponding to batched-together - parameters of a given shape. - - - Args: - group: Dict to look up configuration values. - p: The parameter that we are initializing the state for - state: Dict from string to whatever state we are initializing - """ - size_update_period = group["size_update_period"] - - state["step"] = 0 - - kwargs = {"device": p.device, "dtype": p.dtype} - - # 'delta' implements conventional momentum. There are - # several different kinds of update going on, so rather than - # compute "exp_avg" like in Adam, we store and decay a - # parameter-change "delta", which combines all forms of - # update. this is equivalent to how it's done in Adam, - # except for the first few steps. - state["delta"] = torch.zeros_like( - p, memory_format=torch.preserve_format) - - batch_size = p.shape[0] - numel = p.numel() // batch_size - numel = p.numel() - - if numel > 1: - # "param_rms" just periodically records the scalar root-mean-square value of - # the parameter tensor. - # it has a shape like (batch_size, 1, 1, 1, 1) - param_rms = ( - (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt()) - state["param_rms"] = param_rms - - state["scale_exp_avg_sq"] = torch.zeros_like(param_rms) - state["scale_grads"] = torch.zeros(size_update_period, - *param_rms.shape, **kwargs) - - # exp_avg_sq is the weighted sum of scaled gradients. as in Adam. - state["exp_avg_sq"] = torch.zeros_like( - p, memory_format=torch.preserve_format) - - def _get_clipping_scale(self, - group: dict, - tuples: List[Tuple[Tensor, dict, List[str]]] - ) -> float: - """ - Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients - by this amount before applying the rest of the update. - - Args: - group: the parameter group, an item in self.param_groups - tuples: a list of tuples of (param, state, param_names) - where param is a batched set of parameters, - with a .grad (1st dim is batch dim) - and state is the state-dict where optimization parameters are kept. - param_names is a List[str] while each str is name for a parameter - in batched set of parameters "param". - """ - assert len(tuples) >= 1 - clipping_scale = group["clipping_scale"] - (first_p, first_state, _) = tuples[0] - step = first_state["step"] - if clipping_scale is None or step == 0: - # no clipping. return early on step == 0 because the other - # parameters' state won't have been initialized yet. - return 1.0 - clipping_update_period = group["clipping_update_period"] - - tot_sumsq = torch.tensor(0.0, device=first_p.device) - for (p, state, param_names) in tuples: - grad = p.grad - if grad.is_sparse: - raise RuntimeError( - "ScaledAdam optimizer does not support sparse gradients") - if p.numel() == p.shape[0]: # a batch of scalars - tot_sumsq += (grad**2).sum() # sum() to change shape [1] to [] - else: - tot_sumsq += ((grad * state["param_rms"])**2).sum() - - tot_norm = tot_sumsq.sqrt() - if "model_norms" not in first_state: - first_state["model_norms"] = torch.zeros( - clipping_update_period, device=p.device) - first_state["model_norms"][step % clipping_update_period] = tot_norm - - if step % clipping_update_period == 0: - # Print some stats. - # We don't reach here if step == 0 because we would have returned - # above. - sorted_norms = first_state["model_norms"].sort()[0].to("cpu") - quartiles = [] - for n in range(0, 5): - index = min( - clipping_update_period - 1, - (clipping_update_period // 4) * n, ) - quartiles.append(sorted_norms[index].item()) - - median = quartiles[2] - threshold = clipping_scale * median - first_state["model_norm_threshold"] = threshold - percent_clipped = (first_state["num_clipped"] * 100.0 / - clipping_update_period - if "num_clipped" in first_state else 0.0) - first_state["num_clipped"] = 0 - quartiles = " ".join(["%.3e" % x for x in quartiles]) - logging.info( - f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, " - f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}" - ) - - if step < clipping_update_period: - return 1.0 # We have not yet estimated a norm to clip to. - else: - try: - model_norm_threshold = first_state["model_norm_threshold"] - except KeyError: - logging.info( - "Warning: model_norm_threshold not in state: possibly " - "you changed config when restarting, adding clipping_scale option?" - ) - return 1.0 - ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item()) - if ans < 1.0: - first_state["num_clipped"] += 1 - if ans < 0.1: - logging.warn( - f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}" - ) - if self.show_dominant_parameters: - assert p.shape[0] == len(param_names) - self._show_gradient_dominating_parameter(tuples, tot_sumsq) - return ans - - def _show_gradient_dominating_parameter( - self, tuples: List[Tuple[Tensor, dict, List[str]]], - tot_sumsq: Tensor): - """ - Show information of parameter wihch dominanting tot_sumsq. - - Args: - tuples: a list of tuples of (param, state, param_names) - where param is a batched set of parameters, - with a .grad (1st dim is batch dim) - and state is the state-dict where optimization parameters are kept. - param_names is a List[str] while each str is name for a parameter - in batched set of parameters "param". - tot_sumsq: sumsq of all parameters. Though it's could be calculated - from tuples, we still pass it to save some time. - """ - all_sumsq_orig = {} - for (p, state, batch_param_names) in tuples: - # p is a stacked batch parameters. - batch_grad = p.grad - if p.numel() == p.shape[0]: # a batch of scalars - batch_sumsq_orig = batch_grad**2 - # Dummpy values used by following `zip` statement. - batch_rms_orig = torch.ones(p.shape[0]) - else: - batch_rms_orig = state["param_rms"] - batch_sumsq_orig = ((batch_grad * batch_rms_orig)**2).sum( - dim=list(range(1, batch_grad.ndim))) - - for name, sumsq_orig, rms, grad in zip(batch_param_names, - batch_sumsq_orig, - batch_rms_orig, batch_grad): - - proportion_orig = sumsq_orig / tot_sumsq - all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad) - - assert torch.isclose( - sum([value[0] for value in all_sumsq_orig.values()]).cpu(), - torch.tensor(1.0), ) - sorted_by_proportion = { - k: v - for k, v in sorted( - all_sumsq_orig.items(), - key=lambda item: item[1][0], - reverse=True, ) - } - dominant_param_name = next(iter(sorted_by_proportion)) - (dominant_proportion, dominant_sumsq, dominant_rms, - dominant_grad, ) = sorted_by_proportion[dominant_param_name] - logging.info(f"Parameter Dominanting tot_sumsq {dominant_param_name}" - f" with proportion {dominant_proportion:.2f}," - f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)" - f"={dominant_sumsq:.3e}," - f" grad_sumsq = {(dominant_grad**2).sum():.3e}," - f" orig_rms_sq={(dominant_rms**2).item():.3e}") - - def _step_one_batch(self, - group: dict, - p: Tensor, - state: dict, - clipping_scale: float): - """ - Do the step for one parameter, which is actually going to be a batch of - `real` parameters, with dim 0 as the batch dim. - Args: - group: dict to look up configuration values - p: parameter to update (actually multiple parameters stacked together - as a batch) - state: state-dict for p, to look up the optimizer state - """ - lr = group["lr"] - size_update_period = group["size_update_period"] - beta1 = group["betas"][0] - - grad = p.grad - if clipping_scale != 1.0: - grad = grad * clipping_scale - step = state["step"] - delta = state["delta"] - - delta.mul_(beta1) - batch_size = p.shape[0] - numel = p.numel() // batch_size - if numel > 1: - # Update the size/scale of p, and set param_rms - scale_grads = state["scale_grads"] - scale_grads[step % size_update_period] = (p * grad).sum( - dim=list(range(1, p.ndim)), keepdim=True) - if step % size_update_period == size_update_period - 1: - param_rms = state["param_rms"] # shape: (batch_size, 1, 1, ..) - param_rms.copy_((p**2) - .mean(dim=list(range(1, p.ndim)), keepdim=True) - .sqrt()) - if step > 0: - # self._size_update() learns the overall scale on the - # parameter, by shrinking or expanding it. - self._size_update(group, scale_grads, p, state) - - if numel == 1: - # For parameters with 1 element we just use regular Adam. - # Updates delta. - self._step_scalar(group, p, state) - else: - self._step(group, p, state) - - state["step"] = step + 1 - - def _size_update(self, - group: dict, - scale_grads: Tensor, - p: Tensor, - state: dict) -> None: - """ - Called only where p.numel() > 1, this updates the scale of the parameter. - If we imagine: p = underlying_param * scale.exp(), and we are doing - gradient descent on underlying param and on scale, this function does the update - on `scale`. - - Args: - group: dict to look up configuration values - scale_grads: a tensor of shape (size_update_period, batch_size, 1, 1,...) containing - grads w.r.t. the scales. - p: The parameter to update - state: The state-dict of p - """ - - param_rms = state["param_rms"] - beta1, beta2 = group["betas"] - size_lr = group["lr"] * group["scalar_lr_scale"] - param_min_rms = group["param_min_rms"] - param_max_rms = group["param_max_rms"] - eps = group["eps"] - step = state["step"] - batch_size = p.shape[0] - - size_update_period = scale_grads.shape[0] - # correct beta2 for the size update period: we will have - # faster decay at this level. - beta2_corr = beta2**size_update_period - - scale_exp_avg_sq = state[ - "scale_exp_avg_sq"] # shape: (batch_size, 1, 1, ..) - scale_exp_avg_sq.mul_(beta2_corr).add_( - (scale_grads**2).mean(dim=0), # mean over dim `size_update_period` - alpha=1 - beta2_corr, ) # shape is (batch_size, 1, 1, ...) - - # The 1st time we reach here is when size_step == 1. - size_step = (step + 1) // size_update_period - bias_correction2 = 1 - beta2_corr**size_step - # we don't bother with bias_correction1; this will help prevent divergence - # at the start of training. - - denom = scale_exp_avg_sq.sqrt() + eps - - scale_step = (-size_lr * (bias_correction2**0.5) * - scale_grads.sum(dim=0) / denom) - - is_too_small = param_rms < param_min_rms - is_too_large = param_rms > param_max_rms - - # when the param gets too small, just don't shrink it any further. - scale_step.masked_fill_(is_too_small, 0.0) - # when it gets too large, stop it from getting any larger. - scale_step.masked_fill_(is_too_large, -size_lr * size_update_period) - delta = state["delta"] - # the factor of (1-beta1) relates to momentum. - delta.add_(p * scale_step, alpha=(1 - beta1)) - - def _step(self, group: dict, p: Tensor, state: dict): - """ - This function does the core update of self.step(), in the case where the members of - the batch have more than 1 element. - - Args: - group: A dict which will be used to look up configuration values - p: The parameter to be updated - grad: The grad of p - state: The state-dict corresponding to parameter p - - This function modifies p. - """ - grad = p.grad - lr = group["lr"] - beta1, beta2 = group["betas"] - eps = group["eps"] - param_min_rms = group["param_min_rms"] - step = state["step"] - - exp_avg_sq = state["exp_avg_sq"] - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2)) - - this_step = state["step"] - (state["zero_step"] - if "zero_step" in state else 0) - bias_correction2 = 1 - beta2**(this_step + 1) - if bias_correction2 < 0.99: - # note: not in-place. - exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2) - - denom = exp_avg_sq.sqrt() - denom += eps - grad = grad / denom - - alpha = -lr * (1 - beta1) * state["param_rms"].clamp(min=param_min_rms) - - delta = state["delta"] - delta.add_(grad * alpha) - p.add_(delta) - - def _step_scalar(self, group: dict, p: Tensor, state: dict): - """ - A simplified form of the core update for scalar tensors, where we cannot get a good - estimate of the parameter rms. - """ - beta1, beta2 = group["betas"] - scalar_max = group["scalar_max"] - eps = group["eps"] - lr = group["lr"] * group["scalar_lr_scale"] - grad = p.grad - - exp_avg_sq = state["exp_avg_sq"] # shape: (batch_size,) - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) - - # bias_correction2 is like in Adam. Don't bother with bias_correction1; - # slower update at the start will help stability anyway. - bias_correction2 = 1 - beta2**(state["step"] + 1) - denom = (exp_avg_sq / bias_correction2).sqrt() + eps - - delta = state["delta"] - delta.add_(grad / denom, alpha=-lr * (1 - beta1)) - p.clamp_(min=-scalar_max, max=scalar_max) - p.add_(delta) diff --git a/AR/modules/patched_mha_with_cache.py b/AR/modules/patched_mha_with_cache.py deleted file mode 100644 index 57206703ca97a26ca14727cefa731eb0e508260b..0000000000000000000000000000000000000000 --- a/AR/modules/patched_mha_with_cache.py +++ /dev/null @@ -1,463 +0,0 @@ -from torch.nn.functional import * -from torch.nn.functional import ( - _mha_shape_check, - _canonical_mask, - _none_or_dtype, - _in_projection_packed, -) - -# import torch -# Tensor = torch.Tensor -# from typing import Callable, List, Optional, Tuple, Union - - -def multi_head_attention_forward_patched( - query: Tensor, - key: Tensor, - value: Tensor, - embed_dim_to_check: int, - num_heads: int, - in_proj_weight: Optional[Tensor], - in_proj_bias: Optional[Tensor], - bias_k: Optional[Tensor], - bias_v: Optional[Tensor], - add_zero_attn: bool, - dropout_p: float, - out_proj_weight: Tensor, - out_proj_bias: Optional[Tensor], - training: bool = True, - key_padding_mask: Optional[Tensor] = None, - need_weights: bool = True, - attn_mask: Optional[Tensor] = None, - use_separate_proj_weight: bool = False, - q_proj_weight: Optional[Tensor] = None, - k_proj_weight: Optional[Tensor] = None, - v_proj_weight: Optional[Tensor] = None, - static_k: Optional[Tensor] = None, - static_v: Optional[Tensor] = None, - average_attn_weights: bool = True, - is_causal: bool = False, - cache=None, -) -> Tuple[Tensor, Optional[Tensor]]: - r""" - Args: - query, key, value: map a query and a set of key-value pairs to an output. - See "Attention Is All You Need" for more details. - embed_dim_to_check: total dimension of the model. - num_heads: parallel attention heads. - in_proj_weight, in_proj_bias: input projection weight and bias. - bias_k, bias_v: bias of the key and value sequences to be added at dim=0. - add_zero_attn: add a new batch of zeros to the key and - value sequences at dim=1. - dropout_p: probability of an element to be zeroed. - out_proj_weight, out_proj_bias: the output projection weight and bias. - training: apply dropout if is ``True``. - key_padding_mask: if provided, specified padding elements in the key will - be ignored by the attention. This is an binary mask. When the value is True, - the corresponding value on the attention layer will be filled with -inf. - need_weights: output attn_output_weights. - Default: `True` - Note: `needs_weight` defaults to `True`, but should be set to `False` - For best performance when attention weights are not nedeeded. - *Setting needs_weights to `True` - leads to a significant performance degradation.* - attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all - the batches while a 3D mask allows to specify a different mask for the entries of each batch. - is_causal: If specified, applies a causal mask as attention mask, and ignores - attn_mask for computing scaled dot product attention. - Default: ``False``. - .. warning:: - is_causal is provides a hint that the attn_mask is the - causal mask.Providing incorrect hints can result in - incorrect execution, including forward and backward - compatibility. - use_separate_proj_weight: the function accept the proj. weights for query, key, - and value in different forms. If false, in_proj_weight will be used, which is - a combination of q_proj_weight, k_proj_weight, v_proj_weight. - q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias. - static_k, static_v: static key and value used for attention operators. - average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across heads. - Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an effect - when ``need_weights=True.``. Default: True - - - Shape: - Inputs: - - query: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is - the embedding dimension. - - key: :math:`(S, E)` or :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is - the embedding dimension. - - value: :math:`(S, E)` or :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is - the embedding dimension. - - key_padding_mask: :math:`(S)` or :math:`(N, S)` where N is the batch size, S is the source sequence length. - If a FloatTensor is provided, it will be directly added to the value. - If a BoolTensor is provided, the positions with the - value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. - - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. - 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, - S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked - positions. If a BoolTensor is provided, positions with ``True`` - are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor - is provided, it will be added to the attention weight. - - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length, - N is the batch size, E is the embedding dimension. E/num_heads is the head dimension. - - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length, - N is the batch size, E is the embedding dimension. E/num_heads is the head dimension. - - Outputs: - - attn_output: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size, - E is the embedding dimension. - - attn_output_weights: Only returned when ``need_weights=True``. If ``average_attn_weights=True``, returns - attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or - :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and - :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per - head of shape :math:`(num_heads, L, S)` when input is unbatched or :math:`(N, num_heads, L, S)`. - """ - tens_ops = ( - query, - key, - value, - in_proj_weight, - in_proj_bias, - bias_k, - bias_v, - out_proj_weight, - out_proj_bias, - ) - if has_torch_function(tens_ops): - return handle_torch_function( - multi_head_attention_forward, - tens_ops, - query, - key, - value, - embed_dim_to_check, - num_heads, - in_proj_weight, - in_proj_bias, - bias_k, - bias_v, - add_zero_attn, - dropout_p, - out_proj_weight, - out_proj_bias, - training=training, - key_padding_mask=key_padding_mask, - need_weights=need_weights, - attn_mask=attn_mask, - is_causal=is_causal, - use_separate_proj_weight=use_separate_proj_weight, - q_proj_weight=q_proj_weight, - k_proj_weight=k_proj_weight, - v_proj_weight=v_proj_weight, - static_k=static_k, - static_v=static_v, - average_attn_weights=average_attn_weights, - cache=cache, - ) - - is_batched = _mha_shape_check( - query, key, value, key_padding_mask, attn_mask, num_heads - ) - - # For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input - # is batched, run the computation and before returning squeeze the - # batch dimension so that the output doesn't carry this temporary batch dimension. - if not is_batched: - # unsqueeze if the input is unbatched - query = query.unsqueeze(1) - key = key.unsqueeze(1) - value = value.unsqueeze(1) - if key_padding_mask is not None: - key_padding_mask = key_padding_mask.unsqueeze(0) - - # set up shape vars - tgt_len, bsz, embed_dim = query.shape - src_len, _, _ = key.shape - - key_padding_mask = _canonical_mask( - mask=key_padding_mask, - mask_name="key_padding_mask", - other_type=_none_or_dtype(attn_mask), - other_name="attn_mask", - target_type=query.dtype, - ) - - if is_causal and attn_mask is None: - raise RuntimeError( - "Need attn_mask if specifying the is_causal hint. " - "You may use the Transformer module method " - "`generate_square_subsequent_mask` to create this mask." - ) - - if is_causal and key_padding_mask is None and not need_weights: - # when we have a kpm or need weights, we need attn_mask - # Otherwise, we use the is_causal hint go as is_causal - # indicator to SDPA. - attn_mask = None - else: - attn_mask = _canonical_mask( - mask=attn_mask, - mask_name="attn_mask", - other_type=None, - other_name="", - target_type=query.dtype, - check_other=False, - ) - - if key_padding_mask is not None: - # We have the attn_mask, and use that to merge kpm into it. - # Turn off use of is_causal hint, as the merged mask is no - # longer causal. - is_causal = False - - assert ( - embed_dim == embed_dim_to_check - ), f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}" - if isinstance(embed_dim, torch.Tensor): - # embed_dim can be a tensor when JIT tracing - head_dim = embed_dim.div(num_heads, rounding_mode="trunc") - else: - head_dim = embed_dim // num_heads - assert ( - head_dim * num_heads == embed_dim - ), f"embed_dim {embed_dim} not divisible by num_heads {num_heads}" - if use_separate_proj_weight: - # allow MHA to have different embedding dimensions when separate projection weights are used - assert ( - key.shape[:2] == value.shape[:2] - ), f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}" - else: - assert ( - key.shape == value.shape - ), f"key shape {key.shape} does not match value shape {value.shape}" - - # - # compute in-projection - # - if not use_separate_proj_weight: - assert ( - in_proj_weight is not None - ), "use_separate_proj_weight is False but in_proj_weight is None" - q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias) - else: - assert ( - q_proj_weight is not None - ), "use_separate_proj_weight is True but q_proj_weight is None" - assert ( - k_proj_weight is not None - ), "use_separate_proj_weight is True but k_proj_weight is None" - assert ( - v_proj_weight is not None - ), "use_separate_proj_weight is True but v_proj_weight is None" - if in_proj_bias is None: - b_q = b_k = b_v = None - else: - b_q, b_k, b_v = in_proj_bias.chunk(3) - q, k, v = _in_projection( - query, - key, - value, - q_proj_weight, - k_proj_weight, - v_proj_weight, - b_q, - b_k, - b_v, - ) - if cache != None: - if cache["first_infer"] == 1: - cache["k"][cache["stage"]] = k - # print(0,cache["k"].shape) - cache["v"][cache["stage"]] = v - else: ###12个layer每个都要留自己的cache_kv - # print(1,cache["k"].shape) - cache["k"][cache["stage"]] = torch.cat( - [cache["k"][cache["stage"]], k], 0 - ) ##本来时序是1，但是proj的时候可能transpose了所以时序到0维了 - cache["v"][cache["stage"]] = torch.cat([cache["v"][cache["stage"]], v], 0) - # print(2, cache["k"].shape) - src_len = cache["k"][cache["stage"]].shape[0] - k = cache["k"][cache["stage"]] - v = cache["v"][cache["stage"]] - # if attn_mask is not None: - # attn_mask=attn_mask[-1:,] - # print(attn_mask.shape,attn_mask) - cache["stage"] = (cache["stage"] + 1) % cache["all_stage"] - # print(2333,cache) - # prep attention mask - - attn_mask = _canonical_mask( - mask=attn_mask, - mask_name="attn_mask", - other_type=None, - other_name="", - target_type=q.dtype, - check_other=False, - ) - - if attn_mask is not None: - # ensure attn_mask's dim is 3 - if attn_mask.dim() == 2: - correct_2d_size = (tgt_len, src_len) - if attn_mask.shape != correct_2d_size: - raise RuntimeError( - f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}." - ) - attn_mask = attn_mask.unsqueeze(0) - elif attn_mask.dim() == 3: - correct_3d_size = (bsz * num_heads, tgt_len, src_len) - if attn_mask.shape != correct_3d_size: - raise RuntimeError( - f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}." - ) - else: - raise RuntimeError( - f"attn_mask's dimension {attn_mask.dim()} is not supported" - ) - - # add bias along batch dimension (currently second) - if bias_k is not None and bias_v is not None: - assert static_k is None, "bias cannot be added to static key." - assert static_v is None, "bias cannot be added to static value." - k = torch.cat([k, bias_k.repeat(1, bsz, 1)]) - v = torch.cat([v, bias_v.repeat(1, bsz, 1)]) - if attn_mask is not None: - attn_mask = pad(attn_mask, (0, 1)) - if key_padding_mask is not None: - key_padding_mask = pad(key_padding_mask, (0, 1)) - else: - assert bias_k is None - assert bias_v is None - - # - # reshape q, k, v for multihead attention and make em batch first - # - q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1) - if static_k is None: - k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1) - else: - # TODO finish disentangling control flow so we don't do in-projections when statics are passed - assert ( - static_k.size(0) == bsz * num_heads - ), f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}" - assert ( - static_k.size(2) == head_dim - ), f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}" - k = static_k - if static_v is None: - v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1) - else: - # TODO finish disentangling control flow so we don't do in-projections when statics are passed - assert ( - static_v.size(0) == bsz * num_heads - ), f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}" - assert ( - static_v.size(2) == head_dim - ), f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}" - v = static_v - - # add zero attention along batch dimension (now first) - if add_zero_attn: - zero_attn_shape = (bsz * num_heads, 1, head_dim) - k = torch.cat( - [k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1 - ) - v = torch.cat( - [v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1 - ) - if attn_mask is not None: - attn_mask = pad(attn_mask, (0, 1)) - if key_padding_mask is not None: - key_padding_mask = pad(key_padding_mask, (0, 1)) - - # update source sequence length after adjustments - src_len = k.size(1) - - # merge key padding and attention masks - if key_padding_mask is not None: - assert key_padding_mask.shape == ( - bsz, - src_len, - ), f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}" - key_padding_mask = ( - key_padding_mask.view(bsz, 1, 1, src_len) - .expand(-1, num_heads, -1, -1) - .reshape(bsz * num_heads, 1, src_len) - ) - if attn_mask is None: - attn_mask = key_padding_mask - else: - attn_mask = attn_mask + key_padding_mask - - # adjust dropout probability - if not training: - dropout_p = 0.0 - - # - # (deep breath) calculate attention and out projection - # - - if need_weights: - B, Nt, E = q.shape - q_scaled = q / math.sqrt(E) - - assert not ( - is_causal and attn_mask is None - ), "FIXME: is_causal not implemented for need_weights" - - if attn_mask is not None: - attn_output_weights = torch.baddbmm( - attn_mask, q_scaled, k.transpose(-2, -1) - ) - else: - attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1)) - attn_output_weights = softmax(attn_output_weights, dim=-1) - if dropout_p > 0.0: - attn_output_weights = dropout(attn_output_weights, p=dropout_p) - - attn_output = torch.bmm(attn_output_weights, v) - - attn_output = ( - attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim) - ) - attn_output = linear(attn_output, out_proj_weight, out_proj_bias) - attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1)) - - # optionally average attention weights over heads - attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len) - if average_attn_weights: - attn_output_weights = attn_output_weights.mean(dim=1) - - if not is_batched: - # squeeze the output if input was unbatched - attn_output = attn_output.squeeze(1) - attn_output_weights = attn_output_weights.squeeze(0) - return attn_output, attn_output_weights - else: - # attn_mask can be either (L,S) or (N*num_heads, L, S) - # if attn_mask's shape is (1, L, S) we need to unsqueeze to (1, 1, L, S) - # in order to match the input for SDPA of (N, num_heads, L, S) - if attn_mask is not None: - if attn_mask.size(0) == 1 and attn_mask.dim() == 3: - attn_mask = attn_mask.unsqueeze(0) - else: - attn_mask = attn_mask.view(bsz, num_heads, -1, src_len) - - q = q.view(bsz, num_heads, tgt_len, head_dim) - k = k.view(bsz, num_heads, src_len, head_dim) - v = v.view(bsz, num_heads, src_len, head_dim) - - attn_output = scaled_dot_product_attention( - q, k, v, attn_mask, dropout_p, is_causal - ) - attn_output = ( - attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim) - ) - - attn_output = linear(attn_output, out_proj_weight, out_proj_bias) - attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1)) - if not is_batched: - # squeeze the output if input was unbatched - attn_output = attn_output.squeeze(1) - return attn_output, None diff --git a/AR/modules/patched_mha_with_cache_onnx.py b/AR/modules/patched_mha_with_cache_onnx.py deleted file mode 100644 index 14bdb550a09a2f1dac610ea653848689e6443b4d..0000000000000000000000000000000000000000 --- a/AR/modules/patched_mha_with_cache_onnx.py +++ /dev/null @@ -1,92 +0,0 @@ -from torch.nn.functional import * -from torch.nn.functional import ( - _mha_shape_check, - _canonical_mask, - _none_or_dtype, - _in_projection_packed, -) - -def multi_head_attention_forward_patched( - query, - key, - value, - embed_dim_to_check: int, - num_heads: int, - in_proj_weight, - in_proj_bias: Optional[Tensor], - bias_k: Optional[Tensor], - bias_v: Optional[Tensor], - add_zero_attn: bool, - dropout_p: float, - out_proj_weight: Tensor, - out_proj_bias: Optional[Tensor], - training: bool = True, - key_padding_mask: Optional[Tensor] = None, - need_weights: bool = True, - attn_mask: Optional[Tensor] = None, - use_separate_proj_weight: bool = False, - q_proj_weight: Optional[Tensor] = None, - k_proj_weight: Optional[Tensor] = None, - v_proj_weight: Optional[Tensor] = None, - static_k: Optional[Tensor] = None, - static_v: Optional[Tensor] = None, - average_attn_weights: bool = True, - is_causal: bool = False, - cache=None, -) -> Tuple[Tensor, Optional[Tensor]]: - - # set up shape vars - _, _, embed_dim = query.shape - attn_mask = _canonical_mask( - mask=attn_mask, - mask_name="attn_mask", - other_type=None, - other_name="", - target_type=query.dtype, - check_other=False, - ) - head_dim = embed_dim // num_heads - - proj_qkv = linear(query, in_proj_weight, in_proj_bias) - proj_qkv = proj_qkv.unflatten(-1, (3, query.size(-1))).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous() - q, k, v = proj_qkv[0], proj_qkv[1], proj_qkv[2] - - if cache["first_infer"] == 1: - cache["k"][cache["stage"]] = k - cache["v"][cache["stage"]] = v - else: - cache["k"][cache["stage"]] = torch.cat([cache["k"][cache["stage"]][:-1], k], 0) - cache["v"][cache["stage"]] = torch.cat([cache["v"][cache["stage"]][:-1], v], 0) - k = cache["k"][cache["stage"]] - v = cache["v"][cache["stage"]] - cache["stage"] = (cache["stage"] + 1) % cache["all_stage"] - - attn_mask = _canonical_mask( - mask=attn_mask, - mask_name="attn_mask", - other_type=None, - other_name="", - target_type=q.dtype, - check_other=False, - ) - attn_mask = attn_mask.unsqueeze(0) - - q = q.view(-1, num_heads, head_dim).transpose(0, 1) - k = k.view(-1, num_heads, head_dim).transpose(0, 1) - v = v.view(-1, num_heads, head_dim).transpose(0, 1) - - dropout_p = 0.0 - attn_mask = attn_mask.unsqueeze(0) - q = q.view(num_heads, -1, head_dim).unsqueeze(0) - k = k.view(num_heads, -1, head_dim).unsqueeze(0) - v = v.view(num_heads, -1, head_dim).unsqueeze(0) - attn_output = scaled_dot_product_attention( - q, k, v, attn_mask, dropout_p, is_causal - ) - attn_output = ( - attn_output.permute(2, 0, 1, 3).contiguous().view(-1, embed_dim) - ) - attn_output = linear(attn_output, out_proj_weight, out_proj_bias) - attn_output = attn_output.view(-1, 1, attn_output.size(1)) - - return attn_output diff --git a/AR/modules/scaling.py b/AR/modules/scaling.py deleted file mode 100644 index 80bb8374393073796d25cc985587d1904681dfaf..0000000000000000000000000000000000000000 --- a/AR/modules/scaling.py +++ /dev/null @@ -1,335 +0,0 @@ -# Copyright 2022 Xiaomi Corp. (authors: Daniel Povey) -# -# See ../../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -#import logging -import math -import random -from typing import Optional -from typing import Tuple -from typing import Union - -import torch -import torch.nn as nn -from torch import Tensor - - -class DoubleSwishFunction(torch.autograd.Function): - """ - double_swish(x) = x * torch.sigmoid(x-1) - This is a definition, originally motivated by its close numerical - similarity to swish(swish(x)), where swish(x) = x * sigmoid(x). - - Memory-efficient derivative computation: - double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1) - double_swish'(x) = d/dx double_swish(x) = x * s'(x) + x' * s(x) = x * s'(x) + s(x). - Now, s'(x) = s(x) * (1-s(x)). - double_swish'(x) = x * s'(x) + s(x). - = x * s(x) * (1-s(x)) + s(x). - = double_swish(x) * (1-s(x)) + s(x) - ... so we just need to remember s(x) but not x itself. - """ - - @staticmethod - def forward(ctx, x: Tensor) -> Tensor: - requires_grad = x.requires_grad - x_dtype = x.dtype - if x.dtype == torch.float16: - x = x.to(torch.float32) - - s = torch.sigmoid(x - 1.0) - y = x * s - - if requires_grad: - deriv = y * (1 - s) + s - # notes on derivative of x * sigmoid(x - 1): - # https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29 - # min \simeq -0.043638. Take floor as -0.043637 so it's a lower bund - # max \simeq 1.1990. Take ceil to be 1.2 so it's an upper bound. - # the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which - # floors), should be expectation-preserving. - floor = -0.043637 - ceil = 1.2 - d_scaled = (deriv - floor) * (255.0 / (ceil - floor)) + torch.rand_like( - deriv - ) - if __name__ == "__main__": - # for self-testing only. - assert d_scaled.min() >= 0.0 - assert d_scaled.max() < 256.0 - d_int = d_scaled.to(torch.uint8) - ctx.save_for_backward(d_int) - if x.dtype == torch.float16 or torch.is_autocast_enabled(): - y = y.to(torch.float16) - return y - - @staticmethod - def backward(ctx, y_grad: Tensor) -> Tensor: - (d,) = ctx.saved_tensors - # the same constants as used in forward pass. - floor = -0.043637 - ceil = 1.2 - d = d * ((ceil - floor) / 255.0) + floor - return y_grad * d - - -class DoubleSwish(torch.nn.Module): - def forward(self, x: Tensor) -> Tensor: - """Return double-swish activation function which is an approximation to Swish(Swish(x)), - that we approximate closely with x * sigmoid(x-1). - """ - if torch.jit.is_scripting() or torch.jit.is_tracing(): - return x * torch.sigmoid(x - 1.0) - return DoubleSwishFunction.apply(x) - - -class ActivationBalancerFunction(torch.autograd.Function): - @staticmethod - def forward( - ctx, - x: Tensor, - scale_factor: Tensor, - sign_factor: Optional[Tensor], - channel_dim: int, - ) -> Tensor: - if channel_dim < 0: - channel_dim += x.ndim - ctx.channel_dim = channel_dim - xgt0 = x > 0 - if sign_factor is None: - ctx.save_for_backward(xgt0, scale_factor) - else: - ctx.save_for_backward(xgt0, scale_factor, sign_factor) - return x - - @staticmethod - def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]: - if len(ctx.saved_tensors) == 3: - xgt0, scale_factor, sign_factor = ctx.saved_tensors - for _ in range(ctx.channel_dim, x_grad.ndim - 1): - scale_factor = scale_factor.unsqueeze(-1) - sign_factor = sign_factor.unsqueeze(-1) - factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5) - else: - xgt0, scale_factor = ctx.saved_tensors - for _ in range(ctx.channel_dim, x_grad.ndim - 1): - scale_factor = scale_factor.unsqueeze(-1) - factor = scale_factor * (xgt0.to(x_grad.dtype) - 0.5) - neg_delta_grad = x_grad.abs() * factor - return ( - x_grad - neg_delta_grad, - None, - None, - None, - ) - - -def _compute_scale_factor( - x: Tensor, - channel_dim: int, - min_abs: float, - max_abs: float, - gain_factor: float, - max_factor: float, -) -> Tensor: - if channel_dim < 0: - channel_dim += x.ndim - sum_dims = [d for d in range(x.ndim) if d != channel_dim] - x_abs_mean = torch.mean(x.abs(), dim=sum_dims).to(torch.float32) - - if min_abs == 0.0: - below_threshold = 0.0 - else: - # below_threshold is 0 if x_abs_mean > min_abs, can be at most max_factor if - # x_abs)_mean , min_abs. - below_threshold = ((min_abs - x_abs_mean) * (gain_factor / min_abs)).clamp( - min=0, max=max_factor - ) - - above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp( - min=0, max=max_factor - ) - - return below_threshold - above_threshold - - -def _compute_sign_factor( - x: Tensor, - channel_dim: int, - min_positive: float, - max_positive: float, - gain_factor: float, - max_factor: float, -) -> Tensor: - if channel_dim < 0: - channel_dim += x.ndim - sum_dims = [d for d in range(x.ndim) if d != channel_dim] - proportion_positive = torch.mean((x > 0).to(torch.float32), dim=sum_dims) - if min_positive == 0.0: - factor1 = 0.0 - else: - # 0 if proportion_positive >= min_positive, else can be - # as large as max_factor. - factor1 = ( - (min_positive - proportion_positive) * (gain_factor / min_positive) - ).clamp_(min=0, max=max_factor) - - if max_positive == 1.0: - factor2 = 0.0 - else: - # 0 if self.proportion_positive <= max_positive, else can be - # as large as -max_factor. - factor2 = ( - (proportion_positive - max_positive) * (gain_factor / (1.0 - max_positive)) - ).clamp_(min=0, max=max_factor) - sign_factor = factor1 - factor2 - # require min_positive != 0 or max_positive != 1: - assert not isinstance(sign_factor, float) - return sign_factor - - -class ActivationBalancer(torch.nn.Module): - """ - Modifies the backpropped derivatives of a function to try to encourage, for - each channel, that it is positive at least a proportion `threshold` of the - time. It does this by multiplying negative derivative values by up to - (1+max_factor), and positive derivative values by up to (1-max_factor), - interpolated from 1 at the threshold to those extremal values when none - of the inputs are positive. - - Args: - num_channels: the number of channels - channel_dim: the dimension/axis corresponding to the channel, e.g. - -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative. - min_positive: the minimum, per channel, of the proportion of the time - that (x > 0), below which we start to modify the derivatives. - max_positive: the maximum, per channel, of the proportion of the time - that (x > 0), above which we start to modify the derivatives. - max_factor: the maximum factor by which we modify the derivatives for - either the sign constraint or the magnitude constraint; - e.g. with max_factor=0.02, the the derivatives would be multiplied by - values in the range [0.98..1.02]. - sign_gain_factor: determines the 'gain' with which we increase the - change in gradient once the constraints on min_positive and max_positive - are violated. - scale_gain_factor: determines the 'gain' with which we increase the - change in gradient once the constraints on min_abs and max_abs - are violated. - min_abs: the minimum average-absolute-value difference from the mean - value per channel, which we allow, before we start to modify - the derivatives to prevent this. - max_abs: the maximum average-absolute-value difference from the mean - value per channel, which we allow, before we start to modify - the derivatives to prevent this. - min_prob: determines the minimum probability with which we modify the - gradients for the {min,max}_positive and {min,max}_abs constraints, - on each forward(). This is done randomly to prevent all layers - from doing it at the same time. Early in training we may use - higher probabilities than this; it will decay to this value. - """ - - def __init__( - self, - num_channels: int, - channel_dim: int, - min_positive: float = 0.05, - max_positive: float = 0.95, - max_factor: float = 0.04, - sign_gain_factor: float = 0.01, - scale_gain_factor: float = 0.02, - min_abs: float = 0.2, - max_abs: float = 100.0, - min_prob: float = 0.1, - ): - super(ActivationBalancer, self).__init__() - self.num_channels = num_channels - self.channel_dim = channel_dim - self.min_positive = min_positive - self.max_positive = max_positive - self.max_factor = max_factor - self.min_abs = min_abs - self.max_abs = max_abs - self.min_prob = min_prob - self.sign_gain_factor = sign_gain_factor - self.scale_gain_factor = scale_gain_factor - - # count measures how many times the forward() function has been called. - # We occasionally sync this to a tensor called `count`, that exists to - # make sure it is synced to disk when we load and save the model. - self.cpu_count = 0 - self.register_buffer("count", torch.tensor(0, dtype=torch.int64)) - - def forward(self, x: Tensor) -> Tensor: - if torch.jit.is_scripting() or not x.requires_grad or torch.jit.is_tracing(): - return _no_op(x) - - count = self.cpu_count - self.cpu_count += 1 - - if random.random() < 0.01: - # Occasionally sync self.cpu_count with self.count. - # count affects the decay of 'prob'. don't do this on every iter, - # because syncing with the GPU is slow. - self.cpu_count = max(self.cpu_count, self.count.item()) - self.count.fill_(self.cpu_count) - - # the prob of doing some work exponentially decreases from 0.5 till it hits - # a floor at min_prob (==0.1, by default) - prob = max(self.min_prob, 0.5 ** (1 + (count / 4000.0))) - - if random.random() < prob: - sign_gain_factor = 0.5 - if self.min_positive != 0.0 or self.max_positive != 1.0: - sign_factor = _compute_sign_factor( - x, - self.channel_dim, - self.min_positive, - self.max_positive, - gain_factor=self.sign_gain_factor / prob, - max_factor=self.max_factor, - ) - else: - sign_factor = None - - scale_factor = _compute_scale_factor( - x.detach(), - self.channel_dim, - min_abs=self.min_abs, - max_abs=self.max_abs, - gain_factor=self.scale_gain_factor / prob, - max_factor=self.max_factor, - ) - return ActivationBalancerFunction.apply( - x, - scale_factor, - sign_factor, - self.channel_dim, - ) - else: - return _no_op(x) - - -def BalancedDoubleSwish( - d_model, channel_dim=-1, max_abs=10.0, min_prob=0.25 -) -> nn.Sequential: - """ - ActivationBalancer -> DoubleSwish - """ - balancer = ActivationBalancer( - d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob - ) - return nn.Sequential( - balancer, - DoubleSwish(), - ) diff --git a/AR/modules/transformer.py b/AR/modules/transformer.py deleted file mode 100644 index 7921f48e70bd6849897389d8e0a39d1ac4062b97..0000000000000000000000000000000000000000 --- a/AR/modules/transformer.py +++ /dev/null @@ -1,378 +0,0 @@ -# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/transformer.py -import copy -import numbers -from functools import partial -from typing import Any -from typing import Callable -from typing import List -from typing import Optional -from typing import Tuple -from typing import Union - -import torch -from AR.modules.activation import MultiheadAttention -from AR.modules.scaling import BalancedDoubleSwish -from torch import nn -from torch import Tensor -from torch.nn import functional as F - -_shape_t = Union[int, List[int], torch.Size] - - -class LayerNorm(nn.Module): - __constants__ = ["normalized_shape", "eps", "elementwise_affine"] - normalized_shape: Tuple[int, ...] - eps: float - elementwise_affine: bool - - def __init__( - self, - normalized_shape: _shape_t, - eps: float = 1e-5, - elementwise_affine: bool = True, - device=None, - dtype=None, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(LayerNorm, self).__init__() - if isinstance(normalized_shape, numbers.Integral): - # mypy error: incompatible types in assignment - normalized_shape = (normalized_shape,) # type: ignore[assignment] - self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type] - self.eps = eps - self.elementwise_affine = elementwise_affine - if self.elementwise_affine: - self.weight = nn.Parameter( - torch.empty(self.normalized_shape, **factory_kwargs) - ) - self.bias = nn.Parameter( - torch.empty(self.normalized_shape, **factory_kwargs) - ) - else: - self.register_parameter("weight", None) - self.register_parameter("bias", None) - - self.reset_parameters() - - def reset_parameters(self) -> None: - if self.elementwise_affine: - nn.init.ones_(self.weight) - nn.init.zeros_(self.bias) - - def forward(self, input: Tensor, embedding: Any = None) -> Tensor: - if isinstance(input, tuple): - input, embedding = input - return ( - F.layer_norm( - input, - self.normalized_shape, - self.weight, - self.bias, - self.eps, - ), - embedding, - ) - - assert embedding is None - return F.layer_norm( - input, self.normalized_shape, self.weight, self.bias, self.eps - ) - - def extra_repr(self) -> str: - return ( - "{normalized_shape}, eps={eps}, " - "elementwise_affine={elementwise_affine}".format(**self.__dict__) - ) - - -class IdentityNorm(nn.Module): - def __init__( - self, - d_model: int, - eps: float = 1e-5, - device=None, - dtype=None, - ) -> None: - super(IdentityNorm, self).__init__() - - def forward(self, input: Tensor, embedding: Any = None) -> Tensor: - if isinstance(input, tuple): - return input - - assert embedding is None - return input - - -class TransformerEncoder(nn.Module): - r"""TransformerEncoder is a stack of N encoder layers. Users can build the - BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters. - - Args: - encoder_layer: an instance of the TransformerEncoderLayer() class (required). - num_layers: the number of sub-encoder-layers in the encoder (required). - norm: the layer normalization component (optional). - enable_nested_tensor: if True, input will automatically convert to nested tensor - (and convert back on output). This will improve the overall performance of - TransformerEncoder when padding rate is high. Default: ``True`` (enabled). - - Examples:: - >>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8) - >>> transformer_encoder = TransformerEncoder(encoder_layer, num_layers=6) - >>> src = torch.rand(10, 32, 512) - >>> out = transformer_encoder(src) - """ - __constants__ = ["norm"] - - def __init__(self, encoder_layer, num_layers, norm=None): - super(TransformerEncoder, self).__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward( - self, - src: Tensor, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - return_layer_states: bool = False, - cache=None, - ) -> Tensor: - r"""Pass the input through the encoder layers in turn. - - Args: - src: the sequence to the encoder (required). - mask: the mask for the src sequence (optional). - src_key_padding_mask: the mask for the src keys per batch (optional). - return_layer_states: return layers' state (optional). - - Shape: - see the docs in Transformer class. - """ - if return_layer_states: - layer_states = [] # layers' output - output = src - for mod in self.layers: - output = mod( - output, - src_mask=mask, - src_key_padding_mask=src_key_padding_mask, - cache=cache, - ) - layer_states.append(output[0]) - - if self.norm is not None: - output = self.norm(output) - - return layer_states, output - - output = src - for mod in self.layers: - output = mod( - output, - src_mask=mask, - src_key_padding_mask=src_key_padding_mask, - cache=cache, - ) - - if self.norm is not None: - output = self.norm(output) - - return output - - -class TransformerEncoderLayer(nn.Module): - __constants__ = ["batch_first", "norm_first"] - - def __init__( - self, - d_model: int, - nhead: int, - dim_feedforward: int = 2048, - dropout: float = 0.1, - activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, - batch_first: bool = False, - norm_first: bool = False, - device=None, - dtype=None, - linear1_self_attention_cls: nn.Module = nn.Linear, - linear2_self_attention_cls: nn.Module = nn.Linear, - linear1_feedforward_cls: nn.Module = nn.Linear, - linear2_feedforward_cls: nn.Module = nn.Linear, - layer_norm_cls: nn.Module = LayerNorm, - layer_norm_eps: float = 1e-5, - adaptive_layer_norm=False, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(TransformerEncoderLayer, self).__init__() - # print(233333333333,d_model,nhead) - # import os - # os._exit(2333333) - self.self_attn = MultiheadAttention( - d_model, # 512 16 - nhead, - dropout=dropout, - batch_first=batch_first, - linear1_cls=linear1_self_attention_cls, - linear2_cls=linear2_self_attention_cls, - **factory_kwargs, - ) - - # Implementation of Feedforward model - self.linear1 = linear1_feedforward_cls( - d_model, dim_feedforward, **factory_kwargs - ) - self.dropout = nn.Dropout(dropout) - self.linear2 = linear2_feedforward_cls( - dim_feedforward, d_model, **factory_kwargs - ) - - self.norm_first = norm_first - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - # Legacy string support for activation function. - if isinstance(activation, str): - activation = _get_activation_fn(activation) - elif isinstance(activation, partial): - activation = activation(d_model) - elif activation == BalancedDoubleSwish: - activation = BalancedDoubleSwish(d_model) - - # # We can't test self.activation in forward() in TorchScript, - # # so stash some information about it instead. - # if activation is F.relu or isinstance(activation, torch.nn.ReLU): - # self.activation_relu_or_gelu = 1 - # elif activation is F.gelu or isinstance(activation, torch.nn.GELU): - # self.activation_relu_or_gelu = 2 - # else: - # self.activation_relu_or_gelu = 0 - self.activation = activation - - norm1 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs) - if layer_norm_cls == IdentityNorm: - norm2 = BalancedBasicNorm(d_model, eps=layer_norm_eps, **factory_kwargs) - else: - norm2 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs) - - if adaptive_layer_norm: - self.norm1 = AdaptiveLayerNorm(d_model, norm1) - self.norm2 = AdaptiveLayerNorm(d_model, norm2) - else: - self.norm1 = norm1 - self.norm2 = norm2 - - def __setstate__(self, state): - super(TransformerEncoderLayer, self).__setstate__(state) - if not hasattr(self, "activation"): - self.activation = F.relu - - def forward( - self, - src: Tensor, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - cache=None, - ) -> Tensor: - r"""Pass the input through the encoder layer. - - Args: - src: the sequence to the encoder layer (required). - src_mask: the mask for the src sequence (optional). - src_key_padding_mask: the mask for the src keys per batch (optional). - - Shape: - see the docs in Transformer class. - """ - x, stage_embedding = src, None - is_src_tuple = False - if isinstance(src, tuple): - x, stage_embedding = src - is_src_tuple = True - - if src_key_padding_mask is not None: - _skpm_dtype = src_key_padding_mask.dtype - if _skpm_dtype != torch.bool and not torch.is_floating_point( - src_key_padding_mask - ): - raise AssertionError( - "only bool and floating types of key_padding_mask are supported" - ) - - if self.norm_first: - x = x + self._sa_block( - self.norm1(x, stage_embedding), - src_mask, - src_key_padding_mask, - cache=cache, - ) - x = x + self._ff_block(self.norm2(x, stage_embedding)) - else: - x = self.norm1( - x + self._sa_block(x, src_mask, src_key_padding_mask, cache=cache), - stage_embedding, - ) - x = self.norm2(x + self._ff_block(x), stage_embedding) - - if is_src_tuple: - return (x, stage_embedding) - return x - - # self-attention block - def _sa_block( - self, - x: Tensor, - attn_mask: Optional[Tensor], - key_padding_mask: Optional[Tensor], - cache=None, - ) -> Tensor: - # print(x.shape,attn_mask.shape,key_padding_mask) - # torch.Size([1, 188, 512]) torch.Size([188, 188]) None - # import os - # os._exit(23333) - x = self.self_attn( - x, - x, - x, - attn_mask=attn_mask, - key_padding_mask=key_padding_mask, - need_weights=False, - cache=cache, - )[0] - return self.dropout1(x) - - # feed forward block - def _ff_block(self, x: Tensor) -> Tensor: - x = self.linear2(self.dropout(self.activation(self.linear1(x)))) - return self.dropout2(x) - - -class AdaptiveLayerNorm(nn.Module): - r"""Adaptive Layer Normalization""" - - def __init__(self, d_model, norm) -> None: - super(AdaptiveLayerNorm, self).__init__() - self.project_layer = nn.Linear(d_model, 2 * d_model) - self.norm = norm - self.d_model = d_model - self.eps = self.norm.eps - - def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor: - if isinstance(input, tuple): - input, embedding = input - weight, bias = torch.split( - self.project_layer(embedding), - split_size_or_sections=self.d_model, - dim=-1, - ) - return (weight * self.norm(input) + bias, embedding) - - weight, bias = torch.split( - self.project_layer(embedding), - split_size_or_sections=self.d_model, - dim=-1, - ) - return weight * self.norm(input) + bias - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) diff --git a/AR/modules/transformer_onnx.py b/AR/modules/transformer_onnx.py deleted file mode 100644 index a3f68b43e7a4f3c8d989140009477e07a2d44d19..0000000000000000000000000000000000000000 --- a/AR/modules/transformer_onnx.py +++ /dev/null @@ -1,292 +0,0 @@ -# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/transformer.py -import copy -import numbers -from functools import partial -from typing import Any -from typing import Callable -from typing import List -from typing import Optional -from typing import Tuple -from typing import Union - -import torch -from AR.modules.activation_onnx import MultiheadAttention -from AR.modules.scaling import BalancedDoubleSwish -from torch import nn -from torch import Tensor -from torch.nn import functional as F - -_shape_t = Union[int, List[int], torch.Size] - - -class LayerNorm(nn.Module): - __constants__ = ["normalized_shape", "eps", "elementwise_affine"] - normalized_shape: Tuple[int, ...] - eps: float - elementwise_affine: bool - - def __init__( - self, - normalized_shape: _shape_t, - eps: float = 1e-5, - elementwise_affine: bool = True, - device=None, - dtype=None, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(LayerNorm, self).__init__() - if isinstance(normalized_shape, numbers.Integral): - # mypy error: incompatible types in assignment - normalized_shape = (normalized_shape,) # type: ignore[assignment] - self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type] - self.eps = eps - self.elementwise_affine = elementwise_affine - if self.elementwise_affine: - self.weight = nn.Parameter( - torch.empty(self.normalized_shape, **factory_kwargs) - ) - self.bias = nn.Parameter( - torch.empty(self.normalized_shape, **factory_kwargs) - ) - else: - self.register_parameter("weight", None) - self.register_parameter("bias", None) - - self.reset_parameters() - - def reset_parameters(self) -> None: - if self.elementwise_affine: - nn.init.ones_(self.weight) - nn.init.zeros_(self.bias) - - def forward(self, input: Tensor, embedding: Any = None) -> Tensor: - if isinstance(input, tuple): - input, embedding = input - return ( - F.layer_norm( - input, - self.normalized_shape, - self.weight, - self.bias, - self.eps, - ), - embedding, - ) - - assert embedding is None - return F.layer_norm( - input, self.normalized_shape, self.weight, self.bias, self.eps - ) - - def extra_repr(self) -> str: - return ( - "{normalized_shape}, eps={eps}, " - "elementwise_affine={elementwise_affine}".format(**self.__dict__) - ) - - -class IdentityNorm(nn.Module): - def __init__( - self, - d_model: int, - eps: float = 1e-5, - device=None, - dtype=None, - ) -> None: - super(IdentityNorm, self).__init__() - - def forward(self, input: Tensor, embedding: Any = None) -> Tensor: - if isinstance(input, tuple): - return input - - assert embedding is None - return input - - -class TransformerEncoder(nn.Module): - r"""TransformerEncoder is a stack of N encoder layers. Users can build the - BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters. - - Args: - encoder_layer: an instance of the TransformerEncoderLayer() class (required). - num_layers: the number of sub-encoder-layers in the encoder (required). - norm: the layer normalization component (optional). - enable_nested_tensor: if True, input will automatically convert to nested tensor - (and convert back on output). This will improve the overall performance of - TransformerEncoder when padding rate is high. Default: ``True`` (enabled). - - Examples:: - >>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8) - >>> transformer_encoder = TransformerEncoder(encoder_layer, num_layers=6) - >>> src = torch.rand(10, 32, 512) - >>> out = transformer_encoder(src) - """ - __constants__ = ["norm"] - - def __init__(self, encoder_layer, num_layers, norm=None): - super(TransformerEncoder, self).__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward( - self, - src: Tensor, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - return_layer_states: bool = False, - cache=None, - ) -> Tensor: - output = src - for mod in self.layers: - output = mod( - output, - src_mask=mask, - src_key_padding_mask=src_key_padding_mask, - cache=cache, - ) - - if self.norm is not None: - output = self.norm(output) - - return output - - -class TransformerEncoderLayer(nn.Module): - __constants__ = ["batch_first", "norm_first"] - def __init__( - self, - d_model: int, - nhead: int, - dim_feedforward: int = 2048, - dropout: float = 0.1, - activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, - batch_first: bool = False, - norm_first: bool = False, - device=None, - dtype=None, - linear1_self_attention_cls: nn.Module = nn.Linear, - linear2_self_attention_cls: nn.Module = nn.Linear, - linear1_feedforward_cls: nn.Module = nn.Linear, - linear2_feedforward_cls: nn.Module = nn.Linear, - layer_norm_cls: nn.Module = LayerNorm, - layer_norm_eps: float = 1e-5, - adaptive_layer_norm=False, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(TransformerEncoderLayer, self).__init__() - self.self_attn = MultiheadAttention( - d_model, # 512 16 - nhead, - dropout=dropout, - batch_first=batch_first, - linear1_cls=linear1_self_attention_cls, - linear2_cls=linear2_self_attention_cls, - **factory_kwargs, - ) - self.linear1 = linear1_feedforward_cls( - d_model, dim_feedforward, **factory_kwargs - ) - self.dropout = nn.Dropout(dropout) - self.linear2 = linear2_feedforward_cls( - dim_feedforward, d_model, **factory_kwargs - ) - self.norm_first = norm_first - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - if isinstance(activation, str): - activation = _get_activation_fn(activation) - elif isinstance(activation, partial): - activation = activation(d_model) - elif activation == BalancedDoubleSwish: - activation = BalancedDoubleSwish(d_model) - self.activation = activation - - norm1 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs) - if layer_norm_cls == IdentityNorm: - norm2 = BalancedBasicNorm(d_model, eps=layer_norm_eps, **factory_kwargs) - else: - norm2 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs) - - if adaptive_layer_norm: - self.norm1 = AdaptiveLayerNorm(d_model, norm1) - self.norm2 = AdaptiveLayerNorm(d_model, norm2) - else: - self.norm1 = norm1 - self.norm2 = norm2 - - def __setstate__(self, state): - super(TransformerEncoderLayer, self).__setstate__(state) - if not hasattr(self, "activation"): - self.activation = F.relu - - def forward( - self, - src: Tensor, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - cache=None, - ) -> Tensor: - x = src - stage_embedding = None - x = self.norm1( - x + self._sa_block(x, src_mask, src_key_padding_mask, cache=cache), - stage_embedding, - ) - x = self.norm2(x + self._ff_block(x), stage_embedding) - - return x - - def _sa_block( - self, - x: Tensor, - attn_mask: Optional[Tensor], - key_padding_mask: Optional[Tensor], - cache=None, - ) -> Tensor: - x = self.self_attn( - x, - x, - x, - attn_mask=attn_mask, - key_padding_mask=key_padding_mask, - need_weights=False, - cache=cache, - ) - return self.dropout1(x) - - def _ff_block(self, x: Tensor) -> Tensor: - x = self.linear2(self.dropout(self.activation(self.linear1(x)))) - return self.dropout2(x) - - -class AdaptiveLayerNorm(nn.Module): - r"""Adaptive Layer Normalization""" - - def __init__(self, d_model, norm) -> None: - super(AdaptiveLayerNorm, self).__init__() - self.project_layer = nn.Linear(d_model, 2 * d_model) - self.norm = norm - self.d_model = d_model - self.eps = self.norm.eps - - def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor: - if isinstance(input, tuple): - input, embedding = input - weight, bias = torch.split( - self.project_layer(embedding), - split_size_or_sections=self.d_model, - dim=-1, - ) - return (weight * self.norm(input) + bias, embedding) - - weight, bias = torch.split( - self.project_layer(embedding), - split_size_or_sections=self.d_model, - dim=-1, - ) - return weight * self.norm(input) + bias - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) diff --git a/AR/text_processing/__init__.py b/AR/text_processing/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/AR/text_processing/phonemizer.py b/AR/text_processing/phonemizer.py deleted file mode 100644 index 9fcf5c09f24ee81436d7765d1430ecf508a524bd..0000000000000000000000000000000000000000 --- a/AR/text_processing/phonemizer.py +++ /dev/null @@ -1,78 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/text_processing/phonemizer.py -import itertools -import re -from typing import Dict -from typing import List - -import regex -from gruut import sentences -from gruut.const import Sentence -from gruut.const import Word -from AR.text_processing.symbols import SYMBOL_TO_ID - - -class GruutPhonemizer: - def __init__(self, language: str): - self._phonemizer = sentences - self.lang = language - self.symbol_to_id = SYMBOL_TO_ID - self._special_cases_dict: Dict[str] = { - r"\.\.\.": "... ", - ";": "; ", - ":": ": ", - ",": ", ", - r"\.": ". ", - "!": "! ", - r"\?": "? ", - "—": "—", - "…": "… ", - "«": "«", - "»": "»", - } - self._punctuation_regexp: str = ( - rf"([{''.join(self._special_cases_dict.keys())}])" - ) - - def _normalize_punctuation(self, text: str) -> str: - text = regex.sub(rf"\pZ+{self._punctuation_regexp}", r"\1", text) - text = regex.sub(rf"{self._punctuation_regexp}(\pL)", r"\1 \2", text) - text = regex.sub(r"\pZ+", r" ", text) - return text.strip() - - def _convert_punctuation(self, word: Word) -> str: - if not word.phonemes: - return "" - if word.phonemes[0] in ["‖", "|"]: - return word.text.strip() - - phonemes = "".join(word.phonemes) - # remove modifier characters ˈˌː with regex - phonemes = re.sub(r"[ˈˌː͡]", "", phonemes) - return phonemes.strip() - - def phonemize(self, text: str, espeak: bool = False) -> str: - text_to_phonemize: str = self._normalize_punctuation(text) - sents: List[Sentence] = [ - sent - for sent in self._phonemizer(text_to_phonemize, lang="en-us", espeak=espeak) - ] - words: List[str] = [ - self._convert_punctuation(word) for word in itertools.chain(*sents) - ] - return " ".join(words) - - def transform(self, phonemes): - # convert phonemes to ids - # dictionary is in symbols.py - return [self.symbol_to_id[p] for p in phonemes if p in self.symbol_to_id.keys()] - - -if __name__ == "__main__": - phonemizer = GruutPhonemizer("en-us") - # text -> IPA - phonemes = phonemizer.phonemize("Hello, wor-ld ?") - print("phonemes:", phonemes) - print("len(phonemes):", len(phonemes)) - phoneme_ids = phonemizer.transform(phonemes) - print("phoneme_ids:", phoneme_ids) - print("len(phoneme_ids):", len(phoneme_ids)) diff --git a/AR/text_processing/symbols.py b/AR/text_processing/symbols.py deleted file mode 100644 index c57e2d4133964bea9617081ea8f15b034e3833fb..0000000000000000000000000000000000000000 --- a/AR/text_processing/symbols.py +++ /dev/null @@ -1,9 +0,0 @@ -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/text_processing/symbols.py -PAD = "_" -PUNCTUATION = ';:,.!?¡¿—…"«»“” ' -LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" -IPA_LETTERS = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ" -SYMBOLS = [PAD] + list(PUNCTUATION) + list(LETTERS) + list(IPA_LETTERS) -SPACE_ID = SYMBOLS.index(" ") -SYMBOL_TO_ID = {s: i for i, s in enumerate(SYMBOLS)} -ID_TO_SYMBOL = {i: s for i, s in enumerate(SYMBOLS)} diff --git a/AR/utils/__init__.py b/AR/utils/__init__.py deleted file mode 100644 index c2eaf61adcfee96d6e7ec8fd70a7603e18afb567..0000000000000000000000000000000000000000 --- a/AR/utils/__init__.py +++ /dev/null @@ -1,37 +0,0 @@ -import re - - -def str2bool(str): - return True if str.lower() == 'true' else False - - -def get_newest_ckpt(string_list): - # 定义一个正则表达式模式，用于匹配字符串中的数字 - pattern = r'epoch=(\d+)-step=(\d+)\.ckpt' - - # 使用正则表达式提取每个字符串中的数字信息，并创建一个包含元组的列表 - extracted_info = [] - for string in string_list: - match = re.match(pattern, string) - if match: - epoch = int(match.group(1)) - step = int(match.group(2)) - extracted_info.append((epoch, step, string)) - # 按照 epoch 后面的数字和 step 后面的数字进行排序 - sorted_info = sorted( - extracted_info, key=lambda x: (x[0], x[1]), reverse=True) - # 获取最新的 ckpt 文件名 - newest_ckpt = sorted_info[0][2] - return newest_ckpt - - -# 文本存在且不为空时 return True -def check_txt_file(file_path): - try: - with open(file_path, 'r') as file: - text = file.readline().strip() - assert text.strip() != '' - return text - except Exception: - return False - return False diff --git a/AR/utils/initialize.py b/AR/utils/initialize.py deleted file mode 100644 index 17ff9f92e51c8941973139d6e34d4a1c7cd8daaa..0000000000000000000000000000000000000000 --- a/AR/utils/initialize.py +++ /dev/null @@ -1,38 +0,0 @@ -#!/usr/bin/env python3 -"""Initialize modules for espnet2 neural networks.""" -import torch -from typeguard import check_argument_types - - -def initialize(model: torch.nn.Module, init: str): - """Initialize weights of a neural network module. - - Parameters are initialized using the given method or distribution. - - Custom initialization routines can be implemented into submodules - as function `espnet_initialization_fn` within the custom module. - - Args: - model: Target. - init: Method of initialization. - """ - assert check_argument_types() - print("init with", init) - - # weight init - for p in model.parameters(): - if p.dim() > 1: - if init == "xavier_uniform": - torch.nn.init.xavier_uniform_(p.data) - elif init == "xavier_normal": - torch.nn.init.xavier_normal_(p.data) - elif init == "kaiming_uniform": - torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu") - elif init == "kaiming_normal": - torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu") - else: - raise ValueError("Unknown initialization: " + init) - # bias init - for name, p in model.named_parameters(): - if ".bias" in name and p.dim() == 1: - p.data.zero_() diff --git a/AR/utils/io.py b/AR/utils/io.py deleted file mode 100644 index 52f1f3c991506a9ea5d3fdbc71ded61e914694f1..0000000000000000000000000000000000000000 --- a/AR/utils/io.py +++ /dev/null @@ -1,34 +0,0 @@ -import sys - -import torch -import yaml - - -def load_yaml_config(path): - with open(path) as f: - config = yaml.full_load(f) - return config - - -def save_config_to_yaml(config, path): - assert path.endswith(".yaml") - with open(path, "w") as f: - f.write(yaml.dump(config)) - f.close() - - -def write_args(args, path): - args_dict = dict( - (name, getattr(args, name)) for name in dir(args) if not name.startswith("_") - ) - with open(path, "a") as args_file: - args_file.write("==> torch version: {}\n".format(torch.__version__)) - args_file.write( - "==> cudnn version: {}\n".format(torch.backends.cudnn.version()) - ) - args_file.write("==> Cmd:\n") - args_file.write(str(sys.argv)) - args_file.write("\n==> args:\n") - for k, v in sorted(args_dict.items()): - args_file.write(" %s: %s\n" % (str(k), str(v))) - args_file.close() diff --git a/BV2/MODELS/AW.pth 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differ diff --git a/BV2/__pycache__/transforms.cpython-310.pyc b/BV2/__pycache__/transforms.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..16c64a214a6f92ffcd72db5b8f86c3cc0e00f53e Binary files /dev/null and b/BV2/__pycache__/transforms.cpython-310.pyc differ diff --git a/BV2/__pycache__/utils.cpython-310.pyc b/BV2/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..be8c3a61c33bfcd21933dcd6bf7139e917422ac4 Binary files /dev/null and b/BV2/__pycache__/utils.cpython-310.pyc differ diff --git a/BV2/attentions.py b/BV2/attentions.py new file mode 100644 index 0000000000000000000000000000000000000000..4668035b9f24e08ece7611d305b69d50a1d4e8ac --- /dev/null +++ b/BV2/attentions.py @@ -0,0 +1,343 @@ +import copy +import math +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +from . import commons +from . import modules +from torch.nn.utils import weight_norm, remove_weight_norm +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-5): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = nn.Parameter(torch.ones(channels)) + self.beta = nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + x = x.transpose(1, -1) + x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) + return x.transpose(1, -1) + + + +@torch.jit.script +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a + input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + +class Encoder(nn.Module): + def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, isflow = True, **kwargs): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.window_size = window_size + if isflow: + cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1) + self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1) + self.cond_layer = weight_norm(cond_layer, name='weight') + self.gin_channels = 256 + self.cond_layer_idx = self.n_layers + if 'gin_channels' in kwargs: + self.gin_channels = kwargs['gin_channels'] + if self.gin_channels != 0: + self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels) + # vits2 says 3rd block, so idx is 2 by default + self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2 + print(self.gin_channels, self.cond_layer_idx) + assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers' + self.drop = nn.Dropout(p_dropout) + self.attn_layers = nn.ModuleList() + self.norm_layers_1 = nn.ModuleList() + self.ffn_layers = nn.ModuleList() + self.norm_layers_2 = nn.ModuleList() + for i in range(self.n_layers): + self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size)) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + def forward(self, x, x_mask, g=None): + attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + x = x * x_mask + for i in range(self.n_layers): + if i == self.cond_layer_idx and g is not None: + g = self.spk_emb_linear(g.transpose(1, 2)) + g = g.transpose(1, 2) + x = x + g + x = x * x_mask + y = self.attn_layers[i](x, x, attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class Decoder(nn.Module): + def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.proximal_bias = proximal_bias + self.proximal_init = proximal_init + + self.drop = nn.Dropout(p_dropout) + self.self_attn_layers = nn.ModuleList() + self.norm_layers_0 = nn.ModuleList() + self.encdec_attn_layers = nn.ModuleList() + self.norm_layers_1 = nn.ModuleList() + self.ffn_layers = nn.ModuleList() + self.norm_layers_2 = nn.ModuleList() + for i in range(self.n_layers): + self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init)) + self.norm_layers_0.append(LayerNorm(hidden_channels)) + self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True)) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask, h, h_mask): + """ + x: decoder input + h: encoder output + """ + self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) + encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + x = x * x_mask + for i in range(self.n_layers): + y = self.self_attn_layers[i](x, x, self_attn_mask) + y = self.drop(y) + x = self.norm_layers_0[i](x + y) + + y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class MultiHeadAttention(nn.Module): + def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False): + super().__init__() + assert channels % n_heads == 0 + + self.channels = channels + self.out_channels = out_channels + self.n_heads = n_heads + self.p_dropout = p_dropout + self.window_size = window_size + self.heads_share = heads_share + self.block_length = block_length + self.proximal_bias = proximal_bias + self.proximal_init = proximal_init + self.attn = None + + self.k_channels = channels // n_heads + self.conv_q = nn.Conv1d(channels, channels, 1) + self.conv_k = nn.Conv1d(channels, channels, 1) + self.conv_v = nn.Conv1d(channels, channels, 1) + self.conv_o = nn.Conv1d(channels, out_channels, 1) + self.drop = nn.Dropout(p_dropout) + + if window_size is not None: + n_heads_rel = 1 if heads_share else n_heads + rel_stddev = self.k_channels**-0.5 + self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) + self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) + + nn.init.xavier_uniform_(self.conv_q.weight) + nn.init.xavier_uniform_(self.conv_k.weight) + nn.init.xavier_uniform_(self.conv_v.weight) + if proximal_init: + with torch.no_grad(): + self.conv_k.weight.copy_(self.conv_q.weight) + self.conv_k.bias.copy_(self.conv_q.bias) + + def forward(self, x, c, attn_mask=None): + q = self.conv_q(x) + k = self.conv_k(c) + v = self.conv_v(c) + + x, self.attn = self.attention(q, k, v, mask=attn_mask) + + x = self.conv_o(x) + return x + + def attention(self, query, key, value, mask=None): + # reshape [b, d, t] -> [b, n_h, t, d_k] + b, d, t_s, t_t = (*key.size(), query.size(2)) + query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) + key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + + scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) + if self.window_size is not None: + assert t_s == t_t, "Relative attention is only available for self-attention." + key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) + rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings) + scores_local = self._relative_position_to_absolute_position(rel_logits) + scores = scores + scores_local + if self.proximal_bias: + assert t_s == t_t, "Proximal bias is only available for self-attention." + scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e4) + if self.block_length is not None: + assert t_s == t_t, "Local attention is only available for self-attention." + block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) + scores = scores.masked_fill(block_mask == 0, -1e4) + p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] + p_attn = self.drop(p_attn) + output = torch.matmul(p_attn, value) + if self.window_size is not None: + relative_weights = self._absolute_position_to_relative_position(p_attn) + value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) + output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) + output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] + return output, p_attn + + def _matmul_with_relative_values(self, x, y): + """ + x: [b, h, l, m] + y: [h or 1, m, d] + ret: [b, h, l, d] + """ + ret = torch.matmul(x, y.unsqueeze(0)) + return ret + + def _matmul_with_relative_keys(self, x, y): + """ + x: [b, h, l, d] + y: [h or 1, m, d] + ret: [b, h, l, m] + """ + ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) + return ret + + def _get_relative_embeddings(self, relative_embeddings, length): + max_relative_position = 2 * self.window_size + 1 + # Pad first before slice to avoid using cond ops. + pad_length = max(length - (self.window_size + 1), 0) + slice_start_position = max((self.window_size + 1) - length, 0) + slice_end_position = slice_start_position + 2 * length - 1 + if pad_length > 0: + padded_relative_embeddings = F.pad( + relative_embeddings, + commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) + else: + padded_relative_embeddings = relative_embeddings + used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position] + return used_relative_embeddings + + def _relative_position_to_absolute_position(self, x): + """ + x: [b, h, l, 2*l-1] + ret: [b, h, l, l] + """ + batch, heads, length, _ = x.size() + # Concat columns of pad to shift from relative to absolute indexing. + x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])) + + # Concat extra elements so to add up to shape (len+1, 2*len-1). + x_flat = x.view([batch, heads, length * 2 * length]) + x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]])) + + # Reshape and slice out the padded elements. + x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:] + return x_final + + def _absolute_position_to_relative_position(self, x): + """ + x: [b, h, l, l] + ret: [b, h, l, 2*l-1] + """ + batch, heads, length, _ = x.size() + # padd along column + x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])) + x_flat = x.view([batch, heads, length**2 + length*(length -1)]) + # add 0's in the beginning that will skew the elements after reshape + x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) + x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:] + return x_final + + def _attention_bias_proximal(self, length): + """Bias for self-attention to encourage attention to close positions. + Args: + length: an integer scalar. + Returns: + a Tensor with shape [1, 1, length, length] + """ + r = torch.arange(length, dtype=torch.float32) + diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) + return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) + + +class FFN(nn.Module): + def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.activation = activation + self.causal = causal + + if causal: + self.padding = self._causal_padding + else: + self.padding = self._same_padding + + self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) + self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) + self.drop = nn.Dropout(p_dropout) + + def forward(self, x, x_mask): + x = self.conv_1(self.padding(x * x_mask)) + if self.activation == "gelu": + x = x * torch.sigmoid(1.702 * x) + else: + x = torch.relu(x) + x = self.drop(x) + x = self.conv_2(self.padding(x * x_mask)) + return x * x_mask + + def _causal_padding(self, x): + if self.kernel_size == 1: + return x + pad_l = self.kernel_size - 1 + pad_r = 0 + padding = [[0, 0], [0, 0], [pad_l, pad_r]] + x = F.pad(x, commons.convert_pad_shape(padding)) + return x + + def _same_padding(self, x): + if self.kernel_size == 1: + return x + pad_l = (self.kernel_size - 1) // 2 + pad_r = self.kernel_size // 2 + padding = [[0, 0], [0, 0], [pad_l, pad_r]] + x = F.pad(x, commons.convert_pad_shape(padding)) + return x diff --git a/BV2/bert_gen.py b/BV2/bert_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..467655b2c4171608ad690fe7dec350db85f84f1b --- /dev/null +++ b/BV2/bert_gen.py @@ -0,0 +1,53 @@ +import torch +from torch.utils.data import DataLoader +from multiprocessing import Pool +import commons +import utils +from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate +from tqdm import tqdm +import warnings + +from text import cleaned_text_to_sequence, get_bert + +config_path = 'configs/config.json' +hps = utils.get_hparams_from_file(config_path) + +def process_line(line): + _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|") + phone = phones.split(" ") + tone = [int(i) for i in tone.split(" ")] + word2ph = [int(i) for i in word2ph.split(" ")] + w2pho = [i for i in word2ph] + word2ph = [i for i in word2ph] + phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) + + if hps.data.add_blank: + phone = commons.intersperse(phone, 0) + tone = commons.intersperse(tone, 0) + language = commons.intersperse(language, 0) + for i in range(len(word2ph)): + word2ph[i] = word2ph[i] * 2 + word2ph[0] += 1 + wav_path = f'{_id}' + + bert_path = wav_path.replace(".wav", ".bert.pt") + try: + bert = torch.load(bert_path) + assert bert.shape[-1] == len(phone) + except: + bert = get_bert(text, word2ph, language_str) + assert bert.shape[-1] == len(phone) + torch.save(bert, bert_path) + + +if __name__ == '__main__': + lines = [] + with open(hps.data.training_files, encoding='utf-8' ) as f: + lines.extend(f.readlines()) + + # with open(hps.data.validation_files, encoding='utf-8' ) as f: + # lines.extend(f.readlines()) + + with Pool(processes=2) as pool: #A100 40GB suitable config,if coom,please decrease the processess number. + for _ in tqdm(pool.imap_unordered(process_line, lines)): + pass diff --git a/BV2/commons.py b/BV2/commons.py new file mode 100644 index 0000000000000000000000000000000000000000..9ad0444b61cbadaa388619986c2889c707d873ce --- /dev/null +++ b/BV2/commons.py @@ -0,0 +1,161 @@ +import math +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) + + +def convert_pad_shape(pad_shape): + l = pad_shape[::-1] + pad_shape = [item for sublist in l for item in sublist] + return pad_shape + + +def intersperse(lst, item): + result = [item] * (len(lst) * 2 + 1) + result[1::2] = lst + return result + + +def kl_divergence(m_p, logs_p, m_q, logs_q): + """KL(P||Q)""" + kl = (logs_q - logs_p) - 0.5 + kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q) + return kl + + +def rand_gumbel(shape): + """Sample from the Gumbel distribution, protect from overflows.""" + uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 + return -torch.log(-torch.log(uniform_samples)) + + +def rand_gumbel_like(x): + g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) + return g + + +def slice_segments(x, ids_str, segment_size=4): + ret = torch.zeros_like(x[:, :, :segment_size]) + for i in range(x.size(0)): + idx_str = ids_str[i] + idx_end = idx_str + segment_size + ret[i] = x[i, :, idx_str:idx_end] + return ret + + +def rand_slice_segments(x, x_lengths=None, segment_size=4): + b, d, t = x.size() + if x_lengths is None: + x_lengths = t + ids_str_max = x_lengths - segment_size + 1 + ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) + ret = slice_segments(x, ids_str, segment_size) + return ret, ids_str + + +def get_timing_signal_1d( + length, channels, min_timescale=1.0, max_timescale=1.0e4): + position = torch.arange(length, dtype=torch.float) + num_timescales = channels // 2 + log_timescale_increment = ( + math.log(float(max_timescale) / float(min_timescale)) / + (num_timescales - 1)) + inv_timescales = min_timescale * torch.exp( + torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment) + scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) + signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) + signal = F.pad(signal, [0, 0, 0, channels % 2]) + signal = signal.view(1, channels, length) + return signal + + +def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): + b, channels, length = x.size() + signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) + return x + signal.to(dtype=x.dtype, device=x.device) + + +def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): + b, channels, length = x.size() + signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) + return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) + + +def subsequent_mask(length): + mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) + return mask + + +@torch.jit.script +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a + input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + + +def convert_pad_shape(pad_shape): + l = pad_shape[::-1] + pad_shape = [item for sublist in l for item in sublist] + return pad_shape + + +def shift_1d(x): + x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] + return x + + +def sequence_mask(length, max_length=None): + if max_length is None: + max_length = length.max() + x = torch.arange(max_length, dtype=length.dtype, device=length.device) + return x.unsqueeze(0) < length.unsqueeze(1) + + +def generate_path(duration, mask): + """ + duration: [b, 1, t_x] + mask: [b, 1, t_y, t_x] + """ + device = duration.device + + b, _, t_y, t_x = mask.shape + cum_duration = torch.cumsum(duration, -1) + + cum_duration_flat = cum_duration.view(b * t_x) + path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) + path = path.view(b, t_x, t_y) + path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] + path = path.unsqueeze(1).transpose(2,3) * mask + return path + + +def clip_grad_value_(parameters, clip_value, norm_type=2): + if isinstance(parameters, torch.Tensor): + parameters = [parameters] + parameters = list(filter(lambda p: p.grad is not None, parameters)) + norm_type = float(norm_type) + if clip_value is not None: + clip_value = float(clip_value) + + total_norm = 0 + for p in parameters: + param_norm = p.grad.data.norm(norm_type) + total_norm += param_norm.item() ** norm_type + if clip_value is not None: + p.grad.data.clamp_(min=-clip_value, max=clip_value) + total_norm = total_norm ** (1. / norm_type) + return total_norm diff --git a/configs/s2.json b/BV2/configs/config.json similarity index 62% rename from configs/s2.json rename to BV2/configs/config.json index e44e1eb7d9864a20c76d35201f1ce0b9b15f3e95..5f08648536ee245534ee61ab229fae4cafbc4b66 100644 --- a/configs/s2.json +++ b/BV2/configs/config.json @@ -1,39 +1,48 @@ { "train": { - "log_interval": 100, + "log_interval": 10, "eval_interval": 500, - "seed": 1234, - "epochs": 100, - "learning_rate": 0.0001, + "seed": 52, + "epochs": 2000, + "learning_rate": 0.00015, "betas": [ 0.8, 0.99 ], "eps": 1e-09, - "batch_size": 32, - "fp16_run": true, + "batch_size": 14, + "fp16_run": false, "lr_decay": 0.999875, - "segment_size": 20480, + "segment_size": 16384, "init_lr_ratio": 1, "warmup_epochs": 0, "c_mel": 45, - "c_kl": 1.0, - "text_low_lr_rate": 0.4 + "c_kl": 1.0 }, "data": { + "use_mel_posterior_encoder": false, + "training_files": "filelists/train.list", + "validation_files": "filelists/val.list", "max_wav_value": 32768.0, - "sampling_rate": 32000, + "sampling_rate": 44100, "filter_length": 2048, - "hop_length": 640, + "hop_length": 512, "win_length": 2048, "n_mel_channels": 128, "mel_fmin": 0.0, "mel_fmax": null, "add_blank": true, - "n_speakers": 300, - "cleaned_text": true + "n_speakers": 1, + "cleaned_text": true, + "spk2id": { + "default": 0 + } }, "model": { + "use_spk_conditioned_encoder": true, + "use_noise_scaled_mas": true, + "use_mel_posterior_encoder": false, + "use_duration_discriminator": true, "inter_channels": 192, "hidden_channels": 192, "filter_channels": 768, @@ -65,7 +74,7 @@ ] ], "upsample_rates": [ - 10, + 8, 8, 2, 2, @@ -81,10 +90,6 @@ ], "n_layers_q": 3, "use_spectral_norm": false, - "gin_channels": 512, - "semantic_frame_rate": "25hz", - "freeze_quantizer": true - }, - "s2_ckpt_dir": "logs/s2/big2k1", - "content_module": "cnhubert" + "gin_channels": 256 + } } \ No newline at end of file diff --git a/BV2/data_utils.py b/BV2/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2c98d3dc8b9572bd05859033a74d155425a2a2ab --- /dev/null +++ b/BV2/data_utils.py @@ -0,0 +1,332 @@ +import time +import os +import random +import numpy as np +import torch +import torch.utils.data +import torchaudio +import commons +from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch +from utils import load_wav_to_torch, load_filepaths_and_text +from text import cleaned_text_to_sequence, get_bert + +"""Multi speaker version""" + + +class TextAudioSpeakerLoader(torch.utils.data.Dataset): + """ + 1) loads audio, speaker_id, text pairs + 2) normalizes text and converts them to sequences of integers + 3) computes spectrograms from audio files. + """ + + def __init__(self, audiopaths_sid_text, hparams): + self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text) + self.max_wav_value = hparams.max_wav_value + self.sampling_rate = hparams.sampling_rate + self.filter_length = hparams.filter_length + self.hop_length = hparams.hop_length + self.win_length = hparams.win_length + self.sampling_rate = hparams.sampling_rate + self.spk_map = hparams.spk2id + self.hparams = hparams + + self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False) + if self.use_mel_spec_posterior: + self.n_mel_channels = getattr(hparams, "n_mel_channels", 80) + + self.cleaned_text = getattr(hparams, "cleaned_text", False) + + self.add_blank = hparams.add_blank + self.min_text_len = getattr(hparams, "min_text_len", 1) + self.max_text_len = getattr(hparams, "max_text_len", 300) + + random.seed(1234) + random.shuffle(self.audiopaths_sid_text) + self._filter() + + def _filter(self): + """ + Filter text & store spec lengths + """ + # Store spectrogram lengths for Bucketing + # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) + # spec_length = wav_length // hop_length + + audiopaths_sid_text_new = [] + lengths = [] + skipped = 0 + for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text: + audiopath = f'{_id}' + if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len: + phones = phones.split(" ") + tone = [int(i) for i in tone.split(" ")] + word2ph = [int(i) for i in word2ph.split(" ")] + audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph]) + lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) + else: + skipped += 1 + print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text)) + self.audiopaths_sid_text = audiopaths_sid_text_new + self.lengths = lengths + + def get_audio_text_speaker_pair(self, audiopath_sid_text): + # separate filename, speaker_id and text + audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text + + bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath) + + spec, wav = self.get_audio(audiopath) + sid = torch.LongTensor([int(self.spk_map[sid])]) + return (phones, spec, wav, sid, tone, language, bert) + + def get_audio(self, filename): + audio_norm, sampling_rate = torchaudio.load(filename, frame_offset=0, num_frames=-1, normalize=True, channels_first=True) + ''' + audio, sampling_rate = load_wav_to_torch(filename) + if sampling_rate != self.sampling_rate: + raise ValueError("{} {} SR doesn't match target {} SR".format( + sampling_rate, self.sampling_rate)) + audio_norm = audio / self.max_wav_value + audio_norm = audio_norm.unsqueeze(0) + ''' + spec_filename = filename.replace(".wav", ".spec.pt") + if self.use_mel_spec_posterior: + spec_filename = spec_filename.replace(".spec.pt", ".mel.pt") + if os.path.exists(spec_filename): + spec = torch.load(spec_filename) + else: + if self.use_mel_spec_posterior: + # if os.path.exists(filename.replace(".wav", ".spec.pt")): + # # spec, n_fft, num_mels, sampling_rate, fmin, fmax + # spec = spec_to_mel_torch( + # torch.load(filename.replace(".wav", ".spec.pt")), + # self.filter_length, self.n_mel_channels, self.sampling_rate, + # self.hparams.mel_fmin, self.hparams.mel_fmax) + spec = mel_spectrogram_torch(audio_norm, self.filter_length, + self.n_mel_channels, self.sampling_rate, self.hop_length, + self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False) + else: + spec = spectrogram_torch(audio_norm, self.filter_length, + self.sampling_rate, self.hop_length, self.win_length, + center=False) + spec = torch.squeeze(spec, 0) + torch.save(spec, spec_filename) + return spec, audio_norm + + def get_text(self, text, word2ph, phone, tone, language_str, wav_path): + # print(text, word2ph,phone, tone, language_str) + pold = phone + w2pho = [i for i in word2ph] + word2ph = [i for i in word2ph] + phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) + pold2 = phone + + if self.add_blank: + p1 = len(phone) + phone = commons.intersperse(phone, 0) + p2 = len(phone) + t1 = len(tone) + tone = commons.intersperse(tone, 0) + t2 = len(tone) + language = commons.intersperse(language, 0) + for i in range(len(word2ph)): + word2ph[i] = word2ph[i] * 2 + word2ph[0] += 1 + bert_path = wav_path.replace(".wav", ".bert.pt") + try: + bert = torch.load(bert_path) + assert bert.shape[-1] == len(phone) + except: + bert = get_bert(text, word2ph, language_str) + torch.save(bert, bert_path) + #print(bert.shape[-1], bert_path, text, pold) + assert bert.shape[-1] == len(phone) + + assert bert.shape[-1] == len(phone), ( + bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho) + phone = torch.LongTensor(phone) + tone = torch.LongTensor(tone) + language = torch.LongTensor(language) + return bert, phone, tone, language + + def get_sid(self, sid): + sid = torch.LongTensor([int(sid)]) + return sid + + def __getitem__(self, index): + return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index]) + + def __len__(self): + return len(self.audiopaths_sid_text) + + +class TextAudioSpeakerCollate(): + """ Zero-pads model inputs and targets + """ + + def __init__(self, return_ids=False): + self.return_ids = return_ids + + def __call__(self, batch): + """Collate's training batch from normalized text, audio and speaker identities + PARAMS + ------ + batch: [text_normalized, spec_normalized, wav_normalized, sid] + """ + # Right zero-pad all one-hot text sequences to max input length + _, ids_sorted_decreasing = torch.sort( + torch.LongTensor([x[1].size(1) for x in batch]), + dim=0, descending=True) + + max_text_len = max([len(x[0]) for x in batch]) + max_spec_len = max([x[1].size(1) for x in batch]) + max_wav_len = max([x[2].size(1) for x in batch]) + + text_lengths = torch.LongTensor(len(batch)) + spec_lengths = torch.LongTensor(len(batch)) + wav_lengths = torch.LongTensor(len(batch)) + sid = torch.LongTensor(len(batch)) + + text_padded = torch.LongTensor(len(batch), max_text_len) + tone_padded = torch.LongTensor(len(batch), max_text_len) + language_padded = torch.LongTensor(len(batch), max_text_len) + bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) + + spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) + wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) + text_padded.zero_() + tone_padded.zero_() + language_padded.zero_() + spec_padded.zero_() + wav_padded.zero_() + bert_padded.zero_() + for i in range(len(ids_sorted_decreasing)): + row = batch[ids_sorted_decreasing[i]] + + text = row[0] + text_padded[i, :text.size(0)] = text + text_lengths[i] = text.size(0) + + spec = row[1] + spec_padded[i, :, :spec.size(1)] = spec + spec_lengths[i] = spec.size(1) + + wav = row[2] + wav_padded[i, :, :wav.size(1)] = wav + wav_lengths[i] = wav.size(1) + + sid[i] = row[3] + + tone = row[4] + tone_padded[i, :tone.size(0)] = tone + + language = row[5] + language_padded[i, :language.size(0)] = language + + bert = row[6] + bert_padded[i, :, :bert.size(1)] = bert + + return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded + + +class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): + """ + Maintain similar input lengths in a batch. + Length groups are specified by boundaries. + Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. + + It removes samples which are not included in the boundaries. + Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. + """ + + def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True): + super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) + self.lengths = dataset.lengths + self.batch_size = batch_size + self.boundaries = boundaries + + self.buckets, self.num_samples_per_bucket = self._create_buckets() + self.total_size = sum(self.num_samples_per_bucket) + self.num_samples = self.total_size // self.num_replicas + + def _create_buckets(self): + buckets = [[] for _ in range(len(self.boundaries) - 1)] + for i in range(len(self.lengths)): + length = self.lengths[i] + idx_bucket = self._bisect(length) + if idx_bucket != -1: + buckets[idx_bucket].append(i) + + for i in range(len(buckets) - 1, 0, -1): + if len(buckets[i]) == 0: + buckets.pop(i) + self.boundaries.pop(i + 1) + + num_samples_per_bucket = [] + for i in range(len(buckets)): + len_bucket = len(buckets[i]) + total_batch_size = self.num_replicas * self.batch_size + rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size + num_samples_per_bucket.append(len_bucket + rem) + return buckets, num_samples_per_bucket + + def __iter__(self): + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch) + + indices = [] + if self.shuffle: + for bucket in self.buckets: + indices.append(torch.randperm(len(bucket), generator=g).tolist()) + else: + for bucket in self.buckets: + indices.append(list(range(len(bucket)))) + + batches = [] + for i in range(len(self.buckets)): + bucket = self.buckets[i] + len_bucket = len(bucket) + if (len_bucket == 0): + continue + ids_bucket = indices[i] + num_samples_bucket = self.num_samples_per_bucket[i] + + # add extra samples to make it evenly divisible + rem = num_samples_bucket - len_bucket + ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)] + + # subsample + ids_bucket = ids_bucket[self.rank::self.num_replicas] + + # batching + for j in range(len(ids_bucket) // self.batch_size): + batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]] + batches.append(batch) + + if self.shuffle: + batch_ids = torch.randperm(len(batches), generator=g).tolist() + batches = [batches[i] for i in batch_ids] + self.batches = batches + + assert len(self.batches) * self.batch_size == self.num_samples + return iter(self.batches) + + def _bisect(self, x, lo=0, hi=None): + if hi is None: + hi = len(self.boundaries) - 1 + + if hi > lo: + mid = (hi + lo) // 2 + if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: + return mid + elif x <= self.boundaries[mid]: + return self._bisect(x, lo, mid) + else: + return self._bisect(x, mid + 1, hi) + else: + return -1 + + def __len__(self): + return self.num_samples // self.batch_size diff --git a/BV2/losses.py b/BV2/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..bd077c0aae01b2fa61304a032b4d4156f6028a4d --- /dev/null +++ b/BV2/losses.py @@ -0,0 +1,61 @@ +import torch +from torch.nn import functional as F + +from . import commons + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + rl = rl.float().detach() + gl = gl.float() + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + dr = dr.float() + dg = dg.float() + r_loss = torch.mean((1-dr)**2) + g_loss = torch.mean(dg**2) + loss += (r_loss + g_loss) + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + dg = dg.float() + l = torch.mean((1-dg)**2) + gen_losses.append(l) + loss += l + + return loss, gen_losses + + +def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): + """ + z_p, logs_q: [b, h, t_t] + m_p, logs_p: [b, h, t_t] + """ + z_p = z_p.float() + logs_q = logs_q.float() + m_p = m_p.float() + logs_p = logs_p.float() + z_mask = z_mask.float() + + kl = logs_p - logs_q - 0.5 + kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p) + kl = torch.sum(kl * z_mask) + l = kl / torch.sum(z_mask) + return l diff --git a/BV2/mel_processing.py b/BV2/mel_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..50435ecf88ef4fb6c1d47f3e6edd04c3ea7d3e80 --- /dev/null +++ b/BV2/mel_processing.py @@ -0,0 +1,112 @@ +import math +import os +import random +import torch +from torch import nn +import torch.nn.functional as F +import torch.utils.data +import numpy as np +import librosa +import librosa.util as librosa_util +from librosa.util import normalize, pad_center, tiny +from scipy.signal import get_window +from scipy.io.wavfile import read +from librosa.filters import mel as librosa_mel_fn + +MAX_WAV_VALUE = 32768.0 + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + """ + PARAMS + ------ + C: compression factor + """ + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + """ + PARAMS + ------ + C: compression factor used to compress + """ + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + global hann_window + dtype_device = str(y.dtype) + '_' + str(y.device) + wnsize_dtype_device = str(win_size) + '_' + dtype_device + if wnsize_dtype_device not in hann_window: + hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], + center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) + + spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) + return spec + + +def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): + global mel_basis + dtype_device = str(spec.dtype) + '_' + str(spec.device) + fmax_dtype_device = str(fmax) + '_' + dtype_device + if fmax_dtype_device not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) + spec = torch.matmul(mel_basis[fmax_dtype_device], spec) + spec = spectral_normalize_torch(spec) + return spec + + +def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + global mel_basis, hann_window + dtype_device = str(y.dtype) + '_' + str(y.device) + fmax_dtype_device = str(fmax) + '_' + dtype_device + wnsize_dtype_device = str(win_size) + '_' + dtype_device + if fmax_dtype_device not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) + if wnsize_dtype_device not in hann_window: + hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], + center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) + + spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) + + spec = torch.matmul(mel_basis[fmax_dtype_device], spec) + spec = spectral_normalize_torch(spec) + + return spec diff --git a/BV2/models.py b/BV2/models.py new file mode 100644 index 0000000000000000000000000000000000000000..cefcbe3fede58629a3183f341434d2962f3b4972 --- /dev/null +++ b/BV2/models.py @@ -0,0 +1,707 @@ +import copy +import math +import torch +from torch import nn +from torch.nn import functional as F + +from . import commons +from . import modules +from . import attentions +from .monotonic_align import * + +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm + +from .commons import init_weights, get_padding +from .text import symbols, num_tones, num_languages +class DurationDiscriminator(nn.Module): #vits2 + def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): + super().__init__() + + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.gin_channels = gin_channels + + self.drop = nn.Dropout(p_dropout) + self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) + self.norm_1 = modules.LayerNorm(filter_channels) + self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) + self.norm_2 = modules.LayerNorm(filter_channels) + self.dur_proj = nn.Conv1d(1, filter_channels, 1) + + self.pre_out_conv_1 = nn.Conv1d(2*filter_channels, filter_channels, kernel_size, padding=kernel_size//2) + self.pre_out_norm_1 = modules.LayerNorm(filter_channels) + self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) + self.pre_out_norm_2 = modules.LayerNorm(filter_channels) + + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, in_channels, 1) + + self.output_layer = nn.Sequential( + nn.Linear(filter_channels, 1), + nn.Sigmoid() + ) + + def forward_probability(self, x, x_mask, dur, g=None): + dur = self.dur_proj(dur) + x = torch.cat([x, dur], dim=1) + x = self.pre_out_conv_1(x * x_mask) + x = torch.relu(x) + x = self.pre_out_norm_1(x) + x = self.drop(x) + x = self.pre_out_conv_2(x * x_mask) + x = torch.relu(x) + x = self.pre_out_norm_2(x) + x = self.drop(x) + x = x * x_mask + x = x.transpose(1, 2) + output_prob = self.output_layer(x) + return output_prob + + def forward(self, x, x_mask, dur_r, dur_hat, g=None): + x = torch.detach(x) + if g is not None: + g = torch.detach(g) + x = x + self.cond(g) + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.norm_1(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + x = torch.relu(x) + x = self.norm_2(x) + x = self.drop(x) + + output_probs = [] + for dur in [dur_r, dur_hat]: + output_prob = self.forward_probability(x, x_mask, dur, g) + output_probs.append(output_prob) + + return output_probs + +class TransformerCouplingBlock(nn.Module): + def __init__(self, + channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + n_flows=4, + gin_channels=0, + share_parameter=False + ): + + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.flows = nn.ModuleList() + + self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = self.gin_channels) if share_parameter else None + + for i in range(n_flows): + self.flows.append( + modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout, filter_channels, mean_only=True, wn_sharing_parameter=self.wn, gin_channels = self.gin_channels)) + self.flows.append(modules.Flip()) + + def forward(self, x, x_mask, g=None, reverse=False): + if not reverse: + for flow in self.flows: + x, _ = flow(x, x_mask, g=g, reverse=reverse) + else: + for flow in reversed(self.flows): + x = flow(x, x_mask, g=g, reverse=reverse) + return x + +class StochasticDurationPredictor(nn.Module): + def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): + super().__init__() + filter_channels = in_channels # it needs to be removed from future version. + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.log_flow = modules.Log() + self.flows = nn.ModuleList() + self.flows.append(modules.ElementwiseAffine(2)) + for i in range(n_flows): + self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) + self.flows.append(modules.Flip()) + + self.post_pre = nn.Conv1d(1, filter_channels, 1) + self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) + self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) + self.post_flows = nn.ModuleList() + self.post_flows.append(modules.ElementwiseAffine(2)) + for i in range(4): + self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) + self.post_flows.append(modules.Flip()) + + self.pre = nn.Conv1d(in_channels, filter_channels, 1) + self.proj = nn.Conv1d(filter_channels, filter_channels, 1) + self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, filter_channels, 1) + + def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): + x = torch.detach(x) + x = self.pre(x) + if g is not None: + g = torch.detach(g) + x = x + self.cond(g) + x = self.convs(x, x_mask) + x = self.proj(x) * x_mask + + if not reverse: + flows = self.flows + assert w is not None + + logdet_tot_q = 0 + h_w = self.post_pre(w) + h_w = self.post_convs(h_w, x_mask) + h_w = self.post_proj(h_w) * x_mask + e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask + z_q = e_q + for flow in self.post_flows: + z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) + logdet_tot_q += logdet_q + z_u, z1 = torch.split(z_q, [1, 1], 1) + u = torch.sigmoid(z_u) * x_mask + z0 = (w - u) * x_mask + logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]) + logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q + + logdet_tot = 0 + z0, logdet = self.log_flow(z0, x_mask) + logdet_tot += logdet + z = torch.cat([z0, z1], 1) + for flow in flows: + z, logdet = flow(z, x_mask, g=x, reverse=reverse) + logdet_tot = logdet_tot + logdet + nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot + return nll + logq # [b] + else: + flows = list(reversed(self.flows)) + flows = flows[:-2] + [flows[-1]] # remove a useless vflow + z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale + for flow in flows: + z = flow(z, x_mask, g=x, reverse=reverse) + z0, z1 = torch.split(z, [1, 1], 1) + logw = z0 + return logw + + +class DurationPredictor(nn.Module): + def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): + super().__init__() + + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.gin_channels = gin_channels + + self.drop = nn.Dropout(p_dropout) + self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_1 = modules.LayerNorm(filter_channels) + self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_2 = modules.LayerNorm(filter_channels) + self.proj = nn.Conv1d(filter_channels, 1, 1) + + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, in_channels, 1) + + def forward(self, x, x_mask, g=None): + x = torch.detach(x) + if g is not None: + g = torch.detach(g) + x = x + self.cond(g) + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.norm_1(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + x = torch.relu(x) + x = self.norm_2(x) + x = self.drop(x) + x = self.proj(x * x_mask) + return x * x_mask + + +class TextEncoder(nn.Module): + def __init__(self, + n_vocab, + out_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + gin_channels=0): + super().__init__() + self.n_vocab = n_vocab + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.gin_channels = gin_channels + self.emb = nn.Embedding(len(symbols), hidden_channels) + nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5) + self.tone_emb = nn.Embedding(num_tones, hidden_channels) + nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5) + self.language_emb = nn.Embedding(num_languages, hidden_channels) + nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5) + self.bert_proj = nn.Conv1d(1024, hidden_channels, 1) + + self.encoder = attentions.Encoder( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + gin_channels=self.gin_channels) + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + + def forward(self, x, x_lengths, tone, language, bert, g=None): + x = (self.emb(x)+ self.tone_emb(tone)+ self.language_emb(language)+self.bert_proj(bert).transpose(1,2)) * math.sqrt(self.hidden_channels) # [b, t, h] + x = torch.transpose(x, 1, -1) # [b, h, t] + x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + + x = self.encoder(x * x_mask, x_mask, g=g) + stats = self.proj(x) * x_mask + + m, logs = torch.split(stats, self.out_channels, dim=1) + return x, m, logs, x_mask + + +class ResidualCouplingBlock(nn.Module): + def __init__(self, + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + n_flows=4, + gin_channels=0): + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.flows = nn.ModuleList() + for i in range(n_flows): + self.flows.append( + modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, + gin_channels=gin_channels, mean_only=True)) + self.flows.append(modules.Flip()) + + def forward(self, x, x_mask, g=None, reverse=False): + if not reverse: + for flow in self.flows: + x, _ = flow(x, x_mask, g=g, reverse=reverse) + else: + for flow in reversed(self.flows): + x = flow(x, x_mask, g=g, reverse=reverse) + return x + + +class PosteriorEncoder(nn.Module): + def __init__(self, + in_channels, + out_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=0): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + + self.pre = nn.Conv1d(in_channels, hidden_channels, 1) + self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + + def forward(self, x, x_lengths, g=None): + x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + x = self.pre(x) * x_mask + x = self.enc(x, x_mask, g=g) + stats = self.proj(x) * x_mask + m, logs = torch.split(stats, self.out_channels, dim=1) + z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask + return z, m, logs, x_mask + + +class Generator(torch.nn.Module): + def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, + upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): + super(Generator, self).__init__() + self.num_kernels = len(resblock_kernel_sizes) + self.num_upsamples = len(upsample_rates) + self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) + resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + self.ups.append(weight_norm( + ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)), + k, u, padding=(k - u) // 2))) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): + self.resblocks.append(resblock(ch, k, d)) + + self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) + self.ups.apply(init_weights) + + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) + + def forward(self, x, g=None): + x = self.conv_pre(x) + if g is not None: + x = x + self.cond(g) + + for i in range(self.num_upsamples): + x = F.leaky_relu(x, modules.LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + self.use_spectral_norm = use_spectral_norm + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, modules.LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv1d(1, 16, 15, 1, padding=7)), + norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), + norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, modules.LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(MultiPeriodDiscriminator, self).__init__() + periods = [2, 3, 5, 7, 11] + + discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] + discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] + self.discriminators = nn.ModuleList(discs) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + y_d_gs.append(y_d_g) + fmap_rs.append(fmap_r) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + +class ReferenceEncoder(nn.Module): + ''' + inputs --- [N, Ty/r, n_mels*r] mels + outputs --- [N, ref_enc_gru_size] + ''' + + def __init__(self, spec_channels, gin_channels=0): + + super().__init__() + self.spec_channels = spec_channels + ref_enc_filters = [32, 32, 64, 64, 128, 128] + K = len(ref_enc_filters) + filters = [1] + ref_enc_filters + convs = [weight_norm(nn.Conv2d(in_channels=filters[i], + out_channels=filters[i + 1], + kernel_size=(3, 3), + stride=(2, 2), + padding=(1, 1))) for i in range(K)] + self.convs = nn.ModuleList(convs) + # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) + + out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K) + self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels, + hidden_size=256 // 2, + batch_first=True) + self.proj = nn.Linear(128, gin_channels) + + def forward(self, inputs, mask=None): + N = inputs.size(0) + out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs] + for conv in self.convs: + out = conv(out) + # out = wn(out) + out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K] + + out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K] + T = out.size(1) + N = out.size(0) + out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K] + + self.gru.flatten_parameters() + memory, out = self.gru(out) # out --- [1, N, 128] + + return self.proj(out.squeeze(0)) + + def calculate_channels(self, L, kernel_size, stride, pad, n_convs): + for i in range(n_convs): + L = (L - kernel_size + 2 * pad) // stride + 1 + return L + + +class Synthesizer(nn.Module): + """ + Synthesizer for Training + """ + + def __init__(self, + n_vocab, + spec_channels, + segment_size, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + resblock, + resblock_kernel_sizes, + resblock_dilation_sizes, + upsample_rates, + upsample_initial_channel, + upsample_kernel_sizes, + n_speakers=256, + gin_channels=256, + use_sdp=True, + n_flow_layer = 4, + n_layers_trans_flow = 3, + flow_share_parameter = False, + use_transformer_flow = True, + **kwargs): + + super().__init__() + self.n_vocab = n_vocab + self.spec_channels = spec_channels + self.inter_channels = inter_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.resblock = resblock + self.resblock_kernel_sizes = resblock_kernel_sizes + self.resblock_dilation_sizes = resblock_dilation_sizes + self.upsample_rates = upsample_rates + self.upsample_initial_channel = upsample_initial_channel + self.upsample_kernel_sizes = upsample_kernel_sizes + self.segment_size = segment_size + self.n_speakers = n_speakers + self.gin_channels = gin_channels + self.n_layers_trans_flow = n_layers_trans_flow + self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", True) + self.use_sdp = use_sdp + self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False) + self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01) + self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6) + self.current_mas_noise_scale = self.mas_noise_scale_initial + if self.use_spk_conditioned_encoder and gin_channels > 0: + self.enc_gin_channels = gin_channels + self.enc_p = TextEncoder(n_vocab, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + gin_channels=self.enc_gin_channels) + self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, + upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) + self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, + gin_channels=gin_channels) + if use_transformer_flow: + self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads, n_layers_trans_flow, 5, p_dropout, n_flow_layer, gin_channels=gin_channels,share_parameter= flow_share_parameter) + else: + self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels) + self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) + self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) + + if n_speakers >= 1: + self.emb_g = nn.Embedding(n_speakers, gin_channels) + else: + self.ref_enc = ReferenceEncoder(spec_channels, gin_channels) + + def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert): + if self.n_speakers > 0: + g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] + else: + g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1) + x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g) + z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) + z_p = self.flow(z, y_mask, g=g) + + with torch.no_grad(): + # negative cross-entropy + s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] + neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s] + neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), + s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] + neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] + neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s] + neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 + if self.use_noise_scaled_mas: + epsilon = torch.std(neg_cent) * torch.randn_like(neg_cent) * self.current_mas_noise_scale + neg_cent = neg_cent + epsilon + + attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) + attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() + + w = attn.sum(2) + + l_length_sdp = self.sdp(x, x_mask, w, g=g) + l_length_sdp = l_length_sdp / torch.sum(x_mask) + + logw_ = torch.log(w + 1e-6) * x_mask + logw = self.dp(x, x_mask, g=g) + l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) # for averaging + + l_length = l_length_dp + l_length_sdp + + # expand prior + m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) + logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) + + z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) + o = self.dec(z_slice, g=g) + return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_) + + def infer(self, x, x_lengths, sid, tone, language, bert, noise_scale=.667, length_scale=1, noise_scale_w=0.8, max_len=None, sdp_ratio=0,y=None): + #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert) + # g = self.gst(y) + if self.n_speakers > 0: + g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] + else: + g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1) + x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g) + logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (sdp_ratio) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio) + w = torch.exp(logw) * x_mask * length_scale + w_ceil = torch.ceil(w) + y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() + y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) + attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) + attn = commons.generate_path(w_ceil, attn_mask) + + m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] + logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, + 2) # [b, t', t], [b, t, d] -> [b, d, t'] + + z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale + z = self.flow(z_p, y_mask, g=g, reverse=True) + o = self.dec((z * y_mask)[:, :, :max_len], g=g) + return o, attn, y_mask, (z, z_p, m_p, logs_p) diff --git a/BV2/modules.py b/BV2/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..98e8b6800133a8aff1cc5177412847002d4ff1cf --- /dev/null +++ b/BV2/modules.py @@ -0,0 +1,452 @@ +import copy +import math +import numpy as np +import scipy +import torch +from torch import nn +from torch.nn import functional as F + +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm + +from . import commons +from .commons import init_weights, get_padding +from .transforms import piecewise_rational_quadratic_transform +from .attentions import Encoder + +LRELU_SLOPE = 0.1 + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-5): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = nn.Parameter(torch.ones(channels)) + self.beta = nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + x = x.transpose(1, -1) + x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) + return x.transpose(1, -1) + +class ConvReluNorm(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + assert n_layers > 1, "Number of layers should be larger than 0." + + self.conv_layers = nn.ModuleList() + self.norm_layers = nn.ModuleList() + self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.relu_drop = nn.Sequential( + nn.ReLU(), + nn.Dropout(p_dropout)) + for _ in range(n_layers-1): + self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.proj = nn.Conv1d(hidden_channels, out_channels, 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask): + x_org = x + for i in range(self.n_layers): + x = self.conv_layers[i](x * x_mask) + x = self.norm_layers[i](x) + x = self.relu_drop(x) + x = x_org + self.proj(x) + return x * x_mask + + +class DDSConv(nn.Module): + """ + Dialted and Depth-Separable Convolution + """ + def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): + super().__init__() + self.channels = channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + + self.drop = nn.Dropout(p_dropout) + self.convs_sep = nn.ModuleList() + self.convs_1x1 = nn.ModuleList() + self.norms_1 = nn.ModuleList() + self.norms_2 = nn.ModuleList() + for i in range(n_layers): + dilation = kernel_size ** i + padding = (kernel_size * dilation - dilation) // 2 + self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, + groups=channels, dilation=dilation, padding=padding + )) + self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) + self.norms_1.append(LayerNorm(channels)) + self.norms_2.append(LayerNorm(channels)) + + def forward(self, x, x_mask, g=None): + if g is not None: + x = x + g + for i in range(self.n_layers): + y = self.convs_sep[i](x * x_mask) + y = self.norms_1[i](y) + y = F.gelu(y) + y = self.convs_1x1[i](y) + y = self.norms_2[i](y) + y = F.gelu(y) + y = self.drop(y) + x = x + y + return x * x_mask + + +class WN(torch.nn.Module): + def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): + super(WN, self).__init__() + assert(kernel_size % 2 == 1) + self.hidden_channels =hidden_channels + self.kernel_size = kernel_size, + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + self.p_dropout = p_dropout + + self.in_layers = torch.nn.ModuleList() + self.res_skip_layers = torch.nn.ModuleList() + self.drop = nn.Dropout(p_dropout) + + if gin_channels != 0: + cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') + + for i in range(n_layers): + dilation = dilation_rate ** i + padding = int((kernel_size * dilation - dilation) / 2) + in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, + dilation=dilation, padding=padding) + in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') + self.in_layers.append(in_layer) + + # last one is not necessary + if i < n_layers - 1: + res_skip_channels = 2 * hidden_channels + else: + res_skip_channels = hidden_channels + + res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) + res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') + self.res_skip_layers.append(res_skip_layer) + + def forward(self, x, x_mask, g=None, **kwargs): + output = torch.zeros_like(x) + n_channels_tensor = torch.IntTensor([self.hidden_channels]) + + if g is not None: + g = self.cond_layer(g) + + for i in range(self.n_layers): + x_in = self.in_layers[i](x) + if g is not None: + cond_offset = i * 2 * self.hidden_channels + g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] + else: + g_l = torch.zeros_like(x_in) + + acts = commons.fused_add_tanh_sigmoid_multiply( + x_in, + g_l, + n_channels_tensor) + acts = self.drop(acts) + + res_skip_acts = self.res_skip_layers[i](acts) + if i < self.n_layers - 1: + res_acts = res_skip_acts[:,:self.hidden_channels,:] + x = (x + res_acts) * x_mask + output = output + res_skip_acts[:,self.hidden_channels:,:] + else: + output = output + res_skip_acts + return output * x_mask + + def remove_weight_norm(self): + if self.gin_channels != 0: + torch.nn.utils.remove_weight_norm(self.cond_layer) + for l in self.in_layers: + torch.nn.utils.remove_weight_norm(l) + for l in self.res_skip_layers: + torch.nn.utils.remove_weight_norm(l) + + +class ResBlock1(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + def forward(self, x, x_mask=None): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c2(xt) + x = xt + x + if x_mask is not None: + x = x * x_mask + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.convs = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + def forward(self, x, x_mask=None): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c(xt) + x = xt + x + if x_mask is not None: + x = x * x_mask + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Log(nn.Module): + def forward(self, x, x_mask, reverse=False, **kwargs): + if not reverse: + y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask + logdet = torch.sum(-y, [1, 2]) + return y, logdet + else: + x = torch.exp(x) * x_mask + return x + + +class Flip(nn.Module): + def forward(self, x, *args, reverse=False, **kwargs): + x = torch.flip(x, [1]) + if not reverse: + logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) + return x, logdet + else: + return x + + +class ElementwiseAffine(nn.Module): + def __init__(self, channels): + super().__init__() + self.channels = channels + self.m = nn.Parameter(torch.zeros(channels,1)) + self.logs = nn.Parameter(torch.zeros(channels,1)) + + def forward(self, x, x_mask, reverse=False, **kwargs): + if not reverse: + y = self.m + torch.exp(self.logs) * x + y = y * x_mask + logdet = torch.sum(self.logs * x_mask, [1,2]) + return y, logdet + else: + x = (x - self.m) * torch.exp(-self.logs) * x_mask + return x + + +class ResidualCouplingLayer(nn.Module): + def __init__(self, + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + p_dropout=0, + gin_channels=0, + mean_only=False): + assert channels % 2 == 0, "channels should be divisible by 2" + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.half_channels = channels // 2 + self.mean_only = mean_only + + self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) + self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) + self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) + self.post.weight.data.zero_() + self.post.bias.data.zero_() + + def forward(self, x, x_mask, g=None, reverse=False): + x0, x1 = torch.split(x, [self.half_channels]*2, 1) + h = self.pre(x0) * x_mask + h = self.enc(h, x_mask, g=g) + stats = self.post(h) * x_mask + if not self.mean_only: + m, logs = torch.split(stats, [self.half_channels]*2, 1) + else: + m = stats + logs = torch.zeros_like(m) + + if not reverse: + x1 = m + x1 * torch.exp(logs) * x_mask + x = torch.cat([x0, x1], 1) + logdet = torch.sum(logs, [1,2]) + return x, logdet + else: + x1 = (x1 - m) * torch.exp(-logs) * x_mask + x = torch.cat([x0, x1], 1) + return x + + +class ConvFlow(nn.Module): + def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0): + super().__init__() + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.num_bins = num_bins + self.tail_bound = tail_bound + self.half_channels = in_channels // 2 + + self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) + self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.) + self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask, g=None, reverse=False): + x0, x1 = torch.split(x, [self.half_channels]*2, 1) + h = self.pre(x0) + h = self.convs(h, x_mask, g=g) + h = self.proj(h) * x_mask + + b, c, t = x0.shape + h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] + + unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels) + unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels) + unnormalized_derivatives = h[..., 2 * self.num_bins:] + + x1, logabsdet = piecewise_rational_quadratic_transform(x1, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=reverse, + tails='linear', + tail_bound=self.tail_bound + ) + + x = torch.cat([x0, x1], 1) * x_mask + logdet = torch.sum(logabsdet * x_mask, [1,2]) + if not reverse: + return x, logdet + else: + return x +class TransformerCouplingLayer(nn.Module): + def __init__(self, + channels, + hidden_channels, + kernel_size, + n_layers, + n_heads, + p_dropout=0, + filter_channels=0, + mean_only=False, + wn_sharing_parameter=None, + gin_channels = 0 + ): + assert channels % 2 == 0, "channels should be divisible by 2" + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.half_channels = channels // 2 + self.mean_only = mean_only + + self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) + self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter + self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) + self.post.weight.data.zero_() + self.post.bias.data.zero_() + + def forward(self, x, x_mask, g=None, reverse=False): + x0, x1 = torch.split(x, [self.half_channels]*2, 1) + h = self.pre(x0) * x_mask + h = self.enc(h, x_mask, g=g) + stats = self.post(h) * x_mask + if not self.mean_only: + m, logs = torch.split(stats, [self.half_channels]*2, 1) + else: + m = stats + logs = torch.zeros_like(m) + + if not reverse: + x1 = m + x1 * torch.exp(logs) * x_mask + x = torch.cat([x0, x1], 1) + logdet = torch.sum(logs, [1,2]) + return x, logdet + else: + x1 = (x1 - m) * torch.exp(-logs) * x_mask + x = torch.cat([x0, x1], 1) + return x + + x1, logabsdet = piecewise_rational_quadratic_transform(x1, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=reverse, + tails='linear', + tail_bound=self.tail_bound + ) + + x = torch.cat([x0, x1], 1) * x_mask + logdet = torch.sum(logabsdet * x_mask, [1,2]) + if not reverse: + return x, logdet + else: + return x diff --git a/BV2/monotonic_align/__init__.py b/BV2/monotonic_align/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a323673bb16070d6d0fffddb939b657d0915ff1b --- /dev/null +++ b/BV2/monotonic_align/__init__.py @@ -0,0 +1,20 @@ +from numpy import zeros, int32, float32 +from torch import from_numpy + +from .core import maximum_path_jit + + +def maximum_path(neg_cent, mask): + """ numba optimized version. + neg_cent: [b, t_t, t_s] + mask: [b, t_t, t_s] + """ + device = neg_cent.device + dtype = neg_cent.dtype + neg_cent = neg_cent.data.cpu().numpy().astype(float32) + path = zeros(neg_cent.shape, dtype=int32) + + t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32) + t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32) + maximum_path_jit(path, neg_cent, t_t_max, t_s_max) + return from_numpy(path).to(device=device, dtype=dtype) \ No newline at end of file diff --git a/BV2/monotonic_align/__pycache__/__init__.cpython-310.pyc b/BV2/monotonic_align/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a7f31cf7933984387943aa8d896881a340e9b2a0 Binary files /dev/null and b/BV2/monotonic_align/__pycache__/__init__.cpython-310.pyc differ diff --git a/BV2/monotonic_align/__pycache__/core.cpython-310.pyc b/BV2/monotonic_align/__pycache__/core.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1254ca73c469d62fe887866c98854b41ab76c5ac Binary files /dev/null and b/BV2/monotonic_align/__pycache__/core.cpython-310.pyc differ diff --git a/BV2/monotonic_align/core.c b/BV2/monotonic_align/core.c new file mode 100644 index 0000000000000000000000000000000000000000..5f8af54d32474f821e9d1f4d2679d78128722596 --- /dev/null +++ b/BV2/monotonic_align/core.c @@ -0,0 +1,26530 @@ +/* Generated by Cython 3.0.0 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "name": "monotonic_align.core", + "sources": [ + "core.pyx" + ] + }, + "module_name": "monotonic_align.core" +} +END: Cython Metadata */ + +#ifndef PY_SSIZE_T_CLEAN +#define PY_SSIZE_T_CLEAN +#endif /* PY_SSIZE_T_CLEAN */ +#if defined(CYTHON_LIMITED_API) && 0 + #ifndef Py_LIMITED_API + #if CYTHON_LIMITED_API+0 > 0x03030000 + #define Py_LIMITED_API CYTHON_LIMITED_API + #else + #define Py_LIMITED_API 0x03030000 + #endif + #endif +#endif + +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.7+ or Python 3.3+. +#else +#define CYTHON_ABI "3_0_0" +#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI +#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "." +#define CYTHON_HEX_VERSION 0x030000F0 +#define CYTHON_FUTURE_DIVISION 1 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #define HAVE_LONG_LONG +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#if defined(GRAALVM_PYTHON) + /* For very preliminary testing purposes. Most variables are set the same as PyPy. + The existence of this section does not imply that anything works or is even tested */ + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 1 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) + #endif + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(PYPY_VERSION) + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) + #endif + #if PY_VERSION_HEX < 0x03090000 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00) + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(CYTHON_LIMITED_API) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 1 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_CLINE_IN_TRACEBACK + #define CYTHON_CLINE_IN_TRACEBACK 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 1 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #endif + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 1 + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(PY_NOGIL) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #ifndef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #ifndef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #endif + #ifndef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #ifndef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6) + #endif + #ifndef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1) + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #endif + #if PY_VERSION_HEX < 0x030400a1 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #elif !defined(CYTHON_USE_TP_FINALIZE) + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #if PY_VERSION_HEX < 0x030600B1 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #elif !defined(CYTHON_USE_DICT_VERSIONS) + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX < 0x030C00A5) + #endif + #if PY_VERSION_HEX < 0x030700A3 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #elif !defined(CYTHON_USE_EXC_INFO_STACK) + #define CYTHON_USE_EXC_INFO_STACK 1 + #endif + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if !defined(CYTHON_VECTORCALL) +#define CYTHON_VECTORCALL (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1) +#endif +#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1) +#if CYTHON_USE_PYLONG_INTERNALS + #if PY_MAJOR_VERSION < 3 + #include "longintrepr.h" + #endif + #undef SHIFT + #undef BASE + #undef MASK + #ifdef SIZEOF_VOID_P + enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; + #endif +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(maybe_unused) + #define CYTHON_UNUSED [[maybe_unused]] + #endif + #endif + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR + #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x) +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned short uint16_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int16 uint16_t; + typedef unsigned __int32 uint32_t; + #endif + #endif + #if _MSC_VER < 1300 + #ifdef _WIN64 + typedef unsigned long long __pyx_uintptr_t; + #else + typedef unsigned int __pyx_uintptr_t; + #endif + #else + #ifdef _WIN64 + typedef unsigned __int64 __pyx_uintptr_t; + #else + typedef unsigned __int32 __pyx_uintptr_t; + #endif + #endif +#else + #include + typedef uintptr_t __pyx_uintptr_t; +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(fallthrough) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif +#ifdef __cplusplus + template + struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);}; + #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL::value) +#else + #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0) +#endif +#if CYTHON_COMPILING_IN_PYPY == 1 + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000) +#else + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000) +#endif +#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer)) + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_DefaultClassType PyClass_Type + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_DefaultClassType PyType_Type +#if PY_VERSION_HEX >= 0x030B00A1 + static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL; + PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *empty=NULL; + const char *fn_cstr=NULL; + const char *name_cstr=NULL; + PyCodeObject *co=NULL, *result=NULL; + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + if (!(kwds=PyDict_New())) goto end; + if (!(argcount=PyLong_FromLong(a))) goto end; + if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end; + if (!(posonlyargcount=PyLong_FromLong(p))) goto end; + if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end; + if (!(kwonlyargcount=PyLong_FromLong(k))) goto end; + if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end; + if (!(nlocals=PyLong_FromLong(l))) goto end; + if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end; + if (!(stacksize=PyLong_FromLong(s))) goto end; + if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end; + if (!(flags=PyLong_FromLong(f))) goto end; + if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end; + if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end; + if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end; + if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end; + if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto end; + if (!(empty = PyTuple_New(0))) goto end; + result = (PyCodeObject*) PyObject_Call(replace, empty, kwds); + end: + Py_XDECREF((PyObject*) co); + Py_XDECREF(kwds); + Py_XDECREF(argcount); + Py_XDECREF(posonlyargcount); + Py_XDECREF(kwonlyargcount); + Py_XDECREF(nlocals); + Py_XDECREF(stacksize); + Py_XDECREF(replace); + Py_XDECREF(empty); + if (type) { + PyErr_Restore(type, value, traceback); + } + return result; + } +#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#endif +#endif +#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE) + #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type) +#else + #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is) + #define __Pyx_Py_Is(x, y) Py_Is(x, y) +#else + #define __Pyx_Py_Is(x, y) ((x) == (y)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone) + #define __Pyx_Py_IsNone(ob) Py_IsNone(ob) +#else + #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue) + #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob) +#else + #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse) + #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob) +#else + #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False) +#endif +#define __Pyx_NoneAsNull(obj) (__Pyx_Py_IsNone(obj) ? NULL : (obj)) +#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o) +#else + #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o) +#endif +#ifndef CO_COROUTINE + #define CO_COROUTINE 0x80 +#endif +#ifndef CO_ASYNC_GENERATOR + #define CO_ASYNC_GENERATOR 0x200 +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef Py_TPFLAGS_SEQUENCE + #define Py_TPFLAGS_SEQUENCE 0 +#endif +#ifndef Py_TPFLAGS_MAPPING + #define Py_TPFLAGS_MAPPING 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_METH_FASTCALL + #define __Pyx_METH_FASTCALL METH_FASTCALL + #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast + #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords +#else + #define __Pyx_METH_FASTCALL METH_VARARGS + #define __Pyx_PyCFunction_FastCall PyCFunction + #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords +#endif +#if CYTHON_VECTORCALL + #define __pyx_vectorcallfunc vectorcallfunc + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET PY_VECTORCALL_ARGUMENTS_OFFSET + #define __Pyx_PyVectorcall_NARGS(n) PyVectorcall_NARGS((size_t)(n)) +#elif CYTHON_BACKPORT_VECTORCALL + typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args, + size_t nargsf, PyObject *kwnames); + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET ((size_t)1 << (8 * sizeof(size_t) - 1)) + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)) +#else + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET 0 + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(n)) +#endif +#if PY_VERSION_HEX < 0x030900B1 + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) ((void)m, PyType_FromSpecWithBases(s, b)) + typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *); +#else + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) PyType_FromModuleAndSpec(m, s, b) + #define __Pyx_PyCMethod PyCMethod +#endif +#ifndef METH_METHOD + #define METH_METHOD 0x200 +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyThreadState_Current PyThreadState_Get() +#elif !CYTHON_FAST_THREAD_STATE + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op) +{ + void *result; + result = PyModule_GetState(op); + if (!result) + Py_FatalError("Couldn't find the module state"); + return result; +} +#endif +#define __Pyx_PyObject_GetSlot(obj, name, func_ctype) __Pyx_PyType_GetSlot(Py_TYPE(obj), name, func_ctype) +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((func_ctype) PyType_GetSlot((type), Py_##name)) +#else + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((type)->name) +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif +#if PY_MAJOR_VERSION < 3 + #if CYTHON_COMPILING_IN_PYPY + #if PYPY_VERSION_NUM < 0x07030600 + #if defined(__cplusplus) && __cplusplus >= 201402L + [[deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")]] + #elif defined(__GNUC__) || defined(__clang__) + __attribute__ ((__deprecated__("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6"))) + #elif defined(_MSC_VER) + __declspec(deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")) + #endif + static CYTHON_INLINE int PyGILState_Check(void) { + return 0; + } + #else // PYPY_VERSION_NUM < 0x07030600 + #endif // PYPY_VERSION_NUM < 0x07030600 + #else + static CYTHON_INLINE int PyGILState_Check(void) { + PyThreadState * tstate = _PyThreadState_Current; + return tstate && (tstate == PyGILState_GetThisThreadState()); + } + #endif +#endif +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStrWithError(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) { + PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name); + if (res == NULL) PyErr_Clear(); + return res; +} +#elif PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000) +#define __Pyx_PyDict_GetItemStrWithError PyDict_GetItemWithError +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#else +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) { +#if CYTHON_COMPILING_IN_PYPY + return PyDict_GetItem(dict, name); +#else + PyDictEntry *ep; + PyDictObject *mp = (PyDictObject*) dict; + long hash = ((PyStringObject *) name)->ob_shash; + assert(hash != -1); + ep = (mp->ma_lookup)(mp, name, hash); + if (ep == NULL) { + return NULL; + } + return ep->me_value; +#endif +} +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#endif +#if CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyType_GetFlags(tp) (((PyTypeObject *)tp)->tp_flags) + #define __Pyx_PyType_HasFeature(type, feature) ((__Pyx_PyType_GetFlags(type) & (feature)) != 0) + #define __Pyx_PyObject_GetIterNextFunc(obj) (Py_TYPE(obj)->tp_iternext) +#else + #define __Pyx_PyType_GetFlags(tp) (PyType_GetFlags((PyTypeObject *)tp)) + #define __Pyx_PyType_HasFeature(type, feature) PyType_HasFeature(type, feature) + #define __Pyx_PyObject_GetIterNextFunc(obj) PyIter_Next +#endif +#if CYTHON_USE_TYPE_SPECS && PY_VERSION_HEX >= 0x03080000 +#define __Pyx_PyHeapTypeObject_GC_Del(obj) {\ + PyTypeObject *type = Py_TYPE(obj);\ + assert(__Pyx_PyType_HasFeature(type, Py_TPFLAGS_HEAPTYPE));\ + PyObject_GC_Del(obj);\ + Py_DECREF(type);\ +} +#else +#define __Pyx_PyHeapTypeObject_GC_Del(obj) PyObject_GC_Del(obj) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GetLength(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_ReadChar(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((void)u, 1114111U) + #define __Pyx_PyUnicode_KIND(u) ((void)u, (0)) + #define __Pyx_PyUnicode_DATA(u) ((void*)u) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)k, PyUnicode_ReadChar((PyObject*)(d), i)) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GetLength(u)) +#elif PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_READY(op) (0) + #else + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #endif + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) ((int)PyUnicode_KIND(u)) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, (Py_UCS4) ch) + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) + #else + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) + #else + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) + #endif + #endif +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535U : 1114111U) + #define __Pyx_PyUnicode_KIND(u) ((int)sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = (Py_UNICODE) ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #if !defined(PyUnicode_DecodeUnicodeEscape) + #define PyUnicode_DecodeUnicodeEscape(s, size, errors) PyUnicode_Decode(s, size, "unicode_escape", errors) + #endif + #if !defined(PyUnicode_Contains) || (PY_MAJOR_VERSION == 2 && PYPY_VERSION_NUM < 0x07030500) + #undef PyUnicode_Contains + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) + #endif + #if !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) + #endif + #if !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) + #endif +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#ifndef PyObject_Unicode + #define PyObject_Unicode PyObject_Str +#endif +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#if CYTHON_COMPILING_IN_CPYTHON + #define __Pyx_PySequence_ListKeepNew(obj)\ + (likely(PyList_CheckExact(obj) && Py_REFCNT(obj) == 1) ? __Pyx_NewRef(obj) : PySequence_List(obj)) +#else + #define __Pyx_PySequence_ListKeepNew(obj) PySequence_List(obj) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) __Pyx_IS_TYPE(obj, &PySet_Type) +#endif +#if PY_VERSION_HEX >= 0x030900A4 + #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) +#else + #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) +#else + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define __Pyx_Py3Int_Check(op) PyLong_Check(op) + #define __Pyx_Py3Int_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#else + #define __Pyx_Py3Int_Check(op) (PyLong_Check(op) || PyInt_Check(op)) + #define __Pyx_Py3Int_CheckExact(op) (PyLong_CheckExact(op) || PyInt_CheckExact(op)) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsHash_t +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsSsize_t +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS) + #if !defined(_USE_MATH_DEFINES) + #define _USE_MATH_DEFINES + #endif +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + +#define __PYX_MARK_ERR_POS(f_index, lineno) \ + { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } +#define __PYX_ERR(f_index, lineno, Ln_error) \ + { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } + +#ifdef CYTHON_EXTERN_C + #undef __PYX_EXTERN_C + #define __PYX_EXTERN_C CYTHON_EXTERN_C +#elif defined(__PYX_EXTERN_C) + #ifdef _MSC_VER + #pragma message ("Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead.") + #else + #warning Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead. + #endif +#else + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__monotonic_align__core +#define __PYX_HAVE_API__monotonic_align__core +/* Early includes */ +#include "pythread.h" +#include +#include +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u) +{ + const wchar_t *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#endif +#define __Pyx_PyUnicode_FromOrdinal(o) PyUnicode_FromOrdinal((int)o) +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #if PY_VERSION_HEX >= 0x030C00A7 + #ifndef _PyLong_SIGN_MASK + #define _PyLong_SIGN_MASK 3 + #endif + #ifndef _PyLong_NON_SIZE_BITS + #define _PyLong_NON_SIZE_BITS 3 + #endif + #define __Pyx_PyLong_Sign(x) (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK) + #define __Pyx_PyLong_IsNeg(x) ((__Pyx_PyLong_Sign(x) & 2) != 0) + #define __Pyx_PyLong_IsNonNeg(x) (!__Pyx_PyLong_IsNeg(x)) + #define __Pyx_PyLong_IsZero(x) (__Pyx_PyLong_Sign(x) & 1) + #define __Pyx_PyLong_IsPos(x) (__Pyx_PyLong_Sign(x) == 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) (__Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_SignedDigitCount(x)\ + ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x)) + #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue) + #define __Pyx_PyLong_IsCompact(x) PyUnstable_Long_IsCompact((PyLongObject*) x) + #define __Pyx_PyLong_CompactValue(x) PyUnstable_Long_CompactValue((PyLongObject*) x) + #else + #define __Pyx_PyLong_IsCompact(x) (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_CompactValue(x) ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0]) + #endif + typedef Py_ssize_t __Pyx_compact_pylong; + typedef size_t __Pyx_compact_upylong; + #else // Py < 3.12 + #define __Pyx_PyLong_IsNeg(x) (Py_SIZE(x) < 0) + #define __Pyx_PyLong_IsNonNeg(x) (Py_SIZE(x) >= 0) + #define __Pyx_PyLong_IsZero(x) (Py_SIZE(x) == 0) + #define __Pyx_PyLong_IsPos(x) (Py_SIZE(x) > 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) __Pyx_sst_abs(Py_SIZE(x)) + #define __Pyx_PyLong_SignedDigitCount(x) Py_SIZE(x) + #define __Pyx_PyLong_IsCompact(x) (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1) + #define __Pyx_PyLong_CompactValue(x)\ + ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0])) + typedef sdigit __Pyx_compact_pylong; + typedef digit __Pyx_compact_upylong; + #endif + #if PY_VERSION_HEX >= 0x030C00A5 + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->long_value.ob_digit) + #else + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->ob_digit) + #endif +#endif +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = (char) c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +#if !CYTHON_USE_MODULE_STATE +static PyObject *__pyx_m = NULL; +#endif +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm = __FILE__; +static const char *__pyx_filename; + +/* #### Code section: filename_table ### */ + +static const char *__pyx_f[] = { + "core.pyx", + "", +}; +/* #### Code section: utility_code_proto_before_types ### */ +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* BufferFormatStructs.proto */ +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS +#define __pyx_atomic_int_type int +#define __pyx_nonatomic_int_type int +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__)) + #include +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ + (defined(_MSC_VER) && _MSC_VER >= 1700))) + #include +#endif +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type atomic_int + #define __pyx_atomic_incr_aligned(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed) + #define __pyx_atomic_decr_aligned(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C atomics" + #endif +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ +\ + (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type std::atomic_int + #define __pyx_atomic_incr_aligned(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed) + #define __pyx_atomic_decr_aligned(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C++ atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C++ atomics" + #endif +#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\ + (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2)))) + #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type long + #define __pyx_nonatomic_int_type long + #pragma intrinsic (_InterlockedExchangeAdd) + #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1) + #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview)) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview)) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* #### Code section: numeric_typedefs ### */ +/* #### Code section: complex_type_declarations ### */ +/* #### Code section: type_declarations ### */ + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; +struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each; + +/* "monotonic_align/core.pyx":7 + * @cython.boundscheck(False) + * @cython.wraparound(False) + * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil: # <<<<<<<<<<<<<< + * cdef int x + * cdef int y + */ +struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each { + int __pyx_n; + float max_neg_val; +}; + +/* "View.MemoryView":114 + * @cython.collection_type("sequence") + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ +struct __pyx_array_obj { + PyObject_HEAD + struct __pyx_vtabstruct_array *__pyx_vtab; + char *data; + Py_ssize_t len; + char *format; + int ndim; + Py_ssize_t *_shape; + Py_ssize_t *_strides; + Py_ssize_t itemsize; + PyObject *mode; + PyObject *_format; + void (*callback_free_data)(void *); + int free_data; + int dtype_is_object; +}; + + +/* "View.MemoryView":302 + * + * @cname('__pyx_MemviewEnum') + * cdef class Enum(object): # <<<<<<<<<<<<<< + * cdef object name + * def __init__(self, name): + */ +struct __pyx_MemviewEnum_obj { + PyObject_HEAD + PyObject *name; +}; + + +/* "View.MemoryView":337 + * + * @cname('__pyx_memoryview') + * cdef class memoryview: # <<<<<<<<<<<<<< + * + * cdef object obj + */ +struct __pyx_memoryview_obj { + PyObject_HEAD + struct __pyx_vtabstruct_memoryview *__pyx_vtab; + PyObject *obj; + PyObject *_size; + PyObject *_array_interface; + PyThread_type_lock lock; + __pyx_atomic_int_type acquisition_count; + Py_buffer view; + int flags; + int dtype_is_object; + __Pyx_TypeInfo *typeinfo; +}; + + +/* "View.MemoryView":952 + * @cython.collection_type("sequence") + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ +struct __pyx_memoryviewslice_obj { + struct __pyx_memoryview_obj __pyx_base; + __Pyx_memviewslice from_slice; + PyObject *from_object; + PyObject *(*to_object_func)(char *); + int (*to_dtype_func)(char *, PyObject *); +}; + + + +/* "View.MemoryView":114 + * @cython.collection_type("sequence") + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ + +struct __pyx_vtabstruct_array { + PyObject *(*get_memview)(struct __pyx_array_obj *); +}; +static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; + + +/* "View.MemoryView":337 + * + * @cname('__pyx_memoryview') + * cdef class memoryview: # <<<<<<<<<<<<<< + * + * cdef object obj + */ + +struct __pyx_vtabstruct_memoryview { + char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); + PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); + PyObject *(*_get_base)(struct __pyx_memoryview_obj *); +}; +static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; + + +/* "View.MemoryView":952 + * @cython.collection_type("sequence") + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ + +struct __pyx_vtabstruct__memoryviewslice { + struct __pyx_vtabstruct_memoryview __pyx_base; +}; +static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; +/* #### Code section: utility_code_proto ### */ + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, Py_ssize_t); + void (*DECREF)(void*, PyObject*, Py_ssize_t); + void (*GOTREF)(void*, PyObject*, Py_ssize_t); + void (*GIVEREF)(void*, PyObject*, Py_ssize_t); + void* (*SetupContext)(const char*, Py_ssize_t, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__));\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__));\ + } + #define __Pyx_RefNannyFinishContextNogil() {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __Pyx_RefNannyFinishContext();\ + PyGILState_Release(__pyx_gilstate_save);\ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__)) + #define __Pyx_RefNannyFinishContextNogil() __Pyx_RefNannyFinishContext() +#endif + #define __Pyx_RefNannyFinishContextNogil() {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __Pyx_RefNannyFinishContext();\ + PyGILState_Release(__pyx_gilstate_save);\ + } + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_XINCREF(r) do { if((r) == NULL); else {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) == NULL); else {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) == NULL); else {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) == NULL); else {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContextNogil() + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_Py_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; Py_XDECREF(tmp);\ + } while (0) +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#if PY_VERSION_HEX >= 0x030C00A6 +#define __Pyx_PyErr_Occurred() (__pyx_tstate->current_exception != NULL) +#define __Pyx_PyErr_CurrentExceptionType() (__pyx_tstate->current_exception ? (PyObject*) Py_TYPE(__pyx_tstate->current_exception) : (PyObject*) NULL) +#else +#define __Pyx_PyErr_Occurred() (__pyx_tstate->curexc_type != NULL) +#define __Pyx_PyErr_CurrentExceptionType() (__pyx_tstate->curexc_type) +#endif +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() (PyErr_Occurred() != NULL) +#define __Pyx_PyErr_CurrentExceptionType() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A6 +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* PyObjectGetAttrStrNoError.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* TupleAndListFromArray.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n); +static CYTHON_INLINE PyObject* __Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n); +#endif + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* fastcall.proto */ +#define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i) +#define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds) +#define __Pyx_KwValues_VARARGS(args, nargs) NULL +#define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s) +#define __Pyx_KwargsAsDict_VARARGS(kw, kwvalues) PyDict_Copy(kw) +#if CYTHON_METH_FASTCALL + #define __Pyx_Arg_FASTCALL(args, i) args[i] + #define __Pyx_NumKwargs_FASTCALL(kwds) PyTuple_GET_SIZE(kwds) + #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs)) + static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s); + #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw) +#else + #define __Pyx_Arg_FASTCALL __Pyx_Arg_VARARGS + #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS + #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS + #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS + #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS +#endif +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_ArgsSlice_VARARGS(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_VARARGS(args, start), stop - start) +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_FASTCALL(args, start), stop - start) +#else +#define __Pyx_ArgsSlice_VARARGS(args, start, stop) PyTuple_GetSlice(args, start, stop) +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) PyTuple_GetSlice(args, start, stop) +#endif + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues, + PyObject **argnames[], + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, + const char* function_name); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#if !CYTHON_VECTORCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif +#if !CYTHON_VECTORCALL +#if PY_VERSION_HEX >= 0x03080000 + #include "frameobject.h" +#if PY_VERSION_HEX >= 0x030b00a6 + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif + #define __Pxy_PyFrame_Initialize_Offsets() + #define __Pyx_PyFrame_GetLocalsplus(frame) ((frame)->f_localsplus) +#else + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif +#endif +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectFastCall.proto */ +#define __Pyx_PyObject_FastCall(func, args, nargs) __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL) +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs); + +/* RaiseUnexpectedTypeError.proto */ +static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj); + +/* GCCDiagnostics.proto */ +#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) +#define __Pyx_HAS_GCC_DIAGNOSTIC +#endif + +/* BuildPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, + int prepend_sign, char padding_char); + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char); + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char); + +/* JoinPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* PyObjectFormatSimple.proto */ +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#elif PY_MAJOR_VERSION < 3 + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") :\ + PyObject_Format(s, f)) +#elif CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_repr(s) :\ + likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_repr(s) :\ + PyObject_Format(s, f)) +#else + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#endif + +CYTHON_UNUSED static int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* ObjectGetItem.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key); +#else +#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) +#endif + +/* KeywordStringCheck.proto */ +static int __Pyx_CheckKeywordStrings(PyObject *kw, const char* function_name, int kw_allowed); + +/* DivInt[Py_ssize_t].proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* UnaryNegOverflows.proto */ +#define __Pyx_UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* PyDictVersioning.proto */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) do {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +#define __Pyx_GetModuleGlobalNameUncached(var, name) do {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* AssertionsEnabled.proto */ +#define __Pyx_init_assertions_enabled() +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define __pyx_assertions_enabled() (1) +#elif PY_VERSION_HEX < 0x03080000 || CYTHON_COMPILING_IN_PYPY || defined(Py_LIMITED_API) + #define __pyx_assertions_enabled() (!Py_OptimizeFlag) +#elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030900A6 + static int __pyx_assertions_enabled_flag; + #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag) + #undef __Pyx_init_assertions_enabled + static void __Pyx_init_assertions_enabled(void) { + __pyx_assertions_enabled_flag = ! _PyInterpreterState_GetConfig(__Pyx_PyThreadState_Current->interp)->optimization_level; + } +#else + #define __pyx_assertions_enabled() (!Py_OptimizeFlag) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* ImportDottedModule.proto */ +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple); +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple); +#endif + +/* ssize_strlen.proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2)) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyErr_ExceptionMatches2(err1, err2) __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PySequenceMultiply.proto */ +#define __Pyx_PySequence_Multiply_Left(mul, seq) __Pyx_PySequence_Multiply(seq, mul) +static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul); + +/* SetItemInt.proto */ +#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ + __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, + int is_list, int wraparound, int boundscheck); + +/* RaiseUnboundLocalError.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* DivInt[long].proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long); + +/* PySequenceContains.proto */ +static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) { + int result = PySequence_Contains(seq, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); + +/* ErrOccurredWithGIL.proto */ +static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void); + +/* PyObject_GenericGetAttrNoDict.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr +#endif + +/* PyObject_GenericGetAttr.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr +#endif + +/* IncludeStructmemberH.proto */ +#include + +/* FixUpExtensionType.proto */ +#if CYTHON_USE_TYPE_SPECS +static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type); +#endif + +/* PyObjectCallNoArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); + +/* PyObjectGetMethod.proto */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method); + +/* PyObjectCallMethod0.proto */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name); + +/* ValidateBasesTuple.proto */ +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases); +#endif + +/* PyType_Ready.proto */ +CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t); + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable); + +/* GetVTable.proto */ +static void* __Pyx_GetVtable(PyTypeObject *type); + +/* MergeVTables.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_MergeVtables(PyTypeObject *type); +#endif + +/* SetupReduce.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_setup_reduce(PyObject* type_obj); +#endif + +/* FetchSharedCythonModule.proto */ +static PyObject *__Pyx_FetchSharedCythonABIModule(void); + +/* FetchCommonType.proto */ +#if !CYTHON_USE_TYPE_SPECS +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); +#else +static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases); +#endif + +/* PyMethodNew.proto */ +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { + CYTHON_UNUSED_VAR(typ); + if (!self) + return __Pyx_NewRef(func); + return PyMethod_New(func, self); +} +#else + #define __Pyx_PyMethod_New PyMethod_New +#endif + +/* PyVectorcallFastCallDict.proto */ +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw); +#endif + +/* CythonFunctionShared.proto */ +#define __Pyx_CyFunction_USED +#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 +#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 +#define __Pyx_CYFUNCTION_CCLASS 0x04 +#define __Pyx_CYFUNCTION_COROUTINE 0x08 +#define __Pyx_CyFunction_GetClosure(f)\ + (((__pyx_CyFunctionObject *) (f))->func_closure) +#if PY_VERSION_HEX < 0x030900B1 + #define __Pyx_CyFunction_GetClassObj(f)\ + (((__pyx_CyFunctionObject *) (f))->func_classobj) +#else + #define __Pyx_CyFunction_GetClassObj(f)\ + ((PyObject*) ((PyCMethodObject *) (f))->mm_class) +#endif +#define __Pyx_CyFunction_SetClassObj(f, classobj)\ + __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj)) +#define __Pyx_CyFunction_Defaults(type, f)\ + ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) +#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ + ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) +typedef struct { +#if PY_VERSION_HEX < 0x030900B1 + PyCFunctionObject func; +#else + PyCMethodObject func; +#endif +#if CYTHON_BACKPORT_VECTORCALL + __pyx_vectorcallfunc func_vectorcall; +#endif +#if PY_VERSION_HEX < 0x030500A0 + PyObject *func_weakreflist; +#endif + PyObject *func_dict; + PyObject *func_name; + PyObject *func_qualname; + PyObject *func_doc; + PyObject *func_globals; + PyObject *func_code; + PyObject *func_closure; +#if PY_VERSION_HEX < 0x030900B1 + PyObject *func_classobj; +#endif + void *defaults; + int defaults_pyobjects; + size_t defaults_size; // used by FusedFunction for copying defaults + int flags; + PyObject *defaults_tuple; + PyObject *defaults_kwdict; + PyObject *(*defaults_getter)(PyObject *); + PyObject *func_annotations; + PyObject *func_is_coroutine; +} __pyx_CyFunctionObject; +#define __Pyx_CyFunction_Check(obj) __Pyx_TypeCheck(obj, __pyx_CyFunctionType) +#define __Pyx_IsCyOrPyCFunction(obj) __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type) +#define __Pyx_CyFunction_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_CyFunctionType) +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj); +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, + size_t size, + int pyobjects); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, + PyObject *tuple); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, + PyObject *dict); +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, + PyObject *dict); +static int __pyx_CyFunction_init(PyObject *module); +#if CYTHON_METH_FASTCALL +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +#if CYTHON_BACKPORT_VECTORCALL +#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall) +#else +#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall) +#endif +#endif + +/* CythonFunction.proto */ +static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); +#endif + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag); + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* FormatTypeName.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +typedef PyObject *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%U" +static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp); +#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj) +#else +typedef const char *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%.200s" +#define __Pyx_PyType_GetName(tp) ((tp)->tp_name) +#define __Pyx_DECREF_TypeName(obj) +#endif + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +/* #### Code section: module_declarations ### */ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/ + +/* Module declarations from "cython.view" */ + +/* Module declarations from "cython.dataclasses" */ + +/* Module declarations from "cython" */ + +/* Module declarations from "monotonic_align.core" */ +static PyObject *__pyx_collections_abc_Sequence = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/ +static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ +static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/ +static int __pyx_memoryview_err_no_memory(void); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +/* #### Code section: typeinfo ### */ +static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 'U' : 'I', __PYX_IS_UNSIGNED(int), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; +/* #### Code section: before_global_var ### */ +#define __Pyx_MODULE_NAME "monotonic_align.core" +extern int __pyx_module_is_main_monotonic_align__core; +int __pyx_module_is_main_monotonic_align__core = 0; + +/* Implementation of "monotonic_align.core" */ +/* #### Code section: global_var ### */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin___import__; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_AssertionError; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +/* #### Code section: string_decls ### */ +static const char __pyx_k_[] = ": "; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k__2[] = "."; +static const char __pyx_k__3[] = "*"; +static const char __pyx_k__6[] = "'"; +static const char __pyx_k__7[] = ")"; +static const char __pyx_k_gc[] = "gc"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k__23[] = "?"; +static const char __pyx_k_abc[] = "abc"; +static const char __pyx_k_and[] = " and "; +static const char __pyx_k_got[] = " (got "; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_sys[] = "sys"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_spec[] = "__spec__"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_t_xs[] = "t_xs"; +static const char __pyx_k_t_ys[] = "t_ys"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_count[] = "count"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_index[] = "index"; +static const char __pyx_k_paths[] = "paths"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_enable[] = "enable"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_values[] = "values"; +static const char __pyx_k_disable[] = "disable"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_Sequence[] = "Sequence"; +static const char __pyx_k_core_pyx[] = "core.pyx"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_register[] = "register"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_isenabled[] = "isenabled"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_collections[] = "collections"; +static const char __pyx_k_initializing[] = "_initializing"; +static const char __pyx_k_is_coroutine[] = "_is_coroutine"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_stringsource[] = ""; +static const char __pyx_k_version_info[] = "version_info"; +static const char __pyx_k_class_getitem[] = "__class_getitem__"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_AssertionError[] = "AssertionError"; +static const char __pyx_k_maximum_path_c[] = "maximum_path_c"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_collections_abc[] = "collections.abc"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_monotonic_align_core[] = "monotonic_align.core"; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis "; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '"; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct"; +static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)"; +static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)"; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced"; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; +static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; +static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions"; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got "; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis "; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension "; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +/* #### Code section: decls ### */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_ys, __Pyx_memviewslice __pyx_v_t_xs); /* proto */ +static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +/* #### Code section: late_includes ### */ +/* #### Code section: module_state ### */ +typedef struct { + PyObject *__pyx_d; + PyObject *__pyx_b; + PyObject *__pyx_cython_runtime; + PyObject *__pyx_empty_tuple; + PyObject *__pyx_empty_bytes; + PyObject *__pyx_empty_unicode; + #ifdef __Pyx_CyFunction_USED + PyTypeObject *__pyx_CyFunctionType; + #endif + #ifdef __Pyx_FusedFunction_USED + PyTypeObject *__pyx_FusedFunctionType; + #endif + #ifdef __Pyx_Generator_USED + PyTypeObject *__pyx_GeneratorType; + #endif + #ifdef __Pyx_IterableCoroutine_USED + PyTypeObject *__pyx_IterableCoroutineType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineAwaitType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineType; + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + PyObject *__pyx_type___pyx_array; + PyObject *__pyx_type___pyx_MemviewEnum; + PyObject *__pyx_type___pyx_memoryview; + PyObject *__pyx_type___pyx_memoryviewslice; + #endif + PyTypeObject *__pyx_array_type; + PyTypeObject *__pyx_MemviewEnum_type; + PyTypeObject *__pyx_memoryview_type; + PyTypeObject *__pyx_memoryviewslice_type; + PyObject *__pyx_kp_u_; + PyObject *__pyx_n_s_ASCII; + PyObject 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*__pyx_n_b_O; + PyObject *__pyx_kp_u_Out_of_bounds_on_buffer_access_a; + PyObject *__pyx_n_s_PickleError; + PyObject *__pyx_n_s_Sequence; + PyObject *__pyx_kp_s_Step_may_not_be_zero_axis_d; + PyObject *__pyx_n_s_TypeError; + PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; + PyObject *__pyx_n_s_ValueError; + PyObject *__pyx_n_s_View_MemoryView; + PyObject *__pyx_kp_u__2; + PyObject *__pyx_n_s__23; + PyObject *__pyx_n_s__3; + PyObject *__pyx_kp_u__6; + PyObject *__pyx_kp_u__7; + PyObject *__pyx_n_s_abc; + PyObject *__pyx_n_s_allocate_buffer; + PyObject *__pyx_kp_u_and; + PyObject *__pyx_n_s_asyncio_coroutines; + PyObject *__pyx_n_s_base; + PyObject *__pyx_n_s_c; + PyObject *__pyx_n_u_c; + PyObject *__pyx_n_s_class; + PyObject *__pyx_n_s_class_getitem; + PyObject *__pyx_n_s_cline_in_traceback; + PyObject *__pyx_n_s_collections; + PyObject *__pyx_kp_s_collections_abc; + PyObject *__pyx_kp_s_contiguous_and_direct; + PyObject *__pyx_kp_s_contiguous_and_indirect; + PyObject 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*__pyx_tuple__16; + PyObject *__pyx_tuple__17; + PyObject *__pyx_tuple__18; + PyObject *__pyx_tuple__19; + PyObject *__pyx_tuple__21; + PyObject *__pyx_codeobj__20; + PyObject *__pyx_codeobj__22; +} __pyx_mstate; + +#if CYTHON_USE_MODULE_STATE +#ifdef __cplusplus +namespace { + extern struct PyModuleDef __pyx_moduledef; +} /* anonymous namespace */ +#else +static struct PyModuleDef __pyx_moduledef; +#endif + +#define __pyx_mstate(o) ((__pyx_mstate *)__Pyx_PyModule_GetState(o)) + +#define __pyx_mstate_global (__pyx_mstate(PyState_FindModule(&__pyx_moduledef))) + +#define __pyx_m (PyState_FindModule(&__pyx_moduledef)) +#else +static __pyx_mstate __pyx_mstate_global_static = +#ifdef __cplusplus + {}; +#else + {0}; +#endif +static __pyx_mstate *__pyx_mstate_global = &__pyx_mstate_global_static; +#endif +/* #### Code section: module_state_clear ### */ +#if CYTHON_USE_MODULE_STATE +static int __pyx_m_clear(PyObject *m) { + __pyx_mstate *clear_module_state = __pyx_mstate(m); + if 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Py_CLEAR(clear_module_state->__pyx_n_s_IndexError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Index_out_of_bounds_axis_d); + Py_CLEAR(clear_module_state->__pyx_kp_s_Indirect_dimensions_not_supporte); + Py_CLEAR(clear_module_state->__pyx_kp_u_Invalid_mode_expected_c_or_fortr); + Py_CLEAR(clear_module_state->__pyx_kp_u_Invalid_shape_in_axis); + Py_CLEAR(clear_module_state->__pyx_n_s_MemoryError); + Py_CLEAR(clear_module_state->__pyx_kp_s_MemoryView_of_r_at_0x_x); + Py_CLEAR(clear_module_state->__pyx_kp_s_MemoryView_of_r_object); + Py_CLEAR(clear_module_state->__pyx_n_b_O); + Py_CLEAR(clear_module_state->__pyx_kp_u_Out_of_bounds_on_buffer_access_a); + Py_CLEAR(clear_module_state->__pyx_n_s_PickleError); + Py_CLEAR(clear_module_state->__pyx_n_s_Sequence); + Py_CLEAR(clear_module_state->__pyx_kp_s_Step_may_not_be_zero_axis_d); + Py_CLEAR(clear_module_state->__pyx_n_s_TypeError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Unable_to_convert_item_to_object); + Py_CLEAR(clear_module_state->__pyx_n_s_ValueError); + Py_CLEAR(clear_module_state->__pyx_n_s_View_MemoryView); + Py_CLEAR(clear_module_state->__pyx_kp_u__2); + Py_CLEAR(clear_module_state->__pyx_n_s__23); + Py_CLEAR(clear_module_state->__pyx_n_s__3); + Py_CLEAR(clear_module_state->__pyx_kp_u__6); + Py_CLEAR(clear_module_state->__pyx_kp_u__7); + Py_CLEAR(clear_module_state->__pyx_n_s_abc); + Py_CLEAR(clear_module_state->__pyx_n_s_allocate_buffer); + Py_CLEAR(clear_module_state->__pyx_kp_u_and); + Py_CLEAR(clear_module_state->__pyx_n_s_asyncio_coroutines); + Py_CLEAR(clear_module_state->__pyx_n_s_base); + Py_CLEAR(clear_module_state->__pyx_n_s_c); + Py_CLEAR(clear_module_state->__pyx_n_u_c); + Py_CLEAR(clear_module_state->__pyx_n_s_class); + Py_CLEAR(clear_module_state->__pyx_n_s_class_getitem); + Py_CLEAR(clear_module_state->__pyx_n_s_cline_in_traceback); + Py_CLEAR(clear_module_state->__pyx_n_s_collections); + Py_CLEAR(clear_module_state->__pyx_kp_s_collections_abc); + Py_CLEAR(clear_module_state->__pyx_kp_s_contiguous_and_direct); + Py_CLEAR(clear_module_state->__pyx_kp_s_contiguous_and_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_core_pyx); + Py_CLEAR(clear_module_state->__pyx_n_s_count); + Py_CLEAR(clear_module_state->__pyx_n_s_dict); + Py_CLEAR(clear_module_state->__pyx_kp_u_disable); + Py_CLEAR(clear_module_state->__pyx_n_s_dtype_is_object); + Py_CLEAR(clear_module_state->__pyx_kp_u_enable); + Py_CLEAR(clear_module_state->__pyx_n_s_encode); + Py_CLEAR(clear_module_state->__pyx_n_s_enumerate); + Py_CLEAR(clear_module_state->__pyx_n_s_error); + Py_CLEAR(clear_module_state->__pyx_n_s_flags); + Py_CLEAR(clear_module_state->__pyx_n_s_format); + Py_CLEAR(clear_module_state->__pyx_n_s_fortran); + Py_CLEAR(clear_module_state->__pyx_n_u_fortran); + Py_CLEAR(clear_module_state->__pyx_kp_u_gc); + Py_CLEAR(clear_module_state->__pyx_n_s_getstate); + Py_CLEAR(clear_module_state->__pyx_kp_u_got); + Py_CLEAR(clear_module_state->__pyx_kp_u_got_differing_extents_in_dimensi); + Py_CLEAR(clear_module_state->__pyx_n_s_id); + Py_CLEAR(clear_module_state->__pyx_n_s_import); + Py_CLEAR(clear_module_state->__pyx_n_s_index); + Py_CLEAR(clear_module_state->__pyx_n_s_initializing); + Py_CLEAR(clear_module_state->__pyx_n_s_is_coroutine); + Py_CLEAR(clear_module_state->__pyx_kp_u_isenabled); + Py_CLEAR(clear_module_state->__pyx_n_s_itemsize); + Py_CLEAR(clear_module_state->__pyx_kp_s_itemsize_0_for_cython_array); + Py_CLEAR(clear_module_state->__pyx_n_s_main); + Py_CLEAR(clear_module_state->__pyx_n_s_maximum_path_c); + Py_CLEAR(clear_module_state->__pyx_n_s_memview); + Py_CLEAR(clear_module_state->__pyx_n_s_mode); + Py_CLEAR(clear_module_state->__pyx_n_s_monotonic_align_core); + Py_CLEAR(clear_module_state->__pyx_n_s_name); + Py_CLEAR(clear_module_state->__pyx_n_s_name_2); + Py_CLEAR(clear_module_state->__pyx_n_s_ndim); + Py_CLEAR(clear_module_state->__pyx_n_s_new); + Py_CLEAR(clear_module_state->__pyx_kp_s_no_default___reduce___due_to_non); + Py_CLEAR(clear_module_state->__pyx_n_s_obj); + Py_CLEAR(clear_module_state->__pyx_n_s_pack); + Py_CLEAR(clear_module_state->__pyx_n_s_paths); + Py_CLEAR(clear_module_state->__pyx_n_s_pickle); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_PickleError); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_checksum); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_result); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_state); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_type); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_unpickle_Enum); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_vtable); + Py_CLEAR(clear_module_state->__pyx_n_s_range); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce_cython); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce_ex); + Py_CLEAR(clear_module_state->__pyx_n_s_register); + Py_CLEAR(clear_module_state->__pyx_n_s_setstate); + Py_CLEAR(clear_module_state->__pyx_n_s_setstate_cython); + Py_CLEAR(clear_module_state->__pyx_n_s_shape); + Py_CLEAR(clear_module_state->__pyx_n_s_size); + Py_CLEAR(clear_module_state->__pyx_n_s_spec); + Py_CLEAR(clear_module_state->__pyx_n_s_start); + Py_CLEAR(clear_module_state->__pyx_n_s_step); + Py_CLEAR(clear_module_state->__pyx_n_s_stop); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct_or_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_stringsource); + Py_CLEAR(clear_module_state->__pyx_n_s_struct); + Py_CLEAR(clear_module_state->__pyx_n_s_sys); + Py_CLEAR(clear_module_state->__pyx_n_s_t_xs); + Py_CLEAR(clear_module_state->__pyx_n_s_t_ys); + Py_CLEAR(clear_module_state->__pyx_n_s_test); + Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_array_data); + Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str); + Py_CLEAR(clear_module_state->__pyx_n_s_unpack); + Py_CLEAR(clear_module_state->__pyx_n_s_update); + Py_CLEAR(clear_module_state->__pyx_n_s_values); + Py_CLEAR(clear_module_state->__pyx_n_s_version_info); + Py_CLEAR(clear_module_state->__pyx_int_0); + Py_CLEAR(clear_module_state->__pyx_int_1); + Py_CLEAR(clear_module_state->__pyx_int_3); + Py_CLEAR(clear_module_state->__pyx_int_112105877); + Py_CLEAR(clear_module_state->__pyx_int_136983863); + Py_CLEAR(clear_module_state->__pyx_int_184977713); + Py_CLEAR(clear_module_state->__pyx_int_neg_1); + Py_CLEAR(clear_module_state->__pyx_slice__5); + Py_CLEAR(clear_module_state->__pyx_tuple__4); + Py_CLEAR(clear_module_state->__pyx_tuple__8); + Py_CLEAR(clear_module_state->__pyx_tuple__10); + Py_CLEAR(clear_module_state->__pyx_tuple__11); + Py_CLEAR(clear_module_state->__pyx_tuple__12); + Py_CLEAR(clear_module_state->__pyx_tuple__13); + Py_CLEAR(clear_module_state->__pyx_tuple__14); + Py_CLEAR(clear_module_state->__pyx_tuple__15); + Py_CLEAR(clear_module_state->__pyx_tuple__16); + Py_CLEAR(clear_module_state->__pyx_tuple__17); + Py_CLEAR(clear_module_state->__pyx_tuple__18); + Py_CLEAR(clear_module_state->__pyx_tuple__19); + Py_CLEAR(clear_module_state->__pyx_tuple__21); + Py_CLEAR(clear_module_state->__pyx_codeobj__20); + Py_CLEAR(clear_module_state->__pyx_codeobj__22); + return 0; +} +#endif +/* #### Code section: module_state_traverse ### */ +#if CYTHON_USE_MODULE_STATE +static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) { + __pyx_mstate *traverse_module_state = __pyx_mstate(m); + if (!traverse_module_state) return 0; + Py_VISIT(traverse_module_state->__pyx_d); + Py_VISIT(traverse_module_state->__pyx_b); + Py_VISIT(traverse_module_state->__pyx_cython_runtime); + Py_VISIT(traverse_module_state->__pyx_empty_tuple); + Py_VISIT(traverse_module_state->__pyx_empty_bytes); + Py_VISIT(traverse_module_state->__pyx_empty_unicode); + #ifdef __Pyx_CyFunction_USED + Py_VISIT(traverse_module_state->__pyx_CyFunctionType); + #endif + #ifdef __Pyx_FusedFunction_USED + Py_VISIT(traverse_module_state->__pyx_FusedFunctionType); + #endif + Py_VISIT(traverse_module_state->__pyx_array_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_array); + Py_VISIT(traverse_module_state->__pyx_MemviewEnum_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_MemviewEnum); + Py_VISIT(traverse_module_state->__pyx_memoryview_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryview); + Py_VISIT(traverse_module_state->__pyx_memoryviewslice_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryviewslice); + Py_VISIT(traverse_module_state->__pyx_kp_u_); + Py_VISIT(traverse_module_state->__pyx_n_s_ASCII); + Py_VISIT(traverse_module_state->__pyx_kp_s_All_dimensions_preceding_dimensi); + Py_VISIT(traverse_module_state->__pyx_n_s_AssertionError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Buffer_view_does_not_expose_stri); + Py_VISIT(traverse_module_state->__pyx_kp_s_Can_only_create_a_buffer_that_is); + Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_assign_to_read_only_memor); + Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_create_writable_memory_vi); + Py_VISIT(traverse_module_state->__pyx_kp_u_Cannot_index_with_type); + Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_transpose_memoryview_with); + Py_VISIT(traverse_module_state->__pyx_kp_s_Dimension_d_is_not_direct); + Py_VISIT(traverse_module_state->__pyx_n_s_Ellipsis); + Py_VISIT(traverse_module_state->__pyx_kp_s_Empty_shape_tuple_for_cython_arr); + Py_VISIT(traverse_module_state->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0); + Py_VISIT(traverse_module_state->__pyx_n_s_IndexError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Index_out_of_bounds_axis_d); + Py_VISIT(traverse_module_state->__pyx_kp_s_Indirect_dimensions_not_supporte); + Py_VISIT(traverse_module_state->__pyx_kp_u_Invalid_mode_expected_c_or_fortr); + Py_VISIT(traverse_module_state->__pyx_kp_u_Invalid_shape_in_axis); + Py_VISIT(traverse_module_state->__pyx_n_s_MemoryError); + Py_VISIT(traverse_module_state->__pyx_kp_s_MemoryView_of_r_at_0x_x); + Py_VISIT(traverse_module_state->__pyx_kp_s_MemoryView_of_r_object); + Py_VISIT(traverse_module_state->__pyx_n_b_O); + Py_VISIT(traverse_module_state->__pyx_kp_u_Out_of_bounds_on_buffer_access_a); + Py_VISIT(traverse_module_state->__pyx_n_s_PickleError); + Py_VISIT(traverse_module_state->__pyx_n_s_Sequence); + Py_VISIT(traverse_module_state->__pyx_kp_s_Step_may_not_be_zero_axis_d); + Py_VISIT(traverse_module_state->__pyx_n_s_TypeError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Unable_to_convert_item_to_object); + Py_VISIT(traverse_module_state->__pyx_n_s_ValueError); + Py_VISIT(traverse_module_state->__pyx_n_s_View_MemoryView); + Py_VISIT(traverse_module_state->__pyx_kp_u__2); + Py_VISIT(traverse_module_state->__pyx_n_s__23); + Py_VISIT(traverse_module_state->__pyx_n_s__3); + Py_VISIT(traverse_module_state->__pyx_kp_u__6); + Py_VISIT(traverse_module_state->__pyx_kp_u__7); + Py_VISIT(traverse_module_state->__pyx_n_s_abc); + Py_VISIT(traverse_module_state->__pyx_n_s_allocate_buffer); + Py_VISIT(traverse_module_state->__pyx_kp_u_and); + Py_VISIT(traverse_module_state->__pyx_n_s_asyncio_coroutines); + Py_VISIT(traverse_module_state->__pyx_n_s_base); + Py_VISIT(traverse_module_state->__pyx_n_s_c); + Py_VISIT(traverse_module_state->__pyx_n_u_c); + Py_VISIT(traverse_module_state->__pyx_n_s_class); + Py_VISIT(traverse_module_state->__pyx_n_s_class_getitem); + Py_VISIT(traverse_module_state->__pyx_n_s_cline_in_traceback); + Py_VISIT(traverse_module_state->__pyx_n_s_collections); + Py_VISIT(traverse_module_state->__pyx_kp_s_collections_abc); + Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_direct); + Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_indirect); + Py_VISIT(traverse_module_state->__pyx_kp_s_core_pyx); + Py_VISIT(traverse_module_state->__pyx_n_s_count); + Py_VISIT(traverse_module_state->__pyx_n_s_dict); + Py_VISIT(traverse_module_state->__pyx_kp_u_disable); + Py_VISIT(traverse_module_state->__pyx_n_s_dtype_is_object); + Py_VISIT(traverse_module_state->__pyx_kp_u_enable); + Py_VISIT(traverse_module_state->__pyx_n_s_encode); + Py_VISIT(traverse_module_state->__pyx_n_s_enumerate); + Py_VISIT(traverse_module_state->__pyx_n_s_error); + Py_VISIT(traverse_module_state->__pyx_n_s_flags); + Py_VISIT(traverse_module_state->__pyx_n_s_format); + Py_VISIT(traverse_module_state->__pyx_n_s_fortran); + Py_VISIT(traverse_module_state->__pyx_n_u_fortran); + Py_VISIT(traverse_module_state->__pyx_kp_u_gc); + Py_VISIT(traverse_module_state->__pyx_n_s_getstate); + Py_VISIT(traverse_module_state->__pyx_kp_u_got); + Py_VISIT(traverse_module_state->__pyx_kp_u_got_differing_extents_in_dimensi); + Py_VISIT(traverse_module_state->__pyx_n_s_id); + Py_VISIT(traverse_module_state->__pyx_n_s_import); + Py_VISIT(traverse_module_state->__pyx_n_s_index); + Py_VISIT(traverse_module_state->__pyx_n_s_initializing); + Py_VISIT(traverse_module_state->__pyx_n_s_is_coroutine); + Py_VISIT(traverse_module_state->__pyx_kp_u_isenabled); + Py_VISIT(traverse_module_state->__pyx_n_s_itemsize); + Py_VISIT(traverse_module_state->__pyx_kp_s_itemsize_0_for_cython_array); + Py_VISIT(traverse_module_state->__pyx_n_s_main); + Py_VISIT(traverse_module_state->__pyx_n_s_maximum_path_c); + Py_VISIT(traverse_module_state->__pyx_n_s_memview); + Py_VISIT(traverse_module_state->__pyx_n_s_mode); + Py_VISIT(traverse_module_state->__pyx_n_s_monotonic_align_core); + Py_VISIT(traverse_module_state->__pyx_n_s_name); + Py_VISIT(traverse_module_state->__pyx_n_s_name_2); + Py_VISIT(traverse_module_state->__pyx_n_s_ndim); + Py_VISIT(traverse_module_state->__pyx_n_s_new); + Py_VISIT(traverse_module_state->__pyx_kp_s_no_default___reduce___due_to_non); + Py_VISIT(traverse_module_state->__pyx_n_s_obj); + Py_VISIT(traverse_module_state->__pyx_n_s_pack); + Py_VISIT(traverse_module_state->__pyx_n_s_paths); + Py_VISIT(traverse_module_state->__pyx_n_s_pickle); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_PickleError); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_checksum); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_result); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_state); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_type); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_unpickle_Enum); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_vtable); + Py_VISIT(traverse_module_state->__pyx_n_s_range); + Py_VISIT(traverse_module_state->__pyx_n_s_reduce); + Py_VISIT(traverse_module_state->__pyx_n_s_reduce_cython); + Py_VISIT(traverse_module_state->__pyx_n_s_reduce_ex); + Py_VISIT(traverse_module_state->__pyx_n_s_register); + Py_VISIT(traverse_module_state->__pyx_n_s_setstate); + Py_VISIT(traverse_module_state->__pyx_n_s_setstate_cython); + Py_VISIT(traverse_module_state->__pyx_n_s_shape); + Py_VISIT(traverse_module_state->__pyx_n_s_size); + Py_VISIT(traverse_module_state->__pyx_n_s_spec); + Py_VISIT(traverse_module_state->__pyx_n_s_start); + Py_VISIT(traverse_module_state->__pyx_n_s_step); + Py_VISIT(traverse_module_state->__pyx_n_s_stop); + Py_VISIT(traverse_module_state->__pyx_kp_s_strided_and_direct); + Py_VISIT(traverse_module_state->__pyx_kp_s_strided_and_direct_or_indirect); + Py_VISIT(traverse_module_state->__pyx_kp_s_strided_and_indirect); + Py_VISIT(traverse_module_state->__pyx_kp_s_stringsource); + Py_VISIT(traverse_module_state->__pyx_n_s_struct); + Py_VISIT(traverse_module_state->__pyx_n_s_sys); + Py_VISIT(traverse_module_state->__pyx_n_s_t_xs); + Py_VISIT(traverse_module_state->__pyx_n_s_t_ys); + Py_VISIT(traverse_module_state->__pyx_n_s_test); + Py_VISIT(traverse_module_state->__pyx_kp_s_unable_to_allocate_array_data); + Py_VISIT(traverse_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str); + Py_VISIT(traverse_module_state->__pyx_n_s_unpack); + Py_VISIT(traverse_module_state->__pyx_n_s_update); + Py_VISIT(traverse_module_state->__pyx_n_s_values); + Py_VISIT(traverse_module_state->__pyx_n_s_version_info); + Py_VISIT(traverse_module_state->__pyx_int_0); + Py_VISIT(traverse_module_state->__pyx_int_1); + Py_VISIT(traverse_module_state->__pyx_int_3); + Py_VISIT(traverse_module_state->__pyx_int_112105877); + Py_VISIT(traverse_module_state->__pyx_int_136983863); + Py_VISIT(traverse_module_state->__pyx_int_184977713); + Py_VISIT(traverse_module_state->__pyx_int_neg_1); + Py_VISIT(traverse_module_state->__pyx_slice__5); + Py_VISIT(traverse_module_state->__pyx_tuple__4); + Py_VISIT(traverse_module_state->__pyx_tuple__8); + Py_VISIT(traverse_module_state->__pyx_tuple__10); + Py_VISIT(traverse_module_state->__pyx_tuple__11); + Py_VISIT(traverse_module_state->__pyx_tuple__12); + Py_VISIT(traverse_module_state->__pyx_tuple__13); + Py_VISIT(traverse_module_state->__pyx_tuple__14); + Py_VISIT(traverse_module_state->__pyx_tuple__15); + Py_VISIT(traverse_module_state->__pyx_tuple__16); + Py_VISIT(traverse_module_state->__pyx_tuple__17); + Py_VISIT(traverse_module_state->__pyx_tuple__18); + Py_VISIT(traverse_module_state->__pyx_tuple__19); + Py_VISIT(traverse_module_state->__pyx_tuple__21); + Py_VISIT(traverse_module_state->__pyx_codeobj__20); + Py_VISIT(traverse_module_state->__pyx_codeobj__22); + return 0; +} +#endif +/* #### Code section: module_state_defines ### */ +#define __pyx_d __pyx_mstate_global->__pyx_d +#define __pyx_b __pyx_mstate_global->__pyx_b +#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime +#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple +#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes +#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode +#ifdef __Pyx_CyFunction_USED +#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType +#endif +#ifdef __Pyx_FusedFunction_USED +#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType +#endif +#ifdef __Pyx_Generator_USED +#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType +#endif +#ifdef __Pyx_IterableCoroutine_USED +#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType +#endif +#ifdef __Pyx_Coroutine_USED +#define __pyx_CoroutineAwaitType __pyx_mstate_global->__pyx_CoroutineAwaitType +#endif +#ifdef __Pyx_Coroutine_USED +#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#define __pyx_type___pyx_array __pyx_mstate_global->__pyx_type___pyx_array +#define __pyx_type___pyx_MemviewEnum __pyx_mstate_global->__pyx_type___pyx_MemviewEnum +#define __pyx_type___pyx_memoryview __pyx_mstate_global->__pyx_type___pyx_memoryview +#define __pyx_type___pyx_memoryviewslice __pyx_mstate_global->__pyx_type___pyx_memoryviewslice +#endif +#define __pyx_array_type __pyx_mstate_global->__pyx_array_type +#define __pyx_MemviewEnum_type __pyx_mstate_global->__pyx_MemviewEnum_type +#define __pyx_memoryview_type __pyx_mstate_global->__pyx_memoryview_type +#define __pyx_memoryviewslice_type __pyx_mstate_global->__pyx_memoryviewslice_type +#define __pyx_kp_u_ __pyx_mstate_global->__pyx_kp_u_ +#define __pyx_n_s_ASCII __pyx_mstate_global->__pyx_n_s_ASCII +#define __pyx_kp_s_All_dimensions_preceding_dimensi __pyx_mstate_global->__pyx_kp_s_All_dimensions_preceding_dimensi +#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError +#define __pyx_kp_s_Buffer_view_does_not_expose_stri __pyx_mstate_global->__pyx_kp_s_Buffer_view_does_not_expose_stri +#define __pyx_kp_s_Can_only_create_a_buffer_that_is __pyx_mstate_global->__pyx_kp_s_Can_only_create_a_buffer_that_is +#define __pyx_kp_s_Cannot_assign_to_read_only_memor __pyx_mstate_global->__pyx_kp_s_Cannot_assign_to_read_only_memor +#define __pyx_kp_s_Cannot_create_writable_memory_vi __pyx_mstate_global->__pyx_kp_s_Cannot_create_writable_memory_vi +#define __pyx_kp_u_Cannot_index_with_type __pyx_mstate_global->__pyx_kp_u_Cannot_index_with_type +#define __pyx_kp_s_Cannot_transpose_memoryview_with __pyx_mstate_global->__pyx_kp_s_Cannot_transpose_memoryview_with +#define __pyx_kp_s_Dimension_d_is_not_direct __pyx_mstate_global->__pyx_kp_s_Dimension_d_is_not_direct +#define __pyx_n_s_Ellipsis __pyx_mstate_global->__pyx_n_s_Ellipsis +#define __pyx_kp_s_Empty_shape_tuple_for_cython_arr __pyx_mstate_global->__pyx_kp_s_Empty_shape_tuple_for_cython_arr +#define __pyx_kp_s_Incompatible_checksums_0x_x_vs_0 __pyx_mstate_global->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0 +#define __pyx_n_s_IndexError __pyx_mstate_global->__pyx_n_s_IndexError +#define __pyx_kp_s_Index_out_of_bounds_axis_d __pyx_mstate_global->__pyx_kp_s_Index_out_of_bounds_axis_d +#define __pyx_kp_s_Indirect_dimensions_not_supporte __pyx_mstate_global->__pyx_kp_s_Indirect_dimensions_not_supporte +#define __pyx_kp_u_Invalid_mode_expected_c_or_fortr __pyx_mstate_global->__pyx_kp_u_Invalid_mode_expected_c_or_fortr +#define __pyx_kp_u_Invalid_shape_in_axis __pyx_mstate_global->__pyx_kp_u_Invalid_shape_in_axis +#define __pyx_n_s_MemoryError __pyx_mstate_global->__pyx_n_s_MemoryError +#define __pyx_kp_s_MemoryView_of_r_at_0x_x __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_at_0x_x +#define __pyx_kp_s_MemoryView_of_r_object __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_object +#define __pyx_n_b_O __pyx_mstate_global->__pyx_n_b_O +#define __pyx_kp_u_Out_of_bounds_on_buffer_access_a __pyx_mstate_global->__pyx_kp_u_Out_of_bounds_on_buffer_access_a +#define __pyx_n_s_PickleError __pyx_mstate_global->__pyx_n_s_PickleError +#define __pyx_n_s_Sequence __pyx_mstate_global->__pyx_n_s_Sequence +#define __pyx_kp_s_Step_may_not_be_zero_axis_d __pyx_mstate_global->__pyx_kp_s_Step_may_not_be_zero_axis_d +#define __pyx_n_s_TypeError __pyx_mstate_global->__pyx_n_s_TypeError +#define __pyx_kp_s_Unable_to_convert_item_to_object __pyx_mstate_global->__pyx_kp_s_Unable_to_convert_item_to_object +#define __pyx_n_s_ValueError __pyx_mstate_global->__pyx_n_s_ValueError +#define __pyx_n_s_View_MemoryView __pyx_mstate_global->__pyx_n_s_View_MemoryView +#define __pyx_kp_u__2 __pyx_mstate_global->__pyx_kp_u__2 +#define __pyx_n_s__23 __pyx_mstate_global->__pyx_n_s__23 +#define __pyx_n_s__3 __pyx_mstate_global->__pyx_n_s__3 +#define __pyx_kp_u__6 __pyx_mstate_global->__pyx_kp_u__6 +#define __pyx_kp_u__7 __pyx_mstate_global->__pyx_kp_u__7 +#define __pyx_n_s_abc __pyx_mstate_global->__pyx_n_s_abc +#define __pyx_n_s_allocate_buffer __pyx_mstate_global->__pyx_n_s_allocate_buffer +#define __pyx_kp_u_and __pyx_mstate_global->__pyx_kp_u_and +#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines +#define __pyx_n_s_base __pyx_mstate_global->__pyx_n_s_base +#define __pyx_n_s_c __pyx_mstate_global->__pyx_n_s_c +#define __pyx_n_u_c __pyx_mstate_global->__pyx_n_u_c +#define __pyx_n_s_class __pyx_mstate_global->__pyx_n_s_class +#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem +#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback +#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections +#define __pyx_kp_s_collections_abc __pyx_mstate_global->__pyx_kp_s_collections_abc +#define __pyx_kp_s_contiguous_and_direct __pyx_mstate_global->__pyx_kp_s_contiguous_and_direct +#define __pyx_kp_s_contiguous_and_indirect __pyx_mstate_global->__pyx_kp_s_contiguous_and_indirect +#define __pyx_kp_s_core_pyx __pyx_mstate_global->__pyx_kp_s_core_pyx +#define __pyx_n_s_count __pyx_mstate_global->__pyx_n_s_count +#define __pyx_n_s_dict __pyx_mstate_global->__pyx_n_s_dict +#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable +#define __pyx_n_s_dtype_is_object __pyx_mstate_global->__pyx_n_s_dtype_is_object +#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable +#define __pyx_n_s_encode __pyx_mstate_global->__pyx_n_s_encode +#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate +#define __pyx_n_s_error __pyx_mstate_global->__pyx_n_s_error +#define __pyx_n_s_flags __pyx_mstate_global->__pyx_n_s_flags +#define __pyx_n_s_format __pyx_mstate_global->__pyx_n_s_format +#define __pyx_n_s_fortran __pyx_mstate_global->__pyx_n_s_fortran +#define __pyx_n_u_fortran __pyx_mstate_global->__pyx_n_u_fortran +#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc +#define __pyx_n_s_getstate __pyx_mstate_global->__pyx_n_s_getstate +#define __pyx_kp_u_got __pyx_mstate_global->__pyx_kp_u_got +#define __pyx_kp_u_got_differing_extents_in_dimensi __pyx_mstate_global->__pyx_kp_u_got_differing_extents_in_dimensi +#define __pyx_n_s_id __pyx_mstate_global->__pyx_n_s_id +#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import +#define __pyx_n_s_index __pyx_mstate_global->__pyx_n_s_index +#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing +#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine +#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled +#define __pyx_n_s_itemsize __pyx_mstate_global->__pyx_n_s_itemsize +#define __pyx_kp_s_itemsize_0_for_cython_array __pyx_mstate_global->__pyx_kp_s_itemsize_0_for_cython_array +#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main +#define __pyx_n_s_maximum_path_c __pyx_mstate_global->__pyx_n_s_maximum_path_c +#define __pyx_n_s_memview __pyx_mstate_global->__pyx_n_s_memview +#define __pyx_n_s_mode __pyx_mstate_global->__pyx_n_s_mode +#define __pyx_n_s_monotonic_align_core __pyx_mstate_global->__pyx_n_s_monotonic_align_core +#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name +#define __pyx_n_s_name_2 __pyx_mstate_global->__pyx_n_s_name_2 +#define __pyx_n_s_ndim __pyx_mstate_global->__pyx_n_s_ndim +#define __pyx_n_s_new __pyx_mstate_global->__pyx_n_s_new +#define __pyx_kp_s_no_default___reduce___due_to_non __pyx_mstate_global->__pyx_kp_s_no_default___reduce___due_to_non +#define __pyx_n_s_obj __pyx_mstate_global->__pyx_n_s_obj +#define __pyx_n_s_pack __pyx_mstate_global->__pyx_n_s_pack +#define __pyx_n_s_paths __pyx_mstate_global->__pyx_n_s_paths +#define __pyx_n_s_pickle __pyx_mstate_global->__pyx_n_s_pickle +#define __pyx_n_s_pyx_PickleError __pyx_mstate_global->__pyx_n_s_pyx_PickleError +#define __pyx_n_s_pyx_checksum __pyx_mstate_global->__pyx_n_s_pyx_checksum +#define __pyx_n_s_pyx_result __pyx_mstate_global->__pyx_n_s_pyx_result +#define __pyx_n_s_pyx_state __pyx_mstate_global->__pyx_n_s_pyx_state +#define __pyx_n_s_pyx_type __pyx_mstate_global->__pyx_n_s_pyx_type +#define __pyx_n_s_pyx_unpickle_Enum __pyx_mstate_global->__pyx_n_s_pyx_unpickle_Enum +#define __pyx_n_s_pyx_vtable __pyx_mstate_global->__pyx_n_s_pyx_vtable +#define __pyx_n_s_range __pyx_mstate_global->__pyx_n_s_range +#define __pyx_n_s_reduce __pyx_mstate_global->__pyx_n_s_reduce +#define __pyx_n_s_reduce_cython __pyx_mstate_global->__pyx_n_s_reduce_cython +#define __pyx_n_s_reduce_ex __pyx_mstate_global->__pyx_n_s_reduce_ex +#define __pyx_n_s_register __pyx_mstate_global->__pyx_n_s_register +#define __pyx_n_s_setstate __pyx_mstate_global->__pyx_n_s_setstate +#define __pyx_n_s_setstate_cython __pyx_mstate_global->__pyx_n_s_setstate_cython +#define __pyx_n_s_shape __pyx_mstate_global->__pyx_n_s_shape +#define __pyx_n_s_size __pyx_mstate_global->__pyx_n_s_size +#define __pyx_n_s_spec __pyx_mstate_global->__pyx_n_s_spec +#define __pyx_n_s_start __pyx_mstate_global->__pyx_n_s_start +#define __pyx_n_s_step __pyx_mstate_global->__pyx_n_s_step +#define __pyx_n_s_stop __pyx_mstate_global->__pyx_n_s_stop +#define __pyx_kp_s_strided_and_direct __pyx_mstate_global->__pyx_kp_s_strided_and_direct +#define __pyx_kp_s_strided_and_direct_or_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_direct_or_indirect +#define __pyx_kp_s_strided_and_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_indirect +#define __pyx_kp_s_stringsource __pyx_mstate_global->__pyx_kp_s_stringsource +#define __pyx_n_s_struct __pyx_mstate_global->__pyx_n_s_struct +#define __pyx_n_s_sys __pyx_mstate_global->__pyx_n_s_sys +#define __pyx_n_s_t_xs __pyx_mstate_global->__pyx_n_s_t_xs +#define __pyx_n_s_t_ys __pyx_mstate_global->__pyx_n_s_t_ys +#define __pyx_n_s_test __pyx_mstate_global->__pyx_n_s_test +#define __pyx_kp_s_unable_to_allocate_array_data __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_array_data +#define __pyx_kp_s_unable_to_allocate_shape_and_str __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_shape_and_str +#define __pyx_n_s_unpack __pyx_mstate_global->__pyx_n_s_unpack +#define __pyx_n_s_update __pyx_mstate_global->__pyx_n_s_update +#define __pyx_n_s_values __pyx_mstate_global->__pyx_n_s_values +#define __pyx_n_s_version_info __pyx_mstate_global->__pyx_n_s_version_info +#define __pyx_int_0 __pyx_mstate_global->__pyx_int_0 +#define __pyx_int_1 __pyx_mstate_global->__pyx_int_1 +#define __pyx_int_3 __pyx_mstate_global->__pyx_int_3 +#define __pyx_int_112105877 __pyx_mstate_global->__pyx_int_112105877 +#define __pyx_int_136983863 __pyx_mstate_global->__pyx_int_136983863 +#define __pyx_int_184977713 __pyx_mstate_global->__pyx_int_184977713 +#define __pyx_int_neg_1 __pyx_mstate_global->__pyx_int_neg_1 +#define __pyx_k__9 __pyx_mstate_global->__pyx_k__9 +#define __pyx_slice__5 __pyx_mstate_global->__pyx_slice__5 +#define __pyx_tuple__4 __pyx_mstate_global->__pyx_tuple__4 +#define __pyx_tuple__8 __pyx_mstate_global->__pyx_tuple__8 +#define __pyx_tuple__10 __pyx_mstate_global->__pyx_tuple__10 +#define __pyx_tuple__11 __pyx_mstate_global->__pyx_tuple__11 +#define __pyx_tuple__12 __pyx_mstate_global->__pyx_tuple__12 +#define __pyx_tuple__13 __pyx_mstate_global->__pyx_tuple__13 +#define __pyx_tuple__14 __pyx_mstate_global->__pyx_tuple__14 +#define __pyx_tuple__15 __pyx_mstate_global->__pyx_tuple__15 +#define __pyx_tuple__16 __pyx_mstate_global->__pyx_tuple__16 +#define __pyx_tuple__17 __pyx_mstate_global->__pyx_tuple__17 +#define __pyx_tuple__18 __pyx_mstate_global->__pyx_tuple__18 +#define __pyx_tuple__19 __pyx_mstate_global->__pyx_tuple__19 +#define __pyx_tuple__21 __pyx_mstate_global->__pyx_tuple__21 +#define __pyx_codeobj__20 __pyx_mstate_global->__pyx_codeobj__20 +#define __pyx_codeobj__22 __pyx_mstate_global->__pyx_codeobj__22 +/* #### Code section: module_code ### */ + +/* "View.MemoryView":131 + * cdef bint dtype_is_object + * + * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< + * mode="c", bint allocate_buffer=True): + * + */ + +/* Python wrapper */ +static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_shape = 0; + Py_ssize_t __pyx_v_itemsize; + PyObject *__pyx_v_format = 0; + PyObject *__pyx_v_mode = 0; + int __pyx_v_allocate_buffer; + CYTHON_UNUSED const Py_ssize_t __pyx_nargs = PyTuple_GET_SIZE(__pyx_args); + CYTHON_UNUSED PyObject *const *__pyx_kwvalues = __Pyx_KwValues_VARARGS(__pyx_args, __pyx_nargs); + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + int __pyx_r; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); + { + PyObject **__pyx_pyargnames[] = {&__pyx_n_s_shape,&__pyx_n_s_itemsize,&__pyx_n_s_format,&__pyx_n_s_mode,&__pyx_n_s_allocate_buffer,0}; + PyObject* values[5] = {0,0,0,0,0}; + values[3] = ((PyObject *)__pyx_n_s_c); + if (__pyx_kwds) { + Py_ssize_t kw_args; + switch (__pyx_nargs) { + case 5: values[4] = __Pyx_Arg_VARARGS(__pyx_args, 4); + CYTHON_FALLTHROUGH; + case 4: values[3] = __Pyx_Arg_VARARGS(__pyx_args, 3); + CYTHON_FALLTHROUGH; + case 3: values[2] = __Pyx_Arg_VARARGS(__pyx_args, 2); + CYTHON_FALLTHROUGH; + case 2: values[1] = __Pyx_Arg_VARARGS(__pyx_args, 1); + CYTHON_FALLTHROUGH; + case 1: values[0] = __Pyx_Arg_VARARGS(__pyx_args, 0); + CYTHON_FALLTHROUGH; + case 0: break; + default: goto __pyx_L5_argtuple_error; + } + kw_args = __Pyx_NumKwargs_VARARGS(__pyx_kwds); + switch (__pyx_nargs) { + case 0: + if (likely((values[0] = __Pyx_GetKwValue_VARARGS(__pyx_kwds, __pyx_kwvalues, __pyx_n_s_shape)) != 0)) kw_args--; + else if (unlikely(PyErr_Occurred())) __PYX_ERR(1, 131, __pyx_L3_error) + else goto __pyx_L5_argtuple_error; + CYTHON_FALLTHROUGH; + case 1: + if (likely((values[1] = __Pyx_GetKwValue_VARARGS(__pyx_kwds, __pyx_kwvalues, __pyx_n_s_itemsize)) != 0)) kw_args--; + else if (unlikely(PyErr_Occurred())) __PYX_ERR(1, 131, __pyx_L3_error) + else { + __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 1); __PYX_ERR(1, 131, __pyx_L3_error) + } + CYTHON_FALLTHROUGH; + case 2: + if (likely((values[2] = 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* step = 1 + * + */ + /*else*/ { + __pyx_v_negative_step = 0; + + /* "View.MemoryView":827 + * else: + * negative_step = False + * step = 1 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_step = 1; + } + __pyx_L6:; + + /* "View.MemoryView":830 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + __pyx_t_2 = (__pyx_v_have_start != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":831 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + __pyx_t_2 = (__pyx_v_start < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":832 + * if have_start: + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if start < 0: + * start = 0 + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":833 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + __pyx_t_2 = (__pyx_v_start < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":834 + * start += 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(__pyx_v_shape - 1); + + /* "View.MemoryView":836 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L11; + } + + /* "View.MemoryView":839 + * start = shape - 1 + * else: + * start = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + /*else*/ { + __pyx_v_start = __pyx_v_shape; + } + __pyx_L11:; + + /* "View.MemoryView":835 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + } + __pyx_L9:; + + /* "View.MemoryView":830 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + goto __pyx_L8; + } + + /* "View.MemoryView":841 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + /*else*/ { + if (__pyx_v_negative_step) { + + /* "View.MemoryView":842 + * else: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = 0 + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":841 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L12; + } + + /* "View.MemoryView":844 + * start = shape - 1 + * else: + * start = 0 # <<<<<<<<<<<<<< + * + * if have_stop: + */ + /*else*/ { + __pyx_v_start = 0; + } + __pyx_L12:; + } + __pyx_L8:; + + /* "View.MemoryView":846 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + __pyx_t_2 = (__pyx_v_have_stop != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":847 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + __pyx_t_2 = (__pyx_v_stop < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":848 + * if have_stop: + * if stop < 0: + * stop += shape # <<<<<<<<<<<<<< + * if stop < 0: + * stop = 0 + */ + __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); + + /* "View.MemoryView":849 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + __pyx_t_2 = (__pyx_v_stop < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":850 + * stop += shape + * if stop < 0: + * stop = 0 # <<<<<<<<<<<<<< + * elif stop > shape: + * stop = shape + */ + __pyx_v_stop = 0; + + /* "View.MemoryView":849 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + } + + /* "View.MemoryView":847 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + goto __pyx_L14; + } + + /* "View.MemoryView":851 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + __pyx_t_2 = (__pyx_v_stop > __pyx_v_shape); + if (__pyx_t_2) { + + /* "View.MemoryView":852 + * stop = 0 + * elif stop > shape: + * stop = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + __pyx_v_stop = __pyx_v_shape; + + /* "View.MemoryView":851 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + } + __pyx_L14:; + + /* "View.MemoryView":846 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + goto __pyx_L13; + } + + /* "View.MemoryView":854 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + /*else*/ { + if (__pyx_v_negative_step) { + + /* "View.MemoryView":855 + * else: + * if negative_step: + * stop = -1 # <<<<<<<<<<<<<< + * else: + * stop = shape + */ + __pyx_v_stop = -1L; + + /* "View.MemoryView":854 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + goto __pyx_L16; + } + + /* "View.MemoryView":857 + * stop = -1 + * else: + * stop = shape # <<<<<<<<<<<<<< + * + * + */ + /*else*/ { + __pyx_v_stop = __pyx_v_shape; + } + __pyx_L16:; + } + __pyx_L13:; + + /* "View.MemoryView":861 + * + * with cython.cdivision(True): + * new_shape = (stop - start) // step # <<<<<<<<<<<<<< + * + * if (stop - start) - step * new_shape: + */ + __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); + + /* "View.MemoryView":863 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":864 + * + * if (stop - start) - step * new_shape: + * new_shape += 1 # <<<<<<<<<<<<<< + * + * if new_shape < 0: + */ + __pyx_v_new_shape = (__pyx_v_new_shape + 1); + + /* "View.MemoryView":863 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + } + + /* "View.MemoryView":866 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + __pyx_t_2 = (__pyx_v_new_shape < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":867 + * + * if new_shape < 0: + * new_shape = 0 # <<<<<<<<<<<<<< + * + * 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dst.data)[0] + suboffset + * else: + */ + goto __pyx_L22; + } + + /* "View.MemoryView":885 + * dst.data = ( dst.data)[0] + suboffset + * else: + * _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< + * "must be indexed and not sliced", dim) + * else: + */ + /*else*/ { + + /* "View.MemoryView":886 + * else: + * _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d " + * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< + * else: + * suboffset_dim[0] = new_ndim + */ + __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_kp_s_All_dimensions_preceding_dimensi, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 885, __pyx_L1_error) + } + __pyx_L22:; + + /* "View.MemoryView":881 + * + * if suboffset >= 0: + * if not is_slice: # <<<<<<<<<<<<<< + * if new_ndim == 0: + * dst.data = ( dst.data)[0] + suboffset + */ + goto __pyx_L21; + } + + /* "View.MemoryView":888 + * "must be indexed and not sliced", dim) + * else: + * 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__pyx_t_1; + + /* "View.MemoryView":1110 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) noexcept nogil: + * return -arg if arg < 0 else arg # <<<<<<<<<<<<<< + * + * @cname('__pyx_get_best_slice_order') + */ + if ((__pyx_v_arg < 0)) { + __pyx_t_1 = (-__pyx_v_arg); + } else { + __pyx_t_1 = __pyx_v_arg; + } + __pyx_r = __pyx_t_1; + goto __pyx_L0; + + /* "View.MemoryView":1109 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) noexcept nogil: # <<<<<<<<<<<<<< + * return -arg if arg < 0 else arg + * + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1113 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + +static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_c_stride; + Py_ssize_t __pyx_v_f_stride; + char __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1118 + * """ + * cdef int i + * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< + * cdef Py_ssize_t f_stride = 0 + * + */ + __pyx_v_c_stride = 0; + + /* "View.MemoryView":1119 + * cdef int i + * cdef Py_ssize_t c_stride = 0 + * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_f_stride = 0; + + /* "View.MemoryView":1121 + * cdef Py_ssize_t f_stride = 0 + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1122 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = ((__pyx_v_mslice->shape[__pyx_v_i]) > 1); + if (__pyx_t_2) { + + /* "View.MemoryView":1123 + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1124 + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + goto __pyx_L4_break; + + /* "View.MemoryView":1122 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L4_break:; + + /* "View.MemoryView":1126 + * break + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + */ + __pyx_t_1 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_1; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1127 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = ((__pyx_v_mslice->shape[__pyx_v_i]) > 1); + if (__pyx_t_2) { + + /* "View.MemoryView":1128 + * for i in range(ndim): + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1129 + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + */ + goto __pyx_L7_break; + + /* "View.MemoryView":1127 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L7_break:; + + /* "View.MemoryView":1131 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + __pyx_t_2 = (abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)); + if (__pyx_t_2) { + + /* "View.MemoryView":1132 + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1131 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1134 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1113 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1137 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + Py_ssize_t __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + + /* "View.MemoryView":1144 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + */ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1145 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1146 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = (__pyx_v_ndim == 1); + if (__pyx_t_1) { + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = (__pyx_v_src_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_dst_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1151 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_1 = __pyx_t_2; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1152 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1154 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1155 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1156 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1157 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1159 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, + */ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1160 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, + */ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1164 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1165 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1137 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + + /* function exit code */ +} + +/* "View.MemoryView":1167 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + */ + +static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + + /* "View.MemoryView":1170 + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< + * src.shape, dst.shape, ndim, itemsize) + * + */ + _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1167 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1174 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { + Py_ssize_t __pyx_v_shape; + Py_ssize_t __pyx_v_size; + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + Py_ssize_t *__pyx_t_2; + Py_ssize_t *__pyx_t_3; + Py_ssize_t *__pyx_t_4; + + /* "View.MemoryView":1176 + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< + * + * for shape in src.shape[:ndim]: + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_size = __pyx_t_1; + + /* "View.MemoryView":1178 + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + * + * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< + * size *= shape + * + */ + __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); + for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { + __pyx_t_2 = __pyx_t_4; + __pyx_v_shape = (__pyx_t_2[0]); + + /* "View.MemoryView":1179 + * + * for shape in src.shape[:ndim]: + * size *= shape # <<<<<<<<<<<<<< + * + * return size + */ + __pyx_v_size = (__pyx_v_size * __pyx_v_shape); + } + + /* "View.MemoryView":1181 + * size *= shape + * + * return size # <<<<<<<<<<<<<< + * + * @cname('__pyx_fill_contig_strides_array') + */ + __pyx_r = __pyx_v_size; + goto __pyx_L0; + + /* "View.MemoryView":1174 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1184 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) noexcept nogil: + */ + +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { + int __pyx_v_idx; + Py_ssize_t __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1193 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + __pyx_t_1 = (__pyx_v_order == 'F'); + if (__pyx_t_1) { + + /* "View.MemoryView":1194 + * + * if order == 'F': + * for idx in range(ndim): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride *= shape[idx] + */ + __pyx_t_2 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_idx = __pyx_t_4; + + /* "View.MemoryView":1195 + * if order == 'F': + * for idx in range(ndim): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride *= shape[idx] + * else: + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1196 + * for idx in range(ndim): + * strides[idx] = stride + * stride *= shape[idx] # <<<<<<<<<<<<<< + * else: + * for idx in range(ndim - 1, -1, -1): + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + + /* "View.MemoryView":1193 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1198 + * stride *= shape[idx] + * else: + * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride *= shape[idx] + */ + /*else*/ { + for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { + __pyx_v_idx = __pyx_t_2; + + /* "View.MemoryView":1199 + * else: + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride *= shape[idx] + * + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1200 + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride + * stride *= shape[idx] # <<<<<<<<<<<<<< + * + * return stride + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + } + __pyx_L3:; + + /* "View.MemoryView":1202 + * stride *= shape[idx] + * + * return stride # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_copy_data_to_temp') + */ + __pyx_r = __pyx_v_stride; + goto __pyx_L0; + + /* "View.MemoryView":1184 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) noexcept nogil: + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1205 + * + * @cname('__pyx_memoryview_copy_data_to_temp') + * cdef 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* cdef void *tmpdata = NULL + * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< + * cdef int i + * cdef char order = get_best_order(&src, src_ndim) + */ + __pyx_t_1 = __pyx_v_src.memview->view.itemsize; + __pyx_v_itemsize = __pyx_t_1; + + /* "View.MemoryView":1276 + * cdef size_t itemsize = src.memview.view.itemsize + * cdef int i + * cdef char order = get_best_order(&src, src_ndim) # <<<<<<<<<<<<<< + * cdef bint broadcasting = False + * cdef bint direct_copy = False + */ + __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim); + + /* "View.MemoryView":1277 + * cdef int i + * cdef char order = get_best_order(&src, src_ndim) + * cdef bint broadcasting = False # <<<<<<<<<<<<<< + * cdef bint direct_copy = False + * cdef __Pyx_memviewslice tmp + */ + __pyx_v_broadcasting = 0; + + /* "View.MemoryView":1278 + * cdef char order = get_best_order(&src, src_ndim) + * cdef bint broadcasting = False + * cdef bint direct_copy = False # <<<<<<<<<<<<<< + * cdef __Pyx_memviewslice tmp + * + */ + __pyx_v_direct_copy = 0; + + /* "View.MemoryView":1281 + * cdef __Pyx_memviewslice tmp + * + * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< + * broadcast_leading(&src, src_ndim, dst_ndim) + * elif dst_ndim < src_ndim: + */ + __pyx_t_2 = (__pyx_v_src_ndim < __pyx_v_dst_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1282 + * + * if src_ndim < dst_ndim: + * broadcast_leading(&src, src_ndim, dst_ndim) # <<<<<<<<<<<<<< + * elif dst_ndim < src_ndim: + * broadcast_leading(&dst, dst_ndim, src_ndim) + */ + __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim); + + /* "View.MemoryView":1281 + * cdef __Pyx_memviewslice tmp + * + * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< + * broadcast_leading(&src, src_ndim, dst_ndim) + * elif dst_ndim < src_ndim: + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1283 + * if src_ndim < dst_ndim: + * broadcast_leading(&src, src_ndim, dst_ndim) + * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< + * broadcast_leading(&dst, dst_ndim, src_ndim) + * + */ + __pyx_t_2 = (__pyx_v_dst_ndim < __pyx_v_src_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1284 + * broadcast_leading(&src, src_ndim, dst_ndim) + * elif dst_ndim < src_ndim: + * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< + * + * cdef int ndim = max(src_ndim, dst_ndim) + */ + __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); + + /* "View.MemoryView":1283 + * if src_ndim < dst_ndim: + * broadcast_leading(&src, src_ndim, dst_ndim) + * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< + * broadcast_leading(&dst, dst_ndim, src_ndim) + * + */ + } + __pyx_L3:; + + /* "View.MemoryView":1286 + * broadcast_leading(&dst, dst_ndim, src_ndim) + * + * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + __pyx_t_3 = __pyx_v_dst_ndim; + __pyx_t_4 = __pyx_v_src_ndim; + if ((__pyx_t_3 > __pyx_t_4)) { + __pyx_t_5 = __pyx_t_3; + } else { + __pyx_t_5 = __pyx_t_4; + } + __pyx_v_ndim = __pyx_t_5; + + /* "View.MemoryView":1288 + * cdef int ndim = max(src_ndim, dst_ndim) + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: + */ + __pyx_t_5 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_5; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1289 + * + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< + * if src.shape[i] == 1: + * broadcasting = True + */ + __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])); + if (__pyx_t_2) { + + /* "View.MemoryView":1290 + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: # <<<<<<<<<<<<<< + * broadcasting = True + * src.strides[i] = 0 + */ + __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) == 1); + if (__pyx_t_2) { + + /* "View.MemoryView":1291 + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: + * broadcasting = True # <<<<<<<<<<<<<< + * src.strides[i] = 0 + * else: + */ + __pyx_v_broadcasting = 1; + + /* "View.MemoryView":1292 + * if src.shape[i] == 1: + * broadcasting = True + * src.strides[i] = 0 # <<<<<<<<<<<<<< + * else: + * _err_extents(i, dst.shape[i], src.shape[i]) + */ + (__pyx_v_src.strides[__pyx_v_i]) = 0; + + /* "View.MemoryView":1290 + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: # <<<<<<<<<<<<<< + * broadcasting = True + * src.strides[i] = 0 + */ + goto __pyx_L7; + } + + /* "View.MemoryView":1294 + * src.strides[i] = 0 + * else: + * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< + * + * if src.suboffsets[i] >= 0: + */ + /*else*/ { + __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1294, __pyx_L1_error) + } + __pyx_L7:; + + /* "View.MemoryView":1289 + * + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< + * if src.shape[i] == 1: + * broadcasting = True + */ + } + + /* "View.MemoryView":1296 + * _err_extents(i, dst.shape[i], src.shape[i]) + * + * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + */ + __pyx_t_2 = ((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1297 + * + * if src.suboffsets[i] >= 0: + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< + * + * if slices_overlap(&src, &dst, ndim, itemsize): + */ + __pyx_t_6 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error) + + /* "View.MemoryView":1296 + * _err_extents(i, dst.shape[i], src.shape[i]) + * + * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + */ + } + } + + /* "View.MemoryView":1299 + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): + */ + __pyx_t_2 = __pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); + if (__pyx_t_2) { + + /* "View.MemoryView":1301 + * if slices_overlap(&src, &dst, ndim, itemsize): + * + * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< + * order = get_best_order(&dst, ndim) + * + */ + __pyx_t_2 = (!__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim)); + if (__pyx_t_2) { + + /* "View.MemoryView":1302 + * + * if not slice_is_contig(src, order, ndim): + * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + */ + __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); + + /* "View.MemoryView":1301 + * if slices_overlap(&src, &dst, ndim, itemsize): + * + * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< + * order = get_best_order(&dst, ndim) + * + */ + } + + /* "View.MemoryView":1304 + * order = get_best_order(&dst, ndim) + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< + * src = tmp + * + */ + __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1304, __pyx_L1_error) + __pyx_v_tmpdata = __pyx_t_7; + + /* "View.MemoryView":1305 + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + * src = tmp # <<<<<<<<<<<<<< + * + * if not broadcasting: + */ + __pyx_v_src = __pyx_v_tmp; + + /* "View.MemoryView":1299 + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): + */ + } + + /* "View.MemoryView":1307 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (!__pyx_v_broadcasting); + if (__pyx_t_2) { + + /* "View.MemoryView":1310 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1311 + * + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); + + /* "View.MemoryView":1310 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + goto __pyx_L12; + } + + /* "View.MemoryView":1312 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1313 + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< + * + * if direct_copy: + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); + + /* "View.MemoryView":1312 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + } + __pyx_L12:; + + /* "View.MemoryView":1315 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + if (__pyx_v_direct_copy) { + + /* "View.MemoryView":1317 + * if direct_copy: + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1318 + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) + */ + (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); + + /* "View.MemoryView":1319 + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * free(tmpdata) + * return 0 + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1320 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1321 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1315 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + } + + /* "View.MemoryView":1307 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + if (__pyx_t_2) { + + /* "View.MemoryView":1326 + * + * + * transpose_memslice(&src) # <<<<<<<<<<<<<< + * transpose_memslice(&dst) + * + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1326, __pyx_L1_error) + + /* "View.MemoryView":1327 + * + * transpose_memslice(&src) + * transpose_memslice(&dst) # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1327, __pyx_L1_error) + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1329 + * transpose_memslice(&dst) + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1330 + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * + */ + copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1331 + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * + * free(tmpdata) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1333 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * + * free(tmpdata) # 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(!__pyx_t_14) { + } else { + __pyx_t_8 = __pyx_t_14; + goto __pyx_L13_bool_binop_done; + } + __pyx_t_9 = (__pyx_v_y - 1); + __pyx_t_10 = __pyx_v_index; + __pyx_t_15 = (__pyx_v_y - 1); + __pyx_t_16 = (__pyx_v_index - 1); + __pyx_t_14 = ((*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) ))) < (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_15 * __pyx_v_value.strides[0]) )) + __pyx_t_16)) )))); + __pyx_t_8 = __pyx_t_14; + __pyx_L13_bool_binop_done:; + if (__pyx_t_8) { + + /* "monotonic_align/core.pyx":33 + * path[y, index] = 1 + * if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]): + * index = index - 1 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_index = (__pyx_v_index - 1); + + /* "monotonic_align/core.pyx":32 + * for y in range(t_y - 1, -1, -1): + * path[y, index] = 1 + * if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]): # <<<<<<<<<<<<<< + * index = index - 1 + * + */ + } + } + + /* "monotonic_align/core.pyx":7 + * @cython.boundscheck(False) + * @cython.wraparound(False) + * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil: # <<<<<<<<<<<<<< + * cdef int x + * cdef int y + */ + + /* function exit code */ +} + +/* "monotonic_align/core.pyx":38 + * @cython.boundscheck(False) + * @cython.wraparound(False) + * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil: # <<<<<<<<<<<<<< + * cdef int b = paths.shape[0] + * cdef int i + */ + +static PyObject *__pyx_pw_15monotonic_align_4core_1maximum_path_c(PyObject *__pyx_self, +#if CYTHON_METH_FASTCALL +PyObject *const *__pyx_args, Py_ssize_t __pyx_nargs, PyObject *__pyx_kwds +#else +PyObject *__pyx_args, PyObject *__pyx_kwds +#endif +); /*proto*/ +static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice __pyx_v_paths, 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Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, /*tp_flags*/ + 0, /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_array, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_array, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_array, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif + +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_MemviewEnum_obj *p; + PyObject *o; + #if CYTHON_COMPILING_IN_LIMITED_API + allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_MemviewEnum_obj *)o); + p->name = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_Enum(PyObject *o) { + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_Enum) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + Py_CLEAR(p->name); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + if (p->name) { + e = (*v)(p->name, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_Enum(PyObject *o) { + PyObject* tmp; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + tmp = ((PyObject*)p->name); + p->name = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static PyObject *__pyx_specialmethod___pyx_MemviewEnum___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) { + return __pyx_MemviewEnum___repr__(self); +} + +static PyMethodDef __pyx_methods_Enum[] = { + {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_MemviewEnum___repr__, METH_NOARGS|METH_COEXIST, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_type___pyx_MemviewEnum_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_Enum}, + {Py_tp_repr, (void *)__pyx_MemviewEnum___repr__}, + {Py_tp_traverse, (void *)__pyx_tp_traverse_Enum}, + {Py_tp_clear, (void *)__pyx_tp_clear_Enum}, + {Py_tp_methods, (void *)__pyx_methods_Enum}, + {Py_tp_init, (void *)__pyx_MemviewEnum___init__}, + {Py_tp_new, (void *)__pyx_tp_new_Enum}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_MemviewEnum_spec = { + "monotonic_align.core.Enum", + sizeof(struct __pyx_MemviewEnum_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_MemviewEnum_slots, +}; +#else + +static PyTypeObject __pyx_type___pyx_MemviewEnum = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""Enum", /*tp_name*/ + sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_Enum, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_MemviewEnum___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_Enum, /*tp_traverse*/ + __pyx_tp_clear_Enum, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_Enum, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + __pyx_MemviewEnum___init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_Enum, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif +static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; + +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryview_obj *p; + PyObject *o; + #if CYTHON_COMPILING_IN_LIMITED_API + allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_memoryview) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryview___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + __Pyx_TypeName o_type_name; + o_type_name = __Pyx_PyType_GetName(Py_TYPE(o)); + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name); + __Pyx_DECREF_TypeName(o_type_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyObject *__pyx_specialmethod___pyx_memoryview___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) { + return __pyx_memoryview___repr__(self); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_memoryview___repr__, METH_NOARGS|METH_COEXIST, 0}, + {"is_c_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"is_f_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy_fortran", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, + {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, + {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, + {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, + {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, + {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +#if !CYTHON_COMPILING_IN_LIMITED_API + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; +#endif +static PyType_Slot __pyx_type___pyx_memoryview_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview}, + {Py_tp_repr, (void *)__pyx_memoryview___repr__}, + {Py_sq_length, (void *)__pyx_memoryview___len__}, + {Py_sq_item, (void *)__pyx_sq_item_memoryview}, + {Py_mp_length, (void *)__pyx_memoryview___len__}, + {Py_mp_subscript, (void *)__pyx_memoryview___getitem__}, + {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview}, + {Py_tp_str, (void *)__pyx_memoryview___str__}, + #if defined(Py_bf_getbuffer) + {Py_bf_getbuffer, (void *)__pyx_memoryview_getbuffer}, + #endif + {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview}, + {Py_tp_clear, (void *)__pyx_tp_clear_memoryview}, + {Py_tp_methods, (void *)__pyx_methods_memoryview}, + {Py_tp_getset, (void *)__pyx_getsets_memoryview}, + {Py_tp_new, (void *)__pyx_tp_new_memoryview}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_memoryview_spec = { + "monotonic_align.core.memoryview", + sizeof(struct __pyx_memoryview_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_memoryview_slots, +}; +#else + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc__memoryviewslice) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryviewslice___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XCLEAR_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_type___pyx_memoryviewslice_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc__memoryviewslice}, + {Py_tp_doc, (void *)PyDoc_STR("Internal class for passing memoryview slices to Python")}, + {Py_tp_traverse, (void *)__pyx_tp_traverse__memoryviewslice}, + {Py_tp_clear, (void *)__pyx_tp_clear__memoryviewslice}, + {Py_tp_methods, (void *)__pyx_methods__memoryviewslice}, + {Py_tp_new, (void *)__pyx_tp_new__memoryviewslice}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_memoryviewslice_spec = { + "monotonic_align.core._memoryviewslice", + sizeof(struct __pyx_memoryviewslice_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, + __pyx_type___pyx_memoryviewslice_slots, +}; +#else + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""_memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY || 0 + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY || 0 + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, /*tp_flags*/ + PyDoc_STR("Internal class for passing memoryview slices to Python"), /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new__memoryviewslice, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if 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+ hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* fastcall */ +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s) +{ + Py_ssize_t i, n = PyTuple_GET_SIZE(kwnames); + for (i = 0; i < n; i++) + { + if (s == PyTuple_GET_ITEM(kwnames, i)) return kwvalues[i]; + } + for (i = 0; i < n; i++) + { + int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ); + if (unlikely(eq != 0)) { + if (unlikely(eq < 0)) return NULL; // error + return kwvalues[i]; + } + } + return NULL; // not found (no exception set) +} +#endif + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject *const *kwvalues, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds)); + while (1) { + if (kwds_is_tuple) { + if (pos >= PyTuple_GET_SIZE(kwds)) break; + key = PyTuple_GET_ITEM(kwds, pos); + value = kwvalues[pos]; + pos++; + } + else + { + if (!PyDict_Next(kwds, &pos, &key, &value)) break; + } + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = ( + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key) + ); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + #if PY_MAJOR_VERSION < 3 + PyErr_Format(PyExc_TypeError, + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + __Pyx_TypeName type_name; + __Pyx_TypeName obj_type_name; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + type_name = __Pyx_PyType_GetName(type); + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME + ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name); + __Pyx_DECREF_TypeName(type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + __Pyx_PyThreadState_declare + CYTHON_UNUSED_VAR(cause); + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { + #if PY_VERSION_HEX >= 0x030C00A6 + PyException_SetTraceback(value, tb); + #elif CYTHON_FAST_THREAD_STATE + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#else + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* PyFunctionFastCall */ +#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = Py_TYPE(func)->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallMethO */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectFastCall */ +static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) { + PyObject *argstuple; + PyObject *result; + size_t i; + argstuple = PyTuple_New((Py_ssize_t)nargs); + if (unlikely(!argstuple)) return NULL; + for (i = 0; i < nargs; i++) { + Py_INCREF(args[i]); + PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]); + } + result = __Pyx_PyObject_Call(func, argstuple, kwargs); + Py_DECREF(argstuple); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) { + Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs); +#if CYTHON_COMPILING_IN_CPYTHON + if (nargs == 0 && kwargs == NULL) { +#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG) + if (__Pyx_IsCyOrPyCFunction(func)) +#else + if (PyCFunction_Check(func)) +#endif + { + if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { + return __Pyx_PyObject_CallMethO(func, NULL); + } + } + } + else if (nargs == 1 && kwargs == NULL) { + if (PyCFunction_Check(func)) + { + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, args[0]); + } + } + } +#endif + #if PY_VERSION_HEX < 0x030800B1 + #if CYTHON_FAST_PYCCALL + if (PyCFunction_Check(func)) { + if (kwargs) { + return _PyCFunction_FastCallDict(func, args, nargs, kwargs); + } else { + return _PyCFunction_FastCallKeywords(func, args, nargs, NULL); + } + } + #if PY_VERSION_HEX >= 0x030700A1 + if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) { + return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL); + } + #endif + #endif + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs); + } + #endif + #endif + #if CYTHON_VECTORCALL + vectorcallfunc f = _PyVectorcall_Function(func); + if (f) { + return f(func, args, (size_t)nargs, kwargs); + } + #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL + if (__Pyx_CyFunction_CheckExact(func)) { + __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func); + if (f) return f(func, args, (size_t)nargs, kwargs); + } + #endif + if (nargs == 0) { + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs); + } + return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs); +} + +/* RaiseUnexpectedTypeError */ +static int +__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj) +{ + __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME, + expected, obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* CIntToDigits */ +static const char DIGIT_PAIRS_10[2*10*10+1] = { + "00010203040506070809" + "10111213141516171819" + "20212223242526272829" + "30313233343536373839" + "40414243444546474849" + "50515253545556575859" + "60616263646566676869" + "70717273747576777879" + "80818283848586878889" + "90919293949596979899" +}; +static const char DIGIT_PAIRS_8[2*8*8+1] = { + "0001020304050607" + "1011121314151617" + "2021222324252627" + "3031323334353637" + "4041424344454647" + "5051525354555657" + "6061626364656667" + "7071727374757677" +}; +static const char DIGITS_HEX[2*16+1] = { + "0123456789abcdef" + "0123456789ABCDEF" +}; + +/* BuildPyUnicode */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, + int prepend_sign, char padding_char) { + PyObject *uval; + Py_ssize_t uoffset = ulength - clength; +#if CYTHON_USE_UNICODE_INTERNALS + Py_ssize_t i; +#if CYTHON_PEP393_ENABLED + void *udata; + uval = PyUnicode_New(ulength, 127); + if (unlikely(!uval)) return NULL; + udata = PyUnicode_DATA(uval); +#else + Py_UNICODE *udata; + uval = PyUnicode_FromUnicode(NULL, ulength); + if (unlikely(!uval)) return NULL; + udata = PyUnicode_AS_UNICODE(uval); +#endif + if (uoffset > 0) { + i = 0; + if (prepend_sign) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-'); + i++; + } + for (; i < uoffset; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char); + } + } + for (i=0; i < clength; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]); + } +#else + { + PyObject *sign = NULL, *padding = NULL; + uval = NULL; + if (uoffset > 0) { + prepend_sign = !!prepend_sign; + if (uoffset > prepend_sign) { + padding = PyUnicode_FromOrdinal(padding_char); + if (likely(padding) && uoffset > prepend_sign + 1) { + PyObject *tmp; + PyObject *repeat = PyInt_FromSsize_t(uoffset - prepend_sign); + if (unlikely(!repeat)) goto done_or_error; + tmp = PyNumber_Multiply(padding, repeat); + Py_DECREF(repeat); + Py_DECREF(padding); + padding = tmp; + } + if (unlikely(!padding)) goto done_or_error; + } + if (prepend_sign) { + sign = PyUnicode_FromOrdinal('-'); + if (unlikely(!sign)) goto done_or_error; + } + } + uval = PyUnicode_DecodeASCII(chars, clength, NULL); + if (likely(uval) && padding) { + PyObject *tmp = PyNumber_Add(padding, uval); + Py_DECREF(uval); + uval = tmp; + } + if (likely(uval) && sign) { + PyObject *tmp = PyNumber_Add(sign, uval); + Py_DECREF(uval); + uval = tmp; + } +done_or_error: + Py_XDECREF(padding); + Py_XDECREF(sign); + } +#endif + return uval; +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(int)*3+2]; + char *dpos, *end = digits + sizeof(int)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + int remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (int) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (int) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (int) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(Py_ssize_t)*3+2]; + char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + Py_ssize_t remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (Py_ssize_t) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (Py_ssize_t) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (Py_ssize_t) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* JoinPyUnicode */ +static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char) { +#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + PyObject *result_uval; + int result_ukind, kind_shift; + Py_ssize_t i, char_pos; + void *result_udata; + CYTHON_MAYBE_UNUSED_VAR(max_char); +#if CYTHON_PEP393_ENABLED + result_uval = PyUnicode_New(result_ulength, max_char); + if (unlikely(!result_uval)) return NULL; + result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND; + kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1; + result_udata = PyUnicode_DATA(result_uval); +#else + result_uval = PyUnicode_FromUnicode(NULL, result_ulength); + if (unlikely(!result_uval)) return NULL; + result_ukind = sizeof(Py_UNICODE); + kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1; + result_udata = PyUnicode_AS_UNICODE(result_uval); +#endif + assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0); + char_pos = 0; + for (i=0; i < value_count; i++) { + int ukind; + Py_ssize_t ulength; + void *udata; + PyObject *uval = PyTuple_GET_ITEM(value_tuple, i); + if (unlikely(__Pyx_PyUnicode_READY(uval))) + goto bad; + ulength = __Pyx_PyUnicode_GET_LENGTH(uval); + if (unlikely(!ulength)) + continue; + if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos)) + goto overflow; + ukind = __Pyx_PyUnicode_KIND(uval); + udata = __Pyx_PyUnicode_DATA(uval); + if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) { + memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift)); + } else { + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters) + _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength); + #else + Py_ssize_t j; + for (j=0; j < ulength; j++) { + Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j); + __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar); + } + #endif + } + char_pos += ulength; + } + return result_uval; +overflow: + PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string"); +bad: + Py_DECREF(result_uval); + return NULL; +#else + CYTHON_UNUSED_VAR(max_char); + CYTHON_UNUSED_VAR(result_ulength); + CYTHON_UNUSED_VAR(value_count); + return PyUnicode_Join(__pyx_empty_unicode, value_tuple); +#endif +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (unlikely(!j)) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_subscript) { + PyObject *r, *key = PyInt_FromSsize_t(i); + if (unlikely(!key)) return NULL; + r = mm->mp_subscript(o, key); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return sm->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* PyObjectCallOneArg */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *args[2] = {NULL, arg}; + return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* ObjectGetItem */ +#if CYTHON_USE_TYPE_SLOTS +static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) { + PyObject *runerr = NULL; + Py_ssize_t key_value; + key_value = __Pyx_PyIndex_AsSsize_t(index); + if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { + return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); + } + if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { + __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index)); + PyErr_Clear(); + PyErr_Format(PyExc_IndexError, + "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name); + __Pyx_DECREF_TypeName(index_type_name); + } + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) { + __Pyx_TypeName obj_type_name; + if (likely(PyType_Check(obj))) { + PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem); + if (meth) { + PyObject *result = __Pyx_PyObject_CallOneArg(meth, key); + Py_DECREF(meth); + return result; + } + } + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) { + PyTypeObject *tp = Py_TYPE(obj); + PyMappingMethods *mm = tp->tp_as_mapping; + PySequenceMethods *sm = tp->tp_as_sequence; + if (likely(mm && mm->mp_subscript)) { + return mm->mp_subscript(obj, key); + } + if (likely(sm && sm->sq_item)) { + return __Pyx_PyObject_GetIndex(obj, key); + } + return __Pyx_PyObject_GetItem_Slow(obj, key); +} +#endif + +/* KeywordStringCheck */ +static int __Pyx_CheckKeywordStrings( + PyObject *kw, + const char* function_name, + int kw_allowed) +{ + PyObject* key = 0; + Py_ssize_t pos = 0; +#if CYTHON_COMPILING_IN_PYPY + if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0)) + goto invalid_keyword; + return 1; +#else + if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) { + if (unlikely(PyTuple_GET_SIZE(kw) == 0)) + return 1; + if (!kw_allowed) { + key = PyTuple_GET_ITEM(kw, 0); + goto invalid_keyword; + } +#if PY_VERSION_HEX < 0x03090000 + for (pos = 0; pos < PyTuple_GET_SIZE(kw); pos++) { + key = PyTuple_GET_ITEM(kw, pos); + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; + } +#endif + return 1; + } + while (PyDict_Next(kw, &pos, &key, 0)) { + #if PY_MAJOR_VERSION < 3 + if (unlikely(!PyString_Check(key))) + #endif + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; + } + if (!kw_allowed && unlikely(key)) + goto invalid_keyword; + return 1; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + return 0; +#endif +invalid_keyword: + #if PY_MAJOR_VERSION < 3 + PyErr_Format(PyExc_TypeError, + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif + return 0; +} + +/* DivInt[Py_ssize_t] */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* GetAttr3 */ +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r; +#if CYTHON_USE_TYPE_SLOTS + if (likely(PyString_Check(n))) { + r = __Pyx_PyObject_GetAttrStrNoError(o, n); + if (unlikely(!r) && likely(!PyErr_Occurred())) { + r = __Pyx_NewRef(d); + } + return r; + } +#endif + r = PyObject_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +} + +/* PyDictVersioning */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { + PyObject **dictptr = NULL; + Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; + if (offset) { +#if CYTHON_COMPILING_IN_CPYTHON + dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); +#else + dictptr = _PyObject_GetDictPtr(obj); +#endif + } + return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; +} +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) + return 0; + return obj_dict_version == __Pyx_get_object_dict_version(obj); +} +#endif + +/* GetModuleGlobalName */ +#if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 + result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } else if (unlikely(PyErr_Occurred())) { + return NULL; + } +#elif CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(!__pyx_m)) { + return NULL; + } + result = PyObject_GetAttr(__pyx_m, name); + if (likely(result)) { + return result; + } +#else + result = PyDict_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } +#endif +#else + result = PyObject_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + __Pyx_TypeName obj_type_name; + __Pyx_TypeName type_name; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + type_name = __Pyx_PyType_GetName(type); + PyErr_Format(PyExc_TypeError, + "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME, + obj_type_name, type_name); + __Pyx_DECREF_TypeName(obj_type_name); + __Pyx_DECREF_TypeName(type_name); + return 0; +} + +/* GetTopmostException */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + PyObject *exc_value = exc_info->exc_value; + if (exc_value == NULL || exc_value == Py_None) { + *value = NULL; + *type = NULL; + *tb = NULL; + } else { + *value = exc_value; + Py_INCREF(*value); + *type = (PyObject*) Py_TYPE(exc_value); + Py_INCREF(*type); + *tb = PyException_GetTraceback(exc_value); + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #endif +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + PyObject *tmp_value = exc_info->exc_value; + exc_info->exc_value = value; + Py_XDECREF(tmp_value); + Py_XDECREF(type); + Py_XDECREF(tb); + #else + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); + #endif +} +#endif + +/* GetException */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type = NULL, *local_value, *local_tb = NULL; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if PY_VERSION_HEX >= 0x030C00A6 + local_value = tstate->current_exception; + tstate->current_exception = 0; + if (likely(local_value)) { + local_type = (PyObject*) Py_TYPE(local_value); + Py_INCREF(local_type); + local_tb = PyException_GetTraceback(local_value); + } + #else + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; + #endif +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6 + if (unlikely(tstate->current_exception)) +#elif CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + #if PY_VERSION_HEX >= 0x030B00a4 + tmp_value = exc_info->exc_value; + exc_info->exc_value = local_value; + tmp_type = NULL; + tmp_tb = NULL; + Py_XDECREF(local_type); + Py_XDECREF(local_tb); + #else + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + #endif + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_value = exc_info->exc_value; + exc_info->exc_value = *value; + if (tmp_value == NULL || tmp_value == Py_None) { + Py_XDECREF(tmp_value); + tmp_value = NULL; + tmp_type = NULL; + tmp_tb = NULL; + } else { + tmp_type = (PyObject*) Py_TYPE(tmp_value); + Py_INCREF(tmp_type); + #if CYTHON_COMPILING_IN_CPYTHON + tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback; + Py_XINCREF(tmp_tb); + #else + tmp_tb = PyException_GetTraceback(tmp_value); + #endif + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = *type; + exc_info->exc_value = *value; + exc_info->exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *module = 0; + PyObject *empty_dict = 0; + PyObject *empty_list = 0; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (unlikely(!py_import)) + goto bad; + if (!from_list) { + empty_list = PyList_New(0); + if (unlikely(!empty_list)) + goto bad; + from_list = empty_list; + } + #endif + empty_dict = PyDict_New(); + if (unlikely(!empty_dict)) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { + #if CYTHON_COMPILING_IN_LIMITED_API + module = PyImport_ImportModuleLevelObject( + name, empty_dict, empty_dict, from_list, 1); + #else + module = PyImport_ImportModuleLevelObject( + name, __pyx_d, empty_dict, from_list, 1); + #endif + if (unlikely(!module)) { + if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError))) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (unlikely(!py_level)) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + #if CYTHON_COMPILING_IN_LIMITED_API + module = PyImport_ImportModuleLevelObject( + name, empty_dict, empty_dict, from_list, level); + #else + module = PyImport_ImportModuleLevelObject( + name, __pyx_d, empty_dict, from_list, level); + #endif + #endif + } + } +bad: + Py_XDECREF(empty_dict); + Py_XDECREF(empty_list); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + return module; +} + +/* ImportDottedModule */ +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) { + PyObject *partial_name = NULL, *slice = NULL, *sep = NULL; + if (unlikely(PyErr_Occurred())) { + PyErr_Clear(); + } + if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) { + partial_name = name; + } else { + slice = PySequence_GetSlice(parts_tuple, 0, count); + if (unlikely(!slice)) + goto bad; + sep = PyUnicode_FromStringAndSize(".", 1); + if (unlikely(!sep)) + goto bad; + partial_name = PyUnicode_Join(sep, slice); + } + PyErr_Format( +#if PY_MAJOR_VERSION < 3 + PyExc_ImportError, + "No module named '%s'", PyString_AS_STRING(partial_name)); +#else +#if PY_VERSION_HEX >= 0x030600B1 + PyExc_ModuleNotFoundError, +#else + PyExc_ImportError, +#endif + "No module named '%U'", partial_name); +#endif +bad: + Py_XDECREF(sep); + Py_XDECREF(slice); + Py_XDECREF(partial_name); + return NULL; +} +#endif +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) { + PyObject *imported_module; +#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + return NULL; + imported_module = __Pyx_PyDict_GetItemStr(modules, name); + Py_XINCREF(imported_module); +#else + imported_module = PyImport_GetModule(name); +#endif + return imported_module; +} +#endif +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) { + Py_ssize_t i, nparts; + nparts = PyTuple_GET_SIZE(parts_tuple); + for (i=1; i < nparts && module; i++) { + PyObject *part, *submodule; +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + part = PyTuple_GET_ITEM(parts_tuple, i); +#else + part = PySequence_ITEM(parts_tuple, i); +#endif + submodule = __Pyx_PyObject_GetAttrStrNoError(module, part); +#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(part); +#endif + Py_DECREF(module); + module = submodule; + } + if (unlikely(!module)) { + return __Pyx__ImportDottedModule_Error(name, parts_tuple, i); + } + return module; +} +#endif +static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) { +#if PY_MAJOR_VERSION < 3 + PyObject *module, *from_list, *star = __pyx_n_s__3; + CYTHON_UNUSED_VAR(parts_tuple); + from_list = PyList_New(1); + if (unlikely(!from_list)) + return NULL; + Py_INCREF(star); + PyList_SET_ITEM(from_list, 0, star); + module = __Pyx_Import(name, from_list, 0); + Py_DECREF(from_list); + return module; +#else + PyObject *imported_module; + PyObject *module = __Pyx_Import(name, NULL, 0); + if (!parts_tuple || unlikely(!module)) + return module; + imported_module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(imported_module)) { + Py_DECREF(module); + return imported_module; + } + PyErr_Clear(); + return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple); +#endif +} +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1 + PyObject *module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(module)) { + PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec); + if (likely(spec)) { + PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing); + if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) { + Py_DECREF(spec); + spec = NULL; + } + Py_XDECREF(unsafe); + } + if (likely(!spec)) { + PyErr_Clear(); + return module; + } + Py_DECREF(spec); + Py_DECREF(module); + } else if (PyErr_Occurred()) { + PyErr_Clear(); + } +#endif + return __Pyx__ImportDottedModule(name, parts_tuple); +} + +/* ssize_strlen */ +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) { + size_t len = strlen(s); + if (unlikely(len > PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, "byte string is too long"); + return -1; + } + return (Py_ssize_t) len; +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*); + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (cls == a || cls == b) return 1; + mro = cls->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + PyObject *base = PyTuple_GET_ITEM(mro, i); + if (base == (PyObject *)a || base == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + if (exc_type1) { + return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2); + } else { + return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; itp_as_sequence && type->tp_as_sequence->sq_repeat)) { + return type->tp_as_sequence->sq_repeat(seq, mul); + } else +#endif + { + return __Pyx_PySequence_Multiply_Generic(seq, mul); + } +} + +/* SetItemInt */ +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { + int r; + if (unlikely(!j)) return -1; + r = PyObject_SetItem(o, j, v); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, + CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) { + PyObject* old = PyList_GET_ITEM(o, n); + Py_INCREF(v); + PyList_SET_ITEM(o, n, v); + Py_DECREF(old); + return 1; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_ass_subscript) { + int r; + PyObject *key = PyInt_FromSsize_t(i); + if (unlikely(!key)) return -1; + r = mm->mp_ass_subscript(o, key, v); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_ass_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return -1; + PyErr_Clear(); + } + } + return sm->sq_ass_item(o, i, v); + } + } +#else +#if CYTHON_COMPILING_IN_PYPY + if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) +#else + if (is_list || PySequence_Check(o)) +#endif + { + return PySequence_SetItem(o, i, v); + } +#endif + return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); +} + +/* RaiseUnboundLocalError */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* DivInt[long] */ +static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + const char* module_name_str = 0; + PyObject* module_name = 0; + PyObject* module_dot = 0; + PyObject* full_name = 0; + PyErr_Clear(); + module_name_str = PyModule_GetName(module); + if (unlikely(!module_name_str)) { goto modbad; } + module_name = PyUnicode_FromString(module_name_str); + if (unlikely(!module_name)) { goto modbad; } + module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__2); + if (unlikely(!module_dot)) { goto modbad; } + full_name = PyUnicode_Concat(module_dot, name); + if (unlikely(!full_name)) { goto modbad; } + #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) + { + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + goto modbad; + value = PyObject_GetItem(modules, full_name); + } + #else + value = PyImport_GetModule(full_name); + #endif + modbad: + Py_XDECREF(full_name); + Py_XDECREF(module_dot); + Py_XDECREF(module_name); + } + if (unlikely(!value)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (!r) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} + +/* ErrOccurredWithGIL */ +static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void) { + int err; + #ifdef WITH_THREAD + PyGILState_STATE _save = PyGILState_Ensure(); + #endif + err = !!PyErr_Occurred(); + #ifdef WITH_THREAD + PyGILState_Release(_save); + #endif + return err; +} + +/* PyObject_GenericGetAttrNoDict */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { + __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp); + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", + type_name, attr_name); +#else + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", + type_name, PyString_AS_STRING(attr_name)); +#endif + __Pyx_DECREF_TypeName(type_name); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { + PyObject *descr; + PyTypeObject *tp = Py_TYPE(obj); + if (unlikely(!PyString_Check(attr_name))) { + return PyObject_GenericGetAttr(obj, attr_name); + } + assert(!tp->tp_dictoffset); + descr = _PyType_Lookup(tp, attr_name); + if (unlikely(!descr)) { + return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); + } + Py_INCREF(descr); + #if PY_MAJOR_VERSION < 3 + if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) + #endif + { + descrgetfunc f = Py_TYPE(descr)->tp_descr_get; + if (unlikely(f)) { + PyObject *res = f(descr, obj, (PyObject *)tp); + Py_DECREF(descr); + return res; + } + } + return descr; +} +#endif + +/* PyObject_GenericGetAttr */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { + if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { + return PyObject_GenericGetAttr(obj, attr_name); + } + return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); +} +#endif + +/* FixUpExtensionType */ +#if CYTHON_USE_TYPE_SPECS +static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) { +#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + CYTHON_UNUSED_VAR(spec); + CYTHON_UNUSED_VAR(type); +#else + const PyType_Slot *slot = spec->slots; + while (slot && slot->slot && slot->slot != Py_tp_members) + slot++; + if (slot && slot->slot == Py_tp_members) { + int changed = 0; +#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON) + const +#endif + PyMemberDef *memb = (PyMemberDef*) slot->pfunc; + while (memb && memb->name) { + if (memb->name[0] == '_' && memb->name[1] == '_') { +#if PY_VERSION_HEX < 0x030900b1 + if (strcmp(memb->name, "__weaklistoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_weaklistoffset = memb->offset; + changed = 1; + } + else if (strcmp(memb->name, "__dictoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_dictoffset = memb->offset; + changed = 1; + } +#if CYTHON_METH_FASTCALL + else if (strcmp(memb->name, "__vectorcalloffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); +#if PY_VERSION_HEX >= 0x030800b4 + type->tp_vectorcall_offset = memb->offset; +#else + type->tp_print = (printfunc) memb->offset; +#endif + changed = 1; + } +#endif +#else + if ((0)); +#endif +#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON + else if (strcmp(memb->name, "__module__") == 0) { + PyObject *descr; + assert(memb->type == T_OBJECT); + assert(memb->flags == 0 || memb->flags == READONLY); + descr = PyDescr_NewMember(type, memb); + if (unlikely(!descr)) + return -1; + if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) { + Py_DECREF(descr); + return -1; + } + Py_DECREF(descr); + changed = 1; + } +#endif + } + memb++; + } + if (changed) + PyType_Modified(type); + } +#endif + return 0; +} +#endif + +/* PyObjectCallNoArg */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { + PyObject *arg = NULL; + return __Pyx_PyObject_FastCall(func, (&arg)+1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* PyObjectGetMethod */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) { + PyObject *attr; +#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP + __Pyx_TypeName type_name; + PyTypeObject *tp = Py_TYPE(obj); + PyObject *descr; + descrgetfunc f = NULL; + PyObject **dictptr, *dict; + int meth_found = 0; + assert (*method == NULL); + if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) { + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; + } + if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) { + return 0; + } + descr = _PyType_Lookup(tp, name); + if (likely(descr != NULL)) { + Py_INCREF(descr); +#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR + if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR)) +#elif PY_MAJOR_VERSION >= 3 + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type))) + #endif +#else + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr))) + #endif +#endif + { + meth_found = 1; + } else { + f = Py_TYPE(descr)->tp_descr_get; + if (f != NULL && PyDescr_IsData(descr)) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + } + } + dictptr = _PyObject_GetDictPtr(obj); + if (dictptr != NULL && (dict = *dictptr) != NULL) { + Py_INCREF(dict); + attr = __Pyx_PyDict_GetItemStr(dict, name); + if (attr != NULL) { + Py_INCREF(attr); + Py_DECREF(dict); + Py_XDECREF(descr); + goto try_unpack; + } + Py_DECREF(dict); + } + if (meth_found) { + *method = descr; + return 1; + } + if (f != NULL) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + if (likely(descr != NULL)) { + *method = descr; + return 0; + } + type_name = __Pyx_PyType_GetName(tp); + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", + type_name, name); +#else + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", + type_name, PyString_AS_STRING(name)); +#endif + __Pyx_DECREF_TypeName(type_name); + return 0; +#else + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; +#endif +try_unpack: +#if CYTHON_UNPACK_METHODS + if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) { + PyObject *function = PyMethod_GET_FUNCTION(attr); + Py_INCREF(function); + Py_DECREF(attr); + *method = function; + return 1; + } +#endif + *method = attr; + return 0; +} + +/* PyObjectCallMethod0 */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { + PyObject *method = NULL, *result = NULL; + int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); + if (likely(is_method)) { + result = __Pyx_PyObject_CallOneArg(method, obj); + Py_DECREF(method); + return result; + } + if (unlikely(!method)) goto bad; + result = __Pyx_PyObject_CallNoArg(method); + Py_DECREF(method); +bad: + return result; +} + +/* ValidateBasesTuple */ +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) { + Py_ssize_t i, n = PyTuple_GET_SIZE(bases); + for (i = 1; i < n; i++) + { + PyObject *b0 = PyTuple_GET_ITEM(bases, i); + PyTypeObject *b; +#if PY_MAJOR_VERSION < 3 + if (PyClass_Check(b0)) + { + PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class", + PyString_AS_STRING(((PyClassObject*)b0)->cl_name)); + return -1; + } +#endif + b = (PyTypeObject*) b0; + if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE)) + { + __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); + PyErr_Format(PyExc_TypeError, + "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name); + __Pyx_DECREF_TypeName(b_name); + return -1; + } + if (dictoffset == 0 && b->tp_dictoffset) + { + __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); + PyErr_Format(PyExc_TypeError, + "extension type '%.200s' has no __dict__ slot, " + "but base type '" __Pyx_FMT_TYPENAME "' has: " + "either add 'cdef dict __dict__' to the extension type " + "or add '__slots__ = [...]' to the base type", + type_name, b_name); + __Pyx_DECREF_TypeName(b_name); + return -1; + } + } + return 0; +} +#endif + +/* PyType_Ready */ +static int __Pyx_PyType_Ready(PyTypeObject *t) { +#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION) + (void)__Pyx_PyObject_CallMethod0; +#if CYTHON_USE_TYPE_SPECS + (void)__Pyx_validate_bases_tuple; +#endif + return PyType_Ready(t); +#else + int r; + PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*); + if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1)) + return -1; +#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) + { + int gc_was_enabled; + #if PY_VERSION_HEX >= 0x030A00b1 + gc_was_enabled = PyGC_Disable(); + (void)__Pyx_PyObject_CallMethod0; + #else + PyObject *ret, *py_status; + PyObject *gc = NULL; + #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400) + gc = PyImport_GetModule(__pyx_kp_u_gc); + #endif + if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc); + if (unlikely(!gc)) return -1; + py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled); + if (unlikely(!py_status)) { + Py_DECREF(gc); + return -1; + } + gc_was_enabled = __Pyx_PyObject_IsTrue(py_status); + Py_DECREF(py_status); + if (gc_was_enabled > 0) { + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable); + if (unlikely(!ret)) { + Py_DECREF(gc); + return -1; + } + Py_DECREF(ret); + } else if (unlikely(gc_was_enabled == -1)) { + Py_DECREF(gc); + return -1; + } + #endif + t->tp_flags |= Py_TPFLAGS_HEAPTYPE; +#if PY_VERSION_HEX >= 0x030A0000 + t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE; +#endif +#else + (void)__Pyx_PyObject_CallMethod0; +#endif + r = PyType_Ready(t); +#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) + t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE; + #if PY_VERSION_HEX >= 0x030A00b1 + if (gc_was_enabled) + PyGC_Enable(); + #else + if (gc_was_enabled) { + PyObject *tp, *v, *tb; + PyErr_Fetch(&tp, &v, &tb); + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable); + if (likely(ret || r == -1)) { + Py_XDECREF(ret); + PyErr_Restore(tp, v, tb); + } else { + Py_XDECREF(tp); + Py_XDECREF(v); + Py_XDECREF(tb); + r = -1; + } + } + Py_DECREF(gc); + #endif + } +#endif + return r; +#endif +} + +/* SetVTable */ +static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) { + PyObject *ob = PyCapsule_New(vtable, 0, 0); + if (unlikely(!ob)) + goto bad; +#if CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_n_s_pyx_vtable, ob) < 0)) +#else + if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_n_s_pyx_vtable, ob) < 0)) +#endif + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* GetVTable */ +static void* __Pyx_GetVtable(PyTypeObject *type) { + void* ptr; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_n_s_pyx_vtable); +#else + PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_n_s_pyx_vtable); +#endif + if (!ob) + goto bad; + ptr = PyCapsule_GetPointer(ob, 0); + if (!ptr && !PyErr_Occurred()) + PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); + Py_DECREF(ob); + return ptr; +bad: + Py_XDECREF(ob); + return NULL; +} + +/* MergeVTables */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_MergeVtables(PyTypeObject *type) { + int i; + void** base_vtables; + __Pyx_TypeName tp_base_name; + __Pyx_TypeName base_name; + void* unknown = (void*)-1; + PyObject* bases = type->tp_bases; + int base_depth = 0; + { + PyTypeObject* base = type->tp_base; + while (base) { + base_depth += 1; + base = base->tp_base; + } + } + base_vtables = (void**) malloc(sizeof(void*) * (size_t)(base_depth + 1)); + base_vtables[0] = unknown; + for (i = 1; i < PyTuple_GET_SIZE(bases); i++) { + void* base_vtable = __Pyx_GetVtable(((PyTypeObject*)PyTuple_GET_ITEM(bases, i))); + if (base_vtable != NULL) { + int j; + PyTypeObject* base = type->tp_base; + for (j = 0; j < base_depth; j++) { + if (base_vtables[j] == unknown) { + base_vtables[j] = __Pyx_GetVtable(base); + base_vtables[j + 1] = unknown; + } + if (base_vtables[j] == base_vtable) { + break; + } else if (base_vtables[j] == NULL) { + goto bad; + } + base = base->tp_base; + } + } + } + PyErr_Clear(); + free(base_vtables); + return 0; +bad: + tp_base_name = __Pyx_PyType_GetName(type->tp_base); + base_name = __Pyx_PyType_GetName((PyTypeObject*)PyTuple_GET_ITEM(bases, i)); + PyErr_Format(PyExc_TypeError, + "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name); + __Pyx_DECREF_TypeName(tp_base_name); + __Pyx_DECREF_TypeName(base_name); + free(base_vtables); + return -1; +} +#endif + +/* SetupReduce */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_getstate = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; + PyObject *getstate = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate); +#else + getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate); + if (!getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (getstate) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate); +#else + object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate); + if (!object_getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (object_getstate != getstate) { + goto __PYX_GOOD; + } + } +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); + if (likely(reduce_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (reduce == object_reduce || PyErr_Occurred()) { + goto __PYX_BAD; + } + setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); + if (likely(setstate_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (!setstate || PyErr_Occurred()) { + goto __PYX_BAD; + } + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto __PYX_GOOD; +__PYX_BAD: + if (!PyErr_Occurred()) { + __Pyx_TypeName type_obj_name = + __Pyx_PyType_GetName((PyTypeObject*)type_obj); + PyErr_Format(PyExc_RuntimeError, + "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name); + __Pyx_DECREF_TypeName(type_obj_name); + } + ret = -1; +__PYX_GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); + Py_XDECREF(object_getstate); + Py_XDECREF(getstate); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} +#endif + +/* FetchSharedCythonModule */ +static PyObject *__Pyx_FetchSharedCythonABIModule(void) { + PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME); + if (unlikely(!abi_module)) return NULL; + Py_INCREF(abi_module); + return abi_module; +} + +/* FetchCommonType */ +static int __Pyx_VerifyCachedType(PyObject *cached_type, + const char *name, + Py_ssize_t basicsize, + Py_ssize_t expected_basicsize) { + if (!PyType_Check(cached_type)) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s is not a type object", name); + return -1; + } + if (basicsize != expected_basicsize) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s has the wrong size, try recompiling", + name); + return -1; + } + return 0; +} +#if !CYTHON_USE_TYPE_SPECS +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { + PyObject* abi_module; + const char* object_name; + PyTypeObject *cached_type = NULL; + abi_module = __Pyx_FetchSharedCythonABIModule(); + if (!abi_module) return NULL; + object_name = strrchr(type->tp_name, '.'); + object_name = object_name ? object_name+1 : type->tp_name; + cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name); + if (cached_type) { + if (__Pyx_VerifyCachedType( + (PyObject *)cached_type, + object_name, + cached_type->tp_basicsize, + type->tp_basicsize) < 0) { + goto bad; + } + goto done; + } + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + if (PyType_Ready(type) < 0) goto bad; + if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0) + goto bad; + Py_INCREF(type); + cached_type = type; +done: + Py_DECREF(abi_module); + return cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} +#else +static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) { + PyObject *abi_module, *cached_type = NULL; + const char* object_name = strrchr(spec->name, '.'); + object_name = object_name ? object_name+1 : spec->name; + abi_module = __Pyx_FetchSharedCythonABIModule(); + if (!abi_module) return NULL; + cached_type = PyObject_GetAttrString(abi_module, object_name); + if (cached_type) { + Py_ssize_t basicsize; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *py_basicsize; + py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__"); + if (unlikely(!py_basicsize)) goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; +#else + basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1; +#endif + if (__Pyx_VerifyCachedType( + cached_type, + object_name, + basicsize, + spec->basicsize) < 0) { + goto bad; + } + goto done; + } + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + CYTHON_UNUSED_VAR(module); + cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases); + if (unlikely(!cached_type)) goto bad; + if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad; + if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad; +done: + Py_DECREF(abi_module); + assert(cached_type == NULL || PyType_Check(cached_type)); + return (PyTypeObject *) cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} +#endif + +/* PyVectorcallFastCallDict */ +#if CYTHON_METH_FASTCALL +static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + PyObject *res = NULL; + PyObject *kwnames; + PyObject **newargs; + PyObject **kwvalues; + Py_ssize_t i, pos; + size_t j; + PyObject *key, *value; + unsigned long keys_are_strings; + Py_ssize_t nkw = PyDict_GET_SIZE(kw); + newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0])); + if (unlikely(newargs == NULL)) { + PyErr_NoMemory(); + return NULL; + } + for (j = 0; j < nargs; j++) newargs[j] = args[j]; + kwnames = PyTuple_New(nkw); + if (unlikely(kwnames == NULL)) { + PyMem_Free(newargs); + return NULL; + } + kwvalues = newargs + nargs; + pos = i = 0; + keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS; + while (PyDict_Next(kw, &pos, &key, &value)) { + keys_are_strings &= Py_TYPE(key)->tp_flags; + Py_INCREF(key); + Py_INCREF(value); + PyTuple_SET_ITEM(kwnames, i, key); + kwvalues[i] = value; + i++; + } + if (unlikely(!keys_are_strings)) { + PyErr_SetString(PyExc_TypeError, "keywords must be strings"); + goto cleanup; + } + res = vc(func, newargs, nargs, kwnames); +cleanup: + Py_DECREF(kwnames); + for (i = 0; i < nkw; i++) + Py_DECREF(kwvalues[i]); + PyMem_Free(newargs); + return res; +} +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) { + return vc(func, args, nargs, NULL); + } + return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw); +} +#endif + +/* CythonFunctionShared */ +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) { +#if PY_VERSION_HEX < 0x030900B1 + __Pyx_Py_XDECREF_SET( + __Pyx_CyFunction_GetClassObj(f), + ((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#else + __Pyx_Py_XDECREF_SET( + ((PyCMethodObject *) (f))->mm_class, + (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#endif +} +static PyObject * +__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure) +{ + CYTHON_UNUSED_VAR(closure); + if (unlikely(op->func_doc == NULL)) { + if (((PyCFunctionObject*)op)->m_ml->ml_doc) { +#if PY_MAJOR_VERSION >= 3 + op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); +#else + op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); +#endif + if (unlikely(op->func_doc == NULL)) + return NULL; + } else { + Py_INCREF(Py_None); + return Py_None; + } + } + Py_INCREF(op->func_doc); + return op->func_doc; +} +static int +__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (value == NULL) { + value = Py_None; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_doc, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(op->func_name == NULL)) { +#if PY_MAJOR_VERSION >= 3 + op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); +#else + op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); +#endif + if (unlikely(op->func_name == NULL)) + return NULL; + } + Py_INCREF(op->func_name); + return op->func_name; +} +static int +__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) +#else + if (unlikely(value == NULL || !PyString_Check(value))) +#endif + { + PyErr_SetString(PyExc_TypeError, + "__name__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_name, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + Py_INCREF(op->func_qualname); + return op->func_qualname; +} +static int +__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) +#else + if (unlikely(value == NULL || !PyString_Check(value))) +#endif + { + PyErr_SetString(PyExc_TypeError, + "__qualname__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_qualname, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(op->func_dict == NULL)) { + op->func_dict = PyDict_New(); + if (unlikely(op->func_dict == NULL)) + return NULL; + } + Py_INCREF(op->func_dict); + return op->func_dict; +} +static int +__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(value == NULL)) { + PyErr_SetString(PyExc_TypeError, + "function's dictionary may not be deleted"); + return -1; + } + if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "setting function's dictionary to a non-dict"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_dict, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + Py_INCREF(op->func_globals); + return op->func_globals; +} +static PyObject * +__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(op); + CYTHON_UNUSED_VAR(context); + Py_INCREF(Py_None); + return Py_None; +} +static PyObject * +__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context) +{ + PyObject* result = (op->func_code) ? op->func_code : Py_None; + CYTHON_UNUSED_VAR(context); + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { + int result = 0; + PyObject *res = op->defaults_getter((PyObject *) op); + if (unlikely(!res)) + return -1; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + op->defaults_tuple = PyTuple_GET_ITEM(res, 0); + Py_INCREF(op->defaults_tuple); + op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); + Py_INCREF(op->defaults_kwdict); + #else + op->defaults_tuple = PySequence_ITEM(res, 0); + if (unlikely(!op->defaults_tuple)) result = -1; + else { + op->defaults_kwdict = PySequence_ITEM(res, 1); + if (unlikely(!op->defaults_kwdict)) result = -1; + } + #endif + Py_DECREF(res); + return result; +} +static int +__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyTuple_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__defaults__ must be set to a tuple object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->defaults_tuple, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->defaults_tuple; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_tuple; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__kwdefaults__ must be set to a dict object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->defaults_kwdict; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_kwdict; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value || value == Py_None) { + value = NULL; + } else if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__annotations__ must be set to a dict object"); + return -1; + } + Py_XINCREF(value); + __Pyx_Py_XDECREF_SET(op->func_annotations, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->func_annotations; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + result = PyDict_New(); + if (unlikely(!result)) return NULL; + op->func_annotations = result; + } + Py_INCREF(result); + return result; +} +static PyObject * +__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) { + int is_coroutine; + CYTHON_UNUSED_VAR(context); + if (op->func_is_coroutine) { + return __Pyx_NewRef(op->func_is_coroutine); + } + is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE; +#if PY_VERSION_HEX >= 0x03050000 + if (is_coroutine) { + PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine; + fromlist = PyList_New(1); + if (unlikely(!fromlist)) return NULL; + Py_INCREF(marker); + PyList_SET_ITEM(fromlist, 0, marker); + module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0); + Py_DECREF(fromlist); + if (unlikely(!module)) goto ignore; + op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker); + Py_DECREF(module); + if (likely(op->func_is_coroutine)) { + return __Pyx_NewRef(op->func_is_coroutine); + } +ignore: + PyErr_Clear(); + } +#endif + op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine); + return __Pyx_NewRef(op->func_is_coroutine); +} +static PyGetSetDef __pyx_CyFunction_getsets[] = { + {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, + {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, + {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, + {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0}, + {0, 0, 0, 0, 0} +}; +static PyMemberDef __pyx_CyFunction_members[] = { + {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0}, +#if CYTHON_USE_TYPE_SPECS + {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0}, +#if CYTHON_METH_FASTCALL +#if CYTHON_BACKPORT_VECTORCALL + {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0}, +#else + {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0}, +#endif +#endif +#if PY_VERSION_HEX < 0x030500A0 + {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0}, +#else + {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0}, +#endif +#endif + {0, 0, 0, 0, 0} +}; +static PyObject * +__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args) +{ + CYTHON_UNUSED_VAR(args); +#if PY_MAJOR_VERSION >= 3 + Py_INCREF(m->func_qualname); + return m->func_qualname; +#else + return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name); +#endif +} +static PyMethodDef __pyx_CyFunction_methods[] = { + {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, + {0, 0, 0, 0} +}; +#if PY_VERSION_HEX < 0x030500A0 +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) +#else +#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist) +#endif +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { + PyCFunctionObject *cf = (PyCFunctionObject*) op; + if (unlikely(op == NULL)) + return NULL; + op->flags = flags; + __Pyx_CyFunction_weakreflist(op) = NULL; + cf->m_ml = ml; + cf->m_self = (PyObject *) op; + Py_XINCREF(closure); + op->func_closure = closure; + Py_XINCREF(module); + cf->m_module = module; + op->func_dict = NULL; + op->func_name = NULL; + Py_INCREF(qualname); + op->func_qualname = qualname; + op->func_doc = NULL; +#if PY_VERSION_HEX < 0x030900B1 + op->func_classobj = NULL; +#else + ((PyCMethodObject*)op)->mm_class = NULL; +#endif + op->func_globals = globals; + Py_INCREF(op->func_globals); + Py_XINCREF(code); + op->func_code = code; + op->defaults_pyobjects = 0; + op->defaults_size = 0; + op->defaults = NULL; + op->defaults_tuple = NULL; + op->defaults_kwdict = NULL; + op->defaults_getter = NULL; + op->func_annotations = NULL; + op->func_is_coroutine = NULL; +#if CYTHON_METH_FASTCALL + switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) { + case METH_NOARGS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS; + break; + case METH_O: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O; + break; + case METH_METHOD | METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD; + break; + case METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS; + break; + case METH_VARARGS | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = NULL; + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + Py_DECREF(op); + return NULL; + } +#endif + return (PyObject *) op; +} +static int +__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) +{ + Py_CLEAR(m->func_closure); + Py_CLEAR(((PyCFunctionObject*)m)->m_module); + Py_CLEAR(m->func_dict); + Py_CLEAR(m->func_name); + Py_CLEAR(m->func_qualname); + Py_CLEAR(m->func_doc); + Py_CLEAR(m->func_globals); + Py_CLEAR(m->func_code); +#if PY_VERSION_HEX < 0x030900B1 + Py_CLEAR(__Pyx_CyFunction_GetClassObj(m)); +#else + { + PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class; + ((PyCMethodObject *) (m))->mm_class = NULL; + Py_XDECREF(cls); + } +#endif + Py_CLEAR(m->defaults_tuple); + Py_CLEAR(m->defaults_kwdict); + Py_CLEAR(m->func_annotations); + Py_CLEAR(m->func_is_coroutine); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_XDECREF(pydefaults[i]); + PyObject_Free(m->defaults); + m->defaults = NULL; + } + return 0; +} +static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + if (__Pyx_CyFunction_weakreflist(m) != NULL) + PyObject_ClearWeakRefs((PyObject *) m); + __Pyx_CyFunction_clear(m); + __Pyx_PyHeapTypeObject_GC_Del(m); +} +static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + PyObject_GC_UnTrack(m); + __Pyx__CyFunction_dealloc(m); +} +static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) +{ + Py_VISIT(m->func_closure); + Py_VISIT(((PyCFunctionObject*)m)->m_module); + Py_VISIT(m->func_dict); + Py_VISIT(m->func_name); + Py_VISIT(m->func_qualname); + Py_VISIT(m->func_doc); + Py_VISIT(m->func_globals); + Py_VISIT(m->func_code); + Py_VISIT(__Pyx_CyFunction_GetClassObj(m)); + Py_VISIT(m->defaults_tuple); + Py_VISIT(m->defaults_kwdict); + Py_VISIT(m->func_is_coroutine); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_VISIT(pydefaults[i]); + } + return 0; +} +static PyObject* +__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromFormat("", + op->func_qualname, (void *)op); +#else + return PyString_FromFormat("", + PyString_AsString(op->func_qualname), (void *)op); +#endif +} +static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { + PyCFunctionObject* f = (PyCFunctionObject*)func; + PyCFunction meth = f->m_ml->ml_meth; + Py_ssize_t size; + switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { + case METH_VARARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) + return (*meth)(self, arg); + break; + case METH_VARARGS | METH_KEYWORDS: + return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw); + case METH_NOARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { + size = PyTuple_GET_SIZE(arg); + if (likely(size == 0)) + return (*meth)(self, NULL); + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); + return NULL; + } + break; + case METH_O: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { + size = PyTuple_GET_SIZE(arg); + if (likely(size == 1)) { + PyObject *result, *arg0; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + arg0 = PyTuple_GET_ITEM(arg, 0); + #else + arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL; + #endif + result = (*meth)(self, arg0); + #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(arg0); + #endif + return result; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); + return NULL; + } + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + return NULL; + } + PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", + f->m_ml->ml_name); + return NULL; +} +static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { + return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw); +} +static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { + PyObject *result; + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; +#if CYTHON_METH_FASTCALL + __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc); + if (vc) { +#if CYTHON_ASSUME_SAFE_MACROS + return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw); +#else + (void) &__Pyx_PyVectorcall_FastCallDict; + return PyVectorcall_Call(func, args, kw); +#endif + } +#endif + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + Py_ssize_t argc; + PyObject *new_args; + PyObject *self; + argc = PyTuple_GET_SIZE(args); + new_args = PyTuple_GetSlice(args, 1, argc); + if (unlikely(!new_args)) + return NULL; + self = PyTuple_GetItem(args, 0); + if (unlikely(!self)) { + Py_DECREF(new_args); +#if PY_MAJOR_VERSION > 2 + PyErr_Format(PyExc_TypeError, + "unbound method %.200S() needs an argument", + cyfunc->func_qualname); +#else + PyErr_SetString(PyExc_TypeError, + "unbound method needs an argument"); +#endif + return NULL; + } + result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); + Py_DECREF(new_args); + } else { + result = __Pyx_CyFunction_Call(func, args, kw); + } + return result; +} +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames) +{ + int ret = 0; + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + if (unlikely(nargs < 1)) { + PyErr_Format(PyExc_TypeError, "%.200s() needs an argument", + ((PyCFunctionObject*)cyfunc)->m_ml->ml_name); + return -1; + } + ret = 1; + } + if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name); + return -1; + } + return ret; +} +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + if (unlikely(nargs != 0)) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + def->ml_name, nargs); + return NULL; + } + return def->ml_meth(self, NULL); +} +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + if (unlikely(nargs != 1)) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + def->ml_name, nargs); + return NULL; + } + return def->ml_meth(self, args[0]); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; + PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc); +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, (size_t)nargs, kwnames); +} +#endif +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_CyFunctionType_slots[] = { + {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc}, + {Py_tp_repr, (void *)__Pyx_CyFunction_repr}, + {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod}, + {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse}, + {Py_tp_clear, (void *)__Pyx_CyFunction_clear}, + {Py_tp_methods, (void *)__pyx_CyFunction_methods}, + {Py_tp_members, (void *)__pyx_CyFunction_members}, + {Py_tp_getset, (void *)__pyx_CyFunction_getsets}, + {Py_tp_descr_get, (void *)__Pyx_PyMethod_New}, + {0, 0}, +}; +static PyType_Spec __pyx_CyFunctionType_spec = { + __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, +#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR + Py_TPFLAGS_METHOD_DESCRIPTOR | +#endif +#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL) + _Py_TPFLAGS_HAVE_VECTORCALL | +#endif + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + __pyx_CyFunctionType_slots +}; +#else +static PyTypeObject __pyx_CyFunctionType_type = { + PyVarObject_HEAD_INIT(0, 0) + __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, + (destructor) __Pyx_CyFunction_dealloc, +#if !CYTHON_METH_FASTCALL + 0, +#elif CYTHON_BACKPORT_VECTORCALL + (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall), +#else + offsetof(PyCFunctionObject, vectorcall), +#endif + 0, + 0, +#if PY_MAJOR_VERSION < 3 + 0, +#else + 0, +#endif + (reprfunc) __Pyx_CyFunction_repr, + 0, + 0, + 0, + 0, + __Pyx_CyFunction_CallAsMethod, + 0, + 0, + 0, + 0, +#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR + Py_TPFLAGS_METHOD_DESCRIPTOR | +#endif +#ifdef _Py_TPFLAGS_HAVE_VECTORCALL + _Py_TPFLAGS_HAVE_VECTORCALL | +#endif + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + 0, + (traverseproc) __Pyx_CyFunction_traverse, + (inquiry) __Pyx_CyFunction_clear, + 0, +#if PY_VERSION_HEX < 0x030500A0 + offsetof(__pyx_CyFunctionObject, func_weakreflist), +#else + offsetof(PyCFunctionObject, m_weakreflist), +#endif + 0, + 0, + __pyx_CyFunction_methods, + __pyx_CyFunction_members, + __pyx_CyFunction_getsets, + 0, + 0, + __Pyx_PyMethod_New, + 0, + offsetof(__pyx_CyFunctionObject, func_dict), + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +#if PY_VERSION_HEX >= 0x030400a1 + 0, +#endif +#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, +#endif +#if __PYX_NEED_TP_PRINT_SLOT + 0, +#endif +#if PY_VERSION_HEX >= 0x030C0000 + 0, +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, +#endif +}; +#endif +static int __pyx_CyFunction_init(PyObject *module) { +#if CYTHON_USE_TYPE_SPECS + __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL); +#else + CYTHON_UNUSED_VAR(module); + __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); +#endif + if (unlikely(__pyx_CyFunctionType == NULL)) { + return -1; + } + return 0; +} +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults = PyObject_Malloc(size); + if (unlikely(!m->defaults)) + return PyErr_NoMemory(); + memset(m->defaults, 0, size); + m->defaults_pyobjects = pyobjects; + m->defaults_size = size; + return m->defaults; +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_tuple = tuple; + Py_INCREF(tuple); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_kwdict = dict; + Py_INCREF(dict); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->func_annotations = dict; + Py_INCREF(dict); +} + +/* CythonFunction */ +static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { + PyObject *op = __Pyx_CyFunction_Init( + PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType), + ml, flags, qualname, closure, module, globals, code + ); + if (likely(op)) { + PyObject_GC_Track(op); + } + return op; +} + +/* CLineInTraceback */ +#ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + CYTHON_MAYBE_UNUSED_VAR(tstate); + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} +#endif + +/* AddTraceback */ +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +#if PY_VERSION_HEX >= 0x030b00a6 + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + if (c_line) { + (void) __pyx_cfilenm; + (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line); + } + _PyTraceback_Add(funcname, filename, py_line); +} +#else +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = NULL; + PyObject *py_funcname = NULL; + #if PY_MAJOR_VERSION < 3 + PyObject *py_srcfile = NULL; + py_srcfile = PyString_FromString(filename); + if (!py_srcfile) goto bad; + #endif + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + funcname = PyUnicode_AsUTF8(py_funcname); + if (!funcname) goto bad; + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + if (!py_funcname) goto bad; + #endif + } + #if PY_MAJOR_VERSION < 3 + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + #else + py_code = PyCode_NewEmpty(filename, funcname, py_line); + #endif + Py_XDECREF(py_funcname); // XDECREF since it's only set on Py3 if cline + return py_code; +bad: + Py_XDECREF(py_funcname); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_srcfile); + #endif + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject *ptype, *pvalue, *ptraceback; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) { + /* If the code object creation fails, then we should clear the + fetched exception references and propagate the new exception */ + Py_XDECREF(ptype); + Py_XDECREF(pvalue); + Py_XDECREF(ptraceback); + goto bad; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} +#endif + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + __Pyx_TypeName obj_type_name; + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "'" __Pyx_FMT_TYPENAME "' does not have the buffer interface", + obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + +/* MemviewSliceIsContig */ +static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ +static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t <= '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case '?': return "'bool'"; + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparsable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number, ndim; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ndim = ctx->head->field->type->ndim; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && + (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (unlikely(buf->strides[dim] != sizeof(void *))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (unlikely(buf->strides[dim] != buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (unlikely(stride < buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (unlikely(buf->suboffsets)) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec) +{ + CYTHON_UNUSED_VAR(ndim); + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (unlikely(buf->ndim != ndim)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; + } + if (unlikely((unsigned) buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->len > 0) { + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) + goto fail; + if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) + goto fail; + } + if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) + goto fail; + } + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, + &__Pyx_TypeInfo_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, + &__Pyx_TypeInfo_float, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, + &__Pyx_TypeInfo_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (unlikely(from_mvs->suboffsets[i] >= 0)) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* MemviewSliceInit */ + static int +__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference) +{ + __Pyx_RefNannyDeclarations + int i, retval=-1; + Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (unlikely(memviewslice->memview || memviewslice->data)) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF(memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES) + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + return; + } + old_acquisition_count = __pyx_add_acquisition_count(memview); + if (unlikely(old_acquisition_count <= 0)) { + if (likely(old_acquisition_count == 0)) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count+1, lineno); + } + } +} +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + memslice->memview = NULL; + return; + } + old_acquisition_count = __pyx_sub_acquisition_count(memview); + memslice->data = NULL; + if (likely(old_acquisition_count > 1)) { + memslice->memview = NULL; + } else if (likely(old_acquisition_count == 1)) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count-1, lineno); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(int) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(int) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(int) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (int) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (int) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (int) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (int) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((int) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((int) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(long) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(long) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(long) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (long) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (long) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (long) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (long) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((long) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((long) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const char neg_one = (char) -1, const_zero = (char) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(char) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(char) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(char) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (char) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (char) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (char) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (char) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((char) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((char) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* FormatTypeName */ + #if CYTHON_COMPILING_IN_LIMITED_API +static __Pyx_TypeName +__Pyx_PyType_GetName(PyTypeObject* tp) +{ + PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp, + __pyx_n_s_name_2); + if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) { + PyErr_Clear(); + Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__23)); + } + return name; +} +#endif + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[5]; + int same=1, i, found_dot; + const char* rt_from_call = Py_GetVersion(); + PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + found_dot = 0; + for (i = 0; i < 4; i++) { + if (!ctversion[i]) { + same = (rt_from_call[i] < '0' || rt_from_call[i] > '9'); + break; + } + if (rt_from_call[i] != ctversion[i]) { + same = 0; + break; + } + } + if (!same) { + char rtversion[5] = {'\0'}; + char message[200]; + for (i=0; i<4; ++i) { + if (rt_from_call[i] == '.') { + if (found_dot) break; + found_dot = 1; + } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') { + break; + } + rtversion[i] = rt_from_call[i]; + } + PyOS_snprintf(message, sizeof(message), + "compile time version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* InitStrings */ + #if PY_MAJOR_VERSION >= 3 +static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) { + if (t.is_unicode | t.is_str) { + if (t.intern) { + *str = PyUnicode_InternFromString(t.s); + } else if (t.encoding) { + *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL); + } else { + *str = PyUnicode_FromStringAndSize(t.s, t.n - 1); + } + } else { + *str = PyBytes_FromStringAndSize(t.s, t.n - 1); + } + if (!*str) + return -1; + if (PyObject_Hash(*str) == -1) + return -1; + return 0; +} +#endif +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION >= 3 + __Pyx_InitString(*t, t->p); + #else + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + #endif + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { + __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result)); +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type " __Pyx_FMT_TYPENAME "). " + "The ability to return an instance of a strict subclass of int is deprecated, " + "and may be removed in a future version of Python.", + result_type_name)) { + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; + } + __Pyx_DECREF_TypeName(result_type_name); + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")", + type_name, type_name, result_type_name); + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(__Pyx_PyLong_IsCompact(b))) { + return __Pyx_PyLong_CompactValue(b); + } else { + const digit* digits = __Pyx_PyLong_Digits(b); + const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b); + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) { + if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) { + return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o); +#if PY_MAJOR_VERSION < 3 + } else if (likely(PyInt_CheckExact(o))) { + return PyInt_AS_LONG(o); +#endif + } else { + Py_ssize_t ival; + PyObject *x; + x = PyNumber_Index(o); + if (!x) return -1; + ival = PyInt_AsLong(x); + Py_DECREF(x); + return ival; + } +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +/* #### Code section: utility_code_pragmas_end ### */ +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + + + +/* #### Code section: end ### */ +#endif /* Py_PYTHON_H */ diff --git a/BV2/monotonic_align/core.py b/BV2/monotonic_align/core.py new file mode 100644 index 0000000000000000000000000000000000000000..5ff728cd74c9228346a82ec64a9829cb98ad315e --- /dev/null +++ b/BV2/monotonic_align/core.py @@ -0,0 +1,36 @@ +import numba + + +@numba.jit(numba.void(numba.int32[:, :, ::1], numba.float32[:, :, ::1], numba.int32[::1], numba.int32[::1]), + nopython=True, nogil=True) +def maximum_path_jit(paths, values, t_ys, t_xs): + b = paths.shape[0] + max_neg_val = -1e9 + for i in range(int(b)): + path = paths[i] + value = values[i] + t_y = t_ys[i] + t_x = t_xs[i] + + v_prev = v_cur = 0.0 + index = t_x - 1 + + for y in range(t_y): + for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + if x == y: + v_cur = max_neg_val + else: + v_cur = value[y - 1, x] + if x == 0: + if y == 0: + v_prev = 0. + else: + v_prev = max_neg_val + else: + v_prev = value[y - 1, x - 1] + value[y, x] += max(v_prev, v_cur) + + for y in range(t_y - 1, -1, -1): + path[y, index] = 1 + if index != 0 and (index == y or value[y - 1, index] < value[y - 1, index - 1]): + index = index - 1 \ No newline at end of file diff --git a/BV2/monotonic_align/core.pyx b/BV2/monotonic_align/core.pyx new file mode 100644 index 0000000000000000000000000000000000000000..bfaabd4d21c2299cdd978f0cc0caefa20ad186e5 --- /dev/null +++ b/BV2/monotonic_align/core.pyx @@ -0,0 +1,42 @@ +cimport cython +from cython.parallel import prange + + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil: + cdef int x + cdef int y + cdef float v_prev + cdef float v_cur + cdef float tmp + cdef int index = t_x - 1 + + for y in range(t_y): + for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + if x == y: + v_cur = max_neg_val + else: + v_cur = value[y-1, x] + if x == 0: + if y == 0: + v_prev = 0. + else: + v_prev = max_neg_val + else: + v_prev = value[y-1, x-1] + value[y, x] += max(v_prev, v_cur) + + for y in range(t_y - 1, -1, -1): + path[y, index] = 1 + if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]): + index = index - 1 + + +@cython.boundscheck(False) +@cython.wraparound(False) +cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil: + cdef int b = paths.shape[0] + cdef int i + for i in prange(b, nogil=True): + maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i]) diff --git a/AR/data/__init__.py b/BV2/monotonic_align/monotonic_align/monotonic_align similarity index 100% rename from AR/data/__init__.py rename to BV2/monotonic_align/monotonic_align/monotonic_align diff --git a/BV2/monotonic_align/setup.py b/BV2/monotonic_align/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..30c224807a70faa9df9c9eb75f8e80c8c867b16b --- /dev/null +++ b/BV2/monotonic_align/setup.py @@ -0,0 +1,9 @@ +from distutils.core import setup +from Cython.Build import cythonize +import numpy + +setup( + name = 'monotonic_align', + ext_modules = cythonize("core.pyx"), + include_dirs=[numpy.get_include()] +) diff --git a/BV2/preprocess_text.py b/BV2/preprocess_text.py new file mode 100644 index 0000000000000000000000000000000000000000..44c35fecd9b7f21016e80e9597d6055254cba3f7 --- /dev/null +++ b/BV2/preprocess_text.py @@ -0,0 +1,69 @@ +import json +from random import shuffle + +import tqdm +from text.cleaner import clean_text +from collections import defaultdict +import shutil +stage = [1,2,3] + +transcription_path = 'filelists/short_character_anno.list' +train_path = 'filelists/train.list' +val_path = 'filelists/val.list' +config_path = "configs/config.json" +val_per_spk = 4 +max_val_total = 8 + +if 1 in stage: + with open( transcription_path+'.cleaned', 'w', encoding='utf-8') as f: + for line in tqdm.tqdm(open(transcription_path, encoding='utf-8').readlines()): + try: + utt, spk, language, text = line.strip().split('|') + #language = "ZH" + norm_text, phones, tones, word2ph = clean_text(text, language) + f.write('{}|{}|{}|{}|{}|{}|{}\n'.format(utt, spk, language, norm_text, ' '.join(phones), + " ".join([str(i) for i in tones]), + " ".join([str(i) for i in word2ph]))) + except: + print("err!", utt) + +if 2 in stage: + spk_utt_map = defaultdict(list) + spk_id_map = {} + current_sid = 0 + + with open( transcription_path+'.cleaned', encoding='utf-8') as f: + for line in f.readlines(): + utt, spk, language, text, phones, tones, word2ph = line.strip().split('|') + spk_utt_map[spk].append(line) + if spk not in spk_id_map.keys(): + spk_id_map[spk] = current_sid + current_sid += 1 + train_list = [] + val_list = [] + for spk, utts in spk_utt_map.items(): + shuffle(utts) + val_list+=utts[:val_per_spk] + train_list+=utts[val_per_spk:] + if len(val_list) > max_val_total: + train_list+=val_list[max_val_total:] + val_list = val_list[:max_val_total] + + with open( train_path,"w", encoding='utf-8') as f: + for line in train_list: + f.write(line) + + file_path = transcription_path+'.cleaned' + shutil.copy(file_path,'./filelists/train.list') + + with open(val_path, "w", encoding='utf-8') as f: + for line in val_list: + f.write(line) + +if 3 in stage: + assert 2 in stage + config = json.load(open(config_path)) + config['data']["n_speakers"] = current_sid # + config["data"]['spk2id'] = spk_id_map + with open(config_path, 'w', encoding='utf-8') as f: + json.dump(config, f, indent=2, ensure_ascii=False) diff --git a/BV2/setup_ffmpeg.py b/BV2/setup_ffmpeg.py new file mode 100644 index 0000000000000000000000000000000000000000..7137ab5faebb6d80740b8c843667458f25596839 --- /dev/null +++ b/BV2/setup_ffmpeg.py @@ -0,0 +1,55 @@ +import os +import sys +import re +from pathlib import Path +import winreg + +def check_ffmpeg_path(): + path_list = os.environ['Path'].split(';') + ffmpeg_found = False + + for path in path_list: + if 'ffmpeg' in path.lower() and 'bin' in path.lower(): + ffmpeg_found = True + print("FFmpeg already installed.") + break + + return ffmpeg_found + +def add_ffmpeg_path_to_user_variable(): + ffmpeg_bin_path = Path('.\\ffmpeg\\bin') + if ffmpeg_bin_path.is_dir(): + abs_path = str(ffmpeg_bin_path.resolve()) + + try: + key = winreg.OpenKey( + winreg.HKEY_CURRENT_USER, + r"Environment", + 0, + winreg.KEY_READ | winreg.KEY_WRITE + ) + + try: + current_path, _ = winreg.QueryValueEx(key, "Path") + if abs_path not in current_path: + new_path = f"{current_path};{abs_path}" + winreg.SetValueEx(key, "Path", 0, winreg.REG_EXPAND_SZ, new_path) + print(f"Added FFmpeg path to user variable 'Path': {abs_path}") + else: + print("FFmpeg path already exists in the user variable 'Path'.") + finally: + winreg.CloseKey(key) + except WindowsError: + print("Error: Unable to modify user variable 'Path'.") + sys.exit(1) + + else: + print("Error: ffmpeg\\bin folder not found in the current path.") + sys.exit(1) + +def main(): + if not check_ffmpeg_path(): + add_ffmpeg_path_to_user_variable() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/BV2/text/__init__.py b/BV2/text/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..713a9e16cd26c99a74c3b65e7ddf5bc8f4b7fea2 --- /dev/null +++ b/BV2/text/__init__.py @@ -0,0 +1,28 @@ +#from .text.symbols import * +from .symbols import * + +_symbol_to_id = {s: i for i, s in enumerate(symbols)} + +def cleaned_text_to_sequence(cleaned_text, tones, language): + '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + Args: + text: string to convert to a sequence + Returns: + List of integers corresponding to the symbols in the text + ''' + phones = [_symbol_to_id[symbol] for symbol in cleaned_text] + tone_start = language_tone_start_map[language] + tones = [i + tone_start for i in tones] + lang_id = language_id_map[language] + lang_ids = [lang_id for i in phones] + return phones, tones, lang_ids + +def get_bert(norm_text, word2ph, language): + from .chinese_bert import get_bert_feature as zh_bert + from .english_bert_mock import get_bert_feature as en_bert + lang_bert_func_map = { + 'ZH': zh_bert, + 'EN': en_bert + } + bert = lang_bert_func_map[language](norm_text, word2ph) + return bert diff --git a/BV2/text/__pycache__/__init__.cpython-310.pyc b/BV2/text/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e5855e4bd1f2cd0e031472c801ef0c5fdcffaa97 Binary files /dev/null and b/BV2/text/__pycache__/__init__.cpython-310.pyc differ diff --git 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0000000000000000000000000000000000000000..edf7b105d31a87933d9aa66a2a7d5add5264c90a --- /dev/null +++ b/BV2/text/chinese.py @@ -0,0 +1,193 @@ +import os +import re + +import cn2an +from pypinyin import lazy_pinyin, Style + +from .symbols import * +from .symbols import punctuation +from .tone_sandhi import ToneSandhi + +current_file_path = os.path.dirname(__file__) +pinyin_to_symbol_map = {line.split("\t")[0]: line.strip().split("\t")[1] for line in + open(os.path.join(current_file_path, 'opencpop-strict.txt')).readlines()} + +import jieba.posseg as psg + + +rep_map = { + '：': ',', + '；': ',', + '，': ',', + '。': '.', + '！': '!', + '？': '?', + '\n': '.', + "·": ",", + '、': ",", + '...': '…', + '$': '.', + '“': "'", + '”': "'", + '‘': "'", + '’': "'", + '（': "'", + '）': "'", + '(': "'", + ')': "'", + '《': "'", + '》': "'", + '【': "'", + '】': "'", + '[': "'", + ']': "'", + '—': "-", + '～': "-", + '~': "-", + '「': "'", + '」': "'", + +} + +tone_modifier = ToneSandhi() + +def replace_punctuation(text): + text = text.replace("嗯", "恩").replace("呣","母") + pattern = re.compile('|'.join(re.escape(p) for p in rep_map.keys())) + + replaced_text = pattern.sub(lambda x: rep_map[x.group()], text) + + replaced_text = re.sub(r'[^\u4e00-\u9fa5'+"".join(punctuation)+r']+', '', replaced_text) + + return replaced_text + +def g2p(text): + pattern = r'(?<=[{0}])\s*'.format(''.join(punctuation)) + sentences = [i for i in re.split(pattern, text) if i.strip()!=''] + phones, tones, word2ph = _g2p(sentences) + assert sum(word2ph) == len(phones) + assert len(word2ph) == len(text) #Sometimes it will crash,you can add a try-catch. + phones = ['_'] + phones + ["_"] + tones = [0] + tones + [0] + word2ph = [1] + word2ph + [1] + return phones, tones, word2ph + + +def _get_initials_finals(word): + initials = [] + finals = [] + orig_initials = lazy_pinyin( + word, neutral_tone_with_five=True, style=Style.INITIALS) + orig_finals = lazy_pinyin( + word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) + for c, v in zip(orig_initials, orig_finals): + initials.append(c) + finals.append(v) + return initials, finals + + +def _g2p(segments): + phones_list = [] + tones_list = [] + word2ph = [] + for seg in segments: + pinyins = [] + # Replace all English words in the sentence + seg = re.sub('[a-zA-Z]+', '', seg) + seg_cut = psg.lcut(seg) + initials = [] + finals = [] + seg_cut = tone_modifier.pre_merge_for_modify(seg_cut) + for word, pos in seg_cut: + if pos == 'eng': + continue + sub_initials, sub_finals = _get_initials_finals(word) + sub_finals = tone_modifier.modified_tone(word, pos, + sub_finals) + initials.append(sub_initials) + finals.append(sub_finals) + + # assert len(sub_initials) == len(sub_finals) == len(word) + initials = sum(initials, []) + finals = sum(finals, []) + # + for c, v in zip(initials, finals): + raw_pinyin = c+v + # NOTE: post process for pypinyin outputs + # we discriminate i, ii and iii + if c == v: + assert c in punctuation + phone = [c] + tone = '0' + word2ph.append(1) + else: + v_without_tone = v[:-1] + tone = v[-1] + + pinyin = c+v_without_tone + assert tone in '12345' + + if c: + # 多音节 + v_rep_map = { + "uei": 'ui', + 'iou': 'iu', + 'uen': 'un', + } + if v_without_tone in v_rep_map.keys(): + pinyin = c+v_rep_map[v_without_tone] + else: + # 单音节 + pinyin_rep_map = { + 'ing': 'ying', + 'i': 'yi', + 'in': 'yin', + 'u': 'wu', + } + if pinyin in pinyin_rep_map.keys(): + pinyin = pinyin_rep_map[pinyin] + else: + single_rep_map = { + 'v': 'yu', + 'e': 'e', + 'i': 'y', + 'u': 'w', + } + if pinyin[0] in single_rep_map.keys(): + pinyin = single_rep_map[pinyin[0]]+pinyin[1:] + + assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin) + phone = pinyin_to_symbol_map[pinyin].split(' ') + word2ph.append(len(phone)) + + phones_list += phone + tones_list += [int(tone)] * len(phone) + return phones_list, tones_list, word2ph + + + +def text_normalize(text): + numbers = re.findall(r'\d+(?:\.?\d+)?', text) + for number in numbers: + text = text.replace(number, cn2an.an2cn(number), 1) + text = replace_punctuation(text) + return text + +def get_bert_feature(text, word2ph): + from text import chinese_bert + return chinese_bert.get_bert_feature(text, word2ph) + +if __name__ == '__main__': + from text.chinese_bert import get_bert_feature + text = "啊！但是《原神》是由,米哈\游自主， [研发]的一款全.新开放世界.冒险游戏" + text = text_normalize(text) + print(text) + phones, tones, word2ph = g2p(text) + bert = get_bert_feature(text, word2ph) + + print(phones, tones, word2ph, bert.shape) + + +# # 示例用法 +# text = "这是一个示例文本：,你好！这是一个测试...." +# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试 diff --git a/BV2/text/chinese_bert.py b/BV2/text/chinese_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..f1f9875340544f24f237194f2bbb4846e8a8806f --- /dev/null +++ b/BV2/text/chinese_bert.py @@ -0,0 +1,50 @@ +import torch +from transformers import AutoTokenizer, AutoModelForMaskedLM + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext-large") +model = AutoModelForMaskedLM.from_pretrained("hfl/chinese-roberta-wwm-ext-large").to(device) + +def get_bert_feature(text, word2ph): + with torch.no_grad(): + inputs = tokenizer(text, return_tensors='pt') + for i in inputs: + inputs[i] = inputs[i].to(device) + res = model(**inputs, output_hidden_states=True) + res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu() + + assert len(word2ph) == len(text)+2 + word2phone = word2ph + phone_level_feature = [] + for i in range(len(word2phone)): + repeat_feature = res[i].repeat(word2phone[i], 1) + phone_level_feature.append(repeat_feature) + + phone_level_feature = torch.cat(phone_level_feature, dim=0) + + + return phone_level_feature.T + +if __name__ == '__main__': + # feature = get_bert_feature('你好,我是说的道理。') + import torch + + word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征 + word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1] + + # 计算总帧数 + total_frames = sum(word2phone) + print(word_level_feature.shape) + print(word2phone) + phone_level_feature = [] + for i in range(len(word2phone)): + print(word_level_feature[i].shape) + + # 对每个词重复word2phone[i]次 + repeat_feature = word_level_feature[i].repeat(word2phone[i], 1) + phone_level_feature.append(repeat_feature) + + phone_level_feature = torch.cat(phone_level_feature, dim=0) + print(phone_level_feature.shape) # torch.Size([36, 1024]) + diff --git a/BV2/text/cleaner.py b/BV2/text/cleaner.py new file mode 100644 index 0000000000000000000000000000000000000000..f54cca58ab0075504d062579b33b09163d4a0df5 --- /dev/null +++ b/BV2/text/cleaner.py @@ -0,0 +1,25 @@ +from . import chinese, cleaned_text_to_sequence + +language_module_map = { + 'ZH': chinese +} + +def clean_text(text, language): + language_module = language_module_map[language] + norm_text = language_module.text_normalize(text) + phones, tones, word2ph = language_module.g2p(norm_text) + return norm_text, phones, tones, word2ph + +def clean_text_bert(text, language): + language_module = language_module_map[language] + norm_text = language_module.text_normalize(text) + phones, tones, word2ph = language_module.g2p(norm_text) + bert = language_module.get_bert_feature(norm_text, word2ph) + return phones, tones, bert + +def text_to_sequence(text, language): + norm_text, phones, tones, word2ph = clean_text(text, language) + return cleaned_text_to_sequence(phones, tones, language) + +if __name__ == '__main__': + pass diff --git a/text/cmudict.rep b/BV2/text/cmudict.rep similarity index 100% rename from text/cmudict.rep rename to BV2/text/cmudict.rep diff --git a/BV2/text/english.py b/BV2/text/english.py new file mode 100644 index 0000000000000000000000000000000000000000..781d0a56cef71f66fc67db51d76538be90d3ddd2 --- /dev/null +++ b/BV2/text/english.py @@ -0,0 +1,138 @@ +import pickle +import os +import re +from g2p_en import G2p +from string import punctuation + +from text import symbols + +current_file_path = os.path.dirname(__file__) +CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep') +CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle') +_g2p = G2p() + +arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'} + + +def post_replace_ph(ph): + rep_map = { + '：': ',', + '；': ',', + '，': ',', + '。': '.', + '！': '!', + '？': '?', + '\n': '.', + "·": ",", + '、': ",", + '...': '…', + 'v': "V" + } + if ph in rep_map.keys(): + ph = rep_map[ph] + if ph in symbols: + return ph + if ph not in symbols: + ph = 'UNK' + return ph + +def read_dict(): + g2p_dict = {} + start_line = 49 + with open(CMU_DICT_PATH) as f: + line = f.readline() + line_index = 1 + while line: + if line_index >= start_line: + line = line.strip() + word_split = line.split(' ') + word = word_split[0] + + syllable_split = word_split[1].split(' - ') + g2p_dict[word] = [] + for syllable in syllable_split: + phone_split = syllable.split(' ') + g2p_dict[word].append(phone_split) + + line_index = line_index + 1 + line = f.readline() + + return g2p_dict + + +def cache_dict(g2p_dict, file_path): + with open(file_path, 'wb') as pickle_file: + pickle.dump(g2p_dict, pickle_file) + + +def get_dict(): + if os.path.exists(CACHE_PATH): + with open(CACHE_PATH, 'rb') as pickle_file: + g2p_dict = pickle.load(pickle_file) + else: + g2p_dict = read_dict() + cache_dict(g2p_dict, CACHE_PATH) + + return g2p_dict + +eng_dict = get_dict() + +def refine_ph(phn): + tone = 0 + if re.search(r'\d$', phn): + tone = int(phn[-1]) + 1 + phn = phn[:-1] + return phn.lower(), tone + +def refine_syllables(syllables): + tones = [] + phonemes = [] + for phn_list in syllables: + for i in range(len(phn_list)): + phn = phn_list[i] + phn, tone = refine_ph(phn) + phonemes.append(phn) + tones.append(tone) + return phonemes, tones + + +def text_normalize(text): + # todo: eng text normalize + return text + +def g2p(text): + + phones = [] + tones = [] + words = re.split(r"([,;.\-\?\!\s+])", text) + for w in words: + if w.upper() in eng_dict: + phns, tns = refine_syllables(eng_dict[w.upper()]) + phones += phns + tones += tns + else: + phone_list = list(filter(lambda p: p != " ", _g2p(w))) + for ph in phone_list: + if ph in arpa: + ph, tn = refine_ph(ph) + phones.append(ph) + tones.append(tn) + else: + phones.append(ph) + tones.append(0) + # todo: implement word2ph + word2ph = [1 for i in phones] + + phones = [post_replace_ph(i) for i in phones] + return phones, tones, word2ph + +if __name__ == "__main__": + # print(get_dict()) + # print(eng_word_to_phoneme("hello")) + print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")) + # all_phones = set() + # for k, syllables in eng_dict.items(): + # for group in syllables: + # for ph in group: + # all_phones.add(ph) + # print(all_phones) \ No newline at end of file diff --git a/BV2/text/english_bert_mock.py b/BV2/text/english_bert_mock.py new file mode 100644 index 0000000000000000000000000000000000000000..3b894ced5b6d619a18d6bdd7d7606ba9e6532050 --- /dev/null +++ b/BV2/text/english_bert_mock.py @@ -0,0 +1,5 @@ +import torch + + +def get_bert_feature(norm_text, word2ph): + return torch.zeros(1024, sum(word2ph)) diff --git a/BV2/text/japanese.py b/BV2/text/japanese.py new file mode 100644 index 0000000000000000000000000000000000000000..ddedafa0c5b7986068dc6c91637a86febc3923a9 --- /dev/null +++ b/BV2/text/japanese.py @@ -0,0 +1,104 @@ +# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py +import re +import sys + +import pyopenjtalk + +from text import symbols + +# Regular expression matching Japanese without punctuation marks: +_japanese_characters = re.compile( + r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]') + +# Regular expression matching non-Japanese characters or punctuation marks: +_japanese_marks = re.compile( + r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]') + +# List of (symbol, Japanese) pairs for marks: +_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [ + ('％', 'パーセント') +]] + + +# List of (consonant, sokuon) pairs: +_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [ + (r'Q([↑↓]*[kg])', r'k#\1'), + (r'Q([↑↓]*[tdjʧ])', r't#\1'), + (r'Q([↑↓]*[sʃ])', r's\1'), + (r'Q([↑↓]*[pb])', r'p#\1') +]] + +# List of (consonant, hatsuon) pairs: +_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [ + (r'N([↑↓]*[pbm])', r'm\1'), + (r'N([↑↓]*[ʧʥj])', r'n^\1'), + (r'N([↑↓]*[tdn])', r'n\1'), + (r'N([↑↓]*[kg])', r'ŋ\1') +]] + + + +def post_replace_ph(ph): + rep_map = { + '：': ',', + '；': ',', + '，': ',', + '。': '.', + '！': '!', + '？': '?', + '\n': '.', + "·": ",", + '、': ",", + '...': '…', + 'v': "V" + } + if ph in rep_map.keys(): + ph = rep_map[ph] + if ph in symbols: + return ph + if ph not in symbols: + ph = 'UNK' + return ph + +def symbols_to_japanese(text): + for regex, replacement in _symbols_to_japanese: + text = re.sub(regex, replacement, text) + return text + + +def preprocess_jap(text): + '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html''' + text = symbols_to_japanese(text) + sentences = re.split(_japanese_marks, text) + marks = re.findall(_japanese_marks, text) + text = [] + for i, sentence in enumerate(sentences): + if re.match(_japanese_characters, sentence): + p = pyopenjtalk.g2p(sentence) + text += p.split(" ") + + if i < len(marks): + text += [marks[i].replace(' ', '')] + return text + +def text_normalize(text): + # todo: jap text normalize + return text + +def g2p(norm_text): + phones = preprocess_jap(norm_text) + phones = [post_replace_ph(i) for i in phones] + # todo: implement tones and word2ph + tones = [0 for i in phones] + word2ph = [1 for i in phones] + return phones, tones, word2ph + + +if __name__ == '__main__': + for line in open("../../../Downloads/transcript_utf8.txt").readlines(): + text = line.split(":")[1] + phones, tones, word2ph = g2p(text) + for p in phones: + if p == "z": + print(text, phones) + sys.exit(0) diff --git a/text/opencpop-strict.txt b/BV2/text/opencpop-strict.txt similarity index 100% rename from text/opencpop-strict.txt rename to BV2/text/opencpop-strict.txt diff --git a/BV2/text/symbols.py b/BV2/text/symbols.py new file mode 100644 index 0000000000000000000000000000000000000000..9dfae4e633829f20c4fd767b1c7a9198911ed801 --- /dev/null +++ b/BV2/text/symbols.py @@ -0,0 +1,51 @@ +punctuation = ['!', '?', '…', ",", ".", "'", '-'] +pu_symbols = punctuation + ["SP", "UNK"] +pad = '_' + +# chinese +zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h', + 'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o', + 'ong', + 'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn', + 'w', 'x', 'y', 'z', 'zh', + "AA", "EE", "OO"] +num_zh_tones = 6 + +# japanese +ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky', + 'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z'] +num_ja_tones = 1 + +# English +en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy', + 'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's', + 'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh'] +num_en_tones = 4 + +# combine all symbols +normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols)) +symbols = [pad] + normal_symbols + pu_symbols +sil_phonemes_ids = [symbols.index(i) for i in pu_symbols] + +# combine all tones +num_tones = num_zh_tones + num_ja_tones + num_en_tones + +# language maps +language_id_map = { + 'ZH': 0, + "JA": 1, + "EN": 2 +} +num_languages = len(language_id_map.keys()) + +language_tone_start_map = { + 'ZH': 0, + "JA": num_zh_tones, + "EN": num_zh_tones + num_ja_tones +} + +if __name__ == '__main__': + a = set(zh_symbols) + b = set(en_symbols) + print(sorted(a&b)) + diff --git a/BV2/text/tone_sandhi.py b/BV2/text/tone_sandhi.py new file mode 100644 index 0000000000000000000000000000000000000000..0f45b7a72c5d858bcaab19ac85cfa686bf9a74da --- /dev/null +++ b/BV2/text/tone_sandhi.py @@ -0,0 +1,351 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import List +from typing import Tuple + +import jieba +from pypinyin import lazy_pinyin +from pypinyin import Style + + +class ToneSandhi(): + def __init__(self): + self.must_neural_tone_words = { + '麻烦', '麻利', '鸳鸯', '高粱', '骨头', '骆驼', '马虎', '首饰', '馒头', '馄饨', '风筝', + '难为', '队伍', '阔气', '闺女', '门道', '锄头', '铺盖', '铃铛', '铁匠', '钥匙', '里脊', + '里头', '部分', '那么', '道士', '造化', '迷糊', '连累', '这么', '这个', '运气', '过去', + '软和', '转悠', '踏实', '跳蚤', '跟头', '趔趄', '财主', '豆腐', '讲究', '记性', '记号', + '认识', '规矩', '见识', '裁缝', '补丁', '衣裳', '衣服', '衙门', '街坊', '行李', '行当', + '蛤蟆', '蘑菇', '薄荷', '葫芦', '葡萄', '萝卜', '荸荠', '苗条', '苗头', '苍蝇', '芝麻', + '舒服', '舒坦', '舌头', '自在', '膏药', '脾气', '脑袋', '脊梁', '能耐', '胳膊', '胭脂', + '胡萝', '胡琴', '胡同', '聪明', '耽误', '耽搁', '耷拉', '耳朵', '老爷', '老实', '老婆', + '老头', '老太', '翻腾', '罗嗦', '罐头', '编辑', '结实', '红火', '累赘', '糨糊', '糊涂', + '精神', '粮食', '簸箕', '篱笆', '算计', '算盘', '答应', '笤帚', '笑语', '笑话', '窟窿', + '窝囊', '窗户', '稳当', '稀罕', '称呼', '秧歌', '秀气', '秀才', '福气', '祖宗', '砚台', + '码头', '石榴', '石头', '石匠', '知识', '眼睛', '眯缝', '眨巴', '眉毛', '相声', '盘算', + '白净', '痢疾', '痛快', '疟疾', '疙瘩', '疏忽', '畜生', '生意', '甘蔗', '琵琶', '琢磨', + '琉璃', '玻璃', '玫瑰', '玄乎', '狐狸', '状元', '特务', '牲口', '牙碜', '牌楼', '爽快', + '爱人', '热闹', '烧饼', '烟筒', '烂糊', '点心', '炊帚', '灯笼', '火候', '漂亮', '滑溜', + '溜达', '温和', '清楚', '消息', '浪头', '活泼', '比方', '正经', '欺负', '模糊', '槟榔', + '棺材', '棒槌', '棉花', '核桃', '栅栏', '柴火', '架势', '枕头', '枇杷', '机灵', '本事', + '木头', '木匠', '朋友', '月饼', '月亮', '暖和', '明白', '时候', '新鲜', '故事', '收拾', + '收成', '提防', '挖苦', '挑剔', '指甲', '指头', '拾掇', '拳头', '拨弄', '招牌', '招呼', + '抬举', '护士', '折腾', '扫帚', '打量', '打算', '打点', '打扮', '打听', '打发', '扎实', + '扁担', '戒指', '懒得', '意识', '意思', '情形', '悟性', '怪物', '思量', '怎么', '念头', + '念叨', '快活', '忙活', '志气', '心思', '得罪', '张罗', '弟兄', '开通', '应酬', '庄稼', + '干事', '帮手', '帐篷', '希罕', '师父', '师傅', '巴结', '巴掌', '差事', '工夫', '岁数', + '屁股', '尾巴', '少爷', '小气', '小伙', '将就', '对头', '对付', '寡妇', '家伙', '客气', + '实在', '官司', '学问', '学生', '字号', '嫁妆', '媳妇', '媒人', '婆家', '娘家', '委屈', + '姑娘', '姐夫', '妯娌', '妥当', '妖精', '奴才', '女婿', '头发', '太阳', '大爷', '大方', + '大意', '大夫', '多少', '多么', '外甥', '壮实', '地道', '地方', '在乎', '困难', '嘴巴', + '嘱咐', '嘟囔', '嘀咕', '喜欢', '喇嘛', '喇叭', '商量', '唾沫', '哑巴', '哈欠', '哆嗦', + '咳嗽', '和尚', '告诉', '告示', '含糊', '吓唬', '后头', '名字', '名堂', '合同', '吆喝', + '叫唤', '口袋', '厚道', '厉害', '千斤', '包袱', '包涵', '匀称', '勤快', '动静', '动弹', + '功夫', '力气', '前头', '刺猬', '刺激', '别扭', '利落', '利索', '利害', '分析', '出息', + '凑合', '凉快', '冷战', '冤枉', '冒失', '养活', '关系', '先生', '兄弟', '便宜', '使唤', + '佩服', '作坊', '体面', '位置', '似的', '伙计', '休息', '什么', '人家', '亲戚', '亲家', + '交情', '云彩', '事情', '买卖', '主意', '丫头', '丧气', '两口', '东西', '东家', '世故', + '不由', '不在', '下水', '下巴', '上头', '上司', '丈夫', '丈人', '一辈', '那个', '菩萨', + '父亲', '母亲', '咕噜', '邋遢', '费用', '冤家', '甜头', '介绍', '荒唐', '大人', '泥鳅', + '幸福', '熟悉', '计划', '扑腾', '蜡烛', '姥爷', '照顾', '喉咙', '吉他', '弄堂', '蚂蚱', + '凤凰', '拖沓', '寒碜', '糟蹋', '倒腾', '报复', '逻辑', '盘缠', '喽啰', '牢骚', '咖喱', + '扫把', '惦记' + } + self.must_not_neural_tone_words = { + "男子", "女子", "分子", "原子", "量子", "莲子", "石子", "瓜子", "电子", "人人", "虎虎" + } + self.punc = "：，；。？！“”‘’':,;.?!" + + # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041 + # e.g. + # word: "家里" + # pos: "s" + # finals: ['ia1', 'i3'] + def _neural_sandhi(self, word: str, pos: str, + finals: List[str]) -> List[str]: + + # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺 + for j, item in enumerate(word): + if j - 1 >= 0 and item == word[j - 1] and pos[0] in { + "n", "v", "a" + } and word not in self.must_not_neural_tone_words: + finals[j] = finals[j][:-1] + "5" + ge_idx = word.find("个") + if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶": + finals[-1] = finals[-1][:-1] + "5" + elif len(word) >= 1 and word[-1] in "的地得": + finals[-1] = finals[-1][:-1] + "5" + # e.g. 走了, 看着, 去过 + # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}: + # finals[-1] = finals[-1][:-1] + "5" + elif len(word) > 1 and word[-1] in "们子" and pos in { + "r", "n" + } and word not in self.must_not_neural_tone_words: + finals[-1] = finals[-1][:-1] + "5" + # e.g. 桌上, 地下, 家里 + elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}: + finals[-1] = finals[-1][:-1] + "5" + # e.g. 上来, 下去 + elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开": + finals[-1] = finals[-1][:-1] + "5" + # 个做量词 + elif (ge_idx >= 1 and + (word[ge_idx - 1].isnumeric() or + word[ge_idx - 1] in "几有两半多各整每做是")) or word == '个': + finals[ge_idx] = finals[ge_idx][:-1] + "5" + else: + if word in self.must_neural_tone_words or word[ + -2:] in self.must_neural_tone_words: + finals[-1] = finals[-1][:-1] + "5" + + word_list = self._split_word(word) + finals_list = [finals[:len(word_list[0])], finals[len(word_list[0]):]] + for i, word in enumerate(word_list): + # conventional neural in Chinese + if word in self.must_neural_tone_words or word[ + -2:] in self.must_neural_tone_words: + finals_list[i][-1] = finals_list[i][-1][:-1] + "5" + finals = sum(finals_list, []) + return finals + + def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]: + # e.g. 看不懂 + if len(word) == 3 and word[1] == "不": + finals[1] = finals[1][:-1] + "5" + else: + for i, char in enumerate(word): + # "不" before tone4 should be bu2, e.g. 不怕 + if char == "不" and i + 1 < len(word) and finals[i + + 1][-1] == "4": + finals[i] = finals[i][:-1] + "2" + return finals + + def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]: + # "一" in number sequences, e.g. 一零零, 二一零 + if word.find("一") != -1 and all( + [item.isnumeric() for item in word if item != "一"]): + return finals + # "一" between reduplication words shold be yi5, e.g. 看一看 + elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]: + finals[1] = finals[1][:-1] + "5" + # when "一" is ordinal word, it should be yi1 + elif word.startswith("第一"): + finals[1] = finals[1][:-1] + "1" + else: + for i, char in enumerate(word): + if char == "一" and i + 1 < len(word): + # "一" before tone4 should be yi2, e.g. 一段 + if finals[i + 1][-1] == "4": + finals[i] = finals[i][:-1] + "2" + # "一" before non-tone4 should be yi4, e.g. 一天 + else: + # "一" 后面如果是标点，还读一声 + if word[i + 1] not in self.punc: + finals[i] = finals[i][:-1] + "4" + return finals + + def _split_word(self, word: str) -> List[str]: + word_list = jieba.cut_for_search(word) + word_list = sorted(word_list, key=lambda i: len(i), reverse=False) + first_subword = word_list[0] + first_begin_idx = word.find(first_subword) + if first_begin_idx == 0: + second_subword = word[len(first_subword):] + new_word_list = [first_subword, second_subword] + else: + second_subword = word[:-len(first_subword)] + new_word_list = [second_subword, first_subword] + return new_word_list + + def _three_sandhi(self, word: str, finals: List[str]) -> List[str]: + if len(word) == 2 and self._all_tone_three(finals): + finals[0] = finals[0][:-1] + "2" + elif len(word) == 3: + word_list = self._split_word(word) + if self._all_tone_three(finals): + # disyllabic + monosyllabic, e.g. 蒙古/包 + if len(word_list[0]) == 2: + finals[0] = finals[0][:-1] + "2" + finals[1] = finals[1][:-1] + "2" + # monosyllabic + disyllabic, e.g. 纸/老虎 + elif len(word_list[0]) == 1: + finals[1] = finals[1][:-1] + "2" + else: + finals_list = [ + finals[:len(word_list[0])], finals[len(word_list[0]):] + ] + if len(finals_list) == 2: + for i, sub in enumerate(finals_list): + # e.g. 所有/人 + if self._all_tone_three(sub) and len(sub) == 2: + finals_list[i][0] = finals_list[i][0][:-1] + "2" + # e.g. 好/喜欢 + elif i == 1 and not self._all_tone_three(sub) and finals_list[i][0][-1] == "3" and \ + finals_list[0][-1][-1] == "3": + + finals_list[0][-1] = finals_list[0][-1][:-1] + "2" + finals = sum(finals_list, []) + # split idiom into two words who's length is 2 + elif len(word) == 4: + finals_list = [finals[:2], finals[2:]] + finals = [] + for sub in finals_list: + if self._all_tone_three(sub): + sub[0] = sub[0][:-1] + "2" + finals += sub + + return finals + + def _all_tone_three(self, finals: List[str]) -> bool: + return all(x[-1] == "3" for x in finals) + + # merge "不" and the word behind it + # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error + def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + last_word = "" + for word, pos in seg: + if last_word == "不": + word = last_word + word + if word != "不": + new_seg.append((word, pos)) + last_word = word[:] + if last_word == "不": + new_seg.append((last_word, 'd')) + last_word = "" + return new_seg + + # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听" + # function 2: merge single "一" and the word behind it + # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error + # e.g. + # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')] + # output seg: [['听一听', 'v']] + def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + # function 1 + for i, (word, pos) in enumerate(seg): + if i - 1 >= 0 and word == "一" and i + 1 < len(seg) and seg[i - 1][ + 0] == seg[i + 1][0] and seg[i - 1][1] == "v": + new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0] + else: + if i - 2 >= 0 and seg[i - 1][0] == "一" and seg[i - 2][ + 0] == word and pos == "v": + continue + else: + new_seg.append([word, pos]) + seg = new_seg + new_seg = [] + # function 2 + for i, (word, pos) in enumerate(seg): + if new_seg and new_seg[-1][0] == "一": + new_seg[-1][0] = new_seg[-1][0] + word + else: + new_seg.append([word, pos]) + return new_seg + + # the first and the second words are all_tone_three + def _merge_continuous_three_tones( + self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + sub_finals_list = [ + lazy_pinyin( + word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) + for (word, pos) in seg + ] + assert len(sub_finals_list) == len(seg) + merge_last = [False] * len(seg) + for i, (word, pos) in enumerate(seg): + if i - 1 >= 0 and self._all_tone_three( + sub_finals_list[i - 1]) and self._all_tone_three( + sub_finals_list[i]) and not merge_last[i - 1]: + # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi + if not self._is_reduplication(seg[i - 1][0]) and len( + seg[i - 1][0]) + len(seg[i][0]) <= 3: + new_seg[-1][0] = new_seg[-1][0] + seg[i][0] + merge_last[i] = True + else: + new_seg.append([word, pos]) + else: + new_seg.append([word, pos]) + + return new_seg + + def _is_reduplication(self, word: str) -> bool: + return len(word) == 2 and word[0] == word[1] + + # the last char of first word and the first char of second word is tone_three + def _merge_continuous_three_tones_2( + self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + sub_finals_list = [ + lazy_pinyin( + word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) + for (word, pos) in seg + ] + assert len(sub_finals_list) == len(seg) + merge_last = [False] * len(seg) + for i, (word, pos) in enumerate(seg): + if i - 1 >= 0 and sub_finals_list[i - 1][-1][-1] == "3" and sub_finals_list[i][0][-1] == "3" and not \ + merge_last[i - 1]: + # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi + if not self._is_reduplication(seg[i - 1][0]) and len( + seg[i - 1][0]) + len(seg[i][0]) <= 3: + new_seg[-1][0] = new_seg[-1][0] + seg[i][0] + merge_last[i] = True + else: + new_seg.append([word, pos]) + else: + new_seg.append([word, pos]) + return new_seg + + def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + for i, (word, pos) in enumerate(seg): + if i - 1 >= 0 and word == "儿" and seg[i-1][0] != "#": + new_seg[-1][0] = new_seg[-1][0] + seg[i][0] + else: + new_seg.append([word, pos]) + return new_seg + + def _merge_reduplication( + self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + for i, (word, pos) in enumerate(seg): + if new_seg and word == new_seg[-1][0]: + new_seg[-1][0] = new_seg[-1][0] + seg[i][0] + else: + new_seg.append([word, pos]) + return new_seg + + def pre_merge_for_modify( + self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + seg = self._merge_bu(seg) + try: + seg = self._merge_yi(seg) + except: + print("_merge_yi failed") + seg = self._merge_reduplication(seg) + seg = self._merge_continuous_three_tones(seg) + seg = self._merge_continuous_three_tones_2(seg) + seg = self._merge_er(seg) + return seg + + def modified_tone(self, word: str, pos: str, + finals: List[str]) -> List[str]: + finals = self._bu_sandhi(word, finals) + finals = self._yi_sandhi(word, finals) + finals = self._neural_sandhi(word, pos, finals) + finals = self._three_sandhi(word, finals) + return finals diff --git a/BV2/transforms.py b/BV2/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..4793d67ca5a5630e0ffe0f9fb29445c949e64dae --- /dev/null +++ b/BV2/transforms.py @@ -0,0 +1,193 @@ +import torch +from torch.nn import functional as F + +import numpy as np + + +DEFAULT_MIN_BIN_WIDTH = 1e-3 +DEFAULT_MIN_BIN_HEIGHT = 1e-3 +DEFAULT_MIN_DERIVATIVE = 1e-3 + + +def piecewise_rational_quadratic_transform(inputs, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=False, + tails=None, + tail_bound=1., + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + min_derivative=DEFAULT_MIN_DERIVATIVE): + + if tails is None: + spline_fn = rational_quadratic_spline + spline_kwargs = {} + else: + spline_fn = unconstrained_rational_quadratic_spline + spline_kwargs = { + 'tails': tails, + 'tail_bound': tail_bound + } + + outputs, logabsdet = spline_fn( + inputs=inputs, + unnormalized_widths=unnormalized_widths, + unnormalized_heights=unnormalized_heights, + unnormalized_derivatives=unnormalized_derivatives, + inverse=inverse, + min_bin_width=min_bin_width, + min_bin_height=min_bin_height, + min_derivative=min_derivative, + **spline_kwargs + ) + return outputs, logabsdet + + +def searchsorted(bin_locations, inputs, eps=1e-6): + bin_locations[..., -1] += eps + return torch.sum( + inputs[..., None] >= bin_locations, + dim=-1 + ) - 1 + + +def unconstrained_rational_quadratic_spline(inputs, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=False, + tails='linear', + tail_bound=1., + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + min_derivative=DEFAULT_MIN_DERIVATIVE): + inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) + outside_interval_mask = ~inside_interval_mask + + outputs = torch.zeros_like(inputs) + logabsdet = torch.zeros_like(inputs) + + if tails == 'linear': + unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) + constant = np.log(np.exp(1 - min_derivative) - 1) + unnormalized_derivatives[..., 0] = constant + unnormalized_derivatives[..., -1] = constant + + outputs[outside_interval_mask] = inputs[outside_interval_mask] + logabsdet[outside_interval_mask] = 0 + else: + raise RuntimeError('{} tails are not implemented.'.format(tails)) + + outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline( + inputs=inputs[inside_interval_mask], + unnormalized_widths=unnormalized_widths[inside_interval_mask, :], + unnormalized_heights=unnormalized_heights[inside_interval_mask, :], + unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], + inverse=inverse, + left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound, + min_bin_width=min_bin_width, + min_bin_height=min_bin_height, + min_derivative=min_derivative + ) + + return outputs, logabsdet + +def rational_quadratic_spline(inputs, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=False, + left=0., right=1., bottom=0., top=1., + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + min_derivative=DEFAULT_MIN_DERIVATIVE): + if torch.min(inputs) < left or torch.max(inputs) > right: + raise ValueError('Input to a transform is not within its domain') + + num_bins = unnormalized_widths.shape[-1] + + if min_bin_width * num_bins > 1.0: + raise ValueError('Minimal bin width too large for the number of bins') + if min_bin_height * num_bins > 1.0: + raise ValueError('Minimal bin height too large for the number of bins') + + widths = F.softmax(unnormalized_widths, dim=-1) + widths = min_bin_width + (1 - min_bin_width * num_bins) * widths + cumwidths = torch.cumsum(widths, dim=-1) + cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0) + cumwidths = (right - left) * cumwidths + left + cumwidths[..., 0] = left + cumwidths[..., -1] = right + widths = cumwidths[..., 1:] - cumwidths[..., :-1] + + derivatives = min_derivative + F.softplus(unnormalized_derivatives) + + heights = F.softmax(unnormalized_heights, dim=-1) + heights = min_bin_height + (1 - min_bin_height * num_bins) * heights + cumheights = torch.cumsum(heights, dim=-1) + cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0) + cumheights = (top - bottom) * cumheights + bottom + cumheights[..., 0] = bottom + cumheights[..., -1] = top + heights = cumheights[..., 1:] - cumheights[..., :-1] + + if inverse: + bin_idx = searchsorted(cumheights, inputs)[..., None] + else: + bin_idx = searchsorted(cumwidths, inputs)[..., None] + + input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] + input_bin_widths = widths.gather(-1, bin_idx)[..., 0] + + input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] + delta = heights / widths + input_delta = delta.gather(-1, bin_idx)[..., 0] + + input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] + input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] + + input_heights = heights.gather(-1, bin_idx)[..., 0] + + if inverse: + a = (((inputs - input_cumheights) * (input_derivatives + + input_derivatives_plus_one + - 2 * input_delta) + + input_heights * (input_delta - input_derivatives))) + b = (input_heights * input_derivatives + - (inputs - input_cumheights) * (input_derivatives + + input_derivatives_plus_one + - 2 * input_delta)) + c = - input_delta * (inputs - input_cumheights) + + discriminant = b.pow(2) - 4 * a * c + assert (discriminant >= 0).all() + + root = (2 * c) / (-b - torch.sqrt(discriminant)) + outputs = root * input_bin_widths + input_cumwidths + + theta_one_minus_theta = root * (1 - root) + denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) + * theta_one_minus_theta) + derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2) + + 2 * input_delta * theta_one_minus_theta + + input_derivatives * (1 - root).pow(2)) + logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) + + return outputs, -logabsdet + else: + theta = (inputs - input_cumwidths) / input_bin_widths + theta_one_minus_theta = theta * (1 - theta) + + numerator = input_heights * (input_delta * theta.pow(2) + + input_derivatives * theta_one_minus_theta) + denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) + * theta_one_minus_theta) + outputs = input_cumheights + numerator / denominator + + derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2) + + 2 * input_delta * theta_one_minus_theta + + input_derivatives * (1 - theta).pow(2)) + logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) + + return outputs, logabsdet diff --git a/utils.py b/BV2/utils.py similarity index 52% rename from utils.py rename to BV2/utils.py index 0ce03b33490a71e254b24fe25e3acfa438743b90..c6aa6cfc64c33e2eed33e9845239e831fc1c4a1a 100644 --- a/utils.py +++ b/BV2/utils.py @@ -5,16 +5,9 @@ import argparse import logging import json import subprocess -import traceback - -import librosa import numpy as np from scipy.io.wavfile import read import torch -import logging - -logging.getLogger("numba").setLevel(logging.ERROR) -logging.getLogger("matplotlib").setLevel(logging.ERROR) MATPLOTLIB_FLAG = False @@ -24,83 +17,63 @@ logger = logging def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False): assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location="cpu") - iteration = checkpoint_dict["iteration"] - learning_rate = checkpoint_dict["learning_rate"] - if ( - optimizer is not None - and not skip_optimizer - and checkpoint_dict["optimizer"] is not None - ): - optimizer.load_state_dict(checkpoint_dict["optimizer"]) - saved_state_dict = checkpoint_dict["model"] - if hasattr(model, "module"): + checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') + iteration = checkpoint_dict['iteration'] + learning_rate = checkpoint_dict['learning_rate'] + if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None: + optimizer.load_state_dict(checkpoint_dict['optimizer']) + elif optimizer is None and not skip_optimizer: + #else: #Disable this line if Infer ,and enable the line upper + new_opt_dict = optimizer.state_dict() + new_opt_dict_params = new_opt_dict['param_groups'][0]['params'] + new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups'] + new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params + optimizer.load_state_dict(new_opt_dict) + saved_state_dict = checkpoint_dict['model'] + if hasattr(model, 'module'): state_dict = model.module.state_dict() else: state_dict = model.state_dict() new_state_dict = {} for k, v in state_dict.items(): try: - # assert "quantizer" not in k + #assert "emb_g" not in k # print("load", k) new_state_dict[k] = saved_state_dict[k] - assert saved_state_dict[k].shape == v.shape, ( - saved_state_dict[k].shape, - v.shape, - ) + assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape) except: - traceback.print_exc() - print( - "error, %s is not in the checkpoint" % k - ) # shape不对也会，比如text_embedding当cleaner修改时 + print("error, %s is not in the checkpoint" % k) new_state_dict[k] = v - if hasattr(model, "module"): - model.module.load_state_dict(new_state_dict) + if hasattr(model, 'module'): + model.module.load_state_dict(new_state_dict, strict=False) else: - model.load_state_dict(new_state_dict) + model.load_state_dict(new_state_dict, strict=False) print("load ") - logger.info( - "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration) - ) + logger.info("Loaded checkpoint '{}' (iteration {})".format( + checkpoint_path, iteration)) return model, optimizer, learning_rate, iteration def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): - logger.info( - "Saving model and optimizer state at iteration {} to {}".format( - iteration, checkpoint_path - ) - ) - if hasattr(model, "module"): + logger.info("Saving model and optimizer state at iteration {} to {}".format( + iteration, checkpoint_path)) + if hasattr(model, 'module'): state_dict = model.module.state_dict() else: state_dict = model.state_dict() - torch.save( - { - "model": state_dict, - "iteration": iteration, - "optimizer": optimizer.state_dict(), - "learning_rate": learning_rate, - }, - checkpoint_path, - ) - - -def summarize( - writer, - global_step, - scalars={}, - histograms={}, - images={}, - audios={}, - audio_sampling_rate=22050, -): + torch.save({'model': state_dict, + 'iteration': iteration, + 'optimizer': optimizer.state_dict(), + 'learning_rate': learning_rate}, checkpoint_path) + + +def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050): for k, v in scalars.items(): writer.add_scalar(k, v, global_step) for k, v in histograms.items(): writer.add_histogram(k, v, global_step) for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats="HWC") + writer.add_image(k, v, global_step, dataformats='HWC') for k, v in audios.items(): writer.add_audio(k, v, global_step, audio_sampling_rate) @@ -117,23 +90,23 @@ def plot_spectrogram_to_numpy(spectrogram): global MATPLOTLIB_FLAG if not MATPLOTLIB_FLAG: import matplotlib - matplotlib.use("Agg") MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger("matplotlib") + mpl_logger = logging.getLogger('matplotlib') mpl_logger.setLevel(logging.WARNING) import matplotlib.pylab as plt import numpy as np fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + im = ax.imshow(spectrogram, aspect="auto", origin="lower", + interpolation='none') plt.colorbar(im, ax=ax) plt.xlabel("Frames") plt.ylabel("Channels") plt.tight_layout() fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) plt.close() return data @@ -143,125 +116,96 @@ def plot_alignment_to_numpy(alignment, info=None): global MATPLOTLIB_FLAG if not MATPLOTLIB_FLAG: import matplotlib - matplotlib.use("Agg") MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger("matplotlib") + mpl_logger = logging.getLogger('matplotlib') mpl_logger.setLevel(logging.WARNING) import matplotlib.pylab as plt import numpy as np fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow( - alignment.transpose(), aspect="auto", origin="lower", interpolation="none" - ) + im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', + interpolation='none') fig.colorbar(im, ax=ax) - xlabel = "Decoder timestep" + xlabel = 'Decoder timestep' if info is not None: - xlabel += "\n\n" + info + xlabel += '\n\n' + info plt.xlabel(xlabel) - plt.ylabel("Encoder timestep") + plt.ylabel('Encoder timestep') plt.tight_layout() fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) plt.close() return data def load_wav_to_torch(full_path): - data, sampling_rate = librosa.load(full_path, sr=None) - return torch.FloatTensor(data), sampling_rate + sampling_rate, data = read(full_path) + return torch.FloatTensor(data.astype(np.float32)), sampling_rate def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding="utf-8") as f: + with open(filename, encoding='utf-8') as f: filepaths_and_text = [line.strip().split(split) for line in f] return filepaths_and_text -def get_hparams(init=True, stage=1): +def get_hparams(init=True): parser = argparse.ArgumentParser() - parser.add_argument( - "-c", - "--config", - type=str, - default="./configs/s2.json", - help="JSON file for configuration", - ) - parser.add_argument( - "-p", "--pretrain", type=str, required=False, default=None, help="pretrain dir" - ) - parser.add_argument( - "-rs", - "--resume_step", - type=int, - required=False, - default=None, - help="resume step", - ) - # parser.add_argument('-e', '--exp_dir', type=str, required=False,default=None,help='experiment directory') - # parser.add_argument('-g', '--pretrained_s2G', type=str, required=False,default=None,help='pretrained sovits gererator weights') - # parser.add_argument('-d', '--pretrained_s2D', type=str, required=False,default=None,help='pretrained sovits discriminator weights') + parser.add_argument('-c', '--config', type=str, default="./configs/base.json", + help='JSON file for configuration') + parser.add_argument('-m', '--model', type=str, default="./OUTPUT_MODEL", + help='Model name') + parser.add_argument('--cont', dest='cont', action="store_true", default=False, help="whether to continue training on the latest checkpoint") args = parser.parse_args() + model_dir = os.path.join("./logs", args.model) + + if not os.path.exists(model_dir): + os.makedirs(model_dir) config_path = args.config - with open(config_path, "r") as f: - data = f.read() + config_save_path = os.path.join(model_dir, "config.json") + if init: + with open(config_path, "r") as f: + data = f.read() + with open(config_save_path, "w") as f: + f.write(data) + else: + with open(config_save_path, "r") as f: + data = f.read() config = json.loads(data) hparams = HParams(**config) - hparams.pretrain = args.pretrain - hparams.resume_step = args.resume_step - # hparams.data.exp_dir = args.exp_dir - if stage == 1: - model_dir = hparams.s1_ckpt_dir - else: - model_dir = hparams.s2_ckpt_dir - config_save_path = os.path.join(model_dir, "config.json") - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - with open(config_save_path, "w") as f: - f.write(data) + hparams.model_dir = model_dir + hparams.cont = args.cont return hparams -def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True): +def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True): """Freeing up space by deleting saved ckpts - Arguments: - path_to_models -- Path to the model directory - n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth - sort_by_time -- True -> chronologically delete ckpts - False -> lexicographically delete ckpts - """ + Arguments: + path_to_models -- Path to the model directory + n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth + sort_by_time -- True -> chronologically delete ckpts + False -> lexicographically delete ckpts + """ import re - - ckpts_files = [ - f - for f in os.listdir(path_to_models) - if os.path.isfile(os.path.join(path_to_models, f)) - ] - name_key = lambda _f: int(re.compile("._(\d+)\.pth").match(_f).group(1)) - time_key = lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)) + ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))] + name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1))) + time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))) sort_key = time_key if sort_by_time else name_key - x_sorted = lambda _x: sorted( - [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")], - key=sort_key, - ) - to_del = [ - os.path.join(path_to_models, fn) - for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep]) - ] + x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], + key=sort_key) + to_del = [os.path.join(path_to_models, fn) for fn in + (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])] del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}") del_routine = lambda x: [os.remove(x), del_info(x)] rs = [del_routine(fn) for fn in to_del] - def get_hparams_from_dir(model_dir): config_save_path = os.path.join(model_dir, "config.json") with open(config_save_path, "r") as f: @@ -285,11 +229,9 @@ def get_hparams_from_file(config_path): def check_git_hash(model_dir): source_dir = os.path.dirname(os.path.realpath(__file__)) if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn( - "{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - ) - ) + logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( + source_dir + )) return cur_hash = subprocess.getoutput("git rev-parse HEAD") @@ -298,11 +240,8 @@ def check_git_hash(model_dir): if os.path.exists(path): saved_hash = open(path).read() if saved_hash != cur_hash: - logger.warn( - "git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8] - ) - ) + logger.warn("git hash values are different. {}(saved) != {}(current)".format( + saved_hash[:8], cur_hash[:8])) else: open(path, "w").write(cur_hash) @@ -322,7 +261,7 @@ def get_logger(model_dir, filename="train.log"): return logger -class HParams: +class HParams(): def __init__(self, **kwargs): for k, v in kwargs.items(): if type(v) == dict: @@ -352,11 +291,3 @@ class HParams: def __repr__(self): return self.__dict__.__repr__() - - -if __name__ == "__main__": - print( - load_wav_to_torch( - "/home/fish/wenetspeech/dataset_vq/Y0000022499_wHFSeHEx9CM/S00261.flac" - ) - ) diff --git a/MODELS/21/1.wav b/MODELS/21/1.wav deleted file mode 100644 index 55237fe41e2ed8ab220be95eebb7eb0dfce9370f..0000000000000000000000000000000000000000 --- a/MODELS/21/1.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:60f90a8009bef17813c1902f9bff9724c669880827ab78e8b4ed354c7468a8e3 -size 335918 diff --git a/MODELS/21/11.wav 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configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/app.py b/app.py index c9937a4ee7e706dbe92bf8089ada7ae4262ddd51..e953347675d853c42c57cb5f66c2f950d3963fef 100644 --- a/app.py +++ b/app.py @@ -1,749 +1,162 @@ -import os,re,sys -import LangSegment -import gradio as gr -import librosa,pdb -import numpy as np +import sys, os +sys.path.append('BV2') import torch -from transformers import AutoModelForMaskedLM, AutoTokenizer -from feature_extractor import cnhubert -from time import time as ttime +import argparse +import BV2.commons +import BV2.utils +from BV2.models import Synthesizer +from BV2.text.symbols import symbols +from BV2.text import cleaned_text_to_sequence, get_bert +from BV2.text.cleaner import clean_text +import gradio as gr +import soundfile as sf from datetime import datetime -from AR.models.t2s_lightning_module import Text2SemanticLightningModule -from module.mel_processing import spectrogram_torch -from module.models import SynthesizerTrn -from my_utils import load_audio -from text import cleaned_text_to_sequence -from text.cleaner import clean_text import pytz -import soundfile as sf -from transformers import pipeline -from transformers.pipelines.audio_utils import ffmpeg_read - -if "_CUDA_VISIBLE_DEVICES" in os.environ: - os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] -tz = pytz.timezone('Asia/Singapore') -device = "cuda" if torch.cuda.is_available() else "cpu" - - - -def abs_path(dir): - global_dir = os.path.dirname(os.path.abspath(sys.argv[0])) - return(os.path.join(global_dir, dir)) -gpt_path = abs_path("MODELS/33/33.ckpt") -sovits_path=abs_path("MODELS/33/33.pth") - -cnhubert_base_path = os.environ.get( - "cnhubert_base_path", "pretrained_models/chinese-hubert-base" -) -bert_path = os.environ.get( - "bert_path", "pretrained_models/chinese-roberta-wwm-ext-large" -) - -MODEL_NAME = "openai/whisper-medium" -pipe = pipeline( - task="automatic-speech-recognition", - model=MODEL_NAME, - chunk_length_s=30, - device=device, -) - -from timeit import default_timer as timer -#cnhubert_base_path = "TencentGameMate/chinese-hubert-base" -#bert_path = "hfl/chinese-roberta-wwm-ext-large" -cnhubert.cnhubert_base_path = cnhubert_base_path -is_half = eval( - os.environ.get("is_half", "True" if torch.cuda.is_available() else "False") -) -device = "cuda" if torch.cuda.is_available() else "cpu" - - -tokenizer = AutoTokenizer.from_pretrained(bert_path) -bert_model = AutoModelForMaskedLM.from_pretrained(bert_path) -if is_half == True: - bert_model = bert_model.half().to(device) -else: - bert_model = bert_model.to(device) - - -def get_bert_feature(text, word2ph): - with torch.no_grad(): - inputs = tokenizer(text, return_tensors="pt") - for i in inputs: - inputs[i] = inputs[i].to(device) - res = bert_model(**inputs, output_hidden_states=True) - res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] - assert len(word2ph) == len(text) - phone_level_feature = [] - for i in range(len(word2ph)): - repeat_feature = res[i].repeat(word2ph[i], 1) - phone_level_feature.append(repeat_feature) - phone_level_feature = torch.cat(phone_level_feature, dim=0) - return phone_level_feature.T - - -class DictToAttrRecursive(dict): - def __init__(self, input_dict): - super().__init__(input_dict) - for key, value in input_dict.items(): - if isinstance(value, dict): - value = DictToAttrRecursive(value) - self[key] = value - setattr(self, key, value) - - def __getattr__(self, item): - try: - return self[item] - except KeyError: - raise AttributeError(f"Attribute {item} not found") - - def __setattr__(self, key, value): - if isinstance(value, dict): - value = DictToAttrRecursive(value) - super(DictToAttrRecursive, self).__setitem__(key, value) - super().__setattr__(key, value) - - def __delattr__(self, item): - try: - del self[item] - except KeyError: - raise AttributeError(f"Attribute {item} not found") - - -ssl_model = cnhubert.get_model() -if is_half == True: - ssl_model = ssl_model.half().to(device) -else: - ssl_model = ssl_model.to(device) - -def change_sovits_weights(sovits_path): - global vq_model, hps - dict_s2 = torch.load(sovits_path, map_location="cpu") - hps = dict_s2["config"] - hps = DictToAttrRecursive(hps) - hps.model.semantic_frame_rate = "25hz" - vq_model = SynthesizerTrn( - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model - ) - if ("pretrained" not in sovits_path): - del vq_model.enc_q - if is_half == True: - vq_model = vq_model.half().to(device) - else: - vq_model = vq_model.to(device) - vq_model.eval() - print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) - with open("./sweight.txt", "w", encoding="utf-8") as f: - f.write(sovits_path) - - -change_sovits_weights(sovits_path) - - -def change_gpt_weights(gpt_path): - global hz, max_sec, t2s_model, config - hz = 50 - dict_s1 = torch.load(gpt_path, map_location="cpu") - config = dict_s1["config"] - max_sec = config["data"]["max_sec"] - t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) - t2s_model.load_state_dict(dict_s1["weight"]) - if is_half == True: - t2s_model = t2s_model.half() - t2s_model = t2s_model.to(device) - t2s_model.eval() - total = sum([param.nelement() for param in t2s_model.parameters()]) - print("Number of parameter: %.2fM" % (total / 1e6)) - with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path) - - -change_gpt_weights(gpt_path) - - -def get_spepc(hps, filename): - audio = load_audio(filename, int(hps.data.sampling_rate)) - audio = torch.FloatTensor(audio) - audio_norm = audio - audio_norm = audio_norm.unsqueeze(0) - spec = spectrogram_torch( - audio_norm, - hps.data.filter_length, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - center=False, - ) - return spec - - -dict_language = { - ("中文1"): "all_zh",#全部按中文识别 - ("English"): "en",#全部按英文识别#######不变 - ("日文1"): "all_ja",#全部按日文识别 - ("中文"): "zh",#按中英混合识别####不变 - ("日本語"): "ja",#按日英混合识别####不变 - ("混合"): "auto",#多语种启动切分识别语种 +tz = pytz.timezone('Asia/Shanghai') +net_g = None +models = { + "Mellowdear": "./BV2/MODELS/adorabledarling.pth", + "MistyNikki": "./BV2/MODELS/nikki9400.pth", + "Silverleg": "./BV2/MODELS/J8900.pth", + "Xelo": "./BV2/MODELS/HER_1100.pth", + "Rrabbitt": "./BV2/MODELS/rabbit4900.pth", + "VVV": "./BV2/MODELS/v3.pth", + "AlluWin": "./BV2/MODELS/AW.pth", + "Hypnosia": "./BV2/MODELS/hypno.pth", + "PremJ": "./BV2/MODELS/premj.pth", + "Umemura": "./BV2/MODELS/take2.pth", + "ArasakaAI": "./BV2/MODELS/Arasaka.pth", + "Terra": "./BV2/MODELS/TERRA.pth", } +def get_text(text, language_str, hps): + norm_text, phone, tone, word2ph = clean_text(text, language_str) + phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) -def splite_en_inf(sentence, language): - pattern = re.compile(r'[a-zA-Z ]+') - textlist = [] - langlist = [] - pos = 0 - for match in pattern.finditer(sentence): - start, end = match.span() - if start > pos: - textlist.append(sentence[pos:start]) - langlist.append(language) - textlist.append(sentence[start:end]) - langlist.append("en") - pos = end - if pos < len(sentence): - textlist.append(sentence[pos:]) - langlist.append(language) - # Merge punctuation into previous word - for i in range(len(textlist)-1, 0, -1): - if re.match(r'^[\W_]+$', textlist[i]): - textlist[i-1] += textlist[i] - del textlist[i] - del langlist[i] - # Merge consecutive words with the same language tag - i = 0 - while i < len(langlist) - 1: - if langlist[i] == langlist[i+1]: - textlist[i] += textlist[i+1] - del textlist[i+1] - del langlist[i+1] - else: - i += 1 - - return textlist, langlist - - -def clean_text_inf(text, language): - formattext = "" - language = language.replace("all_","") - for tmp in LangSegment.getTexts(text): - if tmp["lang"] == language: - formattext += tmp["text"] + " " - while " " in formattext: - formattext = formattext.replace(" ", " ") - phones, word2ph, norm_text = clean_text(formattext, language) - phones = cleaned_text_to_sequence(phones) - return phones, word2ph, norm_text - -dtype=torch.float16 if is_half == True else torch.float32 -def get_bert_inf(phones, word2ph, norm_text, language): - language=language.replace("all_","") - if language == "zh": - bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype) - else: - bert = torch.zeros( - (1024, len(phones)), - dtype=torch.float16 if is_half == True else torch.float32, - ).to(device) - - return bert - - -def nonen_clean_text_inf(text, language): - if(language!="auto"): - textlist, langlist = splite_en_inf(text, language) - else: - textlist=[] - langlist=[] - for tmp in LangSegment.getTexts(text): - langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - print(textlist) - print(langlist) - phones_list = [] - word2ph_list = [] - norm_text_list = [] - for i in range(len(textlist)): - lang = langlist[i] - phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) - phones_list.append(phones) - if lang == "zh": - word2ph_list.append(word2ph) - norm_text_list.append(norm_text) - print(word2ph_list) - phones = sum(phones_list, []) - word2ph = sum(word2ph_list, []) - norm_text = ' '.join(norm_text_list) - - return phones, word2ph, norm_text - - -def nonen_get_bert_inf(text, language): - if(language!="auto"): - textlist, langlist = splite_en_inf(text, language) - else: - textlist=[] - langlist=[] - for tmp in LangSegment.getTexts(text): - langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - print(textlist) - print(langlist) - bert_list = [] - for i in range(len(textlist)): - lang = langlist[i] - phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) - bert = get_bert_inf(phones, word2ph, norm_text, lang) - bert_list.append(bert) - bert = torch.cat(bert_list, dim=1) - - return bert - + if hps.data.add_blank: + phone = BV2.commons.intersperse(phone, 0) + tone = BV2.commons.intersperse(tone, 0) + language = BV2.commons.intersperse(language, 0) + for i in range(len(word2ph)): + word2ph[i] = word2ph[i] * 2 + word2ph[0] += 1 + bert = get_bert(norm_text, word2ph, language_str) + del word2ph -splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } + assert bert.shape[-1] == len(phone) + phone = torch.LongTensor(phone) + tone = torch.LongTensor(tone) + language = torch.LongTensor(language) -def get_first(text): - pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" - text = re.split(pattern, text)[0].strip() - return text + return bert, phone, tone, language - -def get_cleaned_text_final(text,language): - if language in {"en","all_zh","all_ja"}: - phones, word2ph, norm_text = clean_text_inf(text, language) - elif language in {"zh", "ja","auto"}: - phones, word2ph, norm_text = nonen_clean_text_inf(text, language) - return phones, word2ph, norm_text - -def get_bert_final(phones, word2ph, text,language,device): - if language == "en": - bert = get_bert_inf(phones, word2ph, text, language) - elif language in {"zh", "ja","auto"}: - bert = nonen_get_bert_inf(text, language) - elif language == "all_zh": - bert = get_bert_feature(text, word2ph).to(device) - else: - bert = torch.zeros((1024, len(phones))).to(device) - return bert - -def merge_short_text_in_array(texts, threshold): - if (len(texts)) < 2: - return texts - result = [] - text = "" - for ele in texts: - text += ele - if len(text) >= threshold: - result.append(text) - text = "" - if (len(text) > 0): - if len(result) == 0: - result.append(text) - else: - result[len(result) - 1] += text - return result - -def tprint(text): - now=datetime.now(tz).strftime('%H:%M:%S') - print(f'UTC+8 - {now} - ✅{text}') - -def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=("Do not split"),playback_speed=1.0, volume_scale=1.0): - t0 = ttime() - startTime=timer() - change_sovits_weights(sovits_path) - tprint(f'LOADED SoVITS Model: {sovits_path}') - change_gpt_weights(gpt_path) - tprint(f'LOADED GPT Model: {gpt_path}') - - prompt_language = dict_language[prompt_language] - text_language = dict_language[text_language] - prompt_text = prompt_text.strip("\n") - if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." - text = text.strip("\n") - if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text - print(("实际输入的参考文本:"), prompt_text) - print(("实际输入的目标文本:"), text) - zero_wav = np.zeros( - int(hps.data.sampling_rate * 0.3), - dtype=np.float16 if is_half == True else np.float32, - ) +def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, model_dir): + global net_g + bert, phones, tones, lang_ids = get_text(text, "ZH", HPS) with torch.no_grad(): - wav16k, sr = librosa.load(ref_wav_path, sr=16000) - if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): - raise OSError(("参考音频在3~10秒范围外，请更换！")) - wav16k = torch.from_numpy(wav16k) - zero_wav_torch = torch.from_numpy(zero_wav) - if is_half == True: - wav16k = wav16k.half().to(device) - zero_wav_torch = zero_wav_torch.half().to(device) - else: - wav16k = wav16k.to(device) - zero_wav_torch = zero_wav_torch.to(device) - wav16k = torch.cat([wav16k, zero_wav_torch]) - ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ - "last_hidden_state" - ].transpose( - 1, 2 - ) # .float() - codes = vq_model.extract_latent(ssl_content) - prompt_semantic = codes[0, 0] - t1 = ttime() - - phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language) - - if (how_to_cut == ("Split into groups of 4 sentences")): - text = cut1(text) - elif (how_to_cut == ("Split every 50 characters")): - text = cut2(text) - elif (how_to_cut == ("Split at CN/JP periods (。)")): - text = cut3(text) - elif (how_to_cut == ("Split at English periods (.)")): - text = cut4(text) - elif (how_to_cut == ("Split at punctuation marks")): - text = cut5(text) - while "\n\n" in text: - text = text.replace("\n\n", "\n") - print(("实际输入的目标文本(切句后):"), text) - texts = text.split("\n") - texts = merge_short_text_in_array(texts, 5) - audio_opt = [] - bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype) - - for text in texts: - if (len(text.strip()) == 0): - continue - if (text[-1] not in splits): text += "。" if text_language != "en" else "." - print(("实际输入的目标文本(每句):"), text) - phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language) - bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype) - bert = torch.cat([bert1, bert2], 1) - - all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) - prompt = prompt_semantic.unsqueeze(0).to(device) - t2 = ttime() - with torch.no_grad(): - # pred_semantic = t2s_model.model.infer( - pred_semantic, idx = t2s_model.model.infer_panel( - all_phoneme_ids, - all_phoneme_len, - prompt, - bert, - # prompt_phone_len=ph_offset, - top_k=config["inference"]["top_k"], - early_stop_num=hz * max_sec, - ) - t3 = ttime() - # print(pred_semantic.shape,idx) - pred_semantic = pred_semantic[:, -idx:].unsqueeze( - 0 - ) # .unsqueeze(0)#mq要多unsqueeze一次 - refer = get_spepc(hps, ref_wav_path) # .to(device) - if is_half == True: - refer = refer.half().to(device) - else: - refer = refer.to(device) - # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0] - audio = ( - vq_model.decode( - pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer - ) - .detach() - .cpu() - .numpy()[0, 0] - ) - max_audio=np.abs(audio).max() - if max_audio>1:audio/=max_audio - audio_opt.append(audio) - audio_opt.append(zero_wav) - t4 = ttime() - print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) - #yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16) - audio_data = (np.concatenate(audio_opt, 0) * 32768).astype(np.int16) - if playback_speed != 1.0: - audio_data_float = audio_data.astype(np.float32) / 32768 - audio_data_stretched = librosa.effects.time_stretch(audio_data_float, rate=playback_speed) - audio_data = (audio_data_stretched * 32768).astype(np.int16) - audio_data = (audio_data.astype(np.float32) * volume_scale).astype(np.int16) - output_wav = "output_audio.wav" - sf.write(output_wav, audio_data, hps.data.sampling_rate) - endTime=timer() - tprint(f'TTS COMPLETE,{round(endTime-startTime,4)}s') - return output_wav - -def split(todo_text): - todo_text = todo_text.replace("……", "。").replace("——", "，") - if todo_text[-1] not in splits: - todo_text += "。" - i_split_head = i_split_tail = 0 - len_text = len(todo_text) - todo_texts = [] - while 1: - if i_split_head >= len_text: - break - if todo_text[i_split_head] in splits: - i_split_head += 1 - todo_texts.append(todo_text[i_split_tail:i_split_head]) - i_split_tail = i_split_head - else: - i_split_head += 1 - return todo_texts - - -def cut1(inp): - inp = inp.strip("\n") - inps = split(inp) - split_idx = list(range(0, len(inps), 4)) - split_idx[-1] = None - if len(split_idx) > 1: - opts = [] - for idx in range(len(split_idx) - 1): - opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) - else: - opts = [inp] - return "\n".join(opts) - - -def cut2(inp): - inp = inp.strip("\n") - inps = split(inp) - if len(inps) < 2: - return inp - opts = [] - summ = 0 - tmp_str = "" - for i in range(len(inps)): - summ += len(inps[i]) - tmp_str += inps[i] - if summ > 50: - summ = 0 - opts.append(tmp_str) - tmp_str = "" - if tmp_str != "": - opts.append(tmp_str) - # print(opts) - if len(opts) > 1 and len(opts[-1]) < 50: - opts[-2] = opts[-2] + opts[-1] - opts = opts[:-1] - return "\n".join(opts) - - -def cut3(inp): - inp = inp.strip("\n") - return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) - - -def cut4(inp): - inp = inp.strip("\n") - return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) - - -# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py -def cut5(inp): - # if not re.search(r'[^\w\s]', inp[-1]): - # inp += '。' - inp = inp.strip("\n") - punds = r'[,.;?!、，。？！;：]' - items = re.split(f'({punds})', inp) - items = ["".join(group) for group in zip(items[::2], items[1::2])] - opt = "\n".join(items) - return opt - - -def custom_sort_key(s): - # 使用正则表达式提取字符串中的数字部分和非数字部分 - parts = re.split('(\d+)', s) - # 将数字部分转换为整数，非数字部分保持不变 - parts = [int(part) if part.isdigit() else part for part in parts] - return parts - -def update_model(choice="女神"): - global gpt_path, sovits_path - model_info = models[choice] - gpt_path = abs_path(model_info["gpt_weight"]) - sovits_path = abs_path(model_info["sovits_weight"]) - model_name = choice - tone_info = model_info["tones"]["tone1"] - tprint(f'SELECT MODEL：{choice}') - # 返回默认tone“tone1” - return ( - tone_info["example_voice_wav"], - tone_info["example_voice_wav_words"], - model_info["default_language"], - model_info["default_language"], - model_name, - "tone1" - ) - -def update_tone(model_choice, tone_choice): - model_info = models[model_choice] - tone_info = model_info["tones"][tone_choice] - example_voice_wav = abs_path(tone_info["example_voice_wav"]) - example_voice_wav_words = tone_info["example_voice_wav_words"] - return example_voice_wav, example_voice_wav_words - -def transcribe(voice): - time1=timer() - tprint('Start transcribe') - task="transcribe" - if voice is None: - print("No audio file submitted! Please upload or record an audio file before submitting your request.") - R = pipe(voice, batch_size=8, generate_kwargs={"task": task}, return_timestamps=True,return_language=True) - text=R['text'] - lang=R['chunks'][0]['language'] - if lang=='english': - language='English' - elif lang =='chinese': - language='中文' - elif lang=='japanese': - language = '日本語' - - time2=timer() - tprint(f'TRANSCRIBE COMPLETE,{round(time2-time1,4)}s') - print(f'language：{language}，words：{text}') - return text,language - -def clone_voice(user_voice,user_text,user_lang): - tprint('Start clone') - time1=timer() - global gpt_path, sovits_path - gpt_path = abs_path("pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") - #tprint(f'Model loaded:{gpt_path}') - sovits_path = abs_path("pretrained_models/s2G488k.pth") - #tprint(f'Model loaded:{sovits_path}') - prompt_text, prompt_language = transcribe(user_voice) - output_wav = get_tts_wav( - user_voice, - prompt_text, - prompt_language, - user_text, - user_lang, - how_to_cut="Do not split", - playback_speed=1.0, - volume_scale=1.0) - time2=timer() - tprint(f'CLONE COMPLETE,{round(time2-time1,4)}s') - return output_wav - - -from info import models -models_by_language = { - "English": [], - "中文": [], - "日本語": [] -} -for model_name, model_info in models.items(): - language = model_info["default_language"] - models_by_language[language].append((model_name, model_info)) - -##########GRADIO########### - -with gr.Blocks(theme='remilia/Ghostly') as app: - gr.HTML(''' -

A TTS GENERATOR

-

- This space is based on the innovative text-to-speech generation solution - GPT-SoVITS . - You can visit the repo's github homepage to learn training and inference.
- 本空间基于新式的文字转语音生成方案 GPT-SoVITS . - 你可以前往项目的github主页学习如何推理和训练。
- Due to using Hugging Face's free CPU for inference in this space, the speed of generating voice - is very slow. If you want to generate voice more quickly, please click the Colab icon below to go to Colab - and use this space, which will greatly improve the generation speed.
- 由于本空间使用huggingface的免费CPU进行推理，因此速度很慢，如果你想获得快速的推理， - 请点击下方的Colab图标，前往Colab使用本空间，会大大提升生成语音的速度 -

- aolab -

-

SELECT A MODEL/选择一个模型

-''') - - default_voice_wav, default_voice_wav_words, default_language, _, default_model_name, _ = update_model("Trump") - english_models = [name for name, _ in models_by_language["English"]] - chinese_models = [name for name, _ in models_by_language["中文"]] - japanese_models = [name for name, _ in models_by_language["日本語"]] - with gr.Row(): - english_choice = gr.Radio(english_models, label="EN|English Model",value="Trump") - chinese_choice = gr.Radio(chinese_models, label="CN|中文模型") - japanese_choice = gr.Radio(japanese_models, label="JP|日本語モデル") - - plsh='Text must match the selected language option to prevent errors, for example, if English is input but Chinese is selected for generation./文字一定要和语言选项匹配，不然要报错，比如输入的是英文，生成语言选中文' - with gr.Row(): - model_name = gr.Textbox(label="Seleted Model/已选模型", value=default_model_name, scale=1) - text = gr.Textbox(label="Input some text for voice generation/输入想要生成语音的文字", lines=5,scale=8, - placeholder=plsh) - - - with gr.Row(): - text_language = gr.Radio( - label="Select language for input text/输入的文字对应语言", - choices=["中文","English","日本語"], - value=default_language, - info='Input text and language must match.',scale=1, - ) - tone_select = gr.Radio( - label="Select Tone/选择语气", - choices=["tone1","tone2","tone3"], - value="tone1", - info='Tone influences the emotional expression ',scale=1) - - how_to_cut = gr.Dropdown( - label=("How to split?"), - choices=[("Do not split"), ("Split into groups of 4 sentences"), ("Split every 50 characters"), - ("Split at CN/JP periods (。)"), ("Split at English periods (.)"), ("Split at punctuation marks"), ], - value=("Split into groups of 4 sentences"), - interactive=True, - info='A suitable splitting method can achieve better generation results',scale=2 - ) - with gr.Accordion(label="Preview selected tone/预览语气", open=False,visible=False): - with gr.Row(visible=True): - inp_ref = gr.Audio(label="Reference audio", type="filepath", value=default_voice_wav, scale=3) - prompt_text = gr.Textbox(label="Reference text", value=default_voice_wav_words, scale=3) - prompt_language = gr.Dropdown(label="Language of the reference audio", choices=["中文", "English", "日本語"], value=default_language, scale=1,interactive=False) - - tone_select.change(update_tone, inputs=[model_name, tone_select], outputs=[inp_ref, prompt_text]) - - with gr.Accordion(label="Additional generation options/附加生成选项", open=False): - volume = gr.Slider(minimum=0.5, maximum=2, value=1, step=0.01, label='Volume') - speed = gr.Slider(minimum=0.5, maximum=1.5, value=1, step=0.05, label='Speed') + x_tst=phones.to(devicee).unsqueeze(0) + tones=tones.to(devicee).unsqueeze(0) + lang_ids=lang_ids.to(devicee).unsqueeze(0) + bert = bert.to(devicee).unsqueeze(0) + x_tst_lengths = torch.LongTensor([phones.size(0)]).to(devicee) + del phones + speakers = torch.LongTensor([HPS.data.spk2id[sid]]).to(devicee) + audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio + , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() + del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers + sf.write("tmp.wav", audio, 44100) + return audio + + + +def convert_wav_to_mp3(wav_file): + now = datetime.now(tz).strftime('%m%d%H%M%S') + os.makedirs('out', exist_ok=True) + output_path_mp3 = os.path.join('out', f"{now}.mp3") + + renamed_input_path = os.path.join('in', f"in.wav") + os.makedirs('in', exist_ok=True) + os.rename(wav_file.name, renamed_input_path) + command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libmp3lame", "-y", output_path_mp3] + os.system(" ".join(command)) + return output_path_mp3 +def tts_generator(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, model): + global net_g,speakers,tz + now = datetime.now(tz).strftime('%m-%d %H:%M:%S') + model_path = models[model] + net_g, _, _, _ = BV2.utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True) + print(f'✨{now}-开始生成：{text}') + try: + with torch.no_grad(): + audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker,model_dir=model) + with open('tmp.wav', 'rb') as wav_file: + mp3 = convert_wav_to_mp3(wav_file) + return "生成语音成功", (HPS.data.sampling_rate, audio), mp3 + except Exception as e: + return "生成语音失败：" + str(e), None, None + +current_dir = os.path.dirname(os.path.abspath(__file__)) +config_path = os.path.join(current_dir, "BV2/configs/config.json") + +if __name__ == "__main__": + HPS = BV2.utils.get_hparams_from_file(config_path) + devicee = "cuda:0" if torch.cuda.is_available() else "cpu" + + net_g = Synthesizer( + len(symbols), + HPS.data.filter_length // 2 + 1, + HPS.train.segment_size // HPS.data.hop_length, + n_speakers=HPS.data.n_speakers, + **HPS.model).to(devicee) + _ = net_g.eval() + + speaker_ids = HPS.data.spk2id + speaker = list(speaker_ids.keys())[0] + theme='remilia/Ghostly' - with gr.Row(): - main_button = gr.Button("✨Generate Voice", variant="primary", scale=1) - output = gr.Audio(label="💾Download it by clicking ⬇️", scale=3) - #info = gr.Textbox(label="INFO", visible=True, readonly=True, scale=1) - - gr.HTML('''

-

Clone custom Voice/克隆自定义声音

-

Need 3~10s audio.This involves voice-to-text conversion followed by text-to-voice conversion, so it takes longer time
- 需要3~10秒语音，这个会涉及语音转文字，之后再转语音，所以耗时比较久 + with gr.Blocks() as app: + with gr.Column(): + with gr.Column(): + gr.HTML('''

+

+ Use English to generate, please go to this SPACE +

+

+ 日本語で生成するために、こちらへ進んでください。

''') - with gr.Row(): - user_voice = gr.Audio(type="filepath", label="（3~10s）Upload or Record audio/上传或录制声音",scale=3) - user_text= gr.Textbox(label="Text for generation/输入想要生成语音的文字", lines=8,scale=5, - placeholder=plsh) - - with gr.Row(): - user_lang = gr.Dropdown(label="Language/生成语言", choices=["中文", "English", "日本語"],scale=1) - user_button = gr.Button("✨Clone Voice", variant="primary",scale=7) - user_output = gr.Audio(label="💾Output wave file,Download it by clicking ⬇️") - gr.HTML('''
visitor badge
''') - english_choice.change(update_model, inputs=[english_choice], outputs=[inp_ref, prompt_text, prompt_language, text_language, model_name, tone_select]) - chinese_choice.change(update_model, inputs=[chinese_choice], outputs=[inp_ref, prompt_text, prompt_language, text_language, model_name, tone_select]) - japanese_choice.change(update_model, inputs=[japanese_choice], outputs=[inp_ref, prompt_text, prompt_language, text_language, model_name, tone_select]) - - - main_button.click( - get_tts_wav, - inputs=[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut,speed,volume], - outputs=[output]) - - user_button.click( - clone_voice, - inputs=[user_voice,user_text,user_lang], - outputs=[user_output]) - -app.launch(share=True) \ No newline at end of file + gr.HTML(''' +
+

以下仅供测试用，质量参差Only read Chinese

+

+ 模型训练以及推理基于开源项目Bert-VITS2 + （具体使用的是9月份的版本，可能后续项目效果更好，请自行尝试训练）

+ ''') + text = gr.TextArea(label="输入需要生成语音的文字(标点也会影响语气)", placeholder="输入文字", + value="今天拿白金了吗", + info="使用huggingface的免费CPU进行推理，因此速度不快，一次性不要输入超过500汉字。字数越多，生成速度越慢，请耐心等待，只会说中文。", + ) + model = gr.Radio(choices=list(models.keys()), value=list(models.keys())[0], label='声音模型') + with gr.Accordion(label="展开设置生成参数", open=False): + sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='SDP/DP混合比',info='可控制一定程度的语调变化') + noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.5, step=0.01, label='感情变化') + noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.9, step=0.01, label='音节长度') + length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成语音总长度',info='数值越大，语速越慢') + btn = gr.Button("✨生成", variant="primary") + with gr.Column(): + audio_output = gr.Audio(label="试听") + MP3_output = gr.File(label="💾下载") + text_output = gr.Textbox(label="调试信息") + gr.Markdown(""" + + """) + btn.click( + tts_generator, + inputs=[text, sdp_ratio, noise_scale, noise_scale_w, length_scale, model], + outputs=[text_output, audio_output,MP3_output] + ) + gr.HTML('''
visitor badge
''') + app.launch(share=True) + \ No newline at end of file diff --git a/configs/s1.yaml b/configs/s1.yaml deleted file mode 100644 index f8ae17d4415cbbacca2d30e1403239ecc554e98b..0000000000000000000000000000000000000000 --- a/configs/s1.yaml +++ /dev/null @@ -1,31 +0,0 @@ -train: - seed: 1234 - epochs: 300 - batch_size: 8 - gradient_accumulation: 4 - save_every_n_epoch: 1 - precision: 16 - gradient_clip: 1.0 -optimizer: - lr: 0.01 - lr_init: 0.00001 - lr_end: 0.0001 - warmup_steps: 2000 - decay_steps: 40000 -data: - max_eval_sample: 8 - max_sec: 54 - num_workers: 1 - pad_val: 1024 # same with EOS in model -model: - vocab_size: 1025 - phoneme_vocab_size: 512 - embedding_dim: 512 - hidden_dim: 512 - head: 16 - linear_units: 2048 - n_layer: 12 - dropout: 0 - EOS: 1024 -inference: - top_k: 5 diff --git a/configs/s1big.yaml b/configs/s1big.yaml deleted file mode 100644 index a811150de4c418be57b3bb932a7fe12cc7b6f679..0000000000000000000000000000000000000000 --- a/configs/s1big.yaml +++ /dev/null @@ -1,31 +0,0 @@ -train: - seed: 1234 - epochs: 300 - batch_size: 8 - gradient_accumulation: 4 - save_every_n_epoch: 1 - precision: 16-mixed - gradient_clip: 1.0 -optimizer: - lr: 0.01 - lr_init: 0.00001 - lr_end: 0.0001 - warmup_steps: 2000 - decay_steps: 40000 -data: - max_eval_sample: 8 - max_sec: 54 - num_workers: 1 - pad_val: 1024 # same with EOS in model -model: - vocab_size: 1025 - phoneme_vocab_size: 512 - embedding_dim: 1024 - hidden_dim: 1024 - head: 16 - linear_units: 2048 - n_layer: 16 - dropout: 0 - EOS: 1024 -inference: - top_k: 5 diff --git a/configs/s1big2.yaml b/configs/s1big2.yaml deleted file mode 100644 index b8b889babbc43ada8c17cee52db68fd484cc0c97..0000000000000000000000000000000000000000 --- a/configs/s1big2.yaml +++ /dev/null @@ -1,31 +0,0 @@ -train: - seed: 1234 - epochs: 300 - batch_size: 12 - gradient_accumulation: 4 - save_every_n_epoch: 1 - precision: 16-mixed - gradient_clip: 1.0 -optimizer: - lr: 0.01 - lr_init: 0.00001 - lr_end: 0.0001 - warmup_steps: 2000 - decay_steps: 40000 -data: - max_eval_sample: 8 - max_sec: 54 - num_workers: 1 - pad_val: 1024 # same with EOS in model -model: - vocab_size: 1025 - phoneme_vocab_size: 512 - embedding_dim: 1024 - hidden_dim: 1024 - head: 16 - linear_units: 2048 - n_layer: 6 - dropout: 0 - EOS: 1024 -inference: - top_k: 5 diff --git a/configs/s1longer.yaml b/configs/s1longer.yaml deleted file mode 100644 index 3f57abd21a85a36107d247488e988797f0f8d484..0000000000000000000000000000000000000000 --- a/configs/s1longer.yaml +++ /dev/null @@ -1,31 +0,0 @@ -train: - seed: 1234 - epochs: 20 - batch_size: 8 - save_every_n_epoch: 1 - precision: 16-mixed - gradient_clip: 1.0 -optimizer: - lr: 0.01 - lr_init: 0.00001 - lr_end: 0.0001 - warmup_steps: 2000 - decay_steps: 40000 -data: - max_eval_sample: 8 - max_sec: 54 - num_workers: 4 - pad_val: 1024 # same with EOS in model -model: - vocab_size: 1025 - phoneme_vocab_size: 512 - embedding_dim: 512 - hidden_dim: 512 - head: 16 - linear_units: 2048 - n_layer: 24 - dropout: 0 - EOS: 1024 - random_bert: 0 -inference: - top_k: 5 diff --git a/configs/s1mq.yaml b/configs/s1mq.yaml deleted file mode 100644 index b554fd34f88faca8bc71eb3eaf6b1bdf301a326b..0000000000000000000000000000000000000000 --- a/configs/s1mq.yaml +++ /dev/null @@ -1,77 +0,0 @@ -train: - seed: 1234 - epochs: 100 - batch_size: 6 - gradient_accumulation: 4 - save_every_n_epoch: 1 - precision: 32 - gradient_clip: 1.0 -optimizer: - lr: 0.01 - lr_init: 0.00001 - lr_end: 0.0001 - warmup_steps: 2000 - decay_steps: 40000 -data: - max_eval_sample: 8 - max_sec: 40 - num_workers: 1 - pad_val: 1024 # same with EOS in model -model: - saving_path: "ckpt/" - resume_checkpoint: null - vocoder_config_path: "quantizer/new_ckpt/config.json" - vocoder_ckpt_path: "quantizer/new_ckpt/g_00600000" - datadir: "/home/liweiche/GigaSpeech/wavs" - metapath: "/home/liweiche/GigaSpeech/train2.json" - val_metapath: "/home/liweiche/GigaSpeech/dev2.json" - sampledir: "logs/" - pretrained_path: null - lr: 0.0001 - batch_size: 200.0 - train_bucket_size: 8192 - training_step: 800000 - optim_flat_percent: 0.0 - warmup_step: 50 - adam_beta1: 0.9 - adam_beta2: 0.98 - ffd_size: 3072 - hidden_size: 768 - enc_nlayers: 6 - dec_nlayers: 6 - nheads: 12 - ar_layer: 4 - ar_ffd_size: 1024 - ar_hidden_size: 256 - ar_nheads: 4 - aligner_softmax_temp: 1.0 - layer_norm_eps: 0.00001 - speaker_embed_dropout: 0.05 - label_smoothing: 0.0 - val_check_interval: 5000 - check_val_every_n_epoch: 1 - precision: "fp16" - nworkers: 16 - distributed: true - accelerator: "ddp" - version: null - accumulate_grad_batches: 1 - use_repetition_token: true - use_repetition_gating: false - repetition_penalty: 1.0 - sampling_temperature: 1.0 - top_k: -1 - min_top_k: 3 - top_p: 0.8 - sample_num: 4 - length_penalty_max_length: 15000 - length_penalty_max_prob: 0.95 - max_input_length: 2048 - max_output_length: 2000 - sample_rate: 16000 - n_codes: 1024 - n_cluster_groups: 1 - phone_context_window: 4 - phoneset_size: 1000 -inference: - top_k: 5 diff --git a/configs/train.yaml b/configs/train.yaml deleted file mode 100644 index be5333571980807b7d8f788165035c02cb0d176c..0000000000000000000000000000000000000000 --- a/configs/train.yaml +++ /dev/null @@ -1,32 +0,0 @@ -gpu: - n_card: 1 - n_process_per_card: 2 -io: - text_path: D:\RVC1006\GPT-SoVITS\GPT_SoVITS - save_every_n_epoch: 1 - precision: 16-mixed - gradient_clip: 1.0 -optimizer: - lr: 0.01 - lr_init: 0.00001 - lr_end: 0.0001 - warmup_steps: 2000 - decay_steps: 40000 -data: - max_eval_sample: 8 - max_sec: 54 - num_workers: 1 - pad_val: 1024 # same with EOS in model -model: - vocab_size: 1025 - phoneme_vocab_size: 512 - embedding_dim: 512 - hidden_dim: 512 - head: 16 - linear_units: 2048 - n_layer: 24 - dropout: 0 - EOS: 1024 - random_bert: 0 -inference: - top_k: 5 diff --git a/feature_extractor/__init__.py b/feature_extractor/__init__.py deleted file mode 100644 index 79aa9294fec8fd823afae7d1a18e3279533dd7cf..0000000000000000000000000000000000000000 --- a/feature_extractor/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from . import cnhubert, whisper_enc - -content_module_map = { - 'cnhubert': cnhubert, - 'whisper': whisper_enc -} \ No newline at end of file diff --git a/feature_extractor/cnhubert.py b/feature_extractor/cnhubert.py deleted file mode 100644 index 2e150e5c4a481ddd3137856fd92b3f432e0db75c..0000000000000000000000000000000000000000 --- a/feature_extractor/cnhubert.py +++ /dev/null @@ -1,104 +0,0 @@ -import time - -import librosa -import torch -import torch.nn.functional as F -import soundfile as sf -#import logging - -#logging.getLogger("numba").setLevel(logging.WARNING) - -from transformers import ( - Wav2Vec2FeatureExtractor, - HubertModel, -) - -import utils -import torch.nn as nn - -cnhubert_base_path = None - - -class CNHubert(nn.Module): - def __init__(self): - super().__init__() - self.model = HubertModel.from_pretrained(cnhubert_base_path) - self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( - cnhubert_base_path - ) - - def forward(self, x): - input_values = self.feature_extractor( - x, return_tensors="pt", sampling_rate=16000 - ).input_values.to(x.device) - feats = self.model(input_values)["last_hidden_state"] - return feats - - -# class CNHubertLarge(nn.Module): -# def __init__(self): -# super().__init__() -# self.model = HubertModel.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-hubert-large") -# self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-hubert-large") -# def forward(self, x): -# input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device) -# feats = self.model(input_values)["last_hidden_state"] -# return feats -# -# class CVec(nn.Module): -# def __init__(self): -# super().__init__() -# self.model = HubertModel.from_pretrained("/data/docker/liujing04/vc-webui-big/hubert_base") -# self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("/data/docker/liujing04/vc-webui-big/hubert_base") -# def forward(self, x): -# input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device) -# feats = self.model(input_values)["last_hidden_state"] -# return feats -# -# class cnw2v2base(nn.Module): -# def __init__(self): -# super().__init__() -# self.model = Wav2Vec2Model.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-wav2vec2-base") -# self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-wav2vec2-base") -# def forward(self, x): -# input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device) -# feats = self.model(input_values)["last_hidden_state"] -# return feats - - -def get_model(): - model = CNHubert() - model.eval() - return model - - -# def get_large_model(): -# model = CNHubertLarge() -# model.eval() -# return model -# -# def get_model_cvec(): -# model = CVec() -# model.eval() -# return model -# -# def get_model_cnw2v2base(): -# model = cnw2v2base() -# model.eval() -# return model - - -def get_content(hmodel, wav_16k_tensor): - with torch.no_grad(): - feats = hmodel(wav_16k_tensor) - return feats.transpose(1, 2) - - -if __name__ == "__main__": - model = get_model() - src_path = "/Users/Shared/原音频2.wav" - wav_16k_tensor = utils.load_wav_to_torch_and_resample(src_path, 16000) - model = model - wav_16k_tensor = wav_16k_tensor - feats = get_content(model, wav_16k_tensor) - print(feats.shape) diff --git a/feature_extractor/whisper_enc.py b/feature_extractor/whisper_enc.py deleted file mode 100644 index 983c3e4d8a96232d29f41847abfedb70c42ebb02..0000000000000000000000000000000000000000 --- a/feature_extractor/whisper_enc.py +++ /dev/null @@ -1,25 +0,0 @@ -import torch - - -def get_model(): - import whisper - - model = whisper.load_model("small", device="cpu") - - return model.encoder - - -def get_content(model=None, wav_16k_tensor=None): - from whisper import log_mel_spectrogram, pad_or_trim - - dev = next(model.parameters()).device - mel = log_mel_spectrogram(wav_16k_tensor).to(dev)[:, :3000] - # if torch.cuda.is_available(): - # mel = mel.to(torch.float16) - feature_len = mel.shape[-1] // 2 - assert mel.shape[-1] < 3000, "输入音频过长，只允许输入30以内音频" - with torch.no_grad(): - feature = model(pad_or_trim(mel, 3000).unsqueeze(0))[ - :1, :feature_len, : - ].transpose(1, 2) - return feature diff --git a/info.py b/info.py deleted file mode 100644 index 63715062ff54c8a04ace9893d745adda2b4d60d5..0000000000000000000000000000000000000000 --- a/info.py +++ /dev/null @@ -1,386 +0,0 @@ -models = { - "Trump": { - "gpt_weight": "MODELS/22/22.ckpt", - "sovits_weight": "MODELS/22/22.pth", - "default_language": "English", - "tones": { - "tone3": { - "example_voice_wav": "MODELS/22/speed.wav", - "example_voice_wav_words": "My father was a worker, and he loved to work. He'd work on Saturdays, he'd work on Sundays, he just liked it.", - }, - "tone2": { - "example_voice_wav": "MODELS/22/slow_calm.wav", - "example_voice_wav_words": "for ourselves, and for our allies.", - }, - "tone1": { - "example_voice_wav": "MODELS/22/passion.wav", - "example_voice_wav_words": "Because when you're doing something that you love, like I do, of course I love my business, but this is a little bit different. Who thought this was going to happen?", - }, - }, - }, - - "Vader": { - "gpt_weight": "MODELS/31/31.ckpt", - "sovits_weight": "MODELS/31/31.pth", - "default_language": "English", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/31/1.wav", - "example_voice_wav_words": "A dying frenzy. You purchase moments with blood.", - }, - "tone2": { - "example_voice_wav": "MODELS/31/148.wav", - "example_voice_wav_words": "Face me. Now you face true power.", - }, - "tone3": { - "example_voice_wav": "MODELS/31/96.wav", - "example_voice_wav_words": "Soon. Prepare to engage.", - }, - }, - }, - - "Boba": { - "gpt_weight": "MODELS/33/33.ckpt", - "sovits_weight": "MODELS/33/33.pth", - "default_language": "English", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/33/1.wav", - "example_voice_wav_words": "Primitive. Animal. Not until we're through.", - }, - "tone2": { - "example_voice_wav": "MODELS/33/56.wav", - "example_voice_wav_words": "There they are! Charging weapons. Weapons online.", - }, - "tone3": { - "example_voice_wav": "MODELS/33/103.wav", - "example_voice_wav_words": "You almost had me. Almost. Interesting tactic.", - }, - }, - }, - - "Sylph": { - "gpt_weight": "MODELS/B2/B2.ckpt", - "sovits_weight": "MODELS/B2/B2.pth", - "default_language": "English", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/B2/1.wav", - "example_voice_wav_words": "The first type of lashes we'll be using today is the Little Devil. It is a type of flare individual lash.", - }, - "tone2": { - "example_voice_wav": "MODELS/B2/16.wav", - "example_voice_wav_words": "Take out an eyelid tape using a tweezer or your hands. Place the front of the tape slightly above your natural eye fold.", - }, - "tone3": { - "example_voice_wav": "MODELS/B2/212.wav", - "example_voice_wav_words": "and own your style. Until we meet again next time!", - }, - }, - }, - - "Emperor": { - "gpt_weight": "MODELS/32/32.ckpt", - "sovits_weight": "MODELS/32/32.pth", - "default_language": "English", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/32/1.wav", - "example_voice_wav_words": "Senator Organa, it has been a long time.", - }, - "tone2": { - "example_voice_wav": "MODELS/32/4.wav", - "example_voice_wav_words": "I wish. So unfortunate.", - }, - "tone3": { - "example_voice_wav": "MODELS/32/14.wav", - "example_voice_wav_words": "Closer. Closer. Ah, the bounty hunter.", - }, - }, - }, - - "Blacksmith": { - "gpt_weight": "MODELS/D1/D1.ckpt", - "sovits_weight": "MODELS/D1/D1.pth", - "default_language": "English", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/D1/1.wav", - "example_voice_wav_words": "Show respect! This is a holy place!", - }, - "tone2": { - "example_voice_wav": "MODELS/D1/29.wav", - "example_voice_wav_words": "Yeah, sign of beast men, no doubt.", - }, - "tone3": { - "example_voice_wav": "MODELS/D1/96.wav", - "example_voice_wav_words": "I'm not an imbecile. I've learned when to speak.", - }, - }, - }, - - - "Pawn": { - "gpt_weight": "MODELS/21/21.ckpt", - "sovits_weight": "MODELS/21/21.pth", - "default_language": "English", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/21/1.wav", - "example_voice_wav_words": "There's an enemy wagon loaded with coin and weapons. We need to capture it.", - }, - "tone2": { - "example_voice_wav": "MODELS/21/11.wav", - "example_voice_wav_words": "I have the artifact you seek, but I think I'll be keeping it. So long.", - }, - "tone3": { - "example_voice_wav": "MODELS/21/191.wav", - "example_voice_wav_words": "I don't have time for this. I can get rough with you if I have to.", - }, - }, - }, - - "月": { - "gpt_weight": "MODELS/Y1/Y1.ckpt", - "sovits_weight": "MODELS/Y1/Y1.pth", - "default_language": "中文", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/Y1/query.wav", - "example_voice_wav_words": "但是子秋明明是神子啊？", - }, - "tone2": { - "example_voice_wav": "MODELS/Y1/25.wav", - "example_voice_wav_words": "我常常想,若将来能遇到一个人", - }, - "tone3": { - "example_voice_wav": "MODELS/Y1/39.wav", - "example_voice_wav_words": "才鼓起勇气说，结果你怎么这么容易就说出来了呀", - }, - }, - }, - - "TC_Nikki": { - "gpt_weight": "MODELS/N1/N1.ckpt", - "sovits_weight": "MODELS/N1/N1.pth", - "default_language": "中文", - "tones": { - "tone3": { - "example_voice_wav": "MODELS/N1/2_33.wav", - "example_voice_wav_words": "他怎么又更新了，有什么想法，直接告诉我就好了。", - }, - "tone2": { - "example_voice_wav": "MODELS/N1/3_10.wav", - "example_voice_wav_words": "不过还有很多没有完成的事呢。", - }, - "tone1": { - "example_voice_wav": "MODELS/N1/3_28.wav", - "example_voice_wav_words": "跟能够自己画设计图的铅笔，梦里不会感觉到痛。", - }, - }, - }, - - "云": { - "gpt_weight": "MODELS/K2/K2.ckpt", - "sovits_weight": "MODELS/K2/K2.pth", - "default_language": "中文", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/K2/1.wav", - "example_voice_wav_words": "小灌木很快就停止了生长，开始做好了开花的准备。", - }, - "tone2": { - "example_voice_wav": "MODELS/K2/0013064.wav", - "example_voice_wav_words": "诶呀，开玩笑的，外面这么冷，不要命啦", - }, - "tone3": { - "example_voice_wav": "MODELS/K2/0013073.wav", - "example_voice_wav_words": "啊，所以今天的食谱是，毛血旺和水煮肉片。", - }, - }, - }, - - "Xiu": { - "gpt_weight": "MODELS/Y2/Y2.ckpt", - "sovits_weight": "MODELS/Y2/Y2.pth", - "default_language": "中文", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/Y2/1.wav", - "example_voice_wav_words": "敖旭对共生之术了如指掌，我怕他为了对付我而向师姐下手。", - }, - "tone2": { - "example_voice_wav": "MODELS/Y2/22.wav", - "example_voice_wav_words": "相亲相爱，形影不离", - }, - "tone3": { - "example_voice_wav": "MODELS/Y2/44.wav", - "example_voice_wav_words": "所以你同我有一样想法，我很开心。", - }, - }, - }, - - - "白い悪魔": { - "gpt_weight": "MODELS/G1/G1.ckpt", - "sovits_weight": "MODELS/G1/G1.pth", - "default_language": "日本語", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/G1/18.wav",#记得重新传18 - "example_voice_wav_words": "モビルスーツならば格闘戦も!サーベルで!ザーッ!見える!", - }, - "tone2": { - "example_voice_wav": "MODELS/G1/30.wav", - "example_voice_wav_words": "敵を一掃した!こいつの戦闘力もなかなかだな!", - }, - "tone3": { - "example_voice_wav": "MODELS/G1/62.wav", - "example_voice_wav_words": "悪くないな、さすがはゼータガンダムを元にしただけのことはある", - }, - }, - }, - - "赤い彗星": { - "gpt_weight": "MODELS/G2/G2.ckpt", - "sovits_weight": "MODELS/G2/G2.pth", - "default_language": "日本語", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/G2/53.wav", - "example_voice_wav_words": "直接攻撃を行う次第である 地球連邦政府がスイートウォーターの試験を認めなければ", - }, - "tone2": { - "example_voice_wav": "MODELS/G2/62.wav", - "example_voice_wav_words": "何だと、こざかしい!こざかしい真似を!うあああああ", - }, - "tone3": { - "example_voice_wav": "MODELS/G2/73.wav", - "example_voice_wav_words": "撤退だ!この注意機から速やかに離脱するぞ!", - }, - }, - }, - - "うみ": { - "gpt_weight": "MODELS/B1/B1.ckpt", - "sovits_weight": "MODELS/B1/B1.pth", - "default_language": "日本語", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/B1/1.wav", - "example_voice_wav_words": "大変ありがたきこと。 提督様の指揮のおかげです。", - }, - "tone2": { - "example_voice_wav": "MODELS/B1/5.wav", - "example_voice_wav_words": "参ります。出撃。特型駆逐艦。", - }, - "tone3": { - "example_voice_wav": "MODELS/B1/24.wav", - "example_voice_wav_words": "肩と肝、話しません!", - }, - }, - }, - - "よろずや": { - "gpt_weight": "MODELS/L1/L1.ckpt", - "sovits_weight": "MODELS/L1/L1.pth", - "default_language": "日本語", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/L1/1.wav", - "example_voice_wav_words": "寺院を各種取り揃えております", - }, - "tone2": { - "example_voice_wav": "MODELS/L1/4.wav", - "example_voice_wav_words": "冒険で手に入れたトレジャーと アイテムの交換ができちゃいます!", - }, - "tone3": { - "example_voice_wav": "MODELS/L1/66.wav", - "example_voice_wav_words": "雪山の氷を仕入れる、氷魚!ぷぷぷぷ", - }, - }, - }, - - "エルレイ": { - "gpt_weight": "MODELS/F1/F1.ckpt", - "sovits_weight": "MODELS/F1/F1.pth", - "default_language": "日本語", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/F1/tiamat_6.wav", - "example_voice_wav_words": "に。喜んだな、人類。いいぞ。", - }, - "tone2": { - "example_voice_wav": "MODELS/F1/tiamat_14.wav", - "example_voice_wav_words": "あ、違った違った 違ってないか", - }, - "tone3": { - "example_voice_wav": "MODELS/F1/tiamat_57.wav", - "example_voice_wav_words": "あ゛あ゛あ゛", - }, - }, - }, - - "迷惑": { - "gpt_weight": "MODELS/R1/R1.ckpt", - "sovits_weight": "MODELS/R1/R1.pth", - "default_language": "日本語", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/R1/1.wav", - "example_voice_wav_words": "こんな便乗商法なんて、お客様を舐めてるとしか思えないわよ!", - }, - "tone2": { - "example_voice_wav": "MODELS/R1/3.wav", - "example_voice_wav_words": "どこがいいわけ まあでもどうしても欲しいって言うなら", - }, - "tone3": { - "example_voice_wav": "MODELS/R1/26.wav", - "example_voice_wav_words": "じゃ、じゃあしょうがないから、真心を込めて", - }, - }, - }, - - "DK": { - "gpt_weight": "MODELS/G3/G3.ckpt", - "sovits_weight": "MODELS/G3/G3.pth", - "default_language": "日本語", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/G3/1.wav", - "example_voice_wav_words": "不動の学年トップかぁ 僕はついに君を追い越せなかったなぁ", - }, - "tone2": { - "example_voice_wav": "MODELS/G3/122.wav", - "example_voice_wav_words": "ある意味忘れられない文化祭だな", - }, - "tone3": { - "example_voice_wav": "MODELS/G3/120.wav", - "example_voice_wav_words": "あ、そうだあの時だな 僕も忘れられないよ", - }, - }, - }, - - "ヴァルキリー": { - "gpt_weight": "MODELS/Z2/Z2.ckpt", - "sovits_weight": "MODELS/Z2/Z2.pth", - "default_language": "日本語", - "tones": { - "tone1": { - "example_voice_wav": "MODELS/Z2/1.wav", - "example_voice_wav_words": "今回作ったムサシコシュラエも 無骨ながらお気に入りかな", - }, - "tone2": { - "example_voice_wav": "MODELS/Z2/7.wav", - "example_voice_wav_words": "よしよし、おさかべひめも出てきたか。しかもサバゲーなんて良い趣味してる!", - }, - "tone3": { - "example_voice_wav": "MODELS/Z2/56.wav", - "example_voice_wav_words": "いざ、決戦の島へ!よーし、乗ってくか!", - }, - }, - }, -} - - - \ No newline at end of file diff --git a/module/__init__.py b/module/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/module/attentions.py b/module/attentions.py deleted file mode 100644 index a2e9e5155ed10884a61531d65f6a9792ec595486..0000000000000000000000000000000000000000 --- a/module/attentions.py +++ /dev/null @@ -1,709 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F - -from module import commons -from module.modules import LayerNorm - - -class Encoder(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size=1, - p_dropout=0.0, - window_size=4, - isflow=False, - **kwargs - ): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - window_size=window_size, - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - if isflow: - cond_layer = torch.nn.Conv1d( - kwargs["gin_channels"], 2 * hidden_channels * n_layers, 1 - ) - self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1) - self.cond_layer = weight_norm_modules(cond_layer, name="weight") - self.gin_channels = kwargs["gin_channels"] - - def forward(self, x, x_mask, g=None): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - if g is not None: - x = self.cond_pre(x) - cond_offset = i * 2 * self.hidden_channels - g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] - x = commons.fused_add_tanh_sigmoid_multiply( - x, g_l, torch.IntTensor([self.hidden_channels]) - ) - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Decoder(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size=1, - p_dropout=0.0, - proximal_bias=False, - proximal_init=True, - **kwargs - ): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.encdec_attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - proximal_bias=proximal_bias, - proximal_init=proximal_init, - ) - ) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.encdec_attn_layers.append( - MultiHeadAttention( - hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - causal=True, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, h, h_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to( - device=x.device, dtype=x.dtype - ) - encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__( - self, - channels, - out_channels, - n_heads, - p_dropout=0.0, - window_size=None, - heads_share=True, - block_length=None, - proximal_bias=False, - proximal_init=False, - ): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - self.emb_rel_v = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - if self.window_size is not None: - assert ( - t_s == t_t - ), "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys( - query / math.sqrt(self.k_channels), key_relative_embeddings - ) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to( - device=scores.device, dtype=scores.dtype - ) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - assert ( - t_s == t_t - ), "Local attention is only available for self-attention." - block_mask = ( - torch.ones_like(scores) - .triu(-self.block_length) - .tril(self.block_length) - ) - scores = scores.masked_fill(block_mask == 0, -1e4) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings( - self.emb_rel_v, t_s - ) - output = output + self._matmul_with_relative_values( - relative_weights, value_relative_embeddings - ) - output = ( - output.transpose(2, 3).contiguous().view(b, d, t_t) - ) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), - ) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[ - :, slice_start_position:slice_end_position - ] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad( - x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) - ) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ - :, :, :length, length - 1 : - ] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad( - x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) - ) - x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__( - self, - in_channels, - out_channels, - filter_channels, - kernel_size, - p_dropout=0.0, - activation=None, - causal=False, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - -import torch.nn as nn -from torch.nn.utils import remove_weight_norm, weight_norm - - -class Depthwise_Separable_Conv1D(nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - bias=True, - padding_mode="zeros", # TODO: refine this type - device=None, - dtype=None, - ): - super().__init__() - self.depth_conv = nn.Conv1d( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=kernel_size, - groups=in_channels, - stride=stride, - padding=padding, - dilation=dilation, - bias=bias, - padding_mode=padding_mode, - device=device, - dtype=dtype, - ) - self.point_conv = nn.Conv1d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=1, - bias=bias, - device=device, - dtype=dtype, - ) - - def forward(self, input): - return self.point_conv(self.depth_conv(input)) - - def weight_norm(self): - self.depth_conv = weight_norm(self.depth_conv, name="weight") - self.point_conv = weight_norm(self.point_conv, name="weight") - - def remove_weight_norm(self): - self.depth_conv = remove_weight_norm(self.depth_conv, name="weight") - self.point_conv = remove_weight_norm(self.point_conv, name="weight") - - -class Depthwise_Separable_TransposeConv1D(nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - output_padding=0, - bias=True, - dilation=1, - padding_mode="zeros", # TODO: refine this type - device=None, - dtype=None, - ): - super().__init__() - self.depth_conv = nn.ConvTranspose1d( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=kernel_size, - groups=in_channels, - stride=stride, - output_padding=output_padding, - padding=padding, - dilation=dilation, - bias=bias, - padding_mode=padding_mode, - device=device, - dtype=dtype, - ) - self.point_conv = nn.Conv1d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=1, - bias=bias, - device=device, - dtype=dtype, - ) - - def forward(self, input): - return self.point_conv(self.depth_conv(input)) - - def weight_norm(self): - self.depth_conv = weight_norm(self.depth_conv, name="weight") - self.point_conv = weight_norm(self.point_conv, name="weight") - - def remove_weight_norm(self): - remove_weight_norm(self.depth_conv, name="weight") - remove_weight_norm(self.point_conv, name="weight") - - -def weight_norm_modules(module, name="weight", dim=0): - if isinstance(module, Depthwise_Separable_Conv1D) or isinstance( - module, Depthwise_Separable_TransposeConv1D - ): - module.weight_norm() - return module - else: - return weight_norm(module, name, dim) - - -def remove_weight_norm_modules(module, name="weight"): - if isinstance(module, Depthwise_Separable_Conv1D) or isinstance( - module, Depthwise_Separable_TransposeConv1D - ): - module.remove_weight_norm() - else: - remove_weight_norm(module, name) - - -class FFT(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers=1, - kernel_size=1, - p_dropout=0.0, - proximal_bias=False, - proximal_init=True, - isflow=False, - **kwargs - ): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - if isflow: - cond_layer = torch.nn.Conv1d( - kwargs["gin_channels"], 2 * hidden_channels * n_layers, 1 - ) - self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1) - self.cond_layer = weight_norm_modules(cond_layer, name="weight") - self.gin_channels = kwargs["gin_channels"] - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - proximal_bias=proximal_bias, - proximal_init=proximal_init, - ) - ) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - causal=True, - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, g=None): - """ - x: decoder input - h: encoder output - """ - if g is not None: - g = self.cond_layer(g) - - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to( - device=x.device, dtype=x.dtype - ) - x = x * x_mask - for i in range(self.n_layers): - if g is not None: - x = self.cond_pre(x) - cond_offset = i * 2 * self.hidden_channels - g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] - x = commons.fused_add_tanh_sigmoid_multiply( - x, g_l, torch.IntTensor([self.hidden_channels]) - ) - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - x = x * x_mask - return x - - -class TransformerCouplingLayer(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - n_layers, - n_heads, - p_dropout=0, - filter_channels=0, - mean_only=False, - wn_sharing_parameter=None, - gin_channels=0, - ): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = ( - Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - isflow=True, - gin_channels=gin_channels, - ) - if wn_sharing_parameter is None - else wn_sharing_parameter - ) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels] * 2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1, 2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x diff --git a/module/attentions_onnx.py b/module/attentions_onnx.py deleted file mode 100644 index bc63a06ff571429b1b8ca267ac7b4b64deaa21ce..0000000000000000000000000000000000000000 --- a/module/attentions_onnx.py +++ /dev/null @@ -1,354 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F - -from module import commons -from module.modules import LayerNorm - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -class Encoder(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size=1, - p_dropout=0.0, - window_size=4, - isflow=True, - **kwargs - ): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - # if isflow: - # cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1) - # self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1) - # self.cond_layer = weight_norm(cond_layer, name='weight') - # self.gin_channels = 256 - self.cond_layer_idx = self.n_layers - if "gin_channels" in kwargs: - self.gin_channels = kwargs["gin_channels"] - if self.gin_channels != 0: - self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels) - # vits2 says 3rd block, so idx is 2 by default - self.cond_layer_idx = ( - kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2 - ) - logging.debug(self.gin_channels, self.cond_layer_idx) - assert ( - self.cond_layer_idx < self.n_layers - ), "cond_layer_idx should be less than n_layers" - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - window_size=window_size, - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, g=None): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - if i == self.cond_layer_idx and g is not None: - g = self.spk_emb_linear(g.transpose(1, 2)) - g = g.transpose(1, 2) - x = x + g - x = x * x_mask - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__( - self, - channels, - out_channels, - n_heads, - p_dropout=0.0, - window_size=None, - heads_share=True, - block_length=None, - proximal_bias=False, - proximal_init=False, - ): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - self.emb_rel_v = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, _ = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3) - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - - if self.window_size is not None: - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - - p_attn = F.softmax(scores, dim=-1) - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) - output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) - - output = (output.transpose(2, 3).contiguous().view(b, d, -1)) - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_l = torch.zeros((1), dtype = torch.int64) + length - (self.window_size + 1) - pad_s = torch.zeros((1), dtype = torch.int64) + (self.window_size + 1) - length - pad_length = torch.max(pad_l, other=torch.zeros((1), dtype = torch.int64)) - slice_start_position = torch.max(pad_s, other=torch.zeros((1), dtype = torch.int64)) - - slice_end_position = slice_start_position + 2 * length - 1 - padded_relative_embeddings = F.pad( - relative_embeddings, - commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), - ) - used_relative_embeddings = padded_relative_embeddings[ - :, slice_start_position:slice_end_position - ] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad( - x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) - ) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ - :, :, :length, length - 1 : - ] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad( - x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) - ) - x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__( - self, - in_channels, - out_channels, - filter_channels, - kernel_size, - p_dropout=0.0, - activation=None, - causal=False, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x diff --git a/module/commons.py b/module/commons.py deleted file mode 100644 index e96cf923515c7f5648af9dfedaa803df3f4f28be..0000000000000000000000000000000000000000 --- a/module/commons.py +++ /dev/null @@ -1,189 +0,0 @@ -import math -import torch -from torch.nn import functional as F - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size * dilation - dilation) / 2) - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def intersperse(lst, item): - result = [item] * (len(lst) * 2 + 1) - result[1::2] = lst - return result - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += ( - 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q) - ) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / ( - num_timescales - 1 - ) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment - ) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2, 3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1.0 / norm_type) - return total_norm - - -def squeeze(x, x_mask=None, n_sqz=2): - b, c, t = x.size() - - t = (t // n_sqz) * n_sqz - x = x[:, :, :t] - x_sqz = x.view(b, c, t // n_sqz, n_sqz) - x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz) - - if x_mask is not None: - x_mask = x_mask[:, :, n_sqz - 1 :: n_sqz] - else: - x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype) - return x_sqz * x_mask, x_mask - - -def unsqueeze(x, x_mask=None, n_sqz=2): - b, c, t = x.size() - - x_unsqz = x.view(b, n_sqz, c // n_sqz, t) - x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz) - - if x_mask is not None: - x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz) - else: - x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype) - return x_unsqz * x_mask, x_mask diff --git a/module/core_vq.py b/module/core_vq.py deleted file mode 100644 index a5e22d667631d22a561189651e18e821800b57a5..0000000000000000000000000000000000000000 --- a/module/core_vq.py +++ /dev/null @@ -1,383 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -# This implementation is inspired from -# https://github.com/lucidrains/vector-quantize-pytorch -# which is released under MIT License. Hereafter, the original license: -# MIT License -# -# Copyright (c) 2020 Phil Wang -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -"""Core vector quantization implementation.""" -import typing as tp - -from einops import rearrange, repeat -import torch -from torch import nn -import torch.nn.functional as F -from tqdm import tqdm - - -def default(val: tp.Any, d: tp.Any) -> tp.Any: - return val if val is not None else d - - -def ema_inplace(moving_avg, new, decay: float): - moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) - - -def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5): - return (x + epsilon) / (x.sum() + n_categories * epsilon) - - -def uniform_init(*shape: int): - t = torch.empty(shape) - nn.init.kaiming_uniform_(t) - return t - - -def sample_vectors(samples, num: int): - num_samples, device = samples.shape[0], samples.device - - if num_samples >= num: - indices = torch.randperm(num_samples, device=device)[:num] - else: - indices = torch.randint(0, num_samples, (num,), device=device) - - return samples[indices] - - -def kmeans(samples, num_clusters: int, num_iters: int = 10): - dim, dtype = samples.shape[-1], samples.dtype - max_kmeans_samples = 500 - samples = samples[:max_kmeans_samples, :] - means = sample_vectors(samples, num_clusters) - - print("kmeans start ... ") - for _ in tqdm(range(num_iters)): - diffs = rearrange(samples, "n d -> n () d") - rearrange(means, "c d -> () c d") - dists = -(diffs**2).sum(dim=-1) - - buckets = dists.max(dim=-1).indices - bins = torch.bincount(buckets, minlength=num_clusters) - zero_mask = bins == 0 - bins_min_clamped = bins.masked_fill(zero_mask, 1) - - new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype) - new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples) - new_means = new_means / bins_min_clamped[..., None] - - means = torch.where(zero_mask[..., None], means, new_means) - - return means, bins - - -class EuclideanCodebook(nn.Module): - """Codebook with Euclidean distance. - Args: - dim (int): Dimension. - codebook_size (int): Codebook size. - kmeans_init (bool): Whether to use k-means to initialize the codebooks. - If set to true, run the k-means algorithm on the first training batch and use - the learned centroids as initialization. - kmeans_iters (int): Number of iterations used for k-means algorithm at initialization. - decay (float): Decay for exponential moving average over the codebooks. - epsilon (float): Epsilon value for numerical stability. - threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes - that have an exponential moving average cluster size less than the specified threshold with - randomly selected vector from the current batch. - """ - - def __init__( - self, - dim: int, - codebook_size: int, - kmeans_init: int = False, - kmeans_iters: int = 10, - decay: float = 0.99, - epsilon: float = 1e-5, - threshold_ema_dead_code: int = 2, - ): - super().__init__() - self.decay = decay - init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = ( - uniform_init if not kmeans_init else torch.zeros - ) - embed = init_fn(codebook_size, dim) - - self.codebook_size = codebook_size - - self.kmeans_iters = kmeans_iters - self.epsilon = epsilon - self.threshold_ema_dead_code = threshold_ema_dead_code - - self.register_buffer("inited", torch.Tensor([not kmeans_init])) - self.register_buffer("cluster_size", torch.zeros(codebook_size)) - self.register_buffer("embed", embed) - self.register_buffer("embed_avg", embed.clone()) - - @torch.jit.ignore - def init_embed_(self, data): - if self.inited: - return - - embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters) - self.embed.data.copy_(embed) - self.embed_avg.data.copy_(embed.clone()) - self.cluster_size.data.copy_(cluster_size) - self.inited.data.copy_(torch.Tensor([True])) - # Make sure all buffers across workers are in sync after initialization - # broadcast_tensors(self.buffers()) - - def replace_(self, samples, mask): - modified_codebook = torch.where( - mask[..., None], sample_vectors(samples, self.codebook_size), self.embed - ) - self.embed.data.copy_(modified_codebook) - - def expire_codes_(self, batch_samples): - if self.threshold_ema_dead_code == 0: - return - - expired_codes = self.cluster_size < self.threshold_ema_dead_code - if not torch.any(expired_codes): - return - - batch_samples = rearrange(batch_samples, "... d -> (...) d") - self.replace_(batch_samples, mask=expired_codes) - # broadcast_tensors(self.buffers()) - - def preprocess(self, x): - x = rearrange(x, "... d -> (...) d") - return x - - def quantize(self, x): - embed = self.embed.t() - dist = -( - x.pow(2).sum(1, keepdim=True) - - 2 * x @ embed - + embed.pow(2).sum(0, keepdim=True) - ) - embed_ind = dist.max(dim=-1).indices - return embed_ind - - def postprocess_emb(self, embed_ind, shape): - return embed_ind.view(*shape[:-1]) - - def dequantize(self, embed_ind): - quantize = F.embedding(embed_ind, self.embed) - return quantize - - def encode(self, x): - shape = x.shape - # pre-process - x = self.preprocess(x) - # quantize - embed_ind = self.quantize(x) - # post-process - embed_ind = self.postprocess_emb(embed_ind, shape) - return embed_ind - - def decode(self, embed_ind): - quantize = self.dequantize(embed_ind) - return quantize - - def forward(self, x): - shape, dtype = x.shape, x.dtype - x = self.preprocess(x) - - self.init_embed_(x) - - embed_ind = self.quantize(x) - embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype) - embed_ind = self.postprocess_emb(embed_ind, shape) - quantize = self.dequantize(embed_ind) - - if self.training: - # We do the expiry of code at that point as buffers are in sync - # and all the workers will take the same decision. - self.expire_codes_(x) - ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay) - embed_sum = x.t() @ embed_onehot - ema_inplace(self.embed_avg, embed_sum.t(), self.decay) - cluster_size = ( - laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon) - * self.cluster_size.sum() - ) - embed_normalized = self.embed_avg / cluster_size.unsqueeze(1) - self.embed.data.copy_(embed_normalized) - - return quantize, embed_ind - - -class VectorQuantization(nn.Module): - """Vector quantization implementation. - Currently supports only euclidean distance. - Args: - dim (int): Dimension - codebook_size (int): Codebook size - codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim. - decay (float): Decay for exponential moving average over the codebooks. - epsilon (float): Epsilon value for numerical stability. - kmeans_init (bool): Whether to use kmeans to initialize the codebooks. - kmeans_iters (int): Number of iterations used for kmeans initialization. - threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes - that have an exponential moving average cluster size less than the specified threshold with - randomly selected vector from the current batch. - commitment_weight (float): Weight for commitment loss. - """ - - def __init__( - self, - dim: int, - codebook_size: int, - codebook_dim: tp.Optional[int] = None, - decay: float = 0.99, - epsilon: float = 1e-5, - kmeans_init: bool = True, - kmeans_iters: int = 50, - threshold_ema_dead_code: int = 2, - commitment_weight: float = 1.0, - ): - super().__init__() - _codebook_dim: int = default(codebook_dim, dim) - - requires_projection = _codebook_dim != dim - self.project_in = ( - nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity() - ) - self.project_out = ( - nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity() - ) - - self.epsilon = epsilon - self.commitment_weight = commitment_weight - - self._codebook = EuclideanCodebook( - dim=_codebook_dim, - codebook_size=codebook_size, - kmeans_init=kmeans_init, - kmeans_iters=kmeans_iters, - decay=decay, - epsilon=epsilon, - threshold_ema_dead_code=threshold_ema_dead_code, - ) - self.codebook_size = codebook_size - - @property - def codebook(self): - return self._codebook.embed - - def encode(self, x): - x = rearrange(x, "b d n -> b n d") - x = self.project_in(x) - embed_in = self._codebook.encode(x) - return embed_in - - def decode(self, embed_ind): - quantize = self._codebook.decode(embed_ind) - quantize = self.project_out(quantize) - quantize = rearrange(quantize, "b n d -> b d n") - return quantize - - def forward(self, x): - device = x.device - x = rearrange(x, "b d n -> b n d") - x = self.project_in(x) - - quantize, embed_ind = self._codebook(x) - - if self.training: - quantize = x + (quantize - x).detach() - - loss = torch.tensor([0.0], device=device, requires_grad=self.training) - - if self.training: - if self.commitment_weight > 0: - commit_loss = F.mse_loss(quantize.detach(), x) - loss = loss + commit_loss * self.commitment_weight - - quantize = self.project_out(quantize) - quantize = rearrange(quantize, "b n d -> b d n") - return quantize, embed_ind, loss - - -class ResidualVectorQuantization(nn.Module): - """Residual vector quantization implementation. - Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf - """ - - def __init__(self, *, num_quantizers, **kwargs): - super().__init__() - self.layers = nn.ModuleList( - [VectorQuantization(**kwargs) for _ in range(num_quantizers)] - ) - - def forward( - self, x, n_q: tp.Optional[int] = None, layers: tp.Optional[list] = None - ): - quantized_out = 0.0 - residual = x - - all_losses = [] - all_indices = [] - out_quantized = [] - - n_q = n_q or len(self.layers) - - for i, layer in enumerate(self.layers[:n_q]): - quantized, indices, loss = layer(residual) - residual = residual - quantized - quantized_out = quantized_out + quantized - - all_indices.append(indices) - all_losses.append(loss) - if layers and i in layers: - out_quantized.append(quantized) - - out_losses, out_indices = map(torch.stack, (all_losses, all_indices)) - return quantized_out, out_indices, out_losses, out_quantized - - def encode( - self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None - ) -> torch.Tensor: - residual = x - all_indices = [] - n_q = n_q or len(self.layers) - st = st or 0 - for layer in self.layers[st:n_q]: - indices = layer.encode(residual) - quantized = layer.decode(indices) - residual = residual - quantized - all_indices.append(indices) - out_indices = torch.stack(all_indices) - return out_indices - - def decode(self, q_indices: torch.Tensor, st: int = 0) -> torch.Tensor: - quantized_out = torch.tensor(0.0, device=q_indices.device) - for i, indices in enumerate(q_indices): - layer = self.layers[st + i] - quantized = layer.decode(indices) - quantized_out = quantized_out + quantized - return quantized_out diff --git a/module/data_utils.py b/module/data_utils.py deleted file mode 100644 index ff4c4f4311db8a3fda0a6052ba00ac0c622a9236..0000000000000000000000000000000000000000 --- a/module/data_utils.py +++ /dev/null @@ -1,332 +0,0 @@ -import time -import logging -import os -import random -import traceback -import numpy as np -import torch -import torch.utils.data -from tqdm import tqdm - -from module import commons -from module.mel_processing import spectrogram_torch -from text import cleaned_text_to_sequence -from utils import load_wav_to_torch, load_filepaths_and_text -import torch.nn.functional as F -from functools import lru_cache -import requests -from scipy.io import wavfile -from io import BytesIO -from my_utils import load_audio - -# ZeroDivisionError fixed by Tybost (https://github.com/RVC-Boss/GPT-SoVITS/issues/79) -class TextAudioSpeakerLoader(torch.utils.data.Dataset): - """ - 1) loads audio, speaker_id, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - - def __init__(self, hparams, val=False): - exp_dir = hparams.exp_dir - self.path2 = "%s/2-name2text.txt" % exp_dir - self.path4 = "%s/4-cnhubert" % exp_dir - self.path5 = "%s/5-wav32k" % exp_dir - assert os.path.exists(self.path2) - assert os.path.exists(self.path4) - assert os.path.exists(self.path5) - names4 = set([name[:-3] for name in list(os.listdir(self.path4))]) # 去除.pt后缀 - names5 = set(os.listdir(self.path5)) - self.phoneme_data = {} - with open(self.path2, "r", encoding="utf8") as f: - lines = f.read().strip("\n").split("\n") - - for line in lines: - tmp = line.split("\t") - if (len(tmp) != 4): - continue - self.phoneme_data[tmp[0]] = [tmp[1]] - - self.audiopaths_sid_text = list(set(self.phoneme_data) & names4 & names5) - tmp = self.audiopaths_sid_text - leng = len(tmp) - min_num = 100 - if (leng < min_num): - self.audiopaths_sid_text = [] - for _ in range(max(2, int(min_num / leng))): - self.audiopaths_sid_text += tmp - self.max_wav_value = hparams.max_wav_value - self.sampling_rate = hparams.sampling_rate - self.filter_length = hparams.filter_length - self.hop_length = hparams.hop_length - self.win_length = hparams.win_length - self.sampling_rate = hparams.sampling_rate - self.val = val - - random.seed(1234) - random.shuffle(self.audiopaths_sid_text) - - print("phoneme_data_len:", len(self.phoneme_data.keys())) - print("wav_data_len:", len(self.audiopaths_sid_text)) - - audiopaths_sid_text_new = [] - lengths = [] - skipped_phone = 0 - skipped_dur = 0 - for audiopath in tqdm(self.audiopaths_sid_text): - try: - phoneme = self.phoneme_data[audiopath][0] - phoneme = phoneme.split(' ') - phoneme_ids = cleaned_text_to_sequence(phoneme) - except Exception: - print(f"{audiopath} not in self.phoneme_data !") - skipped_phone += 1 - continue - - size = os.path.getsize("%s/%s" % (self.path5, audiopath)) - duration = size / self.sampling_rate / 2 - - if duration == 0: - print(f"Zero duration for {audiopath}, skipping...") - skipped_dur += 1 - continue - - if 54 > duration > 0.6 or self.val: - audiopaths_sid_text_new.append([audiopath, phoneme_ids]) - lengths.append(size // (2 * self.hop_length)) - else: - skipped_dur += 1 - continue - - print("skipped_phone: ", skipped_phone, ", skipped_dur: ", skipped_dur) - print("total left: ", len(audiopaths_sid_text_new)) - assert len(audiopaths_sid_text_new) > 1 # 至少能凑够batch size，这里todo - self.audiopaths_sid_text = audiopaths_sid_text_new - self.lengths = lengths - - def get_audio_text_speaker_pair(self, audiopath_sid_text): - audiopath, phoneme_ids = audiopath_sid_text - text = torch.FloatTensor(phoneme_ids) - try: - spec, wav = self.get_audio("%s/%s" % (self.path5, audiopath)) - with torch.no_grad(): - ssl = torch.load("%s/%s.pt" % (self.path4, audiopath), map_location="cpu") - if (ssl.shape[-1] != spec.shape[-1]): - typee = ssl.dtype - ssl = F.pad(ssl.float(), (0, 1), mode="replicate").to(typee) - ssl.requires_grad = False - except: - traceback.print_exc() - spec = torch.zeros(1025, 100) - wav = torch.zeros(1, 100 * self.hop_length) - ssl = torch.zeros(1, 768, 100) - text = text[-1:] - print("load audio or ssl error!!!!!!", audiopath) - return (ssl, spec, wav, text) - - def get_audio(self, filename): - audio_array = load_audio(filename, self.sampling_rate) # load_audio的方法是已经归一化到-1~1之间的，不用再/32768 - audio = torch.FloatTensor(audio_array) # /32768 - audio_norm = audio - audio_norm = audio_norm.unsqueeze(0) - spec = spectrogram_torch(audio_norm, self.filter_length, self.sampling_rate, self.hop_length, self.win_length, - center=False) - spec = torch.squeeze(spec, 0) - return spec, audio_norm - - def get_sid(self, sid): - sid = torch.LongTensor([int(sid)]) - return sid - - def __getitem__(self, index): - # with torch.no_grad(): - return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index]) - - def __len__(self): - return len(self.audiopaths_sid_text) - - def random_slice(self, ssl, wav, mel): - assert abs(ssl.shape[-1] - wav.shape[-1] // self.hop_length) < 3, ( - "first", ssl.shape, wav.shape) - - len_mel = mel.shape[1] - if self.val: - reference_mel = mel[:, :len_mel // 3] - return reference_mel, ssl, wav, mel - dir = random.randint(0, 1) - sep_point = random.randint(int(len_mel // 3), int(len_mel // 3 * 2)) - - if dir == 0: - reference_mel = mel[:, :sep_point] - ssl = ssl[:, :, sep_point:] - wav2 = wav[:, sep_point * self.hop_length:] - mel = mel[:, sep_point:] - else: - reference_mel = mel[:, sep_point:] - ssl = ssl[:, :, :sep_point] - wav2 = wav[:, :sep_point * self.hop_length] - mel = mel[:, :sep_point] - - assert abs(ssl.shape[-1] - wav2.shape[-1] // self.hop_length) < 3, ( - ssl.shape, wav.shape, wav2.shape, mel.shape, sep_point, self.hop_length, sep_point * self.hop_length, dir) - return reference_mel, ssl, wav2, mel - - -class TextAudioSpeakerCollate(): - """ Zero-pads model inputs and targets - """ - - def __init__(self, return_ids=False): - self.return_ids = return_ids - - def __call__(self, batch): - """Collate's training batch from normalized text, audio and speaker identities - PARAMS - ------ - batch: [text_normalized, spec_normalized, wav_normalized, sid] - """ - # Right zero-pad all one-hot text sequences to max input length - _, ids_sorted_decreasing = torch.sort( - torch.LongTensor([x[1].size(1) for x in batch]), - dim=0, descending=True) - - max_ssl_len = max([x[0].size(2) for x in batch]) - max_ssl_len = int(2 * ((max_ssl_len // 2) + 1)) - max_spec_len = max([x[1].size(1) for x in batch]) - max_spec_len = int(2 * ((max_spec_len // 2) + 1)) - max_wav_len = max([x[2].size(1) for x in batch]) - max_text_len = max([x[3].size(0) for x in batch]) - - ssl_lengths = torch.LongTensor(len(batch)) - spec_lengths = torch.LongTensor(len(batch)) - wav_lengths = torch.LongTensor(len(batch)) - text_lengths = torch.LongTensor(len(batch)) - - spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) - wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) - ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len) - text_padded = torch.LongTensor(len(batch), max_text_len) - - spec_padded.zero_() - wav_padded.zero_() - ssl_padded.zero_() - text_padded.zero_() - - for i in range(len(ids_sorted_decreasing)): - row = batch[ids_sorted_decreasing[i]] - - ssl = row[0] - ssl_padded[i, :, :ssl.size(2)] = ssl[0, :, :] - ssl_lengths[i] = ssl.size(2) - - spec = row[1] - spec_padded[i, :, :spec.size(1)] = spec - spec_lengths[i] = spec.size(1) - - wav = row[2] - wav_padded[i, :, :wav.size(1)] = wav - wav_lengths[i] = wav.size(1) - - text = row[3] - text_padded[i, :text.size(0)] = text - text_lengths[i] = text.size(0) - - return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths - - -class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): - """ - Maintain similar input lengths in a batch. - Length groups are specified by boundaries. - Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. - - It removes samples which are not included in the boundaries. - Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. - """ - - def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True): - super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) - self.lengths = dataset.lengths - self.batch_size = batch_size - self.boundaries = boundaries - - self.buckets, self.num_samples_per_bucket = self._create_buckets() - self.total_size = sum(self.num_samples_per_bucket) - self.num_samples = self.total_size // self.num_replicas - - def _create_buckets(self): - buckets = [[] for _ in range(len(self.boundaries) - 1)] - for i in range(len(self.lengths)): - length = self.lengths[i] - idx_bucket = self._bisect(length) - if idx_bucket != -1: - buckets[idx_bucket].append(i) - - i = len(buckets) - 1 - while i >= 0: - if len(buckets[i]) == 0: - buckets.pop(i) - self.boundaries.pop(i + 1) - i -= 1 - - num_samples_per_bucket = [] - for i in range(len(buckets)): - len_bucket = len(buckets[i]) - total_batch_size = self.num_replicas * self.batch_size - rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size - num_samples_per_bucket.append(len_bucket + rem) - return buckets, num_samples_per_bucket - - def __iter__(self): - g = torch.Generator() - g.manual_seed(self.epoch) - - indices = [] - if self.shuffle: - for bucket in self.buckets: - indices.append(torch.randperm(len(bucket), generator=g).tolist()) - else: - for bucket in self.buckets: - indices.append(list(range(len(bucket)))) - - batches = [] - for i in range(len(self.buckets)): - bucket = self.buckets[i] - len_bucket = len(bucket) - ids_bucket = indices[i] - num_samples_bucket = self.num_samples_per_bucket[i] - - rem = num_samples_bucket - len_bucket - ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)] - - ids_bucket = ids_bucket[self.rank::self.num_replicas] - - for j in range(len(ids_bucket) // self.batch_size): - batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]] - batches.append(batch) - - if self.shuffle: - batch_ids = torch.randperm(len(batches), generator=g).tolist() - batches = [batches[i] for i in batch_ids] - self.batches = batches - - assert len(self.batches) * self.batch_size == self.num_samples - return iter(self.batches) - - def _bisect(self, x, lo=0, hi=None): - if hi is None: - hi = len(self.boundaries) - 1 - - if hi > lo: - mid = (hi + lo) // 2 - if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: - return mid - elif x <= self.boundaries[mid]: - return self._bisect(x, lo, mid) - else: - return self._bisect(x, mid + 1, hi) - else: - return -1 - - def __len__(self): - return self.num_samples // self.batch_size \ No newline at end of file diff --git a/module/losses.py b/module/losses.py deleted file mode 100644 index b23fc8c1d5403d8d1ac19bb57e734029dcd67a19..0000000000000000000000000000000000000000 --- a/module/losses.py +++ /dev/null @@ -1,73 +0,0 @@ -import math - -import torch -from torch.nn import functional as F - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - rl = rl.float().detach() - gl = gl.float() - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - dr = dr.float() - dg = dg.float() - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg**2) - loss += r_loss + g_loss - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - dg = dg.float() - l = torch.mean((1 - dg) ** 2) - gen_losses.append(l) - loss += l - - return loss, gen_losses - - -def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): - """ - z_p, logs_q: [b, h, t_t] - m_p, logs_p: [b, h, t_t] - """ - z_p = z_p.float() - logs_q = logs_q.float() - m_p = m_p.float() - logs_p = logs_p.float() - z_mask = z_mask.float() - - kl = logs_p - logs_q - 0.5 - kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p) - kl = torch.sum(kl * z_mask) - l = kl / torch.sum(z_mask) - return l - - -def mle_loss(z, m, logs, logdet, mask): - l = torch.sum(logs) + 0.5 * torch.sum( - torch.exp(-2 * logs) * ((z - m) ** 2) - ) # neg normal likelihood w/o the constant term - l = l - torch.sum(logdet) # log jacobian determinant - l = l / torch.sum( - torch.ones_like(z) * mask - ) # averaging across batch, channel and time axes - l = l + 0.5 * math.log(2 * math.pi) # add the remaining constant term - return l diff --git a/module/mel_processing.py b/module/mel_processing.py deleted file mode 100644 index 503825ec6df0ba395b36739516a5d21842eb72d8..0000000000000000000000000000000000000000 --- a/module/mel_processing.py +++ /dev/null @@ -1,153 +0,0 @@ -import math -import os -import random -import torch -from torch import nn -import torch.nn.functional as F -import torch.utils.data -import numpy as np -import librosa -import librosa.util as librosa_util -from librosa.util import normalize, pad_center, tiny -from scipy.signal import get_window -from scipy.io.wavfile import read -from librosa.filters import mel as librosa_mel_fn - -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.0: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.0: - print("max value is ", torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + "_" + str(y.device) - wnsize_dtype_device = str(win_size) + "_" + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to( - dtype=y.dtype, device=y.device - ) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - spec = torch.stft( - y, - n_fft, - hop_length=hop_size, - win_length=win_size, - window=hann_window[wnsize_dtype_device], - center=center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=False, - ) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + "_" + str(spec.device) - fmax_dtype_device = str(fmax) + "_" + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn( - sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax - ) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to( - dtype=spec.dtype, device=spec.device - ) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch( - y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False -): - if torch.min(y) < -1.0: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.0: - print("max value is ", torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + "_" + str(y.device) - fmax_dtype_device = str(fmax) + "_" + dtype_device - wnsize_dtype_device = str(win_size) + "_" + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn( - sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax - ) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to( - dtype=y.dtype, device=y.device - ) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to( - dtype=y.dtype, device=y.device - ) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - - spec = torch.stft( - y, - n_fft, - hop_length=hop_size, - win_length=win_size, - window=hann_window[wnsize_dtype_device], - center=center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=False, - ) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/module/models.py b/module/models.py deleted file mode 100644 index c99485cf352294a90b47aa85ba9989e3eb4da728..0000000000000000000000000000000000000000 --- a/module/models.py +++ /dev/null @@ -1,989 +0,0 @@ -import copy -import math -import torch -from torch import nn -from torch.nn import functional as F - -from module import commons -from module import modules -from module import attentions - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from module.commons import init_weights, get_padding -from module.mrte_model import MRTE -from module.quantize import ResidualVectorQuantizer -from text import symbols -from torch.cuda.amp import autocast - - -class StochasticDurationPredictor(nn.Module): - def __init__( - self, - in_channels, - filter_channels, - kernel_size, - p_dropout, - n_flows=4, - gin_channels=0, - ): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append( - modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3) - ) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv( - filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout - ) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append( - modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3) - ) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv( - filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout - ) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = ( - torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) - * x_mask - ) - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum( - (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2] - ) - logq = ( - torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2]) - - logdet_tot_q - ) - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = ( - torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2]) - - logdet_tot - ) - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = ( - torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) - * noise_scale - ) - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class DurationPredictor(nn.Module): - def __init__( - self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 - ): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d( - in_channels, filter_channels, kernel_size, padding=kernel_size // 2 - ) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d( - filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 - ) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask - - -class TextEncoder(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - latent_channels=192, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.latent_channels = latent_channels - - self.ssl_proj = nn.Conv1d(768, hidden_channels, 1) - - self.encoder_ssl = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers // 2, - kernel_size, - p_dropout, - ) - - self.encoder_text = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.text_embedding = nn.Embedding(len(symbols), hidden_channels) - - self.mrte = MRTE() - - self.encoder2 = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers // 2, - kernel_size, - p_dropout, - ) - - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, y, y_lengths, text, text_lengths, ge, test=None): - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( - y.dtype - ) - - y = self.ssl_proj(y * y_mask) * y_mask - y = self.encoder_ssl(y * y_mask, y_mask) - - text_mask = torch.unsqueeze( - commons.sequence_mask(text_lengths, text.size(1)), 1 - ).to(y.dtype) - if test == 1: - text[:, :] = 0 - text = self.text_embedding(text).transpose(1, 2) - text = self.encoder_text(text * text_mask, text_mask) - y = self.mrte(y, y_mask, text, text_mask, ge) - - y = self.encoder2(y * y_mask, y_mask) - - stats = self.proj(y) * y_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - return y, m, logs, y_mask - - def extract_latent(self, x): - x = self.ssl_proj(x) - quantized, codes, commit_loss, quantized_list = self.quantizer(x) - return codes.transpose(0, 1) - - def decode_latent(self, codes, y_mask, refer, refer_mask, ge): - quantized = self.quantizer.decode(codes) - - y = self.vq_proj(quantized) * y_mask - y = self.encoder_ssl(y * y_mask, y_mask) - - y = self.mrte(y, y_mask, refer, refer_mask, ge) - - y = self.encoder2(y * y_mask, y_mask) - - stats = self.proj(y) * y_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - return y, m, logs, y_mask, quantized - - -class ResidualCouplingBlock(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0, - ): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer( - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - mean_only=True, - ) - ) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class PosteriorEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - if g != None: - g = g.detach() - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class WNEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.norm = modules.LayerNorm(out_channels) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - out = self.proj(x) * x_mask - out = self.norm(out) - return out - - -class Generator(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=0, - ): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print("Removing weight norm...") - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 1024, - 1024, - (kernel_size, 1), - 1, - padding=(get_padding(kernel_size, 1), 0), - ) - ), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class ReferenceEncoder(nn.Module): - """ - inputs --- [N, Ty/r, n_mels*r] mels - outputs --- [N, ref_enc_gru_size] - """ - - def __init__(self, spec_channels, gin_channels=0): - super().__init__() - self.spec_channels = spec_channels - ref_enc_filters = [32, 32, 64, 64, 128, 128] - K = len(ref_enc_filters) - filters = [1] + ref_enc_filters - convs = [ - weight_norm( - nn.Conv2d( - in_channels=filters[i], - out_channels=filters[i + 1], - kernel_size=(3, 3), - stride=(2, 2), - padding=(1, 1), - ) - ) - for i in range(K) - ] - self.convs = nn.ModuleList(convs) - # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) - - out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K) - self.gru = nn.GRU( - input_size=ref_enc_filters[-1] * out_channels, - hidden_size=256 // 2, - batch_first=True, - ) - self.proj = nn.Linear(128, gin_channels) - - def forward(self, inputs): - N = inputs.size(0) - out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs] - for conv in self.convs: - out = conv(out) - # out = wn(out) - out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K] - - out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K] - T = out.size(1) - N = out.size(0) - out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K] - - self.gru.flatten_parameters() - memory, out = self.gru(out) # out --- [1, N, 128] - - return self.proj(out.squeeze(0)).unsqueeze(-1) - - def calculate_channels(self, L, kernel_size, stride, pad, n_convs): - for i in range(n_convs): - L = (L - kernel_size + 2 * pad) // stride + 1 - return L - - -class Quantizer_module(torch.nn.Module): - def __init__(self, n_e, e_dim): - super(Quantizer_module, self).__init__() - self.embedding = nn.Embedding(n_e, e_dim) - self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e) - - def forward(self, x): - d = ( - torch.sum(x**2, 1, keepdim=True) - + torch.sum(self.embedding.weight**2, 1) - - 2 * torch.matmul(x, self.embedding.weight.T) - ) - min_indicies = torch.argmin(d, 1) - z_q = self.embedding(min_indicies) - return z_q, min_indicies - - -class Quantizer(torch.nn.Module): - def __init__(self, embed_dim=512, n_code_groups=4, n_codes=160): - super(Quantizer, self).__init__() - assert embed_dim % n_code_groups == 0 - self.quantizer_modules = nn.ModuleList( - [ - Quantizer_module(n_codes, embed_dim // n_code_groups) - for _ in range(n_code_groups) - ] - ) - self.n_code_groups = n_code_groups - self.embed_dim = embed_dim - - def forward(self, xin): - # B, C, T - B, C, T = xin.shape - xin = xin.transpose(1, 2) - x = xin.reshape(-1, self.embed_dim) - x = torch.split(x, self.embed_dim // self.n_code_groups, dim=-1) - min_indicies = [] - z_q = [] - for _x, m in zip(x, self.quantizer_modules): - _z_q, _min_indicies = m(_x) - z_q.append(_z_q) - min_indicies.append(_min_indicies) # B * T, - z_q = torch.cat(z_q, -1).reshape(xin.shape) - loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean( - (z_q - xin.detach()) ** 2 - ) - z_q = xin + (z_q - xin).detach() - z_q = z_q.transpose(1, 2) - codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups) - return z_q, loss, codes.transpose(1, 2) - - def embed(self, x): - # idx: N, 4, T - x = x.transpose(1, 2) - x = torch.split(x, 1, 2) - ret = [] - for q, embed in zip(x, self.quantizer_modules): - q = embed.embedding(q.squeeze(-1)) - ret.append(q) - ret = torch.cat(ret, -1) - return ret.transpose(1, 2) # N, C, T - - -class CodePredictor(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - n_q=8, - dims=1024, - ssl_dim=768, - ): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - - self.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1) - self.ref_enc = modules.MelStyleEncoder( - ssl_dim, style_vector_dim=hidden_channels - ) - - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - - self.out_proj = nn.Conv1d(hidden_channels, (n_q - 1) * dims, 1) - self.n_q = n_q - self.dims = dims - - def forward(self, x, x_mask, refer, codes, infer=False): - x = x.detach() - x = self.vq_proj(x * x_mask) * x_mask - g = self.ref_enc(refer, x_mask) - x = x + g - x = self.encoder(x * x_mask, x_mask) - x = self.out_proj(x * x_mask) * x_mask - logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose( - 2, 3 - ) - target = codes[1:].transpose(0, 1) - if not infer: - logits = logits.reshape(-1, self.dims) - target = target.reshape(-1) - loss = torch.nn.functional.cross_entropy(logits, target) - return loss - else: - _, top10_preds = torch.topk(logits, 10, dim=-1) - correct_top10 = torch.any(top10_preds == target.unsqueeze(-1), dim=-1) - top3_acc = 100 * torch.mean(correct_top10.float()).detach().cpu().item() - - print("Top-10 Accuracy:", top3_acc, "%") - - pred_codes = torch.argmax(logits, dim=-1) - acc = 100 * torch.mean((pred_codes == target).float()).detach().cpu().item() - print("Top-1 Accuracy:", acc, "%") - - return pred_codes.transpose(0, 1) - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - n_speakers=0, - gin_channels=0, - use_sdp=True, - semantic_frame_rate=None, - freeze_quantizer=None, - **kwargs - ): - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.n_speakers = n_speakers - self.gin_channels = gin_channels - - self.use_sdp = use_sdp - self.enc_p = TextEncoder( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = Generator( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels - ) - - self.ref_enc = modules.MelStyleEncoder( - spec_channels, style_vector_dim=gin_channels - ) - - ssl_dim = 768 - assert semantic_frame_rate in ["25hz", "50hz"] - self.semantic_frame_rate = semantic_frame_rate - if semantic_frame_rate == "25hz": - self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2) - else: - self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1) - - self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024) - if freeze_quantizer: - self.ssl_proj.requires_grad_(False) - self.quantizer.requires_grad_(False) - # self.enc_p.text_embedding.requires_grad_(False) - # self.enc_p.encoder_text.requires_grad_(False) - # self.enc_p.mrte.requires_grad_(False) - - def forward(self, ssl, y, y_lengths, text, text_lengths): - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( - y.dtype - ) - ge = self.ref_enc(y * y_mask, y_mask) - - with autocast(enabled=False): - ssl = self.ssl_proj(ssl) - quantized, codes, commit_loss, quantized_list = self.quantizer( - ssl, layers=[0] - ) - - if self.semantic_frame_rate == "25hz": - quantized = F.interpolate( - quantized, size=int(quantized.shape[-1] * 2), mode="nearest" - ) - - x, m_p, logs_p, y_mask = self.enc_p( - quantized, y_lengths, text, text_lengths, ge - ) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge) - z_p = self.flow(z, y_mask, g=ge) - - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - o = self.dec(z_slice, g=ge) - return ( - o, - commit_loss, - ids_slice, - y_mask, - y_mask, - (z, z_p, m_p, logs_p, m_q, logs_q), - quantized, - ) - - def infer(self, ssl, y, y_lengths, text, text_lengths, test=None, noise_scale=0.5): - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( - y.dtype - ) - ge = self.ref_enc(y * y_mask, y_mask) - - ssl = self.ssl_proj(ssl) - quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0]) - if self.semantic_frame_rate == "25hz": - quantized = F.interpolate( - quantized, size=int(quantized.shape[-1] * 2), mode="nearest" - ) - - x, m_p, logs_p, y_mask = self.enc_p( - quantized, y_lengths, text, text_lengths, ge, test=test - ) - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - - z = self.flow(z_p, y_mask, g=ge, reverse=True) - - o = self.dec((z * y_mask)[:, :, :], g=ge) - return o, y_mask, (z, z_p, m_p, logs_p) - - @torch.no_grad() - def decode(self, codes, text, refer, noise_scale=0.5): - refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device) - refer_mask = torch.unsqueeze( - commons.sequence_mask(refer_lengths, refer.size(2)), 1 - ).to(refer.dtype) - ge = self.ref_enc(refer * refer_mask, refer_mask) - - y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device) - text_lengths = torch.LongTensor([text.size(-1)]).to(text.device) - - quantized = self.quantizer.decode(codes) - if self.semantic_frame_rate == "25hz": - quantized = F.interpolate( - quantized, size=int(quantized.shape[-1] * 2), mode="nearest" - ) - - x, m_p, logs_p, y_mask = self.enc_p( - quantized, y_lengths, text, text_lengths, ge - ) - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - - z = self.flow(z_p, y_mask, g=ge, reverse=True) - - o = self.dec((z * y_mask)[:, :, :], g=ge) - return o - - def extract_latent(self, x): - ssl = self.ssl_proj(x) - quantized, codes, commit_loss, quantized_list = self.quantizer(ssl) - return codes.transpose(0, 1) diff --git a/module/models_onnx.py b/module/models_onnx.py deleted file mode 100644 index 232fd74d3084e86ab77f595010bb1f975573113b..0000000000000000000000000000000000000000 --- a/module/models_onnx.py +++ /dev/null @@ -1,918 +0,0 @@ -import copy -import math -import torch -from torch import nn -from torch.nn import functional as F - -from module import commons -from module import modules -from module import attentions_onnx as attentions - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from module.commons import init_weights, get_padding -from module.mrte_model import MRTE -from module.quantize import ResidualVectorQuantizer -from text import symbols -from torch.cuda.amp import autocast - - -class StochasticDurationPredictor(nn.Module): - def __init__( - self, - in_channels, - filter_channels, - kernel_size, - p_dropout, - n_flows=4, - gin_channels=0, - ): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append( - modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3) - ) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv( - filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout - ) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append( - modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3) - ) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv( - filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout - ) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = ( - torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) - * x_mask - ) - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum( - (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2] - ) - logq = ( - torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2]) - - logdet_tot_q - ) - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = ( - torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2]) - - logdet_tot - ) - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = ( - torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) - * noise_scale - ) - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class DurationPredictor(nn.Module): - def __init__( - self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 - ): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d( - in_channels, filter_channels, kernel_size, padding=kernel_size // 2 - ) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d( - filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 - ) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask - - -class TextEncoder(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - latent_channels=192, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.latent_channels = latent_channels - - self.ssl_proj = nn.Conv1d(768, hidden_channels, 1) - - self.encoder_ssl = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers // 2, - kernel_size, - p_dropout, - ) - - self.encoder_text = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.text_embedding = nn.Embedding(len(symbols), hidden_channels) - - self.mrte = MRTE() - - self.encoder2 = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers // 2, - kernel_size, - p_dropout, - ) - - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, y, text, ge): - y_mask = torch.ones_like(y[:1,:1,:]) - - y = self.ssl_proj(y * y_mask) * y_mask - y = self.encoder_ssl(y * y_mask, y_mask) - - text_mask = torch.ones_like(text).to(y.dtype).unsqueeze(0) - - text = self.text_embedding(text).transpose(1, 2) - text = self.encoder_text(text * text_mask, text_mask) - y = self.mrte(y, y_mask, text, text_mask, ge) - - y = self.encoder2(y * y_mask, y_mask) - - stats = self.proj(y) * y_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - return y, m, logs, y_mask - - def extract_latent(self, x): - x = self.ssl_proj(x) - quantized, codes, commit_loss, quantized_list = self.quantizer(x) - return codes.transpose(0, 1) - - def decode_latent(self, codes, y_mask, refer, refer_mask, ge): - quantized = self.quantizer.decode(codes) - - y = self.vq_proj(quantized) * y_mask - y = self.encoder_ssl(y * y_mask, y_mask) - - y = self.mrte(y, y_mask, refer, refer_mask, ge) - - y = self.encoder2(y * y_mask, y_mask) - - stats = self.proj(y) * y_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - return y, m, logs, y_mask, quantized - - -class ResidualCouplingBlock(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0, - ): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer( - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - mean_only=True, - ) - ) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class PosteriorEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - if g != None: - g = g.detach() - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class WNEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.norm = modules.LayerNorm(out_channels) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - out = self.proj(x) * x_mask - out = self.norm(out) - return out - - -class Generator(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=0, - ): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print("Removing weight norm...") - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 1024, - 1024, - (kernel_size, 1), - 1, - padding=(get_padding(kernel_size, 1), 0), - ) - ), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class ReferenceEncoder(nn.Module): - """ - inputs --- [N, Ty/r, n_mels*r] mels - outputs --- [N, ref_enc_gru_size] - """ - - def __init__(self, spec_channels, gin_channels=0): - super().__init__() - self.spec_channels = spec_channels - ref_enc_filters = [32, 32, 64, 64, 128, 128] - K = len(ref_enc_filters) - filters = [1] + ref_enc_filters - convs = [ - weight_norm( - nn.Conv2d( - in_channels=filters[i], - out_channels=filters[i + 1], - kernel_size=(3, 3), - stride=(2, 2), - padding=(1, 1), - ) - ) - for i in range(K) - ] - self.convs = nn.ModuleList(convs) - # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) - - out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K) - self.gru = nn.GRU( - input_size=ref_enc_filters[-1] * out_channels, - hidden_size=256 // 2, - batch_first=True, - ) - self.proj = nn.Linear(128, gin_channels) - - def forward(self, inputs): - N = inputs.size(0) - out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs] - for conv in self.convs: - out = conv(out) - # out = wn(out) - out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K] - - out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K] - T = out.size(1) - N = out.size(0) - out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K] - - self.gru.flatten_parameters() - memory, out = self.gru(out) # out --- [1, N, 128] - - return self.proj(out.squeeze(0)).unsqueeze(-1) - - def calculate_channels(self, L, kernel_size, stride, pad, n_convs): - for i in range(n_convs): - L = (L - kernel_size + 2 * pad) // stride + 1 - return L - - -class Quantizer_module(torch.nn.Module): - def __init__(self, n_e, e_dim): - super(Quantizer_module, self).__init__() - self.embedding = nn.Embedding(n_e, e_dim) - self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e) - - def forward(self, x): - d = ( - torch.sum(x**2, 1, keepdim=True) - + torch.sum(self.embedding.weight**2, 1) - - 2 * torch.matmul(x, self.embedding.weight.T) - ) - min_indicies = torch.argmin(d, 1) - z_q = self.embedding(min_indicies) - return z_q, min_indicies - - -class Quantizer(torch.nn.Module): - def __init__(self, embed_dim=512, n_code_groups=4, n_codes=160): - super(Quantizer, self).__init__() - assert embed_dim % n_code_groups == 0 - self.quantizer_modules = nn.ModuleList( - [ - Quantizer_module(n_codes, embed_dim // n_code_groups) - for _ in range(n_code_groups) - ] - ) - self.n_code_groups = n_code_groups - self.embed_dim = embed_dim - - def forward(self, xin): - # B, C, T - B, C, T = xin.shape - xin = xin.transpose(1, 2) - x = xin.reshape(-1, self.embed_dim) - x = torch.split(x, self.embed_dim // self.n_code_groups, dim=-1) - min_indicies = [] - z_q = [] - for _x, m in zip(x, self.quantizer_modules): - _z_q, _min_indicies = m(_x) - z_q.append(_z_q) - min_indicies.append(_min_indicies) # B * T, - z_q = torch.cat(z_q, -1).reshape(xin.shape) - loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean( - (z_q - xin.detach()) ** 2 - ) - z_q = xin + (z_q - xin).detach() - z_q = z_q.transpose(1, 2) - codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups) - return z_q, loss, codes.transpose(1, 2) - - def embed(self, x): - # idx: N, 4, T - x = x.transpose(1, 2) - x = torch.split(x, 1, 2) - ret = [] - for q, embed in zip(x, self.quantizer_modules): - q = embed.embedding(q.squeeze(-1)) - ret.append(q) - ret = torch.cat(ret, -1) - return ret.transpose(1, 2) # N, C, T - - -class CodePredictor(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - n_q=8, - dims=1024, - ssl_dim=768, - ): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - - self.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1) - self.ref_enc = modules.MelStyleEncoder( - ssl_dim, style_vector_dim=hidden_channels - ) - - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - - self.out_proj = nn.Conv1d(hidden_channels, (n_q - 1) * dims, 1) - self.n_q = n_q - self.dims = dims - - def forward(self, x, x_mask, refer, codes, infer=False): - x = x.detach() - x = self.vq_proj(x * x_mask) * x_mask - g = self.ref_enc(refer, x_mask) - x = x + g - x = self.encoder(x * x_mask, x_mask) - x = self.out_proj(x * x_mask) * x_mask - logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose( - 2, 3 - ) - target = codes[1:].transpose(0, 1) - if not infer: - logits = logits.reshape(-1, self.dims) - target = target.reshape(-1) - loss = torch.nn.functional.cross_entropy(logits, target) - return loss - else: - _, top10_preds = torch.topk(logits, 10, dim=-1) - correct_top10 = torch.any(top10_preds == target.unsqueeze(-1), dim=-1) - top3_acc = 100 * torch.mean(correct_top10.float()).detach().cpu().item() - - print("Top-10 Accuracy:", top3_acc, "%") - - pred_codes = torch.argmax(logits, dim=-1) - acc = 100 * torch.mean((pred_codes == target).float()).detach().cpu().item() - print("Top-1 Accuracy:", acc, "%") - - return pred_codes.transpose(0, 1) - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - n_speakers=0, - gin_channels=0, - use_sdp=True, - semantic_frame_rate=None, - freeze_quantizer=None, - **kwargs - ): - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.n_speakers = n_speakers - self.gin_channels = gin_channels - - self.use_sdp = use_sdp - self.enc_p = TextEncoder( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = Generator( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels - ) - - self.ref_enc = modules.MelStyleEncoder( - spec_channels, style_vector_dim=gin_channels - ) - - ssl_dim = 768 - self.ssl_dim = ssl_dim - assert semantic_frame_rate in ["25hz", "50hz"] - self.semantic_frame_rate = semantic_frame_rate - if semantic_frame_rate == "25hz": - self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2) - else: - self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1) - - self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024) - if freeze_quantizer: - self.ssl_proj.requires_grad_(False) - self.quantizer.requires_grad_(False) - # self.enc_p.text_embedding.requires_grad_(False) - # self.enc_p.encoder_text.requires_grad_(False) - # self.enc_p.mrte.requires_grad_(False) - - def forward(self, codes, text, refer): - refer_mask = torch.ones_like(refer[:1,:1,:]) - ge = self.ref_enc(refer * refer_mask, refer_mask) - - quantized = self.quantizer.decode(codes) - if self.semantic_frame_rate == "25hz": - dquantized = torch.cat([quantized, quantized]).permute(1, 2, 0) - quantized = dquantized.contiguous().view(1, self.ssl_dim, -1) - - x, m_p, logs_p, y_mask = self.enc_p( - quantized, text, ge - ) - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) - - z = self.flow(z_p, y_mask, g=ge, reverse=True) - - o = self.dec((z * y_mask)[:, :, :], g=ge) - return o - - def extract_latent(self, x): - ssl = self.ssl_proj(x) - quantized, codes, commit_loss, quantized_list = self.quantizer(ssl) - return codes.transpose(0, 1) \ No newline at end of file diff --git a/module/modules.py b/module/modules.py deleted file mode 100644 index f44474558423ba27b1d2693cf35289b07cc0086f..0000000000000000000000000000000000000000 --- a/module/modules.py +++ /dev/null @@ -1,923 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d -from torch.nn.utils import weight_norm, remove_weight_norm - -from module import commons -from module.commons import init_weights, get_padding -from module.transforms import piecewise_rational_quadratic_transform -import torch.distributions as D - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__( - self, - in_channels, - hidden_channels, - out_channels, - kernel_size, - n_layers, - p_dropout, - ): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append( - nn.Conv1d( - in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) - for _ in range(n_layers - 1): - self.conv_layers.append( - nn.Conv1d( - hidden_channels, - hidden_channels, - kernel_size, - padding=kernel_size // 2, - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size**i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append( - nn.Conv1d( - channels, - channels, - kernel_size, - groups=channels, - dilation=dilation, - padding=padding, - ) - ) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__( - self, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - p_dropout=0, - ): - super(WN, self).__init__() - assert kernel_size % 2 == 1 - self.hidden_channels = hidden_channels - self.kernel_size = (kernel_size,) - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d( - gin_channels, 2 * hidden_channels * n_layers, 1 - ) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") - - for i in range(n_layers): - dilation = dilation_rate**i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d( - hidden_channels, - 2 * hidden_channels, - kernel_size, - dilation=dilation, - padding=padding, - ) - in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:, : self.hidden_channels, :] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:, self.hidden_channels :, :] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels, 1)) - self.logs = nn.Parameter(torch.zeros(channels, 1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1, 2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False, - ): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=p_dropout, - gin_channels=gin_channels, - ) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels] * 2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1, 2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - -class ConvFlow(nn.Module): - def __init__( - self, - in_channels, - filter_channels, - kernel_size, - n_layers, - num_bins=10, - tail_bound=5.0, - ): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0) - self.proj = nn.Conv1d( - filter_channels, self.half_channels * (num_bins * 3 - 1), 1 - ) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt( - self.filter_channels - ) - unnormalized_derivatives = h[..., 2 * self.num_bins :] - - x1, logabsdet = piecewise_rational_quadratic_transform( - x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails="linear", - tail_bound=self.tail_bound, - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1, 2]) - if not reverse: - return x, logdet - else: - return x - - -class LinearNorm(nn.Module): - def __init__( - self, - in_channels, - out_channels, - bias=True, - spectral_norm=False, - ): - super(LinearNorm, self).__init__() - self.fc = nn.Linear(in_channels, out_channels, bias) - - if spectral_norm: - self.fc = nn.utils.spectral_norm(self.fc) - - def forward(self, input): - out = self.fc(input) - return out - - -class Mish(nn.Module): - def __init__(self): - super(Mish, self).__init__() - - def forward(self, x): - return x * torch.tanh(F.softplus(x)) - - -class Conv1dGLU(nn.Module): - """ - Conv1d + GLU(Gated Linear Unit) with residual connection. - For GLU refer to https://arxiv.org/abs/1612.08083 paper. - """ - - def __init__(self, in_channels, out_channels, kernel_size, dropout): - super(Conv1dGLU, self).__init__() - self.out_channels = out_channels - self.conv1 = ConvNorm(in_channels, 2 * out_channels, kernel_size=kernel_size) - self.dropout = nn.Dropout(dropout) - - def forward(self, x): - residual = x - x = self.conv1(x) - x1, x2 = torch.split(x, split_size_or_sections=self.out_channels, dim=1) - x = x1 * torch.sigmoid(x2) - x = residual + self.dropout(x) - return x - - -class ConvNorm(nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size=1, - stride=1, - padding=None, - dilation=1, - bias=True, - spectral_norm=False, - ): - super(ConvNorm, self).__init__() - - if padding is None: - assert kernel_size % 2 == 1 - padding = int(dilation * (kernel_size - 1) / 2) - - self.conv = torch.nn.Conv1d( - in_channels, - out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - bias=bias, - ) - - if spectral_norm: - self.conv = nn.utils.spectral_norm(self.conv) - - def forward(self, input): - out = self.conv(input) - return out - - -class MultiHeadAttention(nn.Module): - """Multi-Head Attention module""" - - def __init__(self, n_head, d_model, d_k, d_v, dropout=0.0, spectral_norm=False): - super().__init__() - - self.n_head = n_head - self.d_k = d_k - self.d_v = d_v - - self.w_qs = nn.Linear(d_model, n_head * d_k) - self.w_ks = nn.Linear(d_model, n_head * d_k) - self.w_vs = nn.Linear(d_model, n_head * d_v) - - self.attention = ScaledDotProductAttention( - temperature=np.power(d_model, 0.5), dropout=dropout - ) - - self.fc = nn.Linear(n_head * d_v, d_model) - self.dropout = nn.Dropout(dropout) - - if spectral_norm: - self.w_qs = nn.utils.spectral_norm(self.w_qs) - self.w_ks = nn.utils.spectral_norm(self.w_ks) - self.w_vs = nn.utils.spectral_norm(self.w_vs) - self.fc = nn.utils.spectral_norm(self.fc) - - def forward(self, x, mask=None): - d_k, d_v, n_head = self.d_k, self.d_v, self.n_head - sz_b, len_x, _ = x.size() - - residual = x - - q = self.w_qs(x).view(sz_b, len_x, n_head, d_k) - k = self.w_ks(x).view(sz_b, len_x, n_head, d_k) - v = self.w_vs(x).view(sz_b, len_x, n_head, d_v) - q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) # (n*b) x lq x dk - k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) # (n*b) x lk x dk - v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_v) # (n*b) x lv x dv - - if mask is not None: - slf_mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x .. - else: - slf_mask = None - output, attn = self.attention(q, k, v, mask=slf_mask) - - output = output.view(n_head, sz_b, len_x, d_v) - output = ( - output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_x, -1) - ) # b x lq x (n*dv) - - output = self.fc(output) - - output = self.dropout(output) + residual - return output, attn - - -class ScaledDotProductAttention(nn.Module): - """Scaled Dot-Product Attention""" - - def __init__(self, temperature, dropout): - super().__init__() - self.temperature = temperature - self.softmax = nn.Softmax(dim=2) - self.dropout = nn.Dropout(dropout) - - def forward(self, q, k, v, mask=None): - attn = torch.bmm(q, k.transpose(1, 2)) - attn = attn / self.temperature - - if mask is not None: - attn = attn.masked_fill(mask, -np.inf) - - attn = self.softmax(attn) - p_attn = self.dropout(attn) - - output = torch.bmm(p_attn, v) - return output, attn - - -class MelStyleEncoder(nn.Module): - """MelStyleEncoder""" - - def __init__( - self, - n_mel_channels=80, - style_hidden=128, - style_vector_dim=256, - style_kernel_size=5, - style_head=2, - dropout=0.1, - ): - super(MelStyleEncoder, self).__init__() - self.in_dim = n_mel_channels - self.hidden_dim = style_hidden - self.out_dim = style_vector_dim - self.kernel_size = style_kernel_size - self.n_head = style_head - self.dropout = dropout - - self.spectral = nn.Sequential( - LinearNorm(self.in_dim, self.hidden_dim), - Mish(), - nn.Dropout(self.dropout), - LinearNorm(self.hidden_dim, self.hidden_dim), - Mish(), - nn.Dropout(self.dropout), - ) - - self.temporal = nn.Sequential( - Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout), - Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout), - ) - - self.slf_attn = MultiHeadAttention( - self.n_head, - self.hidden_dim, - self.hidden_dim // self.n_head, - self.hidden_dim // self.n_head, - self.dropout, - ) - - self.fc = LinearNorm(self.hidden_dim, self.out_dim) - - def temporal_avg_pool(self, x, mask=None): - if mask is None: - out = torch.mean(x, dim=1) - else: - len_ = (~mask).sum(dim=1).unsqueeze(1) - x = x.masked_fill(mask.unsqueeze(-1), 0) - x = x.sum(dim=1) - out = torch.div(x, len_) - return out - - def forward(self, x, mask=None): - x = x.transpose(1, 2) - if mask is not None: - mask = (mask.int() == 0).squeeze(1) - max_len = x.shape[1] - slf_attn_mask = ( - mask.unsqueeze(1).expand(-1, max_len, -1) if mask is not None else None - ) - - # spectral - x = self.spectral(x) - # temporal - x = x.transpose(1, 2) - x = self.temporal(x) - x = x.transpose(1, 2) - # self-attention - if mask is not None: - x = x.masked_fill(mask.unsqueeze(-1), 0) - x, _ = self.slf_attn(x, mask=slf_attn_mask) - # fc - x = self.fc(x) - # temoral average pooling - w = self.temporal_avg_pool(x, mask=mask) - - return w.unsqueeze(-1) - - -class MelStyleEncoderVAE(nn.Module): - def __init__(self, spec_channels, z_latent_dim, emb_dim): - super().__init__() - self.ref_encoder = MelStyleEncoder(spec_channels, style_vector_dim=emb_dim) - self.fc1 = nn.Linear(emb_dim, z_latent_dim) - self.fc2 = nn.Linear(emb_dim, z_latent_dim) - self.fc3 = nn.Linear(z_latent_dim, emb_dim) - self.z_latent_dim = z_latent_dim - - def reparameterize(self, mu, logvar): - if self.training: - std = torch.exp(0.5 * logvar) - eps = torch.randn_like(std) - return eps.mul(std).add_(mu) - else: - return mu - - def forward(self, inputs, mask=None): - enc_out = self.ref_encoder(inputs.squeeze(-1), mask).squeeze(-1) - mu = self.fc1(enc_out) - logvar = self.fc2(enc_out) - posterior = D.Normal(mu, torch.exp(logvar)) - kl_divergence = D.kl_divergence( - posterior, D.Normal(torch.zeros_like(mu), torch.ones_like(logvar)) - ) - loss_kl = kl_divergence.mean() - - z = posterior.rsample() - style_embed = self.fc3(z) - - return style_embed.unsqueeze(-1), loss_kl - - def infer(self, inputs=None, random_sample=False, manual_latent=None): - if manual_latent is None: - if random_sample: - dev = next(self.parameters()).device - posterior = D.Normal( - torch.zeros(1, self.z_latent_dim, device=dev), - torch.ones(1, self.z_latent_dim, device=dev), - ) - z = posterior.rsample() - else: - enc_out = self.ref_encoder(inputs.transpose(1, 2)) - mu = self.fc1(enc_out) - z = mu - else: - z = manual_latent - style_embed = self.fc3(z) - return style_embed.unsqueeze(-1), z - - -class ActNorm(nn.Module): - def __init__(self, channels, ddi=False, **kwargs): - super().__init__() - self.channels = channels - self.initialized = not ddi - - self.logs = nn.Parameter(torch.zeros(1, channels, 1)) - self.bias = nn.Parameter(torch.zeros(1, channels, 1)) - - def forward(self, x, x_mask=None, g=None, reverse=False, **kwargs): - if x_mask is None: - x_mask = torch.ones(x.size(0), 1, x.size(2)).to( - device=x.device, dtype=x.dtype - ) - x_len = torch.sum(x_mask, [1, 2]) - if not self.initialized: - self.initialize(x, x_mask) - self.initialized = True - - if reverse: - z = (x - self.bias) * torch.exp(-self.logs) * x_mask - logdet = None - return z - else: - z = (self.bias + torch.exp(self.logs) * x) * x_mask - logdet = torch.sum(self.logs) * x_len # [b] - return z, logdet - - def store_inverse(self): - pass - - def set_ddi(self, ddi): - self.initialized = not ddi - - def initialize(self, x, x_mask): - with torch.no_grad(): - denom = torch.sum(x_mask, [0, 2]) - m = torch.sum(x * x_mask, [0, 2]) / denom - m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom - v = m_sq - (m**2) - logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6)) - - bias_init = ( - (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype) - ) - logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype) - - self.bias.data.copy_(bias_init) - self.logs.data.copy_(logs_init) - - -class InvConvNear(nn.Module): - def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs): - super().__init__() - assert n_split % 2 == 0 - self.channels = channels - self.n_split = n_split - self.no_jacobian = no_jacobian - - w_init = torch.linalg.qr( - torch.FloatTensor(self.n_split, self.n_split).normal_() - )[0] - if torch.det(w_init) < 0: - w_init[:, 0] = -1 * w_init[:, 0] - self.weight = nn.Parameter(w_init) - - def forward(self, x, x_mask=None, g=None, reverse=False, **kwargs): - b, c, t = x.size() - assert c % self.n_split == 0 - if x_mask is None: - x_mask = 1 - x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t - else: - x_len = torch.sum(x_mask, [1, 2]) - - x = x.view(b, 2, c // self.n_split, self.n_split // 2, t) - x = ( - x.permute(0, 1, 3, 2, 4) - .contiguous() - .view(b, self.n_split, c // self.n_split, t) - ) - - if reverse: - if hasattr(self, "weight_inv"): - weight = self.weight_inv - else: - weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) - logdet = None - else: - weight = self.weight - if self.no_jacobian: - logdet = 0 - else: - logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b] - - weight = weight.view(self.n_split, self.n_split, 1, 1) - z = F.conv2d(x, weight) - - z = z.view(b, 2, self.n_split // 2, c // self.n_split, t) - z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask - if reverse: - return z - else: - return z, logdet - - def store_inverse(self): - self.weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) diff --git a/module/mrte_model.py b/module/mrte_model.py deleted file mode 100644 index b0cd242c3faef30c819cbd7cdbedd90f83fcb1d6..0000000000000000000000000000000000000000 --- a/module/mrte_model.py +++ /dev/null @@ -1,192 +0,0 @@ -# This is Multi-reference timbre encoder - -import torch -from torch import nn -from torch.nn.utils import remove_weight_norm, weight_norm -from module.attentions import MultiHeadAttention - - -class MRTE(nn.Module): - def __init__( - self, - content_enc_channels=192, - hidden_size=512, - out_channels=192, - kernel_size=5, - n_heads=4, - ge_layer=2, - ): - super(MRTE, self).__init__() - self.cross_attention = MultiHeadAttention(hidden_size, hidden_size, n_heads) - self.c_pre = nn.Conv1d(content_enc_channels, hidden_size, 1) - self.text_pre = nn.Conv1d(content_enc_channels, hidden_size, 1) - self.c_post = nn.Conv1d(hidden_size, out_channels, 1) - - def forward(self, ssl_enc, ssl_mask, text, text_mask, ge, test=None): - if ge == None: - ge = 0 - attn_mask = text_mask.unsqueeze(2) * ssl_mask.unsqueeze(-1) - - ssl_enc = self.c_pre(ssl_enc * ssl_mask) - text_enc = self.text_pre(text * text_mask) - if test != None: - if test == 0: - x = ( - self.cross_attention( - ssl_enc * ssl_mask, text_enc * text_mask, attn_mask - ) - + ssl_enc - + ge - ) - elif test == 1: - x = ssl_enc + ge - elif test == 2: - x = ( - self.cross_attention( - ssl_enc * 0 * ssl_mask, text_enc * text_mask, attn_mask - ) - + ge - ) - else: - raise ValueError("test should be 0,1,2") - else: - x = ( - self.cross_attention( - ssl_enc * ssl_mask, text_enc * text_mask, attn_mask - ) - + ssl_enc - + ge - ) - x = self.c_post(x * ssl_mask) - return x - - -class SpeakerEncoder(torch.nn.Module): - def __init__( - self, - mel_n_channels=80, - model_num_layers=2, - model_hidden_size=256, - model_embedding_size=256, - ): - super(SpeakerEncoder, self).__init__() - self.lstm = nn.LSTM( - mel_n_channels, model_hidden_size, model_num_layers, batch_first=True - ) - self.linear = nn.Linear(model_hidden_size, model_embedding_size) - self.relu = nn.ReLU() - - def forward(self, mels): - self.lstm.flatten_parameters() - _, (hidden, _) = self.lstm(mels.transpose(-1, -2)) - embeds_raw = self.relu(self.linear(hidden[-1])) - return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) - - -class MELEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - - def forward(self, x): - # print(x.shape,x_lengths.shape) - x = self.pre(x) - x = self.enc(x) - x = self.proj(x) - return x - - -class WN(torch.nn.Module): - def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers): - super(WN, self).__init__() - assert kernel_size % 2 == 1 - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - - for i in range(n_layers): - dilation = dilation_rate**i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = nn.Conv1d( - hidden_channels, - 2 * hidden_channels, - kernel_size, - dilation=dilation, - padding=padding, - ) - in_layer = weight_norm(in_layer) - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = weight_norm(res_skip_layer, name="weight") - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - - acts = fused_add_tanh_sigmoid_multiply(x_in, n_channels_tensor) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:, : self.hidden_channels, :] - x = x + res_acts - output = output + res_skip_acts[:, self.hidden_channels :, :] - else: - output = output + res_skip_acts - return output - - def remove_weight_norm(self): - for l in self.in_layers: - remove_weight_norm(l) - for l in self.res_skip_layers: - remove_weight_norm(l) - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input, n_channels): - n_channels_int = n_channels[0] - t_act = torch.tanh(input[:, :n_channels_int, :]) - s_act = torch.sigmoid(input[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -if __name__ == "__main__": - content_enc = torch.randn(3, 192, 100) - content_mask = torch.ones(3, 1, 100) - ref_mel = torch.randn(3, 128, 30) - ref_mask = torch.ones(3, 1, 30) - model = MRTE() - out = model(content_enc, content_mask, ref_mel, ref_mask) - print(out.shape) diff --git a/module/quantize.py b/module/quantize.py deleted file mode 100644 index f9a5c632dadbd82e0e2802daee5c0f1fe51b36a7..0000000000000000000000000000000000000000 --- a/module/quantize.py +++ /dev/null @@ -1,119 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -"""Residual vector quantizer implementation.""" - -from dataclasses import dataclass, field -import math -import typing as tp - -import torch -from torch import nn - -from module.core_vq import ResidualVectorQuantization - - -@dataclass -class QuantizedResult: - quantized: torch.Tensor - codes: torch.Tensor - bandwidth: torch.Tensor # bandwidth in kb/s used, per batch item. - penalty: tp.Optional[torch.Tensor] = None - metrics: dict = field(default_factory=dict) - - -class ResidualVectorQuantizer(nn.Module): - """Residual Vector Quantizer. - Args: - dimension (int): Dimension of the codebooks. - n_q (int): Number of residual vector quantizers used. - bins (int): Codebook size. - decay (float): Decay for exponential moving average over the codebooks. - kmeans_init (bool): Whether to use kmeans to initialize the codebooks. - kmeans_iters (int): Number of iterations used for kmeans initialization. - threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes - that have an exponential moving average cluster size less than the specified threshold with - randomly selected vector from the current batch. - """ - - def __init__( - self, - dimension: int = 256, - n_q: int = 8, - bins: int = 1024, - decay: float = 0.99, - kmeans_init: bool = True, - kmeans_iters: int = 50, - threshold_ema_dead_code: int = 2, - ): - super().__init__() - self.n_q = n_q - self.dimension = dimension - self.bins = bins - self.decay = decay - self.kmeans_init = kmeans_init - self.kmeans_iters = kmeans_iters - self.threshold_ema_dead_code = threshold_ema_dead_code - self.vq = ResidualVectorQuantization( - dim=self.dimension, - codebook_size=self.bins, - num_quantizers=self.n_q, - decay=self.decay, - kmeans_init=self.kmeans_init, - kmeans_iters=self.kmeans_iters, - threshold_ema_dead_code=self.threshold_ema_dead_code, - ) - - def forward( - self, - x: torch.Tensor, - n_q: tp.Optional[int] = None, - layers: tp.Optional[list] = None, - ) -> QuantizedResult: - """Residual vector quantization on the given input tensor. - Args: - x (torch.Tensor): Input tensor. - n_q (int): Number of quantizer used to quantize. Default: All quantizers. - layers (list): Layer that need to return quantized. Defalt: None. - Returns: - QuantizedResult: - The quantized (or approximately quantized) representation with - the associated numbert quantizers and layer quantized required to return. - """ - n_q = n_q if n_q else self.n_q - if layers and max(layers) >= n_q: - raise ValueError( - f"Last layer index in layers: A {max(layers)}. Number of quantizers in RVQ: B {self.n_q}. A must less than B." - ) - quantized, codes, commit_loss, quantized_list = self.vq( - x, n_q=n_q, layers=layers - ) - return quantized, codes, torch.mean(commit_loss), quantized_list - - def encode( - self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None - ) -> torch.Tensor: - """Encode a given input tensor with the specified sample rate at the given bandwidth. - The RVQ encode method sets the appropriate number of quantizer to use - and returns indices for each quantizer. - Args: - x (torch.Tensor): Input tensor. - n_q (int): Number of quantizer used to quantize. Default: All quantizers. - st (int): Start to encode input from which layers. Default: 0. - """ - n_q = n_q if n_q else self.n_q - st = st or 0 - codes = self.vq.encode(x, n_q=n_q, st=st) - return codes - - def decode(self, codes: torch.Tensor, st: int = 0) -> torch.Tensor: - """Decode the given codes to the quantized representation. - Args: - codes (torch.Tensor): Input indices for each quantizer. - st (int): Start to decode input codes from which layers. Default: 0. - """ - quantized = self.vq.decode(codes, st=st) - return quantized diff --git a/module/transforms.py b/module/transforms.py deleted file mode 100644 index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000 --- a/module/transforms.py +++ /dev/null @@ -1,209 +0,0 @@ -import torch -from torch.nn import functional as F - -import numpy as np - - -DEFAULT_MIN_BIN_WIDTH = 1e-3 -DEFAULT_MIN_BIN_HEIGHT = 1e-3 -DEFAULT_MIN_DERIVATIVE = 1e-3 - - -def piecewise_rational_quadratic_transform( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails=None, - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if tails is None: - spline_fn = rational_quadratic_spline - spline_kwargs = {} - else: - spline_fn = unconstrained_rational_quadratic_spline - spline_kwargs = {"tails": tails, "tail_bound": tail_bound} - - outputs, logabsdet = spline_fn( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1 - - -def unconstrained_rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails="linear", - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == "linear": - unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) - constant = np.log(np.exp(1 - min_derivative) - 1) - unnormalized_derivatives[..., 0] = constant - unnormalized_derivatives[..., -1] = constant - - outputs[outside_interval_mask] = inputs[outside_interval_mask] - logabsdet[outside_interval_mask] = 0 - else: - raise RuntimeError("{} tails are not implemented.".format(tails)) - - ( - outputs[inside_interval_mask], - logabsdet[inside_interval_mask], - ) = rational_quadratic_spline( - inputs=inputs[inside_interval_mask], - unnormalized_widths=unnormalized_widths[inside_interval_mask, :], - unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], - inverse=inverse, - left=-tail_bound, - right=tail_bound, - bottom=-tail_bound, - top=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - ) - - return outputs, logabsdet - - -def rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0.0, - right=1.0, - bottom=0.0, - top=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError("Input to a transform is not within its domain") - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError("Minimal bin width too large for the number of bins") - if min_bin_height * num_bins > 1.0: - raise ValueError("Minimal bin height too large for the number of bins") - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - - input_heights = heights.gather(-1, bin_idx)[..., 0] - - if inverse: - a = (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) + input_heights * (input_delta - input_derivatives) - b = input_heights * input_derivatives - (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) - c = -input_delta * (inputs - input_cumheights) - - discriminant = b.pow(2) - 4 * a * c - assert (discriminant >= 0).all() - - root = (2 * c) / (-b - torch.sqrt(discriminant)) - outputs = root * input_bin_widths + input_cumwidths - - theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, -logabsdet - else: - theta = (inputs - input_cumwidths) / input_bin_widths - theta_one_minus_theta = theta * (1 - theta) - - numerator = input_heights * ( - input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta - ) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - outputs = input_cumheights + numerator / denominator - - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, logabsdet diff --git a/my_utils.py b/my_utils.py deleted file mode 100644 index 776939dd29bbcd9c45e47665f545a3dddcb16478..0000000000000000000000000000000000000000 --- a/my_utils.py +++ /dev/null @@ -1,21 +0,0 @@ -import ffmpeg -import numpy as np - - -def load_audio(file, sr): - try: - # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26 - # This launches a subprocess to decode audio while down-mixing and resampling as necessary. - # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed. - file = ( - file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) # 防止小白拷路径头尾带了空格和"和回车 - out, _ = ( - ffmpeg.input(file, threads=0) - .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr) - .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True) - ) - except Exception as e: - raise RuntimeError(f"Failed to load audio: {e}") - - return np.frombuffer(out, np.float32).flatten() diff --git a/prepare_datasets/1-get-text.py b/prepare_datasets/1-get-text.py deleted file mode 100644 index 88c9d85848ad1e003d39908e2f45faa5cb0604c4..0000000000000000000000000000000000000000 --- a/prepare_datasets/1-get-text.py +++ /dev/null @@ -1,131 +0,0 @@ -# -*- coding: utf-8 -*- - -import os - -inp_text = os.environ.get("inp_text") -inp_wav_dir = os.environ.get("inp_wav_dir") -exp_name = os.environ.get("exp_name") -i_part = os.environ.get("i_part") -all_parts = os.environ.get("all_parts") -os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES") -opt_dir = os.environ.get("opt_dir") -bert_pretrained_dir = os.environ.get("bert_pretrained_dir") -is_half = eval(os.environ.get("is_half", "True")) -import sys, numpy as np, traceback, pdb -import os.path -from glob import glob -from tqdm import tqdm -from text.cleaner import clean_text -import torch -from transformers import AutoModelForMaskedLM, AutoTokenizer -import numpy as np - -# inp_text=sys.argv[1] -# inp_wav_dir=sys.argv[2] -# exp_name=sys.argv[3] -# i_part=sys.argv[4] -# all_parts=sys.argv[5] -# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6]#i_gpu -# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name -# bert_pretrained_dir="/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large" - -from time import time as ttime -import shutil - - -def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path - dir = os.path.dirname(path) - name = os.path.basename(path) - tmp_path = "%s/%s%s.pth" % (dir, ttime(), i_part) - torch.save(fea, tmp_path) - shutil.move(tmp_path, "%s/%s" % (dir, name)) - - - -txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part) -if os.path.exists(txt_path) == False: - bert_dir = "%s/3-bert" % (opt_dir) - os.makedirs(opt_dir, exist_ok=True) - os.makedirs(bert_dir, exist_ok=True) - if torch.cuda.is_available(): - device = "cuda:0" - elif torch.backends.mps.is_available(): - device = "mps" - else: - device = "cpu" - tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir) - bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir) - if is_half == True: - bert_model = bert_model.half().to(device) - else: - bert_model = bert_model.to(device) - - def get_bert_feature(text, word2ph): - with torch.no_grad(): - inputs = tokenizer(text, return_tensors="pt") - for i in inputs: - inputs[i] = inputs[i].to(device) - res = bert_model(**inputs, output_hidden_states=True) - res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] - - assert len(word2ph) == len(text) - phone_level_feature = [] - for i in range(len(word2ph)): - repeat_feature = res[i].repeat(word2ph[i], 1) - phone_level_feature.append(repeat_feature) - - phone_level_feature = torch.cat(phone_level_feature, dim=0) - - return phone_level_feature.T - - def process(data, res): - for name, text, lan in data: - try: - name = os.path.basename(name) - phones, word2ph, norm_text = clean_text( - text.replace("%", "-").replace("￥", ","), lan - ) - path_bert = "%s/%s.pt" % (bert_dir, name) - if os.path.exists(path_bert) == False and lan == "zh": - bert_feature = get_bert_feature(norm_text, word2ph) - assert bert_feature.shape[-1] == len(phones) - # torch.save(bert_feature, path_bert) - my_save(bert_feature, path_bert) - phones = " ".join(phones) - # res.append([name,phones]) - res.append([name, phones, word2ph, norm_text]) - except: - print(name, text, traceback.format_exc()) - - todo = [] - res = [] - with open(inp_text, "r", encoding="utf8") as f: - lines = f.read().strip("\n").split("\n") - - language_v1_to_language_v2 = { - "ZH": "zh", - "zh": "zh", - "JP": "ja", - "jp": "ja", - "JA": "ja", - "ja": "ja", - "EN": "en", - "en": "en", - "En": "en", - } - for line in lines[int(i_part) :: int(all_parts)]: - try: - wav_name, spk_name, language, text = line.split("|") - # todo.append([name,text,"zh"]) - todo.append( - [wav_name, text, language_v1_to_language_v2.get(language, language)] - ) - except: - print(line, traceback.format_exc()) - - process(todo, res) - opt = [] - for name, phones, word2ph, norm_text in res: - opt.append("%s\t%s\t%s\t%s" % (name, phones, word2ph, norm_text)) - with open(txt_path, "w", encoding="utf8") as f: - f.write("\n".join(opt) + "\n") diff --git a/prepare_datasets/2-get-hubert-wav32k.py b/prepare_datasets/2-get-hubert-wav32k.py deleted file mode 100644 index b8355dd4e73dace0f5ddf5589d18330885a61633..0000000000000000000000000000000000000000 --- a/prepare_datasets/2-get-hubert-wav32k.py +++ /dev/null @@ -1,119 +0,0 @@ -# -*- coding: utf-8 -*- - -import sys,os -inp_text= os.environ.get("inp_text") -inp_wav_dir= os.environ.get("inp_wav_dir") -exp_name= os.environ.get("exp_name") -i_part= os.environ.get("i_part") -all_parts= os.environ.get("all_parts") -os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES") -from feature_extractor import cnhubert -opt_dir= os.environ.get("opt_dir") -cnhubert.cnhubert_base_path= os.environ.get("cnhubert_base_dir") -is_half=eval(os.environ.get("is_half","True")) - -import pdb,traceback,numpy as np,logging -from scipy.io import wavfile -import librosa,torch -now_dir = os.getcwd() -sys.path.append(now_dir) -from my_utils import load_audio - -# from config import cnhubert_base_path -# cnhubert.cnhubert_base_path=cnhubert_base_path -# inp_text=sys.argv[1] -# inp_wav_dir=sys.argv[2] -# exp_name=sys.argv[3] -# i_part=sys.argv[4] -# all_parts=sys.argv[5] -# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6] -# cnhubert.cnhubert_base_path=sys.argv[7] -# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name - -from time import time as ttime -import shutil -def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path - dir=os.path.dirname(path) - name=os.path.basename(path) - tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part) - torch.save(fea,tmp_path) - shutil.move(tmp_path,"%s/%s"%(dir,name)) - -hubert_dir="%s/4-cnhubert"%(opt_dir) -wav32dir="%s/5-wav32k"%(opt_dir) -os.makedirs(opt_dir,exist_ok=True) -os.makedirs(hubert_dir,exist_ok=True) -os.makedirs(wav32dir,exist_ok=True) - -maxx=0.95 -alpha=0.5 -if torch.cuda.is_available(): - device = "cuda:0" -elif torch.backends.mps.is_available(): - device = "mps" -else: - device = "cpu" -model=cnhubert.get_model() -# is_half=False -if(is_half==True): - model=model.half().to(device) -else: - model = model.to(device) - -nan_fails=[] -def name2go(wav_name,wav_path): - hubert_path="%s/%s.pt"%(hubert_dir,wav_name) - if(os.path.exists(hubert_path)):return - tmp_audio = load_audio(wav_path, 32000) - tmp_max = np.abs(tmp_audio).max() - if tmp_max > 2.2: - print("%s-filtered,%s" % (wav_name, tmp_max)) - return - tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * tmp_audio - tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha*1145.14)) + ((1 - alpha)*1145.14) * tmp_audio - tmp_audio = librosa.resample( - tmp_audio32b, orig_sr=32000, target_sr=16000 - )#不是重采样问题 - tensor_wav16 = torch.from_numpy(tmp_audio) - if (is_half == True): - tensor_wav16=tensor_wav16.half().to(device) - else: - tensor_wav16 = tensor_wav16.to(device) - ssl=model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1,2).cpu()#torch.Size([1, 768, 215]) - if np.isnan(ssl.detach().numpy()).sum()!= 0: - nan_fails.append(wav_name) - print("nan filtered:%s"%wav_name) - return - wavfile.write( - "%s/%s"%(wav32dir,wav_name), - 32000, - tmp_audio32.astype("int16"), - ) - my_save(ssl,hubert_path ) - -with open(inp_text,"r",encoding="utf8")as f: - lines=f.read().strip("\n").split("\n") - -for line in lines[int(i_part)::int(all_parts)]: - try: - # wav_name,text=line.split("\t") - wav_name, spk_name, language, text = line.split("|") - if (inp_wav_dir !=None): - wav_name = os.path.basename(wav_name) - wav_path = "%s/%s"%(inp_wav_dir, wav_name) - - else: - wav_path=wav_name - wav_name = os.path.basename(wav_name) - name2go(wav_name,wav_path) - except: - print(line,traceback.format_exc()) - -if(len(nan_fails)>0 and is_half==True): - is_half=False - model=model.float() - for wav_name in nan_fails: - try: - name2go(wav_name) - except: - print(wav_name,traceback.format_exc()) diff --git a/prepare_datasets/3-get-semantic.py b/prepare_datasets/3-get-semantic.py deleted file mode 100644 index 4e7056d1a6c97779828f90aea4f27328003cf7cc..0000000000000000000000000000000000000000 --- a/prepare_datasets/3-get-semantic.py +++ /dev/null @@ -1,95 +0,0 @@ -import os - -inp_text = os.environ.get("inp_text") -exp_name = os.environ.get("exp_name") -i_part = os.environ.get("i_part") -all_parts = os.environ.get("all_parts") -os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES") -opt_dir = os.environ.get("opt_dir") -pretrained_s2G = os.environ.get("pretrained_s2G") -s2config_path = os.environ.get("s2config_path") -is_half = eval(os.environ.get("is_half", "True")) -import math, traceback -import multiprocessing -import sys, pdb - -now_dir = os.getcwd() -sys.path.append(now_dir) -from random import shuffle -import torch.multiprocessing as mp -from glob import glob -from tqdm import tqdm -import logging, librosa, utils, torch -from module.models import SynthesizerTrn - -#logging.getLogger("numba").setLevel(logging.WARNING) -# from config import pretrained_s2G - -# inp_text=sys.argv[1] -# exp_name=sys.argv[2] -# i_part=sys.argv[3] -# all_parts=sys.argv[4] -# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[5] -# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name - - -hubert_dir = "%s/4-cnhubert" % (opt_dir) -semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part) -if os.path.exists(semantic_path) == False: - os.makedirs(opt_dir, exist_ok=True) - - if torch.cuda.is_available(): - device = "cuda" - elif torch.backends.mps.is_available(): - device = "mps" - else: - device = "cpu" - hps = utils.get_hparams_from_file(s2config_path) - vq_model = SynthesizerTrn( - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model - ) - if is_half == True: - vq_model = vq_model.half().to(device) - else: - vq_model = vq_model.to(device) - vq_model.eval() - # utils.load_checkpoint(utils.latest_checkpoint_path(hps.s2_ckpt_dir, "G_*.pth"), vq_model, None, True) - # utils.load_checkpoint(pretrained_s2G, vq_model, None, True) - print( - vq_model.load_state_dict( - torch.load(pretrained_s2G, map_location="cpu")["weight"], strict=False - ) - ) - - def name2go(wav_name, lines): - hubert_path = "%s/%s.pt" % (hubert_dir, wav_name) - if os.path.exists(hubert_path) == False: - return - ssl_content = torch.load(hubert_path, map_location="cpu") - if is_half == True: - ssl_content = ssl_content.half().to(device) - else: - ssl_content = ssl_content.to(device) - codes = vq_model.extract_latent(ssl_content) - semantic = " ".join([str(i) for i in codes[0, 0, :].tolist()]) - lines.append("%s\t%s" % (wav_name, semantic)) - - with open(inp_text, "r", encoding="utf8") as f: - lines = f.read().strip("\n").split("\n") - - lines1 = [] - for line in lines[int(i_part) :: int(all_parts)]: - # print(line) - try: - # wav_name,text=line.split("\t") - wav_name, spk_name, language, text = line.split("|") - wav_name = os.path.basename(wav_name) - # name2go(name,lines1) - name2go(wav_name, lines1) - except: - print(line, traceback.format_exc()) - with open(semantic_path, "w", encoding="utf8") as f: - f.write("\n".join(lines1)) diff --git a/pretrained_models/README.md b/pretrained_models/README.md deleted file mode 100644 index adaf88694058b3a500839f032907f9a480f63431..0000000000000000000000000000000000000000 --- a/pretrained_models/README.md +++ /dev/null @@ -1,4 +0,0 @@ ---- -license: mit ---- -pretrained models used in https://github.com/RVC-Boss/GPT-SoVITS \ No newline at end of file diff --git a/pretrained_models/chinese-hubert-base/config.json b/pretrained_models/chinese-hubert-base/config.json deleted file mode 100644 index 0bb839d4b0cd2ad41cb13c6500fd660800aaefdf..0000000000000000000000000000000000000000 --- a/pretrained_models/chinese-hubert-base/config.json +++ /dev/null @@ -1,72 +0,0 @@ -{ - "_name_or_path": "/data/docker/liujing04/gpt-vits/chinese-hubert-base", - "activation_dropout": 0.1, - "apply_spec_augment": true, - "architectures": [ - "HubertModel" - ], - "attention_dropout": 0.1, - "bos_token_id": 1, - "classifier_proj_size": 256, - "conv_bias": false, - "conv_dim": [ - 512, - 512, - 512, - 512, - 512, - 512, - 512 - ], - "conv_kernel": [ - 10, - 3, - 3, - 3, - 3, - 2, - 2 - ], - "conv_stride": [ - 5, - 2, - 2, - 2, - 2, - 2, - 2 - ], - "ctc_loss_reduction": "sum", - "ctc_zero_infinity": false, - "do_stable_layer_norm": false, - "eos_token_id": 2, - "feat_extract_activation": "gelu", - "feat_extract_norm": "group", - "feat_proj_dropout": 0.0, - "feat_proj_layer_norm": true, - "final_dropout": 0.1, - "hidden_act": "gelu", - "hidden_dropout": 0.1, - "hidden_size": 768, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "layerdrop": 0.1, - "mask_feature_length": 10, - "mask_feature_min_masks": 0, - "mask_feature_prob": 0.0, - "mask_time_length": 10, - "mask_time_min_masks": 2, - "mask_time_prob": 0.05, - "model_type": "hubert", - "num_attention_heads": 12, - "num_conv_pos_embedding_groups": 16, - "num_conv_pos_embeddings": 128, - "num_feat_extract_layers": 7, - "num_hidden_layers": 12, - "pad_token_id": 0, - "torch_dtype": "float16", - "transformers_version": "4.30.2", - "use_weighted_layer_sum": false, - "vocab_size": 32 -} diff --git a/pretrained_models/chinese-hubert-base/preprocessor_config.json b/pretrained_models/chinese-hubert-base/preprocessor_config.json deleted file mode 100644 index 409547bbb99588f69a603019a707505610fa38d3..0000000000000000000000000000000000000000 --- a/pretrained_models/chinese-hubert-base/preprocessor_config.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "do_normalize": true, - "feature_extractor_type": "Wav2Vec2FeatureExtractor", - "feature_size": 1, - "padding_side": "right", - "padding_value": 0, - "return_attention_mask": false, - "sampling_rate": 16000 -} \ No newline at end of file diff --git a/pretrained_models/chinese-hubert-base/pytorch_model.bin b/pretrained_models/chinese-hubert-base/pytorch_model.bin deleted file mode 100644 index a73dedefbc10383bc70e289fe0897d2157c6f66b..0000000000000000000000000000000000000000 --- a/pretrained_models/chinese-hubert-base/pytorch_model.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:24164f129c66499d1346e2aa55f183250c223161ec2770c0da3d3b08cf432d3c -size 188811417 diff --git a/pretrained_models/chinese-roberta-wwm-ext-large/config.json b/pretrained_models/chinese-roberta-wwm-ext-large/config.json deleted file mode 100644 index 3ae58d7f0734373bb28d3b6b9d6858b34f600b4a..0000000000000000000000000000000000000000 --- a/pretrained_models/chinese-roberta-wwm-ext-large/config.json +++ /dev/null @@ -1,34 +0,0 @@ -{ - "_name_or_path": 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diff --git a/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt b/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt deleted file mode 100644 index 83b22408d0d3edd38eef2224a01b2c72a3f50a0b..0000000000000000000000000000000000000000 --- a/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b1c1e17e9c99547a89388f72048cd6e1b41b5a18b170e86a46dfde0324d63eb1 -size 155093966 diff --git a/pretrained_models/s2G488k.pth b/pretrained_models/s2G488k.pth deleted file mode 100644 index a63e37a8755ad141ba68db9ef0109bf36ec020c4..0000000000000000000000000000000000000000 --- a/pretrained_models/s2G488k.pth +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:020a014e1e01e550e510f2f61fae5e5f5b6aab40f15c22f1f12f724df507e835 -size 105973721 diff --git a/requirements.txt b/requirements.txt index 01725fe2a23301a64866bda1b7f8889174eed0df..8d9f4a6c005523fa0548a648a5263bfb861eb249 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,26 +1,31 @@ +librosa==0.9.1 +matplotlib numpy +numba +phonemizer scipy -librosa==0.9.2 -numba==0.56.4 +tensorboard +torch +torchaudio +torchvision +Unidecode +amfm_decompy +jieba +transformers +pypinyin +cn2an +av +mecab-python3 +loguru +unidic-lite +cmudict +fugashi +num2words +Cython==0.29.21 +openai-whisper pytorch-lightning -gradio==4.19.0 +einops ffmpeg-python -onnxruntime -tqdm -cn2an -pypinyin -pyopenjtalk +pyopenjtalk-prebuilt g2p_en -torchaudio -modelscope==1.10.0 -sentencepiece -transformers -chardet -PyYAML -psutil -jieba_fast -jieba -LangSegment -soundfile -git+https://github.com/huggingface/transformers -torch \ No newline at end of file +gradio==4.19.0 \ No newline at end of file diff --git a/text/__init__.py b/text/__init__.py deleted file mode 100644 index a48b9a49a39bdae1801d94a15db645154d6a68d1..0000000000000000000000000000000000000000 --- a/text/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -from text.symbols import * - - -_symbol_to_id = {s: i for i, s in enumerate(symbols)} - -def cleaned_text_to_sequence(cleaned_text): - '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. - Args: - text: string to convert to a sequence - Returns: - List of integers corresponding to the symbols in the text - ''' - phones = [_symbol_to_id[symbol] for symbol in cleaned_text] - return phones - diff --git a/text/chinese.py b/text/chinese.py deleted file mode 100644 index ea41db1f99ee056d00ef4f89fb3ed7f316fb0fc3..0000000000000000000000000000000000000000 --- a/text/chinese.py +++ /dev/null @@ -1,172 +0,0 @@ -import os -import pdb -import re - -import cn2an -from pypinyin import lazy_pinyin, Style - -from text.symbols import punctuation -from text.tone_sandhi import ToneSandhi -from text.zh_normalization.text_normlization import TextNormalizer - -normalizer = lambda x: cn2an.transform(x, "an2cn") - -current_file_path = os.path.dirname(__file__) -pinyin_to_symbol_map = { - line.split("\t")[0]: line.strip().split("\t")[1] - for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines() -} - -import jieba_fast.posseg as psg - - -rep_map = { - "：": ",", - "；": ",", - "，": ",", - "。": ".", - "！": "!", - "？": "?", - "\n": ".", - "·": ",", - "、": ",", - "...": "…", - "$": ".", - "/": ",", - "—": "-", -} - -tone_modifier = ToneSandhi() - - -def replace_punctuation(text): - text = text.replace("嗯", "恩").replace("呣", "母") - pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys())) - - replaced_text = pattern.sub(lambda x: rep_map[x.group()], text) - - replaced_text = re.sub( - r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text - ) - - return replaced_text - - -def g2p(text): - pattern = r"(?<=[{0}])\s*".format("".join(punctuation)) - sentences = [i for i in re.split(pattern, text) if i.strip() != ""] - phones, word2ph = _g2p(sentences) - return phones, word2ph - - -def _get_initials_finals(word): - initials = [] - finals = [] - orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS) - orig_finals = lazy_pinyin( - word, neutral_tone_with_five=True, style=Style.FINALS_TONE3 - ) - for c, v in zip(orig_initials, orig_finals): - initials.append(c) - finals.append(v) - return initials, finals - - -def _g2p(segments): - phones_list = [] - word2ph = [] - for seg in segments: - pinyins = [] - # Replace all English words in the sentence - seg = re.sub("[a-zA-Z]+", "", seg) - seg_cut = psg.lcut(seg) - initials = [] - finals = [] - seg_cut = tone_modifier.pre_merge_for_modify(seg_cut) - for word, pos in seg_cut: - if pos == "eng": - continue - sub_initials, sub_finals = _get_initials_finals(word) - sub_finals = tone_modifier.modified_tone(word, pos, sub_finals) - initials.append(sub_initials) - finals.append(sub_finals) - - # assert len(sub_initials) == len(sub_finals) == len(word) - initials = sum(initials, []) - finals = sum(finals, []) - # - for c, v in zip(initials, finals): - raw_pinyin = c + v - # NOTE: post process for pypinyin outputs - # we discriminate i, ii and iii - if c == v: - assert c in punctuation - phone = [c] - word2ph.append(1) - else: - v_without_tone = v[:-1] - tone = v[-1] - - pinyin = c + v_without_tone - assert tone in "12345" - - if c: - # 多音节 - v_rep_map = { - "uei": "ui", - "iou": "iu", - "uen": "un", - } - if v_without_tone in v_rep_map.keys(): - pinyin = c + v_rep_map[v_without_tone] - else: - # 单音节 - pinyin_rep_map = { - "ing": "ying", - "i": "yi", - "in": "yin", - "u": "wu", - } - if pinyin in pinyin_rep_map.keys(): - pinyin = pinyin_rep_map[pinyin] - else: - single_rep_map = { - "v": "yu", - "e": "e", - "i": "y", - "u": "w", - } - if pinyin[0] in single_rep_map.keys(): - pinyin = single_rep_map[pinyin[0]] + pinyin[1:] - - assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin) - new_c, new_v = pinyin_to_symbol_map[pinyin].split(" ") - new_v = new_v + tone - phone = [new_c, new_v] - word2ph.append(len(phone)) - - phones_list += phone - return phones_list, word2ph - - -def text_normalize(text): - # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization - tx = TextNormalizer() - sentences = tx.normalize(text) - dest_text = "" - for sentence in sentences: - dest_text += replace_punctuation(sentence) - return dest_text - - -if __name__ == "__main__": - text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏" - text = "呣呣呣～就是…大人的鼹鼠党吧？" - text = "你好" - text = text_normalize(text) - print(g2p(text)) - - -# # 示例用法 -# text = "这是一个示例文本：,你好！这是一个测试..." -# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试 diff --git a/text/cleaner.py b/text/cleaner.py deleted file mode 100644 index 0d82d8506ab969d3a9a2f0df47a26124b1a5a462..0000000000000000000000000000000000000000 --- a/text/cleaner.py +++ /dev/null @@ -1,58 +0,0 @@ -from text import chinese, japanese, cleaned_text_to_sequence, symbols, english - -language_module_map = {"zh": chinese, "ja": japanese, "en": english} -special = [ - # ("%", "zh", "SP"), - ("￥", "zh", "SP2"), - ("^", "zh", "SP3"), - # ('@', 'zh', "SP4")#不搞鬼畜了，和第二版保持一致吧 -] - - -def clean_text(text, language): - if(language not in language_module_map): - language="en" - text=" " - for special_s, special_l, target_symbol in special: - if special_s in text and language == special_l: - return clean_special(text, language, special_s, target_symbol) - language_module = language_module_map[language] - norm_text = language_module.text_normalize(text) - if language == "zh": - phones, word2ph = language_module.g2p(norm_text) - assert len(phones) == sum(word2ph) - assert len(norm_text) == len(word2ph) - else: - phones = language_module.g2p(norm_text) - word2ph = None - - for ph in phones: - assert ph in symbols - return phones, word2ph, norm_text - - -def clean_special(text, language, special_s, target_symbol): - """ - 特殊静音段sp符号处理 - """ - text = text.replace(special_s, ",") - language_module = language_module_map[language] - norm_text = language_module.text_normalize(text) - phones = language_module.g2p(norm_text) - new_ph = [] - for ph in phones[0]: - assert ph in symbols - if ph == ",": - new_ph.append(target_symbol) - else: - new_ph.append(ph) - return new_ph, phones[1], norm_text - - -def text_to_sequence(text, language): - phones = clean_text(text) - return cleaned_text_to_sequence(phones) - - -if __name__ == "__main__": - print(clean_text("你好%啊啊啊额、还是到付红四方。", "zh")) diff --git a/text/cmudict-fast.rep b/text/cmudict-fast.rep deleted file mode 100644 index ec1564a87ea73e92c254c7883b410bcc5fca6e26..0000000000000000000000000000000000000000 --- a/text/cmudict-fast.rep +++ /dev/null @@ -1,135010 +0,0 @@ -'BOUT B AW1 T -'CAUSE K AH0 Z -'COURSE K 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AE2 N D AE1 N AH0 Z -BANDAR B AE1 N D AA0 R -BANDED B AE1 N D IH0 D -BANDEL B AE1 N D AH0 L -BANDEMER B AE1 N D IY0 M ER0 -BANDER B AE1 N D ER0 -BANDERAS B AE0 N D ER1 AH0 S -BANDERAS(2) B AA0 N D ER0 AH1 S -BANDICOOT B AE1 N D IH0 K UW2 T -BANDICOOT(2) B AE1 N D IY0 K UW2 T -BANDICOOTS B AE1 N D IH0 K UW2 T S -BANDICOOTS(2) B AE1 N D IY0 K UW2 T S -BANDIED B AE1 N D IY0 D -BANDING B AE1 N D IH0 NG -BANDIT B AE1 N D AH0 T -BANDITRY B AE1 N D AH0 T R IY0 -BANDITS B AE1 N D AH0 T S -BANDLEADER B AE1 N D L IY0 D ER0 -BANDLEADERS B AE1 N D L IY0 D ER0 Z -BANDOLIER B AE2 N D AH0 L IH1 R -BANDOLIERS B AE2 N D AH0 L IH1 R Z -BANDOW B AE1 N D AW2 -BANDOW'S B AE1 N D AW2 Z -BANDS B AE1 N D Z -BANDSHELL B AE0 N D SH EH1 L -BANDSTAND B AE1 N D S T AE2 N D -BANDT B AE1 N T -BANDUCCI B AA0 N D UW1 CH IY0 -BANDWAGON B AE1 N D W AE2 G AH0 N -BANDWIDTH B AE1 N D W IH0 D TH -BANDY B AE1 N D IY0 -BANE B EY1 N -BANEGAS B AE1 N IH0 G AH0 Z -BANERJEE B AH0 N ER1 JH IY0 -BANES B EY1 N Z -BANESTO B AH0 N EH1 S T OW0 -BANESTO'S B AH0 N EH1 S T OW0 Z -BANET B AE1 N IH0 T -BANEY B EY1 N IY0 -BANFF B AE1 N F -BANFIELD B AE1 N F IY2 L D -BANFORD B AE1 N F ER0 D -BANG B AE1 NG -BANGALA B AA0 N G AA1 L AH0 -BANGALOR B AE1 NG G AH0 L AO2 R -BANGALORE B AE1 NG G AH0 L AO2 R -BANGALORE'S B AE1 NG G AH0 L AO2 R Z -BANGE B AE1 N JH -BANGED B AE1 NG D -BANGEE B AE1 N JH IY0 -BANGEMANN B AE1 NG G AH0 M AH0 N -BANGER B AE1 NG ER0 -BANGERS B AE1 NG ER0 Z -BANGERT B EY1 NG G ER0 T -BANGERTER B EY1 NG G ER0 T ER0 -BANGHART B AE1 NG HH AA2 R T -BANGING B AE1 NG IH0 NG -BANGISH B AE1 NG IH0 SH -BANGKOK B AE0 NG K AA1 K -BANGKOK(2) B AE1 NG K AA0 K -BANGKOK'S B AE1 NG K AA0 K S -BANGLA B AE1 NG L AA2 -BANGLADESH B AE1 NG L AH0 D EH2 SH -BANGLADESH'S B AE1 NG L AH0 D EH2 SH IH0 Z -BANGLADESHI B AE1 NG L AH0 D EH2 SH IY0 -BANGLADESHI'S B AE1 NG L AH0 D EH2 SH IY0 Z -BANGLADESHIS B AE1 NG L AH0 D EH2 SH IY0 Z -BANGLE B AE1 NG G AH0 L -BANGLES B AE1 NG G AH0 L Z -BANGO B AA1 NG G OW0 -BANGOR B AE1 NG G ER0 -BANGOR(2) B AE1 NG G AO2 R -BANGS B AE1 NG Z -BANH B AE1 N -BANIA B AA1 N IY0 AH0 -BANICK B AE1 N IH0 K -BANIK B AE1 N IH0 K -BANIS B AE1 N IH0 S -BANISH B AE1 N IH0 SH -BANISHED B AE1 N IH0 SH T -BANISHING B AE1 N IH0 SH IH0 NG -BANISHMENT B AE1 N IH0 SH M AH0 N T -BANISTER B AE1 N IH0 S T ER0 -BANJA B AA1 N Y AH0 -BANJO B AE1 N JH OW2 -BANK B AE1 NG K -BANK'S B AE1 NG K S -BANKABLE B AE1 NG K AH0 B AH0 L -BANKAMERICA B AE2 NG K AH0 M EH1 R IH0 K AH0 -BANKAMERICA'S B AE2 NG K AH0 M EH1 R IH0 K AH0 Z -BANKATLANTIC B AE2 NG K AH0 T L AE1 N T IH0 K -BANKBOOK B AE1 NG K B UH2 K -BANKCARD B AE1 NG K AA2 R D -BANKCARDS B AE1 NG K AA2 R D Z -BANKCORP B AE1 NG K AO2 R P -BANKE B AE1 NG K -BANKEAST B AE2 NG K IY1 S T -BANKED B AE1 NG K T -BANKEN B AE1 NG K AH0 N -BANKER B AE1 NG K ER0 -BANKER'S B AE1 NG K ER0 Z -BANKERS B AE1 NG K ER0 Z -BANKERS' B AE1 NG K ER0 Z -BANKERT B AE1 NG K ER0 T -BANKES B AE1 NG K S -BANKEY B AE1 N K IY2 -BANKHEAD B AE1 NG K HH EH2 D -BANKHOLDING B AE1 NG K HH OW2 L D IH0 NG -BANKING B AE1 NG K IH0 NG -BANKING'S B AE1 NG K IH0 NG Z -BANKNOTE B AE1 NG K N OW2 T -BANKNOTES B AE1 NG K N OW2 T S -BANKO B AE1 NG K OW0 -BANKOWSKI B AH0 NG K AO1 F S K IY0 -BANKROLL B AE1 NG K R OW2 L -BANKROLLED B AE1 NG K R OW2 L D -BANKROLLING B AE1 NG K R OW2 L IH0 NG -BANKROLLS B AE1 NG K R OW2 L Z -BANKRUPCTY B AE1 NG K R AH0 P T S IY0 -BANKRUPT B AE1 NG K R AH0 P T -BANKRUPTCIES B AE1 NG K R AH0 P T S IY0 Z -BANKRUPTCY B AE1 NG K R AH0 P S IY0 -BANKRUPTCY(2) B AE1 NG K R AH0 P T S IY0 -BANKRUPTCY'S B AE1 NG K R AH0 P S IY0 Z -BANKRUPTED B AE1 NG K R AH0 P T IH0 D -BANKRUPTING B AE1 NG K R AH2 P T IH0 NG -BANKS B AE1 NG K S -BANKS' B AE1 NG K S -BANKS'S B AE1 NG K S IH0 Z -BANKSHARE B AE1 NG K SH EH2 R -BANKSHARES B AE1 NG K SH EH2 R Z -BANKSON B AE1 NG K S AH0 N -BANKSTON B AE1 NG K S T AH0 N -BANKVEREIN B AE1 NG K V ER0 AY2 N -BANKVERMONT B AE1 NG K V ER0 M AA1 N T -BANKWORCESTER B AE1 NG K W ER1 CH EH2 S T ER0 -BANKWORCESTER(2) B AE1 NG K W UW1 S T ER0 -BANN B AE1 N -BANNAN B AE1 N AH0 N -BANNED B AE1 N D -BANNER B AE1 N ER0 -BANNER'S B AE1 N ER0 Z -BANNERMAN B AE1 N ER0 M AH0 N -BANNERS B AE1 N ER0 Z -BANNICK B AE1 N IH0 K -BANNING B AE1 N IH0 NG -BANNINGS B AE1 N IH0 NG Z -BANNISTER B AE1 N AH0 S T ER0 -BANNISTER(2) B AE1 N IH0 S T ER0 -BANNON B AE1 N AH0 N -BANOS B AA1 N OW0 Z -BANOUN B AH0 N UW1 N -BANPAIS B AE2 N P EY1 -BANPONCE B AE1 N P AA0 N S -BANQUE B AE1 NG K -BANQUET B AE1 NG K W AH0 T -BANQUETS B AE1 NG K W AH0 T S -BANQUO'S B AE1 NG K W OW0 Z -BANS B AE1 N Z -BANSAL B AA0 N S AA1 L -BANSHEE B AE0 N SH IY1 -BANSHEE(2) B AE1 N SH IY0 -BANTA B AE1 N T AH0 -BANTAM B AE1 N T AH0 M -BANTAM'S B AE1 N T AH0 M Z -BANTAMS B AE1 N T AH0 M Z -BANTER B AE1 N T ER0 -BANTERED B AE1 N T ER0 D -BANTERING B AE1 N T ER0 IH0 NG -BANTLE B AE1 N T AH0 L -BANTON B AE1 N T AH0 N -BANTU B AE1 N T UW0 -BANTZ B AE1 N T S -BANUELOS B AA0 N W EH1 L OW0 Z -BANVILLE B AA1 N V IH0 L -BANWART B AE1 N W AO2 R T -BANXQUOTE B AE1 NG K S K W OW2 T -BANYA B AA1 N Y AH0 -BANYA(2) B AE1 N Y AH0 -BANYALUCA B AE1 N Y AH0 L UW2 K AH0 -BANYAN B AE1 N Y AH0 N -BANYAS B AA1 N Y AH0 Z -BANYAS(2) B AE1 N Y AH0 Z -BANYU B AA1 N Y UW2 -BANZHAF B AE1 N Z HH AH0 F -BAO B AW1 -BAOGUANG B AA1 AO0 G W AE2 NG -BAPLEY B AE1 P L IY0 -BAPNA B AA1 P N AH0 -BAPTISM B AE1 P T IH0 Z AH0 M -BAPTISMAL B AE0 P T IH1 Z M AH0 L -BAPTISMS B AE1 P T IH2 Z AH0 M Z -BAPTIST B AE1 P T AH0 S T -BAPTIST(2) B AE1 P T IH0 S T -BAPTISTA B AE2 P T IH1 S T AH0 -BAPTISTE B AH0 P T IH1 S T -BAPTISTERY B AE1 P T AH0 S T R IY0 -BAPTISTS B AE1 P T AH0 S T S -BAPTISTS(2) B AE1 P T AH0 S -BAPTIZE B AE0 P T AY1 Z -BAPTIZED B AE0 P T AY1 Z D -BAPTIZED(2) B AE1 P T AY2 Z D -BAR B AA1 R -BAR'S B AA1 R Z -BAR-MITZVAH B AA1 R M IH1 T S V AH0 -BARA B AA1 R AH0 -BARABAR B EH1 R AH0 B AA0 R -BARACH B AH0 R AA1 K -BARACH(2) B ER0 AA1 K -BARACK B AA1 R AH0 K -BARACK(2) B AE0 R AA1 K -BARAFF B AA0 R AA1 F -BARAGI B AH0 R AA1 G IY0 -BARAHONA B AE2 R AH0 HH OW1 N AH0 -BARAJAS B AA0 R AA1 Y AA0 Z -BARAK B AA1 R AH0 K -BARAKAT B AA1 R AH0 K AA2 T -BARAM B EH1 R AE0 M -BARAN B AA0 R AA1 N -BARANEK B AE1 R AH0 N IH0 K -BARANOSKI B ER0 AH0 N AW1 S K IY0 -BARANOWSKI B ER0 AH0 N AO1 F S K IY0 -BARANSKI B ER0 AE1 N S K IY0 -BARANY B ER0 AO1 N IY0 -BARASCH B AE1 R AH0 SH -BARASH B AE1 R AH0 SH -BARATH B AE1 R AH0 TH -BARATTA B AA0 R AA1 T AH0 -BARB B AA1 R B -BARB'S B AA1 R B Z -BARBA B AA1 R B AH0 -BARBADOS B AA0 R B EY1 D OW0 S -BARBAGALLO B AA2 R B AH0 G AE1 L OW0 -BARBAKOW B AA1 R B AH0 K AW2 -BARBANEL B AA1 R B AH0 N AH0 L -BARBANO B AA0 R B AA1 N OW0 -BARBARA B AA1 R B ER0 AH0 -BARBARA(2) B AA1 R B R AH0 -BARBARA'S B AA1 R B ER0 AH0 Z -BARBAREE B AA1 R B ER0 IY1 -BARBARIAN B AA0 R B EH1 R IY0 AH0 N -BARBARIANS B AA0 R B EH1 R IY0 AH0 N Z -BARBARIC B AA0 R B AE1 R IH0 K -BARBARIC(2) B AA0 R B EH1 R IH0 K -BARBARINO B AA0 R B AA0 R IY1 N OW0 -BARBARISM B AA1 R B ER0 IH2 Z AH0 M -BARBARITY B AA0 R B AE1 R AH0 T IY0 -BARBARITY(2) B AA0 R B EH1 R AH0 T IY0 -BARBARO B AA0 R B AA1 R OW0 -BARBAROUS B AA1 R B ER0 AH0 S -BARBARY B AA1 R B ER0 IY0 -BARBASH B AA1 R B AE2 SH -BARBATO B AA0 R B AA1 T OW0 -BARBE B AA1 R B -BARBEAU B AA0 R B OW1 -BARBECUE B AA1 R B IH0 K Y UW2 -BARBECUE'S B AA1 R B IH0 K Y UW2 Z -BARBECUED B AA1 R B IH0 K Y UW2 D -BARBECUEING B AA1 R B IH0 K Y UW2 IH0 NG -BARBECUES B AA1 R B IH0 K Y UW2 Z -BARBECUING B AA1 R B IH0 K Y UW2 IH0 NG -BARBED B AA1 R B D -BARBED-WIRE B AA1 R B D W AY1 R -BARBEE B AA1 R B IY1 -BARBEL B AA1 R B EH2 L -BARBELL B AA1 R B EH2 L -BARBELLA B AA2 R B EH1 L AH0 -BARBELLS B AA1 R B EH2 L Z -BARBELS B AA1 R B AH0 L Z -BARBEQUE B AA1 R B IH0 K Y UW2 -BARBEQUED B AA1 R B IH0 K Y UW2 D -BARBEQUEING B AA1 R B IH0 K Y UW2 IH0 NG -BARBEQUES B AA1 R B IH0 K Y UW2 Z -BARBER B AA1 R B ER0 -BARBER'S B AA1 R B ER0 Z -BARBERA B AA0 R B EH1 R AH0 -BARBERI B AA0 R B EH1 R IY0 -BARBERIO B AA2 R B IY1 R IY0 OW0 -BARBERIS B AA1 R B ER0 IH0 S -BARBERO B AA0 R B EH1 R OW0 -BARBERS B AA1 R B ER0 Z -BARBERSHOP B AA1 R B ER0 SH AA2 P -BARBETTE B AA0 R B EH1 T -BARBIAN B AA1 R B IY0 AH0 N -BARBIE B AA1 R B IY0 -BARBIE'S B AA1 R B IY0 Z -BARBIER B AA1 R B IY0 ER0 -BARBIERI B AA0 R B IH1 R IY0 -BARBIERI'S B AA0 R B IH1 R IY0 Z -BARBIES B AA1 R B IY0 Z -BARBIN B AA1 R B IH0 N -BARBITURATE B AA0 R B IH1 CH ER0 AH0 T -BARBITURATES B AA0 R B IH1 CH ER0 AH0 T S -BARBO B AA1 R B OW0 -BARBONE B AA1 R B OW2 N -BARBOSA B AA0 R B OW1 S AH0 -BARBOUR B AA1 R B ER0 -BARBOUR'S B AA1 R B ER0 Z -BARBOURSVILLE B AA1 B ER0 Z V IH0 L -BARBOZA B AA0 R B OW1 Z AH0 -BARBRA B AA1 R B R AH0 -BARBRE B AA1 R B ER0 -BARBS B AA1 R B Z -BARBUDA B AA0 R B UW1 D AH0 -BARBY B AA1 R B IY0 -BARCA B AA1 R K AH0 -BARCELLA B AA2 R S EH1 L AH0 -BARCELLOS B AA0 R S EH1 L OW0 Z -BARCELO B AA0 R CH EH1 L OW0 -BARCELONA B AA2 R S IH0 L OW1 N AH0 -BARCENAS B AA1 R S IH0 N AH0 Z -BARCH B AA1 R K -BARCHEFSKY B AA0 CH EH1 F S K IY0 -BARCIA B AA1 R CH AH0 -BARCLAY B AA1 R K L EY2 -BARCLAY(2) B AA1 R K L IY2 -BARCLAY'S B AA1 R K L IY0 Z -BARCLAY'S(2) B AA1 R K L EY0 Z -BARCLAYS B AA1 R K L IY0 Z -BARCLAYS(2) B AA1 R K L EY0 Z -BARCLAYS' B AA1 R K L IY0 Z -BARCLAYS'(2) B AA1 R K L EY0 Z -BARCLAYS'S B AA1 R K L IY2 Z IH0 Z -BARCLAYS'S(2) B AA1 R K L EY2 Z IH0 Z -BARCLIFT B AA1 R K L IH0 F T -BARCO B AA1 R K OW0 -BARCO'S B AA1 R K OW0 Z -BARCOMB B AA1 R K AH0 M -BARCROFT B AA1 R K R AO2 F T -BARCUS B AA1 R K AH0 S -BARCZAK B AA1 R CH AE0 K -BARD B AA1 R D -BARD'S B AA1 R D Z -BARDELL B AA0 R D EH1 L -BARDEN B AA1 R D AH0 N -BARDERA B AA2 R D EH1 R AH0 -BARDIN B AA1 R D IH0 N -BARDO B AA1 R D OW0 -BARDOLF B AA1 R D OW2 L F -BARDOLPH B AA1 R D AA0 L F -BARDON B AA0 R D AO1 N -BARDRICK B AA1 R D R IH0 K -BARDSLEY B AA1 R D S L IY0 -BARDULF B AA1 R D AH0 L F -BARDULPH B AA1 R D AH0 L F -BARDWELL B AA1 R D W EH2 L -BARE B EH1 R -BARED B EH1 R D -BAREFIELD B AE1 R IH0 F IY0 L D -BAREFIELD(2) B AE1 R F IY0 L D -BAREFOOT B EH1 R F UH2 T -BAREIS B AE1 R AY0 Z -BARELA B AA0 R EH1 L AH0 -BARELY B EH1 R L IY0 -BARENBOIM B EH1 R AH0 N B OY2 M -BARENBOIM'S B EH1 R AH0 N B OY2 M Z -BARENS B EH1 R AH0 N Z -BARENTINE B AA0 R EH0 N T IY1 N IY0 -BARENTINE(2) B EH1 R AH0 N T IY1 N -BARENTINE(3) B EH1 R AH0 N T AY1 N -BARENTS B EH1 R AH0 N T S -BARES B EH1 R Z -BAREST B EH1 R AH0 S T -BAREY B EH1 R IY0 -BARF B AA1 R F -BARFIELD B AA1 R F IY2 L D -BARFKNECHT B AA1 R F K N EH0 K T -BARFKNECHT(2) B AA1 R F N EH0 K T -BARFOOT B AA1 R F UH2 T -BARFUSS B AA1 R F AH2 S -BARG B AA1 R G -BARGA B AA1 R G AH0 -BARGAIN B AA1 R G AH0 N -BARGAIN(2) B AA1 R G IH0 N -BARGAINED B AA1 R G AH0 N D -BARGAINER B AA1 R G IH0 N ER0 -BARGAINERS B AA1 R G IH0 N ER0 Z -BARGAINING B AA1 R G IH0 N IH0 NG -BARGAINS B AA1 R G AH0 N Z -BARGAINS(2) B AA1 R G IH0 N Z -BARGANIER B AA1 R G AH0 N IY0 ER0 -BARGAR B AA0 R G AA1 R -BARGAS B AA1 R G AH0 Z -BARGE B AA1 R JH -BARGED B AA1 R JH D -BARGER B AA1 R JH ER0 -BARGERON B AA1 R G ER0 AH0 N -BARGES B AA1 R JH AH0 Z -BARGES(2) B AA1 R JH IH0 Z -BARGING B AA1 R JH IH0 NG -BARGMAN B AA1 R G M AH0 N -BARGMANN B AA1 R G M AH0 N -BARGO B AA1 R G OW2 -BARHAM B AA1 R HH AH0 M -BARHORST B AA1 R HH AO0 R S T -BARI B AA1 R IY0 -BARI'S B AA1 R IY0 Z -BARIBEAU B AE1 R IH0 B OW0 -BARICH B AE1 R IH0 K -BARIL B EH1 R AH0 L -BARILE B AA1 R AH0 L -BARILLARI B EH2 R IH0 L EH1 R IY0 -BARILLARI'S B EH2 R IH0 L EH1 R IY0 Z -BARILLO B ER0 IH1 L OW0 -BARINCO B ER0 IH1 NG K OW2 -BARING B EH1 R IH0 NG -BARING'S B EH1 R IH0 NG Z -BARINGER B EH1 R IH0 NG ER0 -BARINGS B EH1 R IH0 NG Z -BARINGS' B EH1 R IH0 NG Z -BARIS B AA1 R IY0 Z -BARISH B EH1 R IH0 SH -BARISTA B AA0 R IY1 S T AH0 -BARISTAS B AA0 R IY1 S T AH0 Z -BARITE B EH1 R AY0 T -BARITES B EH1 R AY0 T S -BARITONE B EH1 R AH0 T OW2 N -BARITONES B EH1 R AH0 T OW2 N Z -BARIUM B EH1 R IY0 AH0 M -BARK B AA1 R K -BARKAI B AA0 R K AY1 -BARKALOW B AA1 R K AH0 L OW2 -BARKAN B AA1 R K AH0 N -BARKDOLL B AA1 R K D AH0 L -BARKDULL B AA1 R K D AH0 L -BARKE B AA1 R K -BARKED B AA1 R K T -BARKELEY B AA1 R K L IY0 -BARKER B AA1 R K ER0 -BARKERS B AA1 R K ER0 Z -BARKES B AA1 R K S -BARKETT B AA1 R K IH0 T -BARKEY B AA1 R K IY2 -BARKHURST B AA1 R K HH ER0 S T -BARKIN B AA1 R K IH0 N -BARKING B AA1 R K IH0 NG -BARKLEY B AA1 R K L IY0 -BARKLOW B AA1 R K L OW2 -BARKMAN B AA1 R K M AH0 N -BARKO B AA1 R K OW0 -BARKOCY B AA1 R K AH0 S IY0 -BARKOW B AA1 R K OW0 -BARKS B AA1 R K S -BARKSDALE B AA1 R K S D EY2 L -BARKSHIRE B AA1 R K SH AY2 R -BARKUS B AA1 R K AH0 S -BARLAGE B AA1 R L IH0 JH -BARLETT B AA1 R L IH0 T -BARLETTA B AA0 R L EH1 T AH0 -BARLETTESVILLE B AA1 R L AH0 T S V IH2 L -BARLEY B AA1 R L IY0 -BARLOON B AA0 R L UW1 N -BARLOW B AA1 R L OW2 -BARLOWE B AA1 R L OW2 -BARMAN B AA1 R M AH0 N -BARMORE B AA1 R M AO0 R -BARN B AA1 R N -BARNA B AA1 R N AH0 -BARNABAS B AA1 R N AH0 B AH0 S -BARNABY B AA1 R N AH0 B IY0 -BARNABY'S B AA1 R N AH0 B IY0 Z -BARNACLE B AA1 R N AH0 K AH0 L -BARNACLES B AA1 R N AH0 K AH0 L Z -BARNARD B AA1 R N AA0 R D -BARNARD'S B AA1 R N AA0 R D Z -BARNARDS B AA1 R N ER0 D Z -BARNAS B AA1 R N AH0 Z -BARNDT B AA1 R N T -BARNELL B AA1 R N AH0 L -BARNER B AA1 R N ER0 -BARNES B AA1 R N Z 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T R AH0 N -BELTS B EH1 L T S -BELTSVILLE B EH1 L T S V IH2 L -BELTWAY B EH1 L T W EY2 -BELTZ B EH1 L T S -BELUE B EH1 L Y UW0 -BELUGA B IH0 L UW1 G AH0 -BELUSHI B EH0 L UW1 SH IY0 -BELVA B EY1 L V AH0 -BELVEAL B EH1 L V AH0 L -BELVEDERE B EH2 L V AH0 D IH1 R -BELVEDERE(2) B EH2 L V IH0 D IH1 R -BELVIA B EH1 L V IY0 AH0 -BELVIDERE B EH1 L V IH0 D IH2 R -BELVILLE B EH1 L V IH2 L -BELVIN B EH1 L V IH0 N -BELVOIR B EH0 L V W AA1 R -BELY B AH0 L AY1 -BELYEA B EH0 L IY1 AH0 -BELYEU B EH2 L IY0 UW1 -BELYING B IH0 L AY1 IH0 NG -BELZ B EH1 L Z -BELZBERG B EH1 L T S B ER0 G -BELZBERGS B EH1 L T S B ER0 G Z -BELZBERGS' B EH1 L Z B ER0 G Z -BELZER B EH1 L Z ER0 -BEM B EH1 M -BEMAN B IY1 M AH0 N -BEMBENEK B EH1 M B IH0 N AH0 K -BEMBRY B EH1 M B R IY0 -BEMENT B IY1 M AH0 N T -BEMIDJI B AH0 M IH1 D JH IY0 -BEMIS B IY1 M AH0 S -BEMISS B EH1 M IH0 S -BEMOAN B IH0 M OW1 N -BEMOANED B IH0 M OW1 N D -BEMOANING B IH0 M OW1 N IH0 NG -BEMOANS B IH0 M OW1 N Z -BEMUSE B IH0 M Y UW1 Z -BEMUSED B IH0 M Y UW1 Z D 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T S -BENIGN B IH0 N AY1 N -BENIGNA B EH0 N IY1 G N AH0 -BENIGNLY B AH0 N AY1 N L IY0 -BENIGNO B EH2 N IY1 N Y OW0 -BENIGNO(2) B EH1 N IH0 G N OW0 -BENIHANA B EH2 N IH0 HH AA1 N AH0 -BENIHANA(2) B EH2 N IY0 HH AA1 N AH0 -BENIN B IY1 N IH0 N -BENINATI B EH0 N IY0 N AA1 T IY0 -BENINCASA B EH0 N IY0 N K AA1 S AH0 -BENING B EH1 N IH0 NG -BENISH B EH1 N IH0 SH -BENITA B AH0 N IY1 T AH0 -BENITES B EH1 N AY0 T S -BENITEZ B EY0 N IY1 T EH0 Z -BENITO B EH0 N IY1 T OW0 -BENITO(2) B AH0 N IY1 T OW0 -BENITO(3) B IH0 N IY1 T OW2 -BENJA B EH1 N JH AH0 -BENJAMIN B EH1 N JH AH0 M AH0 N -BENJAMIN'S B EH1 N JH AH0 M AH0 N Z -BENJIMEN B EH1 N JH AH0 M IH0 N -BENJY B EH1 N JH IY0 -BENKE B EH1 NG K -BENKER B EH1 NG K ER0 -BENKERT B EH1 NG K ER0 T -BENKO B EH1 NG K OW0 -BENLATE B EH1 N L EY2 T -BENLOX B EH1 N L AA0 K S -BENN B EH1 N -BENNARDO B AH0 N AA1 R D OW0 -BENNE B EH1 N -BENNEFIELD B EH1 N IH0 F IY0 L D -BENNER B EH1 N ER0 -BENNET B EH1 N IH0 T -BENNETT B EH1 N AH0 T -BENNETT(2) B EH1 N IH0 T 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IH0 N -BIVINS B IH1 V IH0 N Z -BIVONA B IH0 V OW1 N AH0 -BIVOUAC B IH1 V W AE0 K -BIWEEKLY B AY0 W IY1 K L IY0 -BIX B IH1 K S -BIXBY B IH1 K S B IY0 -BIXEL B IH1 K S AH0 L -BIXLER B IH1 K S L ER0 -BIZ B IH1 Z -BIZANGO B IH0 Z AE1 NG G OW0 -BIZARRE B AH0 Z AA1 R -BIZARRE(2) B IH0 Z AA1 R -BIZET B IH0 Z EH1 T -BIZMART B IH1 Z M AA2 R T -BIZRATE B IH1 Z EY2 T -BIZUB B IH1 Z AH0 B -BIZZARO B IH0 Z AA1 R OW0 -BIZZELL B IH1 Z AH0 L -BIZZY B IH1 Z IY0 -BJELASNICA B Y EH0 L AE1 S N IH0 K AH0 -BJELLAND B Y EH1 L AH0 N D -BJERKE B Y ER1 K -BJOERN B Y AO1 R N -BJORGE B Y AO1 R G -BJORK B Y AO1 R K -BJORKLUND B Y AO1 R K L AH0 N D -BJORKMAN B Y AO1 R K M AH0 N -BJORN B Y AO1 R N -BJORNSTAD B Y AO1 R N S T AH0 D -BLAB B L AE1 B -BLABBED B L AE1 B D -BLABBER B L AE1 B ER0 -BLABBERS B L AE1 B ER0 Z -BLACHLY B L AA1 CH L IY0 -BLACHLY(2) B L AA1 K L IY0 -BLACK B L AE1 K -BLACK'S B L AE1 K S -BLACKARD B L AE1 K ER0 D -BLACKBALL B L AE1 K B AO2 L -BLACKBERRIES B L AE1 K B EH2 R IY0 Z -BLACKBERRY B L AE1 K B EH2 R IY0 -BLACKBIRD B L AE1 K B ER0 D -BLACKBIRDS B L AE1 K B ER0 D Z -BLACKBOARD B L AE1 K B AO2 R D -BLACKBOARDS B L AE1 K B AO2 R D Z -BLACKBURN B L AE1 K B ER0 N -BLACKED B L AE1 K T -BLACKEN B L AE1 K AH0 N -BLACKENED B L AE1 K AH0 N D -BLACKENING B L AE1 K AH0 N IH0 NG -BLACKENING(2) B L AE1 K N IH0 NG -BLACKENS B L AE1 K AH0 N Z -BLACKER B L AE1 K ER0 -BLACKERBY B L AE1 K ER0 B IY0 -BLACKEST B L AE1 K AH0 S T -BLACKETER B L AE1 K IY0 T ER0 -BLACKETT B L AE1 K IH0 T -BLACKFOOT B L AE1 K F UH2 T -BLACKFORD B L AE1 K F ER0 D -BLACKHAM B L AE1 K HH AH0 M -BLACKHAWK B L AE1 K HH AO2 K -BLACKHAWK'S B L AE1 K HH AO2 K S -BLACKHAWKS B L AE1 K HH AO2 K S -BLACKHEATH B L AE1 K HH IY2 TH -BLACKHURST B L AE1 K HH ER0 S T -BLACKIE B L AE1 K IY0 -BLACKJACK B L AE1 K JH AE2 K -BLACKLEDGE B L AE1 K L EH2 JH -BLACKLEY B L AE1 K L IY0 -BLACKLIST B L AE1 K L IH2 S T -BLACKLISTED B L AE1 K L IH2 S T IH0 D -BLACKLISTING B L AE1 K L IH2 S T IH0 NG -BLACKLOCK B L AE1 K L AA2 K -BLACKMAIL B L AE1 K M EY2 L -BLACKMAILED B L AE1 K M EY2 L D -BLACKMAILING B L AE1 K M EY2 L IH0 NG -BLACKMAN B L AE1 K M AH0 N -BLACKMER B L AE1 K M ER0 -BLACKMON B L AE1 K M AH0 N -BLACKMORE B L AE1 K M AO0 R -BLACKMUN B L AE1 K M AH0 N -BLACKMUN'S B L AE1 K M AH0 N Z -BLACKNESS B L AE1 K N AH0 S -BLACKOUT B L AE1 K AW2 T -BLACKOUTS B L AE1 K AW2 T S -BLACKPOOL B L AE1 K P UW2 L -BLACKROCK B L AE1 K R AA2 K -BLACKS B L AE1 K S -BLACKS' B L AE1 K S -BLACKSBURG B L AE1 K S B ER0 G -BLACKSHEAR B L AE1 K SH IH0 R -BLACKSHER B L AE1 K SH ER0 -BLACKSHIRE B L AE1 K SH AY2 R -BLACKSMITH B L AE1 K S M IH2 TH -BLACKSON B L AE1 K S AH0 N -BLACKSTOCK B L AE1 K S T AA2 K -BLACKSTON B L AE1 K S T AH0 N -BLACKSTONE B L AE1 K S T OW2 N -BLACKSTONE'S B L AE1 K S T OW2 N Z -BLACKTOP B L AE1 K T AA2 P -BLACKWELDER B L AE1 K W EH2 L D ER0 -BLACKWELL B L AE1 K W EH2 L -BLACKWOOD B L AE1 K W UH2 D -BLADDER B L AE1 D ER0 -BLADDERS B L AE1 D ER0 Z -BLADE B L EY1 D -BLADED B L EY1 D IH0 D -BLADEN B L EY1 D AH0 N -BLADES B L EY1 D Z -BLADING B L EY1 D IH0 NG -BLADOW B L AE1 D OW0 -BLAESE B L EY1 Z -BLAESING B L EH1 S IH0 NG -BLAGDEN B L AE1 G D AH0 N -BLAGG B L AE1 G -BLAH B L AA1 -BLAHA B L AA1 HH AH0 -BLAHNIK B L AA1 N IH0 K -BLAHUT B L AE1 HH AH0 T -BLAICH B L EY1 CH -BLAIKIE B L EY1 K IY0 -BLAIN B L EY1 N -BLAINE B L EY1 N -BLAIR B L EH1 R -BLAIR'S B L EH1 R Z -BLAIS B L EH1 Z -BLAISDELL B L EY1 S D AH0 L -BLAISE B L EY1 Z -BLAISER B L EY1 Z ER0 -BLAIZE B L EY1 Z -BLAKE B L EY1 K -BLAKE'S B L EY1 K S -BLAKELEY B L EY1 K L IY0 -BLAKELEY'S B L EY1 K L IY0 Z -BLAKELY B L EY1 K L IY0 -BLAKELY'S B L EY1 K L IY0 Z -BLAKEMAN B L EY1 K M AH0 N -BLAKEMORE B L EY1 K M AO0 R -BLAKENEY B L EY1 K N IY0 -BLAKENEY'S B L EY1 K N IY0 Z -BLAKENHAM B L EY1 K AH0 N HH AE2 M -BLAKENSHIP B L EY1 K AH0 N SH IH0 P -BLAKER B L EY1 K ER0 -BLAKES B L EY1 K S -BLAKESLEE B L EY1 K S L IY0 -BLAKESLEY B L EY1 K S L IY0 -BLAKEY B L EY1 K IY0 -BLAKLEY B L AE1 K L IY0 -BLAKNEY B L AE1 K N IY0 -BLALACK B L AE1 L AH0 K -BLALOCK B L AE1 L AA0 K -BLAME B L EY1 M -BLAMED B L EY1 M D -BLAMELESS B L EY1 M L AH0 S -BLAMES B L EY1 M Z -BLAMESTRORM B L EY1 M S T AO2 R M -BLAMING B L EY1 M IH0 NG -BLAMPIED B L AE1 M P IY0 D -BLAN B L AE1 N -BLANC B L AE1 NG K -BLANCA B L AA1 NG K AH0 -BLANCETT B L AE1 N S IH0 T -BLANCH B L AE1 N CH -BLANCHARD B L AE1 N CH ER0 D -BLANCHARD'S B L AE1 N CH ER0 D Z -BLANCHARDS B L AE1 N CH ER0 D Z -BLANCHE B L AE1 N CH -BLANCHET B L AE1 N K IH0 T -BLANCHETT B L AE1 N CH IH0 T -BLANCHETTE B L AH0 N SH EH1 T -BLANCHFIELD B L AE1 N CH F IY2 L D -BLANCK B L AE1 NG K -BLANCMANGE B L AH0 M AA1 N JH -BLANCO B L AE1 NG K OW0 -BLAND B L AE1 N D -BLANDA B L AE1 N D AH0 -BLANDER B L AE1 N D ER0 -BLANDFORD B L AE1 N D F AO0 R D -BLANDIN B L AE1 N D IH0 N -BLANDING B L AE1 N D IH0 NG -BLANDINO B L AA0 N D IY1 N OW0 -BLANDISHMENT B L AE1 N D IH0 SH M AH0 N T -BLANDISHMENTS B L AE1 N D IH0 SH M AH0 N T S -BLANDLY B L AE1 N D L IY0 -BLANDNESS B L AE1 N D N AH0 S -BLANDO B L AE1 N D OW0 -BLANDON B L AE1 N D IH0 N -BLANE B L EY1 N -BLANEY B L EY1 N IY0 -BLANFORD B L AE1 N F ER0 D -BLANK B L AE1 NG K -BLANKE B L AE1 NG K -BLANKED B L AE1 NG K T -BLANKEN B L AE1 NG K AH0 N -BLANKENBAKER B L AE1 NG K AH0 N B EY2 K ER0 -BLANKENBECKLER B L AE1 NG K AH0 N B EH2 K L ER0 -BLANKENBURG B L AE1 NG K AH0 N B ER0 G -BLANKENHORN B L AE1 NG K IH0 N HH ER0 N -BLANKENSHIP B L AE1 NG K AH0 N SH IH2 P -BLANKET B L AE1 NG K AH0 T -BLANKET(2) B L AE1 NG K IH0 T -BLANKETED B L AE1 NG K AH0 T IH0 D -BLANKETING B L AE1 NG K AH0 T IH0 NG -BLANKETS B L AE1 NG K AH0 T S -BLANKIE B L AE1 NG K IY2 -BLANKIES B L AE1 NG K IY2 Z -BLANKING B L AE1 NG K IH0 NG -BLANKINSHIP B L AE1 NG K IH0 N SH IH0 P -BLANKLEY B L AE1 NG K L IY0 -BLANKLY B L AE1 NG K L IY0 -BLANKLY'S B L AE1 NG K L IY0 Z -BLANKS B L AE1 NG K S -BLANKY B L AE1 NG K IY2 -BLANN B L AE1 N -BLANQUITA B L AA0 N K IY1 T AH0 -BLANSETT B L AE1 N S IH0 T -BLANTON B L AE1 N T AH0 N -BLARE B L EH1 R -BLARED B L EH1 R D -BLARES B L EH1 R Z -BLARING B L EH1 R IH0 NG -BLAS B L AA1 S -BLASCHKE B L AE1 SH K -BLASCO B L AA1 S K OW0 -BLASDEL B L AE1 S D AH0 L -BLASDELL B L AE1 S D AH0 L -BLASE B L EY1 Z -BLASER B L EY1 Z ER0 -BLASI B L EY1 Z IY0 -BLASIA B L AA1 S IY0 AH0 -BLASIER B L EY1 Z IY0 ER0 -BLASIER(2) B L EY1 ZH ER0 -BLASIER'S B L EY1 Z IY0 ER0 Z -BLASIER'S(2) B L EY1 ZH ER0 Z -BLASING B L EY1 Z IH0 NG -BLASINGAME B L AA0 S IH0 NG G AA1 M IY0 -BLASINI B L AH0 S IY1 N IY0 -BLASIO B L AE1 Z IY0 OW2 -BLASIUS B L EY1 S IY0 IH0 S -BLASKE B L EY1 S K -BLASKO B L AA1 S K OW0 -BLASPHEMOUS B L AE1 S F AH0 M AH0 S -BLASPHEMY B L AE1 S F AH0 M IY0 -BLASS B L AE1 S -BLASSIE B L AE1 S IY0 -BLASSIE'S B L AE1 S IY0 Z -BLASSINGAME B L AA0 S IH0 NG G AA1 M IY0 -BLAST B L AE1 S T -BLASTDOWN B L AE1 S T D AW2 N -BLASTED B L AE1 S T AH0 D -BLASTED(2) B L AE1 S T IH0 D -BLASTER B L AE1 S T ER0 -BLASTFURNACE B L AE1 S T F ER2 N AH0 S -BLASTING B L AE1 S T IH0 NG -BLASTODERM B L AE1 S T AH0 D ER0 M -BLASTOFF B L AE1 S T AO2 F -BLASTOMA B L AE2 S T OW1 M AH0 -BLASTS B L AE1 S T S -BLASZAK B L AA1 SH AH0 K -BLASZCZYK B L AA1 SH CH IH0 K -BLATANT B L EY1 T AH0 N T -BLATANTLY B L EY1 T AH0 N T L IY0 -BLATCHFORD B L AE1 CH F ER0 D -BLATCHLEY B L AE1 CH L IY0 -BLATHER B L AE1 DH ER0 -BLATNIK B L AE1 T N IH0 K -BLATT B L AE1 T -BLATTER B L AE1 T ER0 -BLATTNER B L AE1 T N ER0 -BLATZ B L AE1 T S -BLAU B L AW1 -BLAUCH B L AO1 CH -BLAUER B L AW1 R -BLAUM B L AO1 M -BLAUSER B L AW1 S ER0 -BLAUSTEIN B L AW1 S T AY0 N -BLAUSTEIN(2) B L AW1 S T IY0 N -BLAUVELT B L AW1 V IH0 L T -BLAY B L EY1 -BLAYDES B L EY1 D Z -BLAYDON B L EY1 D AH0 N -BLAYLOCK B L EY1 L AH0 K -BLAYNE B L EY1 N -BLAYNEY B L EY1 N IY0 -BLAYZE B L EY1 Z -BLAZE B L EY1 Z -BLAZED B L EY1 Z D -BLAZEJEWSKI B L AH0 Z EY0 EH1 F S K IY0 -BLAZEK B L AA1 Z EH0 K -BLAZER B L EY1 Z ER0 -BLAZERS B L EY1 Z ER0 Z -BLAZES B L EY1 Z IH0 Z -BLAZIER B L EY1 Z IY0 ER0 -BLAZINA B L AA0 Z IY1 N AH0 -BLAZING B L EY1 Z IH0 NG -BLEA B L IY1 -BLEACH B L IY1 CH -BLEACHED B L IY1 CH T -BLEACHER B L IY1 CH ER0 -BLEACHERS B L IY1 CH ER0 Z -BLEACHING B L IY1 CH IH0 NG -BLEAK B L IY1 K -BLEAKER B L IY1 K ER0 -BLEAKEST B L IY1 K AH0 S T -BLEAKLEY B L IY1 K L IY0 -BLEAKNESS B L IY1 K N AH0 S -BLEAKNEY B L IY1 K N IY0 -BLEAM B L IY1 M -BLEARY B L IH1 R IY0 -BLEAU B L OW1 -BLECH B L EH1 K -BLECH'S B L EH1 K S -BLECHA B L EH1 CH AH0 -BLECHER B L EH1 K ER0 -BLECHLEY B L EH1 K L IY0 -BLECHMAN B L EH1 K M AH0 N -BLECK B L EH1 K -BLECKER B L EH1 K ER0 -BLED B L EH1 D -BLEDSOE B L EH1 D S OW0 -BLEECKER B L IY1 K ER0 -BLEED B L IY1 D -BLEEDING B L IY1 D IH0 NG -BLEEDS B L IY1 D Z -BLEEKER B L IY1 K ER0 -BLEEP B L IY1 P -BLEEPING B L IY1 P IH0 NG -BLEGEN B L EH1 G AH0 N -BLEHM B L EH1 M -BLEICH B L AY1 K -BLEICHER B L AY1 K ER0 -BLEIER B L AY1 ER0 -BLEIL B L IY1 L -BLEILER B L AY1 L ER0 -BLEILERS B L AY1 L ER0 Z -BLELLOCH B L EH1 L AA0 K -BLEMISH B L EH1 M IH0 SH -BLEMISHED B L EH1 M IH0 SH T -BLEMISHES B L EH1 M IH0 SH IH0 Z -BLEND B L EH1 N D -BLENDA B L EH1 N D AH0 -BLENDAX B L EH1 N D AE2 K S -BLENDED B L EH1 N D IH0 D -BLENDER B L EH1 N D ER0 -BLENDERS B L EH1 N D ER0 Z -BLENDING B L EH1 N D IH0 NG -BLENDS B L EH1 N D Z -BLENHEIM B L EH1 N HH AY2 M -BLEPHARISMA B L EH2 F ER0 IH1 Z M AH0 -BLESER B L IY1 Z ER0 -BLESS B L EH1 S -BLESSED B L EH1 S T -BLESSEDLY B L EH1 S AH0 D L IY0 -BLESSEDNESS B L EH1 S AH0 D N AH0 S -BLESSES B L EH1 S IH0 Z -BLESSING B L EH1 S IH0 NG -BLESSINGER B L EH1 S IH0 NG ER0 -BLESSINGS B L EH1 S IH0 NG Z -BLESSINGTON B L EH1 S IH0 NG T AH0 N -BLEST B L EH1 S T -BLETHEN B L EH1 TH AH0 N -BLEU B L UW1 -BLEVENS B L IY1 V AH0 N Z -BLEVINS B L EH1 V IH0 N Z -BLEW B L UW1 -BLEWETT B L UW1 IH0 T -BLEWITT B L UW1 IH0 T -BLEY B L EY1 -BLICK B L IH1 K -BLICKENSTAFF B L IH1 K IH0 N S T AH0 F -BLIDA B L AY1 D AH0 -BLIGH B L AY1 -BLIGHT B L AY1 T -BLIGHTED B L AY1 T IH0 D -BLILEY B L AY1 L IY0 -BLIMP B L IH1 M P -BLIMPS B L IH1 M P S -BLINCOE B L IH0 N K OW1 -BLIND B L AY1 N D -BLINDED B L AY1 N D IH0 D -BLINDER B L AY1 N D ER0 -BLINDER'S B L AY1 N D ER0 Z -BLINDERS B L AY1 N D ER0 Z -BLINDFOLD B L AY1 N D F OW2 L D -BLINDFOLDED B L AY1 N D F OW2 L D IH0 D -BLINDING B L AY1 N D IH0 NG -BLINDLY B L AY1 N D L IY0 -BLINDNESS B L AY1 N D N AH0 S -BLINDS B L AY1 N D Z -BLINDSIDE B L AY1 N D S AY2 D -BLINDSIDED B L AY1 N D S AY2 D IH0 D -BLINK B L IH1 NG K -BLINKED B L IH1 NG K T -BLINKEN B L IH1 NG K AH0 N -BLINKING B L IH1 NG K IH0 NG -BLINKS B L IH1 NG K S -BLINN B L IH1 N -BLIP B L IH1 P -BLIPS B L IH1 P S -BLISCOLL B L IH1 S K AO0 L -BLISH B L IH1 SH -BLISS B L IH1 S -BLISS' B L IH1 S -BLISSETT B L IH1 S IH0 T -BLISSFUL B L IH1 S F AH0 L -BLISSFULLY B L IH1 S F AH0 L IY0 -BLISTER B L IH1 S T ER0 -BLISTERED B L IH1 S T ER0 D -BLISTERING B L IH1 S T ER0 IH0 NG -BLISTERS B L IH1 S T ER0 Z -BLITCH B L IH1 CH -BLITHE B L AY1 DH -BLITHELY B L AY1 TH L IY0 -BLITSTEIN B L IH1 T S T IY0 N -BLITSTEIN(2) B L IH1 T S T AY0 N -BLITZ B L IH1 T S -BLITZED B L IH1 T S T -BLITZEN B L IH1 T S AH0 N -BLITZER B L IH1 T S ER0 -BLITZER'S B L IH1 T S ER0 Z -BLITZES B L IH1 T S IH0 Z -BLITZKRIEG B L IH1 T S K R IY2 G -BLIVEN B L AY1 V AH0 N -BLIXT B L IH1 K S T -BLIZARD B L IH1 Z ER0 D -BLIZZARD B L IH1 Z ER0 D -BLIZZARDS B L IH1 Z ER0 D Z -BLOAT B L OW1 T -BLOATED B L OW1 T IH0 D -BLOATEDNESS B L OW1 T IH0 D N AH0 S -BLOATING B L OW1 T IH0 NG -BLOATS B L OW1 T S -BLOB B L AA1 B -BLOBBY B L AA1 B IY0 -BLOBS B L AA1 B Z -BLOC B L AA1 K -BLOC'S B L AA1 K S -BLOCH B L AA1 K -BLOCH'S B L AA1 K S -BLOCHER B L AA1 K ER0 -BLOCK B L AA1 K -BLOCK'S B L AA1 K S -BLOCKADE B L AA2 K EY1 D -BLOCKADED B L AA2 K EY1 D IH0 D -BLOCKADER B L AA2 K EY1 D ER0 -BLOCKADERS B L AA2 K EY1 D ER0 Z -BLOCKADES B L AA2 K EY1 D Z -BLOCKADING B L AA2 K EY1 D IH0 NG -BLOCKAGE B L AA1 K IH0 JH -BLOCKAGES B L AA1 K IH0 JH IH0 Z -BLOCKBUSTER B L AA1 K B AH2 S T ER0 -BLOCKBUSTER'S B L AA1 K B AH2 S T ER0 Z -BLOCKBUSTERS B L AA1 K B AH2 S T ER0 Z -BLOCKCHAIN B L AA1 K CH EY1 N -BLOCKED B L AA1 K T -BLOCKER B L AA1 K ER0 -BLOCKERS B L AA1 K ER0 Z -BLOCKHEAD B L AA1 K HH EH2 D -BLOCKHEADS B L AA1 K HH EH2 D Z -BLOCKHOUSE B L AA1 K HH AW2 S -BLOCKHOUSES B L AA1 K HH AW2 S AH0 Z -BLOCKING B L AA1 K IH0 NG -BLOCKS B L AA1 K S -BLOCS B L AA1 K S -BLODGETT B L AA1 JH IH0 T -BLOEDEL B L OW1 D AH0 L -BLOEDORN B L OW1 D ER0 N -BLOEM B L OW1 M -BLOEMER B L OW1 M ER0 -BLOEMKER B L OW1 M K ER0 -BLOG B L AO1 G -BLOGGER B L AO1 G ER0 -BLOGGERS B L AO1 G ER0 Z -BLOGGING B L AO1 G IH0 NG -BLOGOSPHERE B L AO1 G AH0 S F IH2 R -BLOGOSPHERE(2) B L AO1 G AO0 S F IY1 R -BLOGS B L AO1 G Z -BLOHM B L OW1 M -BLOK B L AA1 K -BLOKE B L OW1 K -BLOKES B L OW1 K S -BLOM B L AA1 M -BLOMBERG B L AA1 M B ER0 G -BLOME B L OW1 M -BLOMGREN B L AA1 M G R EH0 N -BLOMKVIST B L AA1 M K W IH2 S T -BLOMQUIST B L AA1 M K W IH2 S T -BLOMSTROM B L AA1 M S T R AH0 M -BLOND B L AA1 N D -BLONDE B L AA1 N D -BLONDE'S B L AA1 N D Z -BLONDELL B L AA1 N D AH0 L -BLONDER B L AA1 N D ER0 -BLONDES B L AA1 N D Z -BLONDEST B L AA1 N D AH0 S T -BLONDIE B L AA1 N D IY0 -BLONDIN B L AA1 N D IH0 N -BLONDS B L AA1 N D Z -BLOOD B L AH1 D -BLOOD'S B L AH1 D Z -BLOODBATH B L AH1 D B AE2 TH -BLOODED B L AH1 D IH0 D -BLOODGOOD B L AH1 D G UH2 D -BLOODHOUND B L AH1 D HH AW0 N D -BLOODHOUNDS B L AH1 D HH AW0 N D Z -BLOODHOUNDS(2) B L AH1 D HH AW0 N Z -BLOODIED B L AH1 D IY0 D -BLOODIER B L AH1 D IY0 ER0 -BLOODIEST B L AH1 D IY0 AH0 S T -BLOODLESS B L AH1 D L AH0 S -BLOODLETTING B L AH1 D L EH2 T IH0 NG -BLOODLINE B L AH1 D L AY2 N -BLOODLINES B L AH1 D L AY2 N Z -BLOODS B L AH1 D Z -BLOODSHED B L AH1 D SH EH2 D -BLOODSTAIN B L AH1 D S T EY2 N -BLOODSTAINED B L AH1 D S T EY2 N D -BLOODSTAINS B L AH1 D S T EY2 N Z -BLOODSTONE B L AH1 D S T OW2 N -BLOODSTREAM B L AH1 D S T R IY2 M -BLOODSUCKER B L AH1 D S AH2 K ER0 -BLOODSUCKERS B L AH1 D S AH2 K ER0 Z -BLOODSUCKING B L AH1 D S AH2 K IH0 NG -BLOODSWORTH B L AH1 D Z W ER2 TH -BLOODTHIRSTY B L AH1 D TH ER2 S T IY0 -BLOODWORTH B L AH1 D W ER2 TH -BLOODY B L AH1 D IY0 -BLOOM B L UW1 M -BLOOM'S B L UW1 M Z -BLOOMBERG B L UW1 M B ER0 G -BLOOMBERG'S B L UW1 M B ER0 G Z -BLOOMED B L UW1 M D -BLOOMER B L UW1 M ER0 -BLOOMERS B L UW1 M ER0 Z -BLOOMFIELD B L UW1 M F IY2 L D -BLOOMING B L UW1 M IH0 NG -BLOOMINGDALE B L UW1 M IH0 NG D EY2 L -BLOOMINGDALE'S B L UW1 M IH0 NG D EY2 L Z -BLOOMINGDALES B L UW1 M IH0 NG D EY2 L Z -BLOOMINGTON B L UW1 M IH0 NG T AH0 N -BLOOMQUIST B L UW1 M K W IH2 S T -BLOOMS B L UW1 M Z -BLOOPER B L UW1 P ER0 -BLOOPERS B L UW1 P ER0 Z -BLOOR B L UH1 R -BLOSE B L OW1 Z -BLOSS B L AO1 S -BLOSSER B L AO1 S ER0 -BLOSSOM B L AA1 S AH0 M -BLOSSOMED B L AA1 S AH0 M D -BLOSSOMING B L AA1 S AH0 M IH0 NG -BLOSSOMS B L AA1 S AH0 M Z -BLOT B L AA1 T -BLOTCH B L AA1 T CH -BLOTCHED B L AA1 T CH T -BLOTCHES B L AA1 T CH AH0 Z -BLOTCHIER B L AA1 T CH IY0 ER0 -BLOTCHIEST B L AA1 T CH IY0 AH0 S T -BLOTCHING B L AA1 T CH IH0 NG -BLOTCHY B L AA1 T CH IY0 -BLOTNICK B L AA1 T N IH0 K -BLOTS B L AA1 T S -BLOTTED B L AA1 T AH0 D -BLOTTER B L AA1 T ER0 -BLOTTING B L AA1 T IH0 NG -BLOUCH B L AW1 CH -BLOUGH B L AW1 -BLOUIN B L W IY1 N -BLOUNT B L AW1 N T -BLOUSE B L AW1 S -BLOUSES B L AW1 S AH0 Z -BLOUSES(2) B L AW1 S IH0 Z -BLOW B L OW1 -BLOWDRIED B L OW1 D R AY2 D -BLOWDRIES B L OW1 D R AY2 Z -BLOWDRY B L OW1 D R AY2 -BLOWDRYER B L OW1 D R AY2 ER0 -BLOWDRYERS B L OW1 D R AY2 ER0 Z -BLOWDRYING B L OW1 D R AY2 IH0 NG -BLOWE B L OW1 -BLOWED B L OW1 D -BLOWER B L OW1 ER0 -BLOWERS B L OW1 ER0 Z -BLOWFISH B L OW1 F IH0 SH -BLOWGUN B L OW1 G AH2 N -BLOWGUNS B L OW1 G AH2 N Z -BLOWHARD B L OW1 HH AA2 R D -BLOWHARDS B L OW1 HH AA2 R D Z -BLOWHOLE B L OW1 HH OW2 L -BLOWHOLES B L OW1 HH OW2 L Z -BLOWIER B L OW1 IY0 ER0 -BLOWIEST B L OW1 IY0 AH0 S T -BLOWING B L OW1 IH0 NG -BLOWJOB B L OW1 JH AA2 B -BLOWJOBS B L OW1 JH AA2 B Z -BLOWN B L OW1 N -BLOWOUT B L OW1 AW2 T -BLOWOUTS B L OW1 AW2 T S -BLOWPIPE B L OW1 P AY2 P -BLOWPIPES B L OW1 P AY2 P S -BLOWS B L OW1 Z -BLOWTORCH B L OW1 T AO2 R CH -BLOWTORCHES B L OW1 T AO2 R CH AH0 Z -BLOWUP B L OW1 AH2 P -BLOWY B L OW1 IY0 -BLOXHAM B L AA1 K S AH0 M -BLOXOM B L AA1 K S AH0 M -BLOXSOM B L AA1 K S AH0 M -BLOYD B L OY1 D -BLOYER B L OY1 ER0 -BLUBAUGH B L AH1 B AO0 -BLUBBER B L AH1 B ER0 -BLUDGEON B L AH1 JH AH0 N -BLUDGEONED B L AH1 JH AH0 N D -BLUDGEONING B L AH1 JH AH0 N IH0 NG -BLUE B L UW1 -BLUE'S B L UW1 Z -BLUEBEARD B L UW1 B IY2 R D -BLUEBELL B L UW1 B EH2 L -BLUEBELLS B L UW1 B EH2 L Z -BLUEBERRIES B L UW1 B EH2 R IY0 Z -BLUEBERRY B L UW1 B EH2 R IY0 -BLUEBIRD B L UW1 B ER2 D -BLUEBIRDS B L UW1 B ER2 D Z -BLUEBLOOD B L UW1 B L AH2 D -BLUEBLOODED B L UW1 B L AH2 D AH0 D -BLUEBLOODS B L UW1 B L AH2 D Z -BLUEBONNET B L UW1 B AA2 N AH0 T -BLUEBONNETS B L UW1 B AA2 N AH0 T S -BLUEBOTTLE B L UW1 B AO2 T AH0 L -BLUEBOTTLES B L UW1 B AO2 T AH0 L Z -BLUECHIP B L UW1 CH IH2 P -BLUECOLLAR B L UW1 K AO2 L ER0 -BLUED B L UW1 D -BLUEEYED B L UW1 AY0 D -BLUEFIELD B L UW1 F IY2 L D -BLUEGRASS B L UW1 G R AE2 S -BLUEING B L UW1 IH0 NG -BLUEISH B L UW1 IH0 SH -BLUEJACKET B L UW1 JH AE2 K IH0 T -BLUEJAY B L UW1 JH EY2 -BLUEJEANS B L UW1 JH IY0 N Z -BLUELAW B L UW1 L AA2 -BLUEMEL B L UH1 M AH0 L -BLUENESS B L UW1 N AH0 S -BLUEPENCIL B L UW1 P EH2 N S AH0 L -BLUEPENCILLED B L UW1 P EH2 N S AH0 L D -BLUEPENCILLING B L UW1 P EH2 N S AH0 L IH0 NG -BLUEPENCILS B L UW1 P EH2 N S AH0 L Z -BLUEPRINT B L UW1 P R IH2 N T -BLUEPRINTS B L UW1 P R IH2 N T S -BLUER B L UW1 ER0 -BLUES B L UW1 Z -BLUES' B L UW1 Z -BLUEST B L UW1 AH0 S T -BLUESTEIN B L UH1 S T AY0 N -BLUESTEIN(2) B L UH1 S T IY0 N -BLUESTINE B L UW1 S T AY2 N -BLUESTONE B L UW1 S T OW2 N -BLUESY B L UW1 Z IY0 -BLUESY(2) B L UW1 AH0 S IY0 -BLUETOOTH B L UW1 T UW2 TH -BLUETT B L UW1 T -BLUEY B L UW1 IY0 -BLUFF B L AH1 F -BLUFFED B L AH1 F T -BLUFFER B L AH1 F ER0 -BLUFFING B L AH1 F IH0 NG -BLUFFS B L AH1 F S -BLUFORD B L UW1 F ER0 D -BLUHDORN B L AH1 D AO2 R N -BLUHM B L AH1 M -BLUING B L UW1 IH0 NG -BLUISH B L UW1 IH0 SH -BLUITT B L UW1 T -BLUM B L UW1 M -BLUMBERG B L AH1 M B ER0 G -BLUME B L UW1 M -BLUMENBERG B L UW1 M EH0 N B ER0 G -BLUMENFELD B L UW1 M IH0 N F EH0 L D -BLUMENSCHEIN B L AH1 M IH0 N SH AY0 N -BLUMENSHINE B L AH1 M IH0 N SH AY0 N -BLUMENSTEIN B L UW1 M EH0 N S T AY0 N -BLUMENSTEIN(2) B L UW1 M EH0 N S T IY0 N -BLUMENSTOCK B L UW1 M EH0 N S T AA0 K -BLUMENTHAL B L UW1 M AH0 N TH AO2 L -BLUMER B L UW1 M ER0 -BLUMQUIST B L AH1 M K W IH0 S T -BLUMSTEIN B L AH1 M S T AY0 N -BLUMSTEIN(2) B L AH1 M S T IY0 N -BLUNCK B L AH1 NG K -BLUNDALL B L AH1 N D AH0 L -BLUNDELL B L AH1 N D AH0 L -BLUNDER B L AH1 N D ER0 -BLUNDERED B L AH1 N D ER0 D -BLUNDERING B L AH1 N D ER0 IH0 NG -BLUNDERS B L AH1 N D ER0 Z -BLUNK B L AH1 NG K -BLUNT B L AH1 N T -BLUNTED B L AH1 N T AH0 D -BLUNTED(2) B L AH1 N T IH0 D -BLUNTER B L AH1 N T ER0 -BLUNTEST B L AH1 N T AH0 S T -BLUNTING B L AH1 N T IH0 NG -BLUNTLY B L AH1 N T L IY0 -BLUNTNESS B L AH1 N T N AH0 S -BLUNTS B L AH1 N T S -BLUR B L ER1 -BLURB B L ER1 B -BLURBS B L ER1 B Z -BLURRED B L ER1 D -BLURRING B L ER1 IH0 NG -BLURRY B L ER1 IY0 -BLURS B L ER1 Z -BLURT B L ER1 T -BLURTED B L ER1 T IH0 D -BLURTON B L ER1 T AH0 N -BLURTS B L ER1 T S -BLUSH B L AH1 SH -BLUSHED B L AH1 SH T 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AO1 D AH0 N -BROADENED B R AO1 D AH0 N D -BROADENING B R AO1 D AH0 N IH0 NG -BROADENING(2) B R AO1 D N IH0 NG -BROADENS B R AO1 D AH0 N Z -BROADER B R AO1 D ER0 -BROADEST B R AO1 D IH0 S T -BROADHEAD B R AO1 D HH EH2 D -BROADHURST B R AO1 D HH ER0 S T -BROADIE B R AO1 D IY0 -BROADLEY B R AO1 D L IY0 -BROADLY B R AO1 D L IY0 -BROADNAX B R AO1 D N AE0 K S -BROADPFOOT B R AO1 D F UH0 T -BROADRICK B R AO1 D R IH0 K -BROADSIDE B R AO1 D S AY2 D -BROADSIDED B R AO1 D S AY2 D IH0 D -BROADSTREET B R AO1 D S T R IY2 T -BROADSWORD B R AO1 D S AO2 R D -BROADUS B R OW1 D AH0 S -BROADVIEW B R AO1 D V Y UW2 -BROADWAY B R AO1 D W EY2 -BROADWAY'S B R AO1 D W EY2 Z -BROADWELL B R AO1 D W EH2 L -BROADY B R AO1 D IY0 -BROBDINGNAGIAN B R AO2 B D IH0 G N AE1 G IY0 AH0 N -BROBDINGNAGIANS B R AO2 B D IH0 G N AE1 G IY0 AH0 N Z -BROBECK B R OW1 B EH2 K -BROBERG B R OW1 B ER0 G -BROBST B R AA1 B S T -BROC B R AA1 K -BROCADE B R OW0 K EY1 D -BROCADES B R OW0 K EY1 D Z -BROCATO B R OW0 K AA1 T OW0 -BROCCO B R AA1 K OW0 -BROCCOLI B R AA1 K AH0 L IY0 -BROCCOLI(2) B R AA1 K L IY0 -BROCE B R OW1 S -BROCHU B R OW1 K UW0 -BROCHURE B R OW0 SH UH1 R -BROCHURES B R OW0 SH UH1 R Z -BROCIOUS B R AH0 SH IY1 S -BROCK B R AA1 K -BROCK'S B R AA1 K S -BROCKBANK B R AA1 K B AH0 NG K -BROCKEL B R AA1 K AH0 L -BROCKER B R AA1 K ER0 -BROCKERT B R AA1 K ER0 T -BROCKETT B R AA1 K IH0 T -BROCKHAUS B R AA1 K HH AW2 S -BROCKHOFF B R AA1 K HH AO2 F -BROCKHOUSE B R AA1 K HH AW2 S -BROCKIE B R AA1 K IY0 -BROCKINGTON B R AA1 K IH0 NG T AH0 N -BROCKLEHURST B R AA1 K AH0 L HH ER0 S T -BROCKLEY B R AA1 K L IY0 -BROCKLIN B R AA1 K L IH0 N -BROCKLIN'S B R AA1 K L IH0 N Z -BROCKMAN B R AA1 K M AH0 N -BROCKMANN B R AA1 K M AH0 N -BROCKMEIER B R AA1 K M AY0 ER0 -BROCKMEYER B R AA1 K M AY0 ER0 -BROCKNER B R AA1 K N ER0 -BROCKSMITH B R AA1 K S M IH2 TH -BROCKTON B R AA1 K T AH0 N -BROCKWAY B R AA1 K W EY2 -BROCKWAY'S B R AA1 K W EY2 Z -BROCKWELL B R AA1 K W EH2 L -BROD B R AA1 D -BRODA B R OW1 D AH0 -BRODBECK B R AA1 D B EH2 K 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R OY1 CH -BROIL B R OY1 L -BROILED B R OY1 L D -BROILER B R OY1 L ER0 -BROILERS B R OY1 L ER0 Z -BROILING B R OY1 L IH0 NG -BROIN B R OY1 N -BROK B R AA1 K -BROKAW B R OW1 K AO0 -BROKE B R OW1 K -BROKEN B R OW1 K AH0 N -BROKEN-TOOTH B R OW1 K AH0 N T UW2 TH -BROKEN-WIND B R OW1 K AH0 N W IH1 N D -BROKEN-WINDED B R OW1 K AH0 N W IH1 N D IH0 D -BROKER B R OW1 K ER0 -BROKER'S B R OW1 K ER0 Z -BROKERAGE B R OW1 K ER0 IH0 JH -BROKERAGE(2) B R OW1 K R IH0 JH -BROKERAGE'S B R OW1 K ER0 IH0 JH IH0 Z -BROKERAGE'S(2) B R OW1 K R IH0 JH IH0 Z -BROKERAGES B R OW1 K ER0 IH0 JH IH0 Z -BROKERAGES(2) B R OW1 K R IH0 JH IH0 Z -BROKERAGES' B R OW1 K ER0 IH0 JH IH0 Z -BROKERAGES'(2) B R OW1 K R IH0 JH IH0 Z -BROKERED B R OW1 K ER0 D -BROKERING B R OW1 K ER0 IH0 NG -BROKERS B R OW1 K ER0 Z -BROKERS' B R OW1 K ER0 Z -BROKING B R OW1 K IH0 NG -BROKINI B R AH0 K IY1 N IY0 -BROLIN B R OW1 L IH0 N -BROLLY B R AA1 L IY0 -BROM B R AA1 M -BROMAN B R OW1 M AH0 N -BROMBERG B R AA1 M B ER0 G -BROMFIELD B R AA1 M F IY2 L D -BROMFIELD'S B R AA1 M F IY2 L D Z -BROMIDE B R OW1 M AY2 D -BROMIDES B R OW1 M AY2 D Z -BROMINE B R OW1 M IY2 N -BROMLEY B R AA1 M L IY0 -BROMM B R AA1 M -BROMMER B R AA1 M ER0 -BROMONT B R OW1 M AA2 N T -BROMPHERIL B R AA1 M F EH0 R IH0 L -BROMWELL B R AA1 M W EH2 L -BROMWICH B R AA1 M W IH0 CH -BRONAUGH B R AA1 N AO0 -BRONC B R AA1 NG K -BRONCHIAL B R AA1 N CH IY0 AH0 L -BRONCHITIS B R AA0 NG K AY1 T AH0 S -BRONCHOSCOPE B R AA1 N K AH0 S K OW1 P -BRONCO B R AA1 NG K OW0 -BRONCOS B R AA1 NG K OW0 Z -BRONDER B R AA1 N D ER0 -BRONER B R OW1 N ER0 -BRONFMAN B R AA1 N F M AH0 N -BRONFMAN'S B R AA1 N F M AH0 N Z -BRONFMANS B R AA1 N F M AH0 N Z -BRONK B R AA1 NG K -BRONKEMA B R AH0 NG K IY1 M AH0 -BRONN B R AA1 N -BRONNER B R AA1 N ER0 -BRONS B R AA1 N Z -BRONSON B R AA1 N S AH0 N -BRONSTEIN B R AA1 N S T IY2 N -BRONSTEIN(2) B R AA1 N S T AY2 N -BRONSTON B R AA1 N S T AH0 N -BRONTE B R AA1 N T IY0 -BRONTE'S B R AA1 N T IY0 Z -BRONTOSAURUS B R AO2 N T AH0 S AO1 R AH0 S 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AY2 D -CITYTRUST S IH1 T IY0 T R AH2 S T -CITYWIDE S IH1 T IY0 W AY2 D -CIUCCI CH UW1 CH IY0 -CIUDAD S IY2 UW0 D AE1 D -CIULLA CH UW1 L AH0 -CIULLO CH UW1 L OW0 -CIVET S IH1 V AH0 T -CIVIC S IH1 V IH0 K -CIVICS S IH1 V IH0 K S -CIVIL S IH1 V AH0 L -CIVILETTI S IY2 V IH0 L EH1 T IY0 -CIVILIAN S AH0 V IH1 L Y AH0 N -CIVILIANS S AH0 V IH1 L Y AH0 N Z -CIVILITY S AH0 V IH1 L AH0 T IY0 -CIVILIZATION S IH2 V AH0 L IH0 Z EY1 SH AH0 N -CIVILIZATIONS S IH2 V AH0 L IH0 Z EY1 SH AH0 N Z -CIVILIZE S IH1 V AH0 L AY2 Z -CIVILIZED S IH1 V AH0 L AY2 Z D -CIVILLY S IH1 V IH0 L IY0 -CIVITELLO CH IY0 V IY0 T EH1 L OW0 -CIZEK CH IH1 Z EH0 K -CIZIK S IY1 Z IH0 K -CIZNEROS S IH2 Z N EH1 R OW0 S -CLAAR K L AA1 R -CLAASSEN K L AA1 S AH0 N -CLABAUGH K L AE1 B AO0 -CLABIR K L AE1 B IH0 R -CLABO K L AA1 B OW0 -CLABORN K L AE1 B ER0 N -CLABOUGH K L AE1 B AW0 -CLACK K L AE1 K -CLACKAMAS K L AE1 K AH0 M AH0 S -CLAD K L AE1 D -CLADDAGH K L AE1 D AH2 -CLADIFICATION K L AE2 D AH0 F AH0 K EY1 SH AH0 N -CLAES K L EY1 Z -CLAEYS K L EY1 Z -CLAFFEY K L AE1 F IY0 -CLAFLIN K L AE1 F L IH0 N -CLAGETT K L AE1 JH IH0 T -CLAGG K L AE1 G -CLAGGETT K L AE1 G IH0 T -CLAGUE K L AA1 G -CLAIBORN K L EY1 B ER0 N -CLAIBORNE K L EY1 B ER0 N -CLAIBORNE'S K L EY1 B AO0 R N Z -CLAIBORNE'S(2) K L EY1 B ER0 N Z -CLAIM K L EY1 M -CLAIMANT K L EY1 M AH0 N T -CLAIMANTS K L EY1 M AH0 N T S -CLAIMANTS' K L EY1 M AH0 N T S -CLAIMED K L EY1 M D -CLAIMING K L EY1 M IH0 NG -CLAIMS K L EY1 M Z -CLAIR K L EH1 R -CLAIRE K L EH1 R -CLAIRE'S K L EH1 R Z -CLAIRMONT K L EH1 R M AH0 N T -CLAIROL K L EH1 R AA0 L -CLAIRSON K L EH1 R S AH0 N -CLAIRVOYANCE K L EH0 R V OY1 AH0 N S -CLAIRVOYANT K L EH0 R V OY1 AH0 N T -CLAM K L AE1 M -CLAMBER K L AE1 M B ER0 -CLAMBERED K L AE1 M B ER0 D -CLAMEN K L EY1 M AH0 N -CLAMMED K L AE1 M D -CLAMMY K L AE1 M IY0 -CLAMOR K L AE1 M ER0 -CLAMORED K L AE1 M ER0 D -CLAMORING K L AE1 M ER0 IH0 NG -CLAMOUR K L AE1 M ER0 -CLAMP K L AE1 M P -CLAMPDOWN K L AE1 M P D AW2 N -CLAMPED K L AE1 M P T -CLAMPETT K L AE1 M P AH0 T -CLAMPING K L AE1 M P IH0 NG -CLAMPITT K L AH0 M P IH1 T -CLAMPS K L AE1 M P S -CLAMS K L AE1 M Z -CLAMSHELL K L AE1 M SH EH2 L -CLAN K L AE1 N -CLANCEY K L AE1 N S IY0 -CLANCY K L AE1 N S IY0 -CLANCY'S K L AE1 N S IY0 Z -CLANDESTINE K L AE0 N D EH1 S T IH0 N -CLANDESTINELY K L AE0 N D EH1 S T AH0 N L IY0 -CLANG K L AE1 NG -CLANGING K L AE1 NG IH0 NG -CLANIN K L AE1 N IH0 N -CLANK K L AE1 NG K -CLANKING K L AE1 NG K IH0 NG -CLANNISH K L AE1 N IH0 SH -CLANS K L AE1 N Z -CLANTON K L AE1 N T AH0 N -CLAP K L AE1 P -CLAPBOARD K L AE1 P B AO2 R D -CLAPBOARDS K L AE1 P B AO2 R D Z -CLAPHAM K L AE1 F AH0 M -CLAPMAN K L AE1 P M AH0 N -CLAPP K L AE1 P -CLAPPED K L AE1 P T -CLAPPER K L AE1 P ER0 -CLAPPING K L AE1 P IH0 NG -CLAPS K L AE1 P S -CLAPSADDLE K L AE1 P S AE2 D AH0 L -CLAPTON K L AE1 P T AH0 N -CLAPTON'S K L AE1 P T AH0 N Z -CLAR K L AA1 R -CLARA K L AE1 R AH0 -CLARA(2) K L EH1 R AH0 -CLARA'S K L AE1 R AH0 Z -CLARA'S(2) K L EH1 R AH0 Z -CLARABELLE K L AE1 R AH0 B AH0 L -CLARABELLE(2) K L AE1 R AH0 B EH2 L -CLARAMAE K L AA0 R AA1 M AY0 -CLARAN K L EH1 R AH0 N -CLARCOR K L AA1 R K AO2 R -CLARDY K L AA1 R D IY0 -CLARE K L EH1 R -CLAREMONT K L EH1 R M AA2 N T -CLAREN K L AE1 R AH0 N -CLARENCE K L EH1 R AH0 N S -CLARENDON K L EH1 R AH0 N D AH0 N -CLARESTA K L AA0 R EH1 S T AH0 -CLARETTE K L ER0 EH1 T -CLAREY K L AE1 R IY0 -CLARIBEL K L EH1 R AH0 B EH2 L -CLARICE K L ER0 IY1 S -CLARIDA K L AA0 R IY1 D AH0 -CLARIDGE K L EH1 R IH0 JH -CLARIDGES K L EH1 R IH0 JH IH0 Z -CLARIDON K L EH1 R AH0 D AA0 N -CLARIFICATION K L EH2 R AH0 F AH0 K EY1 SH AH0 N -CLARIFICATIONS K L EH2 R IH0 F IH0 K EY1 SH AH0 N Z -CLARIFIED K L EH1 R AH0 F AY2 D -CLARIFIES K L EH1 R AH0 F AY2 Z -CLARIFY K L EH1 R AH0 F AY2 -CLARIFYING K L EH1 R AH0 F AY2 IH0 NG -CLARIMOND K L AE1 R IH0 M AH0 N D -CLARINDA K L ER0 IH1 N D AH0 -CLARINE K L EH1 R IY0 N -CLARINET K L EH2 R AH0 N EH1 T -CLARINETIST K L EH2 R AH0 N EH1 T IH0 S T -CLARINETISTS K L EH2 R AH0 N EH1 T IH0 S T S -CLARINS K L EH1 R IH0 N Z -CLARION K L EH1 R IY0 AH0 N -CLARIS K L EH1 R IH0 S -CLARISSA K L ER0 IH1 S AH0 -CLARISSE K L AE1 R IH0 S -CLARISSE(2) K L AH0 R IY1 S -CLARITA K L AA0 R IY1 T AH0 -CLARITIN K L EH1 R IH0 T IH0 N -CLARITY K L EH1 R AH0 T IY0 -CLARITY(2) K L EH1 R IH0 T IY0 -CLARK K L AA1 R K -CLARK'S K L AA1 R K S -CLARKE K L AA1 R K -CLARKE'S K L AA1 R K S -CLARKEN K L AA1 R K EH0 N -CLARKIN K L AA1 R K IH0 N -CLARKS K L AA1 R K S -CLARKSBURG K L AA1 R K S B ER0 G -CLARKSON K L AA1 R K S AH0 N -CLARKSTON K L AA1 R K S T AH0 N -CLARKSVILLE K L AA1 R K S V IH2 L -CLARO K L AA1 R OW0 -CLAROSTAT K L EH1 R AH0 S T AE2 T -CLARRIDGE K L AE1 R IH0 JH -CLARRISSE K L AE1 R IH0 S -CLARY K L EH1 R IY0 -CLASBY K L AE1 S B IY0 -CLASEN K L EY1 S AH0 N -CLASH K L AE1 SH -CLASHED K L AE1 SH T -CLASHES K L AE1 SH IH0 Z -CLASHING K L AE1 SH IH0 NG -CLASON K L AE1 S AH0 N -CLASP K L AE1 S P -CLASPED K L AE1 S P T -CLASS K L AE1 S -CLASS'S K L AE1 S IH0 Z -CLASSACTION K L AE1 S AE1 K SH AH0 N -CLASSACTIONS K L AE1 S AE1 K SH AH0 N Z -CLASSED K L AE1 S T -CLASSEN K L AE1 S AH0 N -CLASSES K L AE1 S AH0 Z -CLASSES(2) K L AE1 S IH0 Z -CLASSIC K L AE1 S IH0 K -CLASSICAL K L AE1 S IH0 K AH0 L -CLASSICALLY K L AE1 S IH0 K L IY0 -CLASSICISM K L AE1 S IH0 S IH2 Z AH0 M -CLASSICIST K L AE1 S AH0 S AH0 S T -CLASSICS K L AE1 S IH0 K S -CLASSIER K L AE1 S IY0 ER0 -CLASSIFIABLE K L AE1 S AH0 F AY2 AH0 B AH0 L -CLASSIFICATION K L AE2 S AH0 F AH0 K EY1 SH AH0 N -CLASSIFICATIONS K L AE2 S AH0 F AH0 K EY1 SH AH0 N Z -CLASSIFIED K L AE1 S AH0 F AY2 D -CLASSIFIEDS K L AE1 S AH0 F AY2 D Z -CLASSIFIES K L AE1 S AH0 F AY2 Z -CLASSIFY K L AE1 S AH0 F AY2 -CLASSIFYING K L AE1 S AH0 F AY2 IH0 NG -CLASSING K L AE1 S IH0 NG -CLASSLESS K L AE1 S L AH0 S -CLASSMAN K L AE1 S M AH0 N -CLASSMATE K L AE1 S M EY2 T -CLASSMATES K L AE1 S M EY2 T S -CLASSMEN K L AE1 S M EH0 N -CLASSON K L AE1 S AH0 N -CLASSROOM K L AE1 S R UW2 M -CLASSROOMS K L AE1 S R UW2 M Z -CLASSY K L AE1 S IY0 -CLATTER K L AE1 T ER0 -CLATTERING K L AE1 T ER0 IH0 NG -CLAUD K L AO1 D -CLAUDE K L AO1 D -CLAUDET K L AO0 D EH1 T -CLAUDETTE K L OW0 D EH1 T -CLAUDIA K L AO1 D IY0 AH0 -CLAUDIAN K L AO1 D IY0 AH0 N -CLAUDIE K L AO1 D IY0 -CLAUDINA K L AO1 D IH0 N AH0 -CLAUDINA(2) K L AO0 D IY1 N AH0 -CLAUDINE K L AO0 D IY1 N -CLAUDIO K L AO1 D IY0 OW2 -CLAUDIUS K L AO1 D IY0 AH0 S -CLAUDSON K L AO1 D S AH0 N -CLAUNCH K L AO1 N CH -CLAUS K L AO1 Z -CLAUS' K L AO1 Z -CLAUSE K L AO1 Z -CLAUSELL K L AO1 Z AH0 L -CLAUSEN K L AW1 S AH0 N -CLAUSER K L AO1 Z ER0 -CLAUSES K L AO1 Z AH0 Z -CLAUSES(2) K L AO1 Z IH0 Z -CLAUSING K L AO1 Z IH0 NG -CLAUSON K L AO1 Z AH0 N -CLAUSS K L AO1 S -CLAUSSEN K L AO1 Z S AH0 N -CLAUSTROPHOBIA K L AO2 S T R AH0 F OW1 B IY0 AH0 -CLAUSTROPHOBIC K L AO2 S T R AH0 F OW1 B IH0 K -CLAVETTE K L AH0 V EH1 T -CLAVICHORD K L AE1 V AH0 K AO2 R D -CLAVICLE K L AE1 V AH0 K AH0 L -CLAVICLE(2) K L AE1 V IH0 K AH0 L -CLAVIN K L AE1 V IH0 N -CLAW K L AO1 -CLAWED K L AO1 D -CLAWING K L AO1 IH0 NG -CLAWS K L AO1 Z -CLAWSON K L AO1 S AH0 N -CLAXON K L AE1 K S AH0 N -CLAXTON K L AE1 K S T AH0 N -CLAY K L EY1 -CLAYBAUGH K L EY1 B AO2 -CLAYBORN K L EY1 B ER0 N -CLAYBORNE K L EY1 B ER0 N -CLAYBOURNE K L EY1 B ER0 N -CLAYBROOK K L EY1 B R UH2 K -CLAYBROOKS K L EY1 B R UH2 K S -CLAYBURN K L EY1 B ER2 N -CLAYCOMB K L EY1 K AH0 M -CLAYEY K L EY1 IY0 -CLAYMAN K L EY1 M AH0 N -CLAYMATION K L EY1 M EY2 SH AH0 N -CLAYMONT K L EY1 M AA2 N T -CLAYMORE K L EY1 M AO2 R -CLAYPOOL K L EY1 P UW2 L -CLAYPOOLE K L EY1 P UW2 L -CLAYS K L EY1 Z -CLAYSON K L EY1 Z AH0 N -CLAYTON K L EY1 T AH0 N -CLAYTON'S K L EY1 T AH0 N Z -CLAYTOR K L EY1 T ER0 -CLAYWELL K L EY1 W EH2 L -CLEAH K IY1 AH0 -CLEAN K L IY1 N -CLEANED K L IY1 N D -CLEANER K L IY1 N ER0 -CLEANERS K L IY1 N ER0 Z -CLEANEST K L IY1 N AH0 S T -CLEANING K L IY1 N IH0 NG -CLEANLINESS K L EH1 N L IY0 N IH0 S -CLEANLY K L IY1 N L IY0 -CLEANNESS K L IY1 N IH0 S -CLEANS K L IY1 N Z -CLEANSE K L EH1 N Z -CLEANSED K L EH1 N Z D -CLEANSER K L EH1 N Z ER0 -CLEANSERS K L EH1 N Z ER0 Z -CLEANSING K L EH1 N Z IH0 NG -CLEANTHA K L IY1 N TH AH0 -CLEANUP K L IY1 N AH2 P -CLEANUPS K L IY1 N AH2 P S -CLEAR K L IH1 R -CLEAR-CUT K L IH1 R K AH2 T -CLEARANCE K L IH1 R AH0 N S -CLEARANCES K L IH1 R AH0 N S IH0 Z -CLEARCUT K L IH1 R K AH2 T -CLEARCUTS K L IH1 R K AH2 T S -CLEARCUTTING K L IH1 R K AH2 T IH0 NG -CLEARED K L IH1 R D -CLEARER K L IH1 R ER0 -CLEAREST K L IH1 R IH0 S T -CLEARING K L IH1 R IH0 NG -CLEARINGHOUSE K L IH1 R IH0 NG HH AW2 S -CLEARINGHOUSES K L IY1 R IH0 NG HH AW2 S IH0 Z -CLEARLY K L IH1 R L IY0 -CLEARMAN K L IH1 R M AH0 N -CLEARNESS K L IH1 R N AH0 S -CLEARS K L IH1 R Z -CLEARWATER K L IH1 R W AO2 T ER0 -CLEARY K L IH1 R IY0 -CLEAT K L IY1 T -CLEATS K L IY1 T S -CLEAVAGE K L IY1 V AH0 JH -CLEAVAGE(2) K L IY1 V IH0 JH -CLEAVE K L IY1 V -CLEAVELAND K L IY1 V L AH0 N D -CLEAVENGER K L IY1 V IH0 N JH ER0 -CLEAVER K L IY1 V ER0 -CLEAVES K L IY1 V Z -CLECKLER K L EH1 K L ER0 -CLECKLEY K L EH1 K L IY0 -CLECKNER K L EH1 K N ER0 -CLEEK K L IY1 K -CLEERE K L IH1 R -CLEESE K L IY1 S -CLEETON K L IY1 T AH0 N -CLEF K L EH1 F -CLEFT K L EH1 F T -CLEGG K L EH1 G -CLEGHORN K L EH1 G HH ER0 N -CLELAND K L EH1 L AH0 N D -CLELL K L EH1 L -CLELLAND K L EH1 L AH0 N D -CLEM K L EH1 M -CLEMANS K L EH1 M AH0 N Z -CLEMATIS K L EH1 M AH0 T IH0 S -CLEMATIS(2) K L AH0 M AE1 T IH0 S -CLEMEN K L EH1 M AH0 N -CLEMENCE K L EH1 M AH0 N S -CLEMENCY K L EH1 M AH0 N S IY0 -CLEMENS K L EH1 M AH0 N Z -CLEMENSEN K L EH1 M AH0 N S AH0 N -CLEMENSON K L EH1 M IH0 N S AH0 N -CLEMENT K L EH1 M AH0 N T -CLEMENTE K L AH0 M EH1 N T EY0 -CLEMENTE(2) K L AH0 M EH1 N T IY0 -CLEMENTE'S K L AH0 M EH1 N T EY0 Z -CLEMENTE'S(2) K L AH0 M EH1 N T IY0 Z -CLEMENTES K L AH0 M EH1 N T EY0 Z -CLEMENTES(2) K L AH0 M EH1 N T IY0 Z -CLEMENTI K L EY0 M EY1 N T IY0 -CLEMENTIA K L EY0 M EY1 N SH AH0 -CLEMENTINE K L EH1 M AH0 N T AY2 N -CLEMENTINE(2) K L EH1 M AH0 N T IY2 N -CLEMENTS K L EH1 M AH0 N T S -CLEMENTSON K L EH1 M IH0 N T S AH0 N -CLEMENTZ K L EH1 M IH0 N T S -CLEMMER K L EH1 M ER0 -CLEMMIE K L EH1 M IY0 -CLEMMONS K L EH1 M AH0 N Z -CLEMMY K L EH1 M IY0 -CLEMO K L EY1 M OW0 -CLEMONS K L EH1 M AH0 N Z -CLEMSON K L EH1 M S AH0 N -CLENCH K L EH1 N CH -CLENCHED K L EH1 N CH T -CLENCHER K L EH1 N CH ER0 -CLENCHES K L EH1 N CH AH0 Z -CLENDANIEL K L EH1 N D AH0 N IY2 L -CLENDENEN K L EH1 N D AH0 N AH0 N -CLENDENIN K L EH1 N D IH0 N IH0 N -CLENDENING K L EH1 N D AH0 N IH0 NG -CLENDENNING K L EH2 N D EH1 N IH0 NG -CLENNEY K L EH1 N IY0 -CLENWAR K L EH1 N W ER0 -CLEO K L IY1 OW0 -CLEOPATRA K L IY2 AH0 P AE1 T R AH0 -CLEOPATRA'S K L IY2 AH0 P AE1 T R AH0 Z -CLEPHANE K L EH1 F EY2 N -CLEPPER K L EH1 P ER0 -CLERC K L ER1 K -CLERCQ K L ER1 K -CLERGY K L ER1 JH IY0 -CLERGYMAN K L ER1 JH IY0 M AH0 N -CLERGYMAN(2) K L ER1 JH IY0 M AE2 N -CLERGYMEN K L ER1 JH IY0 M IH0 N -CLERGYMEN(2) K L ER1 JH IY0 M EH2 N -CLERIC K L EH1 R IH0 K -CLERICAL K L EH1 R AH0 K AH0 L -CLERICAL(2) K L EH1 R IH0 K AH0 L -CLERICS K L EH1 R IH0 K S -CLERISSA K L ER0 IY1 S AH0 -CLERK K L ER1 K -CLERK'S K L ER1 K S -CLERKIN K L ER1 K IH0 N -CLERKS K L ER1 K S -CLERKS' K L ER1 K S -CLERMONT K L EH1 R M AA2 N T -CLEVA K L IY1 V AH0 -CLEVE K L IY1 V -CLEVELAND K L IY1 V L AH0 N D -CLEVELAND'S K L IY1 V L AH0 N D Z -CLEVELANDER K L IY1 V L AH0 N D ER0 -CLEVELANDERS K L IY1 V L AH0 N D ER0 Z -CLEVEN K L IY1 V AH0 N -CLEVENGER K L EH1 V IH0 N JH ER0 -CLEVER K L EH1 V ER0 -CLEVERER K L EH1 V AH0 R ER2 -CLEVERLY K L EH1 V ER0 L IY0 -CLEVERNESS K L EH1 V ER0 N AH0 S -CLEVETRUST K L IY1 V T R AH1 S T -CLEVIE K L IY1 V IY0 -CLEVINGER K L EH1 V IH0 NG ER0 -CLEVITE K L AH0 V AY1 T -CLEVITE'S K L AH0 V AY1 T S -CLEWELL K L EH1 W EH0 L -CLEWIS K L UW1 IH0 S -CLEWISTON K L UW1 AH0 S T AH0 N -CLEWS K L UW1 Z -CLIANTHA K L IY0 AE1 N TH AH0 -CLIBURN K L AY1 B ER0 N -CLICHE K L IY0 SH EY1 -CLICHED K L IY2 SH EY1 D -CLICHES K L IY0 SH EY1 Z -CLICK K L IH1 K -CLICKBAIT K L IH1 K B EY2 T -CLICKED K L IH1 K T -CLICKER K L IH1 K ER0 -CLICKING K L IH1 K IH0 NG -CLICKNER K L IH1 K N ER0 -CLICKS K L IH1 K S -CLIENT K L AY1 AH0 N T -CLIENT'S K L AY1 AH0 N T S -CLIENTELE K L AY2 AH0 N T EH1 L -CLIENTS K L AY1 AH0 N T S -CLIENTS' K L AY1 AH0 N T S -CLIETT K L IY1 T -CLIF K L IH1 F -CLIFF K L IH1 F -CLIFF'S K L IH1 F S -CLIFFE K L IH1 F -CLIFFHANGER K L IH1 F HH AE2 NG ER0 -CLIFFHANGERS K L IH1 F HH AE2 NG ER0 Z -CLIFFORD K L IH1 F ER0 D -CLIFFORD'S K L IH1 F ER0 D Z -CLIFFS K L IH1 F S -CLIFFS' K L IH1 F S -CLIFT K L IH1 F T -CLIFTON K L IH1 F T AH0 N -CLIGGOTT K L IH1 G AH0 T -CLIM K L IH1 M -CLIMACO K L IH1 M AH0 K OW0 -CLIMACTIC K L AY0 M AE1 K T IH0 K -CLIMATE K L AY1 M AH0 T -CLIMATE(2) K L AY1 M IH0 T -CLIMATES K L AY1 M AH0 T S -CLIMATES(2) K L AY1 M IH0 T S -CLIMATIC K L AY0 M AE1 T IH0 K -CLIMATOLOGIST K L IH2 M AH0 T AA1 L AH0 JH IH0 S T -CLIMATOLOGIST(2) K L AY2 M AH0 T AA1 L AH0 JH IH0 S T -CLIMATOLOGISTS K L IH2 M AH0 T AA1 L AH0 JH IH0 S T S -CLIMATOLOGISTS(2) K L AY2 M AH0 T AA1 L AH0 JH IH0 S T S -CLIMATOLOGISTS(3) K L IH2 M AH0 T AA1 L AH0 JH IH0 S -CLIMATOLOGISTS(4) K L AY2 M AH0 T AA1 L AH0 JH IH0 S -CLIMAX K L AY1 M AE2 K S -CLIMAXED K L AY1 M AE2 K S T -CLIMAXES K L IH1 M AE0 K S IH0 Z -CLIMAXES(2) K L AY1 M AE0 K S IH0 Z -CLIMB K L AY1 M -CLIMBED K L AY1 M D -CLIMBER K L AY1 M ER0 -CLIMBERS K L AY1 M ER0 Z -CLIMBERS' K L AY1 M ER0 Z -CLIMBING K L AY1 M IH0 NG -CLIMBS K L AY1 M Z -CLIMER K L AY1 M ER0 -CLIMES K L AY1 M Z -CLINARD K L IH1 N ER0 D -CLINCH K L IH1 N CH -CLINCHED K L IH1 N CH T -CLINCHER K L IH1 N CH ER0 -CLINCHES K L IH1 N CH AH0 Z -CLINCHES(2) K L IH1 N CH IH0 Z -CLINCHING K L IH1 N CH IH0 NG -CLINE K L AY1 N -CLINES K L AY1 N Z -CLINES'S K L AY1 N Z IH0 Z -CLING K L IH1 NG -CLINGAN K L IH1 NG G AH0 N -CLINGENPEEL K L IH0 NG G EH1 N P IY0 L -CLINGER K L IH1 NG ER0 -CLINGER'S K L IH1 NG ER0 Z -CLINGERMAN K L IH1 NG ER0 M AH0 N -CLINGING K L IH1 NG IH0 NG -CLINGMAN K L IH1 NG M AH0 N -CLINGS K L IH1 NG Z -CLINI K L IY1 N IY0 -CLINIC K L IH1 N IH0 K -CLINIC'S K L IH1 N IH0 K S -CLINICAL K L IH1 N AH0 K AH0 L -CLINICAL(2) K L IH1 N IH0 K AH0 L -CLINICAL'S K L IH1 N IH0 K AH0 L Z -CLINICALLY K L IH1 N IH0 K AH0 L IY0 -CLINICALLY(2) K L IH1 N IH0 K L IY0 -CLINICIAN K L IH0 N IH1 SH AH0 N -CLINICIANS K L IH0 N IH1 SH AH0 N Z -CLINICS K L IH1 N IH0 K S -CLINIQUE K L AH0 N IY1 K -CLINK K L IH1 NG K -CLINKENBEARD K L IH0 NG K EH1 N B ER0 D -CLINKSCALE K L IH1 NG K S K EY2 L -CLINKSCALES K L IH1 NG K S K EY2 L Z -CLINT K L IH1 N T -CLINTON K L IH1 N T AH0 N -CLINTON'S K L IH1 N T AH0 N Z -CLINTONITE K L IH1 N T AH0 N AY2 T -CLINTONITES K L IH1 N T AH0 N AY2 T S -CLINTONOMICS K L IH2 N T AH0 N AA1 M IH0 K S -CLINTONS K L IH1 N T AH0 N Z -CLINTONS' K L IH1 N T AH0 N Z -CLIO K L IY1 OW0 -CLIP K L IH1 P -CLIPBOARD K L IH1 P B AO2 R D -CLIPPARD K L IH1 P ER0 D -CLIPPED K L IH1 P T -CLIPPER K L IH1 P ER0 -CLIPPERS K L IH1 P ER0 Z -CLIPPING K L IH1 P IH0 NG -CLIPPINGER K L IH1 P IH0 NG ER0 -CLIPPINGS K L IH1 P IH0 NG Z -CLIPS K L IH1 P S -CLIQUE K L IY1 K -CLIQUES K L IH1 K S -CLITES K L AY1 T S -CLITORIS K L AY0 T AO1 R IH0 S -CLIVE K L AY1 V -CLIVER K L AY1 V ER0 -CLO S IY2 EH2 L OW1 -CLOAK K L OW1 K -CLOAKED K L OW1 K T -CLOAKING K L OW1 K IH0 NG -CLOAKROOM K L OW1 K R UW2 M -CLOBBER K L AA1 B ER0 -CLOBBERED K L AA1 B ER0 D -CLOBBERING K L AA1 B ER0 IH0 NG -CLOCK K L AA1 K -CLOCK'S K L AA1 K S -CLOCKED K L AA1 K T -CLOCKER K L AA1 K ER0 -CLOCKERS K L AA1 K ER0 Z -CLOCKING K L AA1 K IH0 NG -CLOCKS K L AA1 K S -CLOCKWISE K L AA1 K W AY2 Z -CLOCKWORK K L AA1 K W ER2 K -CLODFELTER K L AA1 D F EH2 L T ER0 -CLODOVEO K L OW0 D OW1 V IY0 OW0 -CLOE K L OW1 -CLOER K L OW1 ER0 -CLOEY K L OW1 IY0 -CLOG K L AA1 G -CLOGGED K L AO1 G D -CLOGGING K L AA1 G IH0 NG -CLOGGING(2) K L AO1 G IH0 NG -CLOGS K L AA1 G Z -CLOGSTON K L AA1 G S T AH0 N -CLOHERTY K L AA1 HH ER0 T IY0 -CLOHESSY K L AA1 HH IH0 S IY0 -CLOISTER K L OY1 S T ER0 -CLOISTERED K L OY1 S T ER0 D -CLOISTERS K L OY1 S T ER0 Z -CLOKE K L OW1 K -CLOMIPRAMINE K L OW1 M IH0 P R AH0 M AY2 N -CLONCH K L AA1 N CH -CLONE K L OW1 N -CLONED K L OW1 N D -CLONES K L OW1 N Z -CLONES' K L OW1 N Z -CLONIDINE K L AA1 N IH0 D IY2 N -CLONING K L OW1 N IH0 NG -CLONINGER K L OW1 N IH0 NG ER0 -CLONTS K L AA1 N T S -CLONTZ K L AA1 N T S -CLOONAN K L UW1 N AH0 N -CLOONEY K L UW1 N IY0 -CLOOTIE K L UW1 T IY0 -CLOPPER K L AA1 P ER0 -CLOPTON K L AA1 P T AH0 N -CLORE K L AO1 R -CLORE'S K L AO1 R Z -CLORINDA K L AO0 R IY1 N D AH0 -CLOROX K L AO1 R AA0 K S -CLOS K L AA1 S -CLOSE K L OW1 S -CLOSE(2) K L OW1 Z -CLOSE-UP K L OW1 S AH2 P -CLOSED K L OW1 Z D -CLOSEDOWN K L OW1 Z D AW2 N -CLOSEDOWNS K L OW1 Z D AW2 N Z -CLOSELY K L OW1 S L IY0 -CLOSENESS K L OW1 S N IH0 S -CLOSER K L OW1 S ER0 -CLOSER(2) K L OW1 Z ER0 -CLOSES K L OW1 Z IH0 Z -CLOSEST K L OW1 S AH0 S T -CLOSET K L AA1 Z AH0 T -CLOSETED K L AA1 Z AH0 T IH0 D -CLOSETS K L AA1 Z AH0 T S -CLOSEUP K L OW1 S AH1 P -CLOSEUPS K L OW1 S AH1 P S -CLOSING K L OW1 Z IH0 NG -CLOSINGS K L OW1 Z IH0 NG Z -CLOSS K L AO1 S -CLOSSER K L AO1 S ER0 -CLOSSON K L AA1 S AH0 N -CLOSURE K L OW1 ZH ER0 -CLOSURES K L OW1 ZH ER0 Z -CLOT K L AA1 T -CLOTFELTER K L AA1 T F EH2 L T ER0 -CLOTH K L AO1 TH -CLOTHE K L OW1 DH -CLOTHED K L OW1 DH D -CLOTHES K L OW1 DH Z -CLOTHES(2) K L OW1 Z -CLOTHESHORSE K L OW1 Z HH AO2 R S -CLOTHESTIME K L OW1 DH Z T AY1 M -CLOTHIER K L OW1 DH Y ER0 -CLOTHIERS K L OW1 DH Y ER0 Z -CLOTHILDA K L AH0 TH IH1 L D AH0 -CLOTHILDE K L AA1 TH IH0 L D -CLOTHING K L OW1 DH IH0 NG -CLOTHS K L AO1 TH S -CLOTILDA K L AH0 T IH1 L D AH0 -CLOTS K L AA1 T S -CLOTT K L AA1 T -CLOTTED K L AA1 T AH0 D -CLOTTED(2) K L AA1 T IH0 D -CLOTTING K L AA1 T IH0 NG -CLOTURE K L OW1 CH ER0 -CLOUATRE K L AW1 AH0 T R -CLOUD K L AW1 D -CLOUDBURST K L AW1 D B ER2 S T -CLOUDED K L AW1 D IH0 D -CLOUDINESS K L AW1 D IY0 N IH0 S -CLOUDING K L AW1 D IH0 NG -CLOUDLESS K L AW1 D L AH0 S -CLOUDS K L AW1 D Z -CLOUDY K L AW1 D IY0 -CLOUGH K L AW1 -CLOUGHERTY K L AW1 ER0 T IY0 -CLOUSE K L AW1 S -CLOUSER K L AW1 S ER0 -CLOUT K L AW1 T -CLOUTHIER K L AW1 TH IY0 ER0 -CLOUTHIER(2) K L OW1 TH IY0 ER0 -CLOUTHIER(3) K L OW1 DH IY0 ER0 -CLOUTIER K L AW1 T IY0 ER0 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L AH1 M P IH0 NG -CLUMPS K L AH1 M P S -CLUMPY K L AH1 M P IY0 -CLUMSILY K L AH1 M S AH0 L IY0 -CLUMSINESS K L AH1 M Z IY0 N AH0 S -CLUMSY K L AH1 M Z IY0 -CLUNE K L UW1 N -CLUNG K L AH1 NG -CLUNK K L AH1 NG K -CLUNKER K L AH1 NG K ER0 -CLUNKERS K L AH1 NG K ER0 Z -CLUNKS K L AH1 NG K S -CLUNKY K L AH1 NG K IY0 -CLUNY K L UW1 N IY0 -CLUSTER K L AH1 S T ER0 -CLUSTERED K L AH1 S T ER0 D -CLUSTERING K L AH1 S T ER0 IH0 NG -CLUSTERS K L AH1 S T ER0 Z -CLUTCH K L AH1 CH -CLUTCHED K L AH1 CH T -CLUTCHES K L AH1 CH AH0 Z -CLUTCHES(2) K L AH1 CH IH0 Z -CLUTCHING K L AH1 CH IH0 NG -CLUTE K L UW1 T -CLUTTER K L AH1 T ER0 -CLUTTERED K L AH1 T ER0 D -CLUTTERING K L AH1 T ER0 IH0 NG -CLUTTS K L AH1 T S -CLYATT K L AY1 AH0 T -CLYBURN K L IH1 B ER0 N -CLYDE K L AY1 D -CLYDESDALE K L AY1 D Z D EY2 L -CLYMENE K L IH0 M IY1 N -CLYMER K L AY1 M ER0 -CLYNE K L AY1 N -CLYTE K L AY1 T -CLYTIE K L IH1 T IY0 -CLYVE K L AY1 V -CMOS S IY1 M OW0 S -CMOS(2) S IY1 EH1 M OW1 EH1 S -CMU S IY1 EH1 M Y UW1 -CMUDICT S IY2 EH2 M Y UW1 D IH2 K T -CMX K AH0 M EH1 K S -CNBC'S S IY1 EH1 N B IY1 S IY1 Z -CNET S IY1 N EH2 T -CNN S IY1 EH1 N EH1 N -CNN'S S IY1 EH1 N EH1 N Z -CNN.COM S IY1 EH1 N EH1 N D AA1 T K AA1 M -CNNFN S IY1 EH1 N EH1 N EH1 F EH1 N -CO K OW1 -CO-FOUNDER K OW1 F AW1 N D ER0 -CO-LEADER K OW0 L IY1 D ER0 -CO-LEADERS K OW0 L IY1 D ER0 Z -CO-OP K OW1 AA2 P -CO-OPERATIVE K OW2 AA1 P ER2 AH0 T IH0 V -CO-OPERATIVE(2) K OW2 AA1 P R AH0 T IH0 V -CO-OPT K OW0 AA1 P T -CO-OPTED K OW0 AA1 P T AH0 D -CO-OWNER K OW1 OW2 N ER0 -CO-WIFE K OW1 W AY1 F -CO-WORKER K OW1 W ER1 K ER0 -CO-WORKERS K OW1 W ER1 K ER0 Z -CO. K OW1 -CO.(2) K AH1 P AH0 N IY0 -COACH K OW1 CH -COACH'S K OW1 CH IH0 Z -COACHED K OW1 CH T -COACHES K OW1 CH IH0 Z -COACHING K OW1 CH IH0 NG -COACHMAN K OW1 CH M AH0 N -COAD K OW1 D -COADY K OW1 D IY0 -COAGULATE K OW0 AE1 G Y AH0 L EY2 T -COAGULATING K OW0 AE1 G Y AH0 L EY2 T IH0 NG -COAGULATION K OW0 AE1 G Y AH0 L EY1 SH AH0 N -COAKLEY K OW1 K L IY0 -COAL K OW1 L -COAL'S K OW1 L Z -COALE K OW1 L -COALESCE K OW2 AH0 L EH1 S -COALESCED K OW2 AH0 L EH1 S T -COALESCENCE K OW2 AH0 L EH1 S AH0 N S -COALESCES K OW2 AH0 L EH1 S IH0 Z -COALESCING K OW2 AH0 L EH1 S IH0 NG -COALITION K OW2 AH0 L IH1 SH AH0 N -COALITION'S K OW2 AH0 L IH1 SH AH0 N Z -COALITIONS K OW2 AH0 L IH1 SH AH0 N Z -COALS K OW1 L Z -COALSON K OW1 L S AH0 N -COAN K OW1 N -COAR K AO1 R -COARSE K AO1 R S -COARSENING K AO1 R S IH0 N IH0 NG -COARSER K AO1 R S ER0 -COARTICULATE K OW2 AA0 R T IH1 K Y UW0 L EY0 T -COARTICULATED K OW2 AA0 R T IH1 K Y UW0 L EY0 T AH0 D -COARTICULATES K OW2 AA0 R T IH1 K Y UW0 L EY0 T S 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-COAXING K OW1 K S IH0 NG -COAXUM K OW1 K S AH0 M -COB K AA1 B -COBAIN K OW1 B EY2 N -COBAIN'S K OW1 B EY2 N Z -COBAINE K OW1 B EY2 N -COBALT K OW1 B AO2 L T -COBAUGH K AA1 B AO0 -COBB K AA1 B -COBBETT K AA1 B IH0 T -COBBINS K AA1 B IH0 N Z -COBBLE K AA1 B AH0 L -COBBLED K AA1 B AH0 L D -COBBLER K AA1 B L ER0 -COBBLER'S K AA1 B L ER0 Z -COBBLERS K AA1 B L ER0 Z -COBBLESTONE K AA1 B AH0 L S T OW2 N -COBBLESTONES K AA1 B AH0 L S T OW2 N Z -COBBS K AA1 B Z -COBE K OW1 B -COBEN K OW1 B AH0 N -COBEPA K OW0 B EY1 P AH0 -COBERLY K OW1 B ER0 L IY0 -COBERN K AA1 B ER0 N -COBERT K AA1 B ER0 T -COBEY K OW1 B IY0 -COBIA K OW1 B IY0 AH0 -COBIAN K OW1 B IY0 AH0 N -COBIN K OW1 B IH0 N -COBLE K OW1 B AH0 L -COBLEIGH K AA1 B L AH0 -COBLENTZ K AA1 B L IH0 N T S -COBLER K OW1 B AH0 L ER0 -COBLER(2) K OW1 B L ER0 -COBLINER K AA1 B L AY0 N ER0 -COBO K OW1 B OW0 -COBOL K OW1 B AO0 L -COBOS K OW1 B OW0 Z -COBRA K OW1 B R AH0 -COBRAS K OW1 B R AH0 Z -COBRE K AA1 B R AH0 -COBRIN K AA1 B R IH0 N -COBS K AA1 B Z -COBURN K OW1 B ER0 N -COBWEB K AA1 B W EH2 B -COBWEBS K AA1 B W EH2 B Z -COBY K OW1 B IY0 -COCA K OW1 K AH0 -COCA-COLA K OW2 K AH0 K OW1 L AA0 -COCAINE K OW0 K EY1 N -COCANINO K OW2 K AH0 N IY1 N OW0 -COCANOUGHER K AA1 K AH0 N AH2 F ER0 -COCCA K OW1 K AH0 -COCCARO K OW0 K AA1 R OW0 -COCCHI K OW1 K IY0 -COCCIA K OW1 CH AH0 -COCCO K OW1 K OW0 -COCCUS K AA1 K AH0 S -COCHAIRMAN K OW1 CH EH2 R M AH0 N -COCHENOUR K AA1 SH IH0 N UH0 R -COCHIN K OW1 CH IH0 N -COCHLEA K AA1 K L IY0 AH0 -COCHLEAR K AA1 K L IY0 ER0 -COCHLES K AA1 K AH0 L Z -COCHRAN K AA1 K R AH0 N -COCHRAN'S K AA1 K R AH0 N Z -COCHRANE K AA1 K R AH0 N -COCHRANTON K AA1 K R AH0 N T AH0 N -COCK K AA1 K -COCKAMAMIE K AO2 K AH0 M EY1 M IY0 -COCKATOO K AA1 K AH0 T UW2 -COCKATOOS K AA1 K AH0 T UW2 Z -COCKBURN K AA1 K B ER2 N -COCKBURN'S K OW1 B ER0 N Z -COCKBURN'S(2) K AA1 K B ER2 N Z -COCKE K OW1 K -COCKED K AA1 K T -COCKED(2) K AO1 K T -COCKER K AA1 K ER0 -COCKERELL K AA1 K ER0 EH2 L -COCKERHAM K AA1 K ER0 HH AE2 M -COCKERILL K AA1 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B L IY0 -COMPARABLY(2) K AA1 M P R AH0 B L IY0 -COMPARATIVE K AH0 M P EH1 R AH0 T IH0 V -COMPARATIVELY K AH0 M P EH1 R AH0 T IH0 V L IY0 -COMPARATOR K AH0 M P ER1 AH0 T ER0 -COMPARE K AH0 M P EH1 R -COMPARED K AH0 M P EH1 R D -COMPARES K AH0 M P EH1 R Z -COMPARING K AH0 M P EH1 R IH0 NG -COMPARISON K AH0 M P EH1 R AH0 S AH0 N -COMPARISONS K AH0 M P EH1 R AH0 S AH0 N Z -COMPARTMENT K AH0 M P AA1 R T M AH0 N T -COMPARTMENTAL K AH0 M P AA2 R T M EH1 N T AH0 L -COMPARTMENTALIZE K AH0 M P AA2 R T M EH1 N T AH0 L AY2 Z -COMPARTMENTALIZED K AA2 M P AA0 R T M EH1 N T AH0 L AY0 Z D -COMPARTMENTS K AH0 M P AA1 R T M AH0 N T S -COMPAS K AA1 M P AH0 S -COMPASS K AH1 M P AH0 S -COMPASSION K AH0 M P AE1 SH AH0 N -COMPASSIONATE K AH0 M P AE1 SH AH0 N AH0 T -COMPASSIONATELY K AH0 M P AE1 SH AH0 N AH0 T L IY2 -COMPATIBILITY K AH0 M P AE2 T AH0 B IH1 L AH0 T IY2 -COMPATIBLE K AH0 M P AE1 T AH0 B AH0 L -COMPATIBLES K AH0 M P AE1 T IH0 B AH0 L Z -COMPATRIOT K AH0 M P EY1 T R IY0 AH0 T -COMPATRIOTS K AH0 M P EY1 T R IY0 AH0 T S -COMPEAN K AA1 M P AH0 N -COMPEAU K AH0 M P OW1 -COMPEL K AH0 M P EH1 L -COMPELLED K AH0 M P EH1 L D -COMPELLING K AH0 M P EH1 L IH0 NG -COMPELLINGLY K AH0 M P EH1 L IH0 NG L IY0 -COMPELS K AH0 M P EH1 L Z -COMPENDIUM K AH0 M P EH1 N D IY0 AH0 M -COMPENSABLE K AH0 M P EH1 N S AH0 B AH0 L -COMPENSATE K AA1 M P AH0 N S EY2 T -COMPENSATED K AA1 M P AH0 N S EY2 T AH0 D -COMPENSATES K AA1 M P AH0 N S EY2 T S -COMPENSATING K AA1 M P AH0 N S EY2 T IH0 NG -COMPENSATION K AA2 M P AH0 N S EY1 SH AH0 N -COMPENSATIONS K AA2 M P AH0 N S EY1 SH AH0 N Z -COMPENSATORY K AH0 M P EH1 N S AH0 T AO2 R IY0 -COMPETE K AH0 M P IY1 T -COMPETED K AH0 M P IY1 T IH0 D -COMPETENCE K AA1 M P AH0 T IH0 N S -COMPETENCIES K AA1 M P AH0 T IH0 N S IY0 Z -COMPETENCY K AA1 M P AH0 T IH0 N S IY0 -COMPETENT K AA1 M P AH0 T IH0 N T -COMPETENTLY K AA1 M P AH0 T IH0 N T L IY0 -COMPETES K AH0 M P IY1 T S -COMPETING K AH0 M P IY1 T IH0 NG -COMPETITION K AA2 M P AH0 T IH1 SH AH0 N -COMPETITION'S K AA2 M P 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IY1 D Z -CONCEDING K AH0 N S IY1 D IH0 NG -CONCEIT K AH0 N S IY1 T -CONCEITED K AH0 N S IY1 T AH0 D -CONCEIVABLE K AH0 N S IY1 V AH0 B AH0 L -CONCEIVABLY K AH0 N S IY1 V AH0 B L IY0 -CONCEIVE K AH0 N S IY1 V -CONCEIVED K AH0 N S IY1 V D -CONCEIVING K AH0 N S IY1 V IH0 NG -CONCENTRATE K AA1 N S AH0 N T R EY2 T -CONCENTRATED K AA1 N S AH0 N T R EY2 T IH0 D -CONCENTRATES K AA1 N S AH0 N T R EY2 T S -CONCENTRATING K AA1 N S AH0 N T R EY2 T IH0 NG -CONCENTRATION K AA2 N S AH0 N T R EY1 SH AH0 N -CONCENTRATIONS K AA2 N S AH0 N T R EY1 SH AH0 N Z -CONCENTRIC K AH0 N S EH1 N T R IH0 K -CONCEPCION K AH0 N S EH2 P S IY0 OW1 N -CONCEPT K AA1 N S EH0 P T -CONCEPTION K AH0 N S EH1 P SH AH0 N -CONCEPTIONS K AH0 N S EH1 P SH AH0 N Z -CONCEPTS K AA1 N S EH0 P T S -CONCEPTS(2) K AA1 N S EH0 P S -CONCEPTUAL K AH0 N S EH1 P CH UW0 AH0 L -CONCEPTUALIZATION K AH0 N S EH1 P CH W AH0 L IH0 Z EY2 SH AH0 N -CONCEPTUALIZE K AH0 N S EH1 P CH W AH0 L AY2 Z -CONCEPTUALIZES K AH0 N S EH1 P CH W AH0 L AY2 Z IH0 Z 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AH0 N S IH2 L IY0 EY1 SH AH0 N -CONCILIATOR K AH0 N S IH1 L IY0 EY2 T ER0 -CONCILIATOR'S K AH0 N S IH1 L IY0 EY2 T ER0 Z -CONCILIATORY K AH0 N S IH1 L IY2 AH0 T AO2 R IY0 -CONCILIATORY(2) K AH0 N S IH1 L Y AH0 T AO2 R IY0 -CONCISE K AH0 N S AY1 S -CONCISELY K AH0 N S AY1 S L IY0 -CONCLAVE K AA1 N K L EY2 V -CONCLUDE K AH0 N K L UW1 D -CONCLUDED K AH0 N K L UW1 D AH0 D -CONCLUDED(2) K AH0 N K L UW1 D IH0 D -CONCLUDES K AH0 N K L UW1 D Z -CONCLUDING K AH0 N K L UW1 D IH0 NG -CONCLUSION K AH0 N K L UW1 ZH AH0 N -CONCLUSIONS K AH0 N K L UW1 ZH AH0 N Z -CONCLUSIVE K AH0 N K L UW1 S IH0 V -CONCLUSIVELY K AH0 N K L UW1 S IH0 V L IY0 -CONCOCT K AH0 N K AA1 K T -CONCOCTED K AH0 N K AA1 K T AH0 D -CONCOCTING K AH0 N K AA1 K T IH0 NG -CONCOCTION K AH0 N K AA1 K SH AH0 N -CONCOCTIONS K AH0 N K AA1 K SH AH0 N Z -CONCOMITANT K AA2 N K AA1 M AH0 T AH0 N T -CONCOMITANT(2) K AA2 N K AH0 M IH1 T AH0 N T -CONCOMITANTLY K AA2 N K AA1 M AH0 T AH0 N T L IY0 -CONCOMITANTLY(2) K AA2 N K AH0 M IH1 T AH0 N T L IY0 -CONCORD K AA1 N K AO2 R D -CONCORD(2) K AA1 N K ER0 D -CONCORD'S K AA1 N K AO2 R D Z -CONCORD'S(2) K AA1 N K ER0 D Z -CONCORDE K AA1 N K AO2 R D -CONCOURSE K AA1 N K AO2 R S -CONCOURSES K AA1 N K AO2 R S IH0 Z -CONCRETE K AH0 N K R IY1 T -CONCRETE(2) K AA1 N K R IY0 T -CONCRETELY K AA1 N K R IY2 T L IY0 -CONCUBINAGE K AA0 N K Y UW1 B AH0 N AH0 JH -CONCUBINE K AA1 N K Y AH0 B AY2 N -CONCUBINES K AA1 N K Y AH0 B AY2 N Z -CONCUR K AH0 N K ER1 -CONCURRED K AH0 N K ER1 D -CONCURRENCE K AH0 N K ER1 AH0 N S -CONCURRENT K AH0 N K ER1 AH0 N T -CONCURRENTLY K AH0 N K ER1 AH0 N T L IY0 -CONCURRING K AH0 N K ER1 IH0 NG -CONCURS K AH0 N K ER1 Z -CONCUSSION K AH0 N K AH1 SH AH0 N -CONCUSSIONS K AH0 N K AH1 SH AH0 N Z -CONDE K AA1 N D -CONDELLO K AH0 N D EH1 L OW0 -CONDEMN K AH0 N D EH1 M -CONDEMNATION K AA2 N D AH0 M N EY1 SH AH0 N -CONDEMNATIONS K AA2 N D AH0 M N EY1 SH AH0 N Z -CONDEMNED K AH0 N D EH1 M D -CONDEMNING K AH0 N D EH1 M IH0 NG -CONDEMNS K AH0 N D EH1 M Z -CONDENSATE K AA1 N D AH0 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AA1 N D OW0 -CONDOLENCE K AH0 N D OW1 L AH0 N S -CONDOLENCES K AH0 N D OW1 L AH0 N S AH0 Z -CONDOM K AA1 N D AH0 M -CONDOMINIUM K AA2 N D AH0 M IH1 N IY0 AH0 M -CONDOMINIUMS K AA2 N D AH0 M IH1 N IY0 AH0 M Z -CONDOMS K AA1 N D AH0 M Z -CONDON K AA1 N D AH0 N -CONDONE K AH0 N D OW1 N -CONDONED K AH0 N D OW1 N D -CONDONES K AH0 N D OW1 N Z -CONDONING K AH0 N D OW1 N IH0 NG -CONDOR K AA1 N D ER0 -CONDORS K AA1 N D ER0 Z -CONDOS K AA1 N D OW0 Z -CONDRA K AA1 N D R AH0 -CONDRACKY K AA2 N D R AE1 K IY0 -CONDRACKY'S K AA2 N D R AE1 K IY0 Z -CONDRAY K AA1 N D R EY0 -CONDREN K AA1 N D ER0 AH0 N -CONDREY K AA1 N D R IY0 -CONDRON K AA1 N D R AH0 N -CONDRY K AA1 N D ER0 IY0 -CONDUCIVE K AH0 N D UW1 S IH0 V -CONDUCT K AA1 N D AH0 K T -CONDUCT(2) K AA0 N D AH1 K T -CONDUCTED K AH0 N D AH1 K T AH0 D -CONDUCTING K AH0 N D AH1 K T IH0 NG -CONDUCTION K AH0 N D AH1 K SH AH0 N -CONDUCTIVE K AH0 N D AH1 K T IH0 V -CONDUCTIVITY K AA2 N D AH2 K T IH1 V AH0 T IY0 -CONDUCTOR K AH0 N D AH1 K T ER0 -CONDUCTORS K AH0 N D AH1 K T ER0 Z -CONDUCTS K AH0 N D AH1 K T S -CONDUIT K AA1 N D UW0 IH0 T -CONDUIT(2) K AA1 N JH UW0 IH0 T -CONDUIT(3) K AA1 N D W IH0 T -CONDUITS K AA1 N D UW0 AH0 T S -CONDUITS(2) K AA1 N D W AH0 T S -CONE K OW1 N -CONE'S K OW1 N Z -CONEFLOWER K OW1 N F L AW2 ER0 -CONEHEAD K OW1 N HH EH0 D -CONEHEADS K OW1 N HH EH0 D Z -CONELY K OW1 N L IY0 -CONERLY K OW1 N ER0 L IY0 -CONERY K OW1 N ER0 IY0 -CONES K OW1 N Z -CONESTOGA K AA2 N AH0 S T OW1 G AH0 -CONEY K OW1 N IY0 -CONFABULATION K AH0 N F AE2 B Y AH0 L EY1 SH AH0 N -CONFAIR K AA1 N F EH2 R -CONFECT K AH0 N F EH1 K T -CONFECTION K AH0 N F EH1 K SH AH0 N -CONFECTIONARIES K AH0 N F EH1 K SH AH0 N EH2 R IY0 Z -CONFECTIONARY K AH0 N F EH1 K SH AH0 N EH2 R IY0 -CONFECTIONER K AH0 N F EH1 K SH AH0 N ER0 -CONFECTIONERS K AH0 N F EH1 K SH AH0 N ER0 Z -CONFECTIONERY K AH0 N F EH1 K SH AH0 N EH2 R IY0 -CONFECTIONS K AH0 N F EH1 K SH AH0 N Z -CONFEDERACY K AH0 N F EH1 D ER0 AH0 S IY0 -CONFEDERACY(2) K AH0 N F EH1 D R AH0 S IY0 -CONFEDERACY'S K AH0 N F EH1 D ER0 AH0 S IY0 Z -CONFEDERACY'S(2) K AH0 N F EH1 D R AH0 S IY0 Z -CONFEDERATE K AH0 N F EH1 D ER0 AH0 T -CONFEDERATE(2) K AH0 N F EH1 D ER0 EY2 T -CONFEDERATES K AH0 N F EH1 D ER0 AH0 T S -CONFEDERATION K AH0 N F EH2 D ER0 EY1 SH AH0 N -CONFER K AH0 N F ER1 -CONFEREE K AA2 N F ER0 IY1 -CONFEREES K AA2 N F ER0 IY1 Z -CONFERENCE K AA1 N F ER0 AH0 N S -CONFERENCE(2) K AA1 N F R AH0 N S -CONFERENCE'S K AA1 N F ER0 AH0 N S IH0 Z -CONFERENCE'S(2) K AA1 N F R AH0 N S IH0 Z -CONFERENCES K AA1 N F ER0 AH0 N S AH0 Z -CONFERENCES(2) K AA1 N F R AH0 N S AH0 Z -CONFERENCING K AA1 N F R AH0 N S IH0 NG -CONFERRED K AH0 N F ER1 D -CONFERRING K AH0 N F ER1 IH0 NG -CONFERS K AH0 N F ER1 Z -CONFESS K AH0 N F EH1 S -CONFESSED K AH0 N F EH1 S T -CONFESSES K AH0 N F EH1 S IH0 Z -CONFESSING K AH0 N F EH1 S IH0 NG -CONFESSION K AH0 N F EH1 SH AH0 N -CONFESSIONAL K AH0 N F EH1 SH AH0 N AH0 L -CONFESSIONALS K AH0 N F EH1 SH AH0 N AH0 L Z -CONFESSIONS K AH0 N F EH1 SH AH0 N Z 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AH0 N F R AH1 N T AH0 D -CONFRONTED(2) K AH0 N F R AH1 N T IH0 D -CONFRONTING K AH0 N F R AH1 N T IH0 NG -CONFRONTS K AH0 N F R AH1 N T S -CONFUCIAN K AH0 N F Y UW1 SH AH0 N -CONFUCIANISM K AH0 N F Y UW1 SH AH0 N IH2 Z AH0 M -CONFUCIUS K AH0 N F Y UW1 SH AH0 S -CONFUSE K AH0 N F Y UW1 Z -CONFUSED K AH0 N F Y UW1 Z D -CONFUSES K AH0 N F Y UW1 Z IH0 Z -CONFUSING K AH0 N F Y UW1 Z IH0 NG -CONFUSINGLY K AH0 N F Y UW1 Z IH0 NG L IY0 -CONFUSION K AH0 N F Y UW1 ZH AH0 N -CONFUSIONS K AH0 N F Y UW1 ZH AH0 N Z -CONG K AO1 NG -CONGA K AO1 NG G AH0 -CONGDON K AA1 NG D AH0 N -CONGEAL K AH0 N JH IY1 L -CONGEALED K AH0 N JH IY1 L D -CONGENIAL K AH0 N JH IY1 N Y AH0 L -CONGENIALITY K AH0 N JH IY2 N IY0 AE1 L AH0 T IY0 -CONGENITAL K AH0 N JH EH1 N AH0 T AH0 L -CONGER K AO1 NG ER0 -CONGERIES K AO1 NG JH AH0 R IY2 Z -CONGEST K AH0 N JH EH1 S T -CONGESTED K AH0 N JH EH1 S T AH0 D -CONGESTED(2) K AH0 N JH EH1 S T IH0 D -CONGESTION K AH0 N JH EH1 S CH AH0 N -CONGESTIVE K AH0 N JH EH1 S T IH0 V -CONGLETON K AA1 NG G AH0 L T AA0 N -CONGLOMERATE K AH0 N G L AA1 M ER0 AH0 T -CONGLOMERATE'S K AH0 N G L AA1 M ER0 AH0 T S -CONGLOMERATES K AH0 N G L AA1 M ER0 AH0 T S -CONGLOMERATION K AH0 N G L AA2 M ER0 EY1 SH AH0 N -CONGO K AA1 NG G OW0 -CONGO'S K AA1 NG G OW0 Z -CONGOLESE K AA2 N G AH0 L IY1 Z -CONGRATS K AH0 N G R AE1 T S -CONGRATULATE K AH0 N G R AE1 CH AH0 L EY2 T -CONGRATULATED K AH0 N G R AE1 CH AH0 L EY2 T IH0 D -CONGRATULATING K AH0 N G R AE1 CH AH0 L EY2 T IH0 NG -CONGRATULATION K AH0 N G R AE2 CH AH0 L EY1 SH AH0 N -CONGRATULATIONS K AH0 N G R AE2 CH AH0 L EY1 SH AH0 N Z -CONGRATULATORY K AH0 N G R AE1 CH AH0 L AH0 T AO2 R IY0 -CONGREGANT K AA1 NG G R AH0 G AH0 N T -CONGREGANTS K AA1 NG G R AH0 G AH0 N T S -CONGREGATE K AA1 NG G R AH0 G EY2 T -CONGREGATED K AA1 NG G R IH0 G EY2 T IH0 D -CONGREGATION K AA2 NG G R AH0 G EY1 SH AH0 N -CONGREGATION'S K AA2 NG G R AH0 G EY1 SH AH0 N Z -CONGREGATIONAL K AA2 NG G R AH0 G EY1 SH AH0 N AH0 L -CONGREGATIONS K AA2 NG G R AH0 G EY1 SH AH0 N Z -CONGRESS K AA1 NG G R AH0 S -CONGRESS' K AA1 N G R AH0 S IH0 Z -CONGRESS'(2) K AA1 NG G R AH0 S -CONGRESS'S K AA1 NG G R AH0 S IH0 Z -CONGRESSES K AA1 NG G R AH0 S IH0 Z -CONGRESSIONAL K AH0 N G R EH1 SH AH0 N AH0 L -CONGRESSIONALLY K AH0 N G R EH1 SH AH0 N AH0 L IY0 -CONGRESSIONALLY(2) K AH0 N G R EH1 SH N AH0 L IY0 -CONGRESSMAN K AA1 NG G R AH0 S M AH0 N -CONGRESSMAN'S K AA1 NG G R AH0 S M AH0 N Z -CONGRESSMEN K AA1 NG G R AH0 S M IH0 N -CONGRESSPEOPLE K AA1 NG G R AH0 S P IY2 P AH0 L -CONGRESSPERSON K AA1 NG G R AH0 S P ER2 S AH0 N -CONGRESSPERSONS K AA1 NG G R AH0 S P ER2 S AH0 N Z -CONGRESSWOMAN K AA1 NG G R AH0 S W UH2 M AH0 N -CONGRESSWOMAN'S K AA1 NG G R AH0 S W UH2 M AH0 N Z -CONGRESSWOMEN K AA1 NG G R AH0 S W IH2 M IH0 N -CONGROVE K AA1 NG G R AH0 V -CONGRUENCE K AO1 N G R UW0 AH0 N S -CONGRUENT K AO1 N G R UW0 EH2 N T -CONGRUITY K AH0 N G R UW1 AH0 T IY0 -CONIC K AA1 N IH0 K -CONIC(2) K OW1 N IH0 K -CONICAL K AA1 N IH0 K AH0 L -CONICAL(2) K OW1 N IH0 K AH0 L -CONICS K AA1 N IH0 K S -CONICS(2) K OW1 N IH0 K S -CONIFER K AA1 N AH0 F ER0 -CONIFER'S K AA1 N AH0 F ER0 Z -CONIFEROUS K AH0 N IH1 F ER0 AH0 S -CONIFERS K AA1 N AH0 F ER0 Z -CONIGLIARO K AH0 N IH2 G L IY0 AA1 R OW0 -CONIGLIO K AH0 N IH1 G L IY0 OW0 -CONISTON K AA1 N AH0 S T AH0 N -CONJECTURE K AH0 N JH EH1 K CH ER0 -CONJECTURE(2) K AH0 N JH EH1 K SH ER0 -CONJECTURES K AH0 N JH EH1 K CH ER0 Z -CONJECTURES(2) K AH0 N JH EH1 K SH ER0 Z -CONJOIN K AA2 N JH OY1 N -CONJOINED K AA2 N JH OY1 N D -CONJUGAL K AA1 N JH AH0 G AH0 L -CONJUGATE K AA2 N JH AH0 G EY1 T -CONJUGATE(2) K AA1 N JH AH0 G EY2 T -CONJUGATED K AA2 N JH AH0 G EY1 T IH0 D -CONJUGATED(2) K AA1 N JH AH0 G EY2 T IH0 D -CONJUGATES K AA2 N JH AH0 G EY1 T S -CONJUGATES(2) K AA1 N JH AH0 G EY2 T S -CONJUGATION K AA2 N JH AH0 G EY1 SH AH0 N -CONJUGATIONS K AA2 N JH AH0 G EY1 SH AH0 N Z -CONJUL K AA1 N JH AH0 L -CONJUNCTION K AH0 N JH AH1 NG K SH AH0 N -CONJUNCTIONS K AH0 N JH AH1 NG K SH AH0 N Z -CONJUNCTIVA K AA2 N JH AH0 NG K T AY1 V AH0 -CONJURE K AA1 N JH ER0 -CONJURED K AA1 N JH ER0 D -CONJURER K AA1 N JH AH0 R AH2 R -CONJURES K AA1 N JH ER0 Z -CONJURING K AA1 N JH ER0 IH0 NG -CONJUROR K AA1 N JH ER0 ER0 -CONK K AA1 NG K -CONKEL K AA1 NG K AH0 L -CONKEY K AA1 N K IY0 -CONKIN K AA1 NG K IH0 N -CONKLE K AA1 NG K AH0 L -CONKLIN K AA1 NG K L IH0 N -CONKLING K AA1 NG K L IH0 NG -CONKRIGHT K AA1 NG K R AY2 T -CONLAN K AA1 N L AH0 N -CONLEE K AA1 N L IY0 -CONLEY K AA1 N L IY0 -CONLIN K AA1 N L IH0 N -CONLON K AA1 N L AH0 N -CONLOW K AA1 N L OW0 -CONLY K AA1 N L IY0 -CONN K AA1 N -CONN. K AA1 N -CONN.(2) K AH0 N EH1 T AH0 K AH0 T -CONNALLY K AA1 N AH0 L IY0 -CONNALLY'S K AA1 N AH0 L IY0 Z -CONNAN K AA1 N AH0 N -CONNAUGHT K AA1 N AO0 T -CONNAUGHT'S K AA1 N AO0 T S -CONNAUGHTON K AA1 N AO0 T AA0 N -CONNAWAY K AA1 N AH0 W EY2 -CONNECT K AH0 N EH1 K T -CONNECTED K AH0 N EH1 K T IH0 D -CONNECTER K AH0 N EH1 K T ER0 -CONNECTICUT K AH0 N EH1 T AH0 K AH0 T -CONNECTICUT'S K AH0 N EH1 T AH0 K AH0 T S -CONNECTING K AH0 N EH1 K T IH0 NG -CONNECTION K AH0 N EH1 K SH AH0 N -CONNECTIONS K AH0 N EH1 K SH AH0 N Z -CONNECTIVE K AH0 N EH1 K T IH0 V -CONNECTIVITY K AH0 N EH0 K T IH1 V IH0 T IY0 -CONNECTOR K AH0 N EH1 K T ER0 -CONNECTORS K AH0 N EH1 K T ER0 Z -CONNECTS K AH0 N EH1 K T S -CONNED K AA1 N D -CONNEELY K AH0 N IY1 L IY0 -CONNELL K AA1 N AH0 L -CONNELLEY K AA1 N IH0 L IY0 -CONNELLY K AA1 N AH0 L IY0 -CONNELLY'S K AA1 N AH0 L IY0 Z -CONNELY K AA1 N L IY0 -CONNER K AA1 N ER0 -CONNER'S K AA1 N ER0 Z -CONNERLY K AA1 N ER0 L IY0 -CONNERS K AA1 N ER0 Z -CONNERY K AA1 N ER0 IY0 -CONNERY'S K AA1 N ER0 IY0 Z -CONNETT K AA1 N IH0 T -CONNEXION K AH0 N EH1 K SH AH0 N -CONNEXIONS K AH0 N EH1 K SH AH0 N Z -CONNICK K AA1 N IH0 K -CONNIE K AO1 N IY0 -CONNIE'S K AO1 N IY0 Z -CONNIFF K AA1 N IH0 F -CONNING K AA1 N IH0 NG -CONNIPTION K AH0 N IH1 P SH AH0 N -CONNIVANCE K AH0 N AY1 V AH0 N S -CONNIVE K AH0 N AY1 V -CONNIVING K AH0 N AY1 V IH0 NG -CONNOISSEUR K AA2 N AH0 S ER1 -CONNOISSEURS K AA2 N AH0 S ER1 Z -CONNOLE K AA1 N AH0 L -CONNOLLY K AO1 N AH0 L IY0 -CONNON K AA1 N AH0 N -CONNOR K AA1 N ER0 -CONNORS K AA1 N ER0 Z -CONNOTATION K AA2 N AH0 T EY1 SH AH0 N -CONNOTATIONAL K AA2 N AH0 T EY1 SH AH0 N AH0 L -CONNOTATIONS K AA2 N AH0 T EY1 SH AH0 N Z -CONNOTE K AH0 N OW1 T -CONNOTES K AH0 N OW1 T S -CONNY K AA1 N IY0 -CONOCO K AA1 N AH0 K OW0 -CONOCO(2) K AH0 N AA1 K OW0 -CONOCO'S K AA1 N AH0 K OW0 Z -CONOLLY K AA1 N OW0 L IY0 -CONOLY K AA1 N OW0 L IY0 -CONOVER K AA1 N AH0 V ER0 -CONQUER K AA1 NG K ER0 -CONQUERED K AA1 NG K ER0 D -CONQUERING K AA1 NG K ER0 IH0 NG -CONQUEROR K AA1 NG K ER0 ER0 -CONQUEROR'S K AA1 NG K ER0 ER0 Z -CONQUERORS K AA1 NG K ER0 ER0 Z -CONQUERS K AA1 NG K ER0 Z -CONQUEST K AA1 NG K W EH0 S T -CONQUEST'S K AA1 NG K W EH0 S T S -CONQUESTS K AA1 N K W EH2 S T S -CONRAC K AA1 N R AE0 K -CONRAD K AA1 N R AE0 D -CONRAD'S K AA1 N R AE0 D Z -CONRADES K AH0 N R EY1 D Z -CONRADI K AA0 N R AA1 D IY0 -CONRADINE K AA1 N R AH0 D AY2 N -CONRADS K AA1 N R AE0 D Z -CONRADT K AA1 N R AE0 T -CONRADY K AH0 N R AA1 D IY0 -CONRAIL K AA1 N R EY2 L -CONRAIL'S K AA1 N R EY2 L Z -CONRAN K AA1 N R AH0 N -CONRATH K AA1 N R AH0 TH -CONREY K AA1 N R IY0 -CONROE K AA1 N R OW0 -CONROW K AA1 N R OW0 -CONROY K AO1 N R OY0 -CONRY K AA1 N R IY0 -CONS K AA1 N Z -CONSALVO K AA0 N S AA1 L V OW0 -CONSCIENCE K AA1 N SH AH0 N S -CONSCIENCES K AA1 N CH IH0 N S IH0 Z -CONSCIENTIOUS K AA2 N SH IY0 EH1 N SH AH0 S -CONSCIENTIOUSLY K AA2 N CH IY0 EH1 N CH AH0 S L IY0 -CONSCIOUS K AA1 N SH AH0 S -CONSCIOUSLY K AA1 N SH AH0 S L IY0 -CONSCIOUSNESS K AA1 N SH AH0 S N AH0 S -CONSCRIPT K AA1 N S K R IH2 P T -CONSCRIPT(2) K AH0 N S K R IH1 P T -CONSCRIPTED K AH0 N S K R IH1 P T IH0 D -CONSCRIPTION K AH0 N S K R IH1 P SH AH0 N -CONSCRIPTS K AA1 N S K R IH0 P T S -CONSECO K AA0 N S EY1 K OW0 -CONSECO(2) K AH0 N S EY1 K OW0 -CONSECO'S K AA0 N S EY1 K OW0 Z -CONSECO'S(2) K AH0 N S EY1 K OW0 Z -CONSECRATE K AA1 N S AH0 K R EY2 T -CONSECRATED K AA1 N S AH0 K R EY2 T IH0 D -CONSECRATION K AA2 N S AH0 K R EY1 SH AH0 N -CONSECRATIONS K AA2 N S AH0 K R EY1 SH AH0 N Z -CONSECUTIVE K AH0 N S EH1 K Y AH0 T IH0 V -CONSECUTIVELY K AH0 N S EH1 K Y AH0 T IH0 V L IY0 -CONSENSUAL K AH0 N S EH1 N S UW0 AH0 L -CONSENSUAL(2) K AH0 N S EH1 N SH UW0 AH0 L -CONSENSUS K AH0 N S EH1 N S AH0 S -CONSENT K AH0 N S EH1 N T -CONSENTED K AH0 N S EH1 N T IH0 D -CONSENTED(2) K AH0 N S EH1 N IH0 D -CONSENTING K AH0 N S EH1 N T IH0 NG -CONSENTING(2) K AH0 N S EH1 N IH0 NG -CONSENTINO K AA0 N S EH0 N T IY1 N OW0 -CONSENTS K AH0 N S EH1 N T S -CONSEQUENCE K AA1 N S AH0 K W AH0 N S -CONSEQUENCES K AA1 N S AH0 K W EH2 N S AH0 Z -CONSEQUENT K AA1 N S AH0 K W AH0 N T -CONSEQUENTIAL K AA2 N S AH0 K W EH1 N SH AH0 L -CONSEQUENTLY K AA1 N S AH0 K W AH0 N T L IY0 -CONSEQUENTLY(2) K AA1 N S AH0 K W EH2 N T L IY0 -CONSER K AA1 N S ER0 -CONSERVANCY K AH0 N S ER1 V AH0 N S IY0 -CONSERVANCY'S K AH0 N S ER1 V AH0 N S IY0 Z -CONSERVATION K AA2 N S ER0 V EY1 SH AH0 N -CONSERVATIONIST K AA2 N S ER0 V EY1 SH AH0 N IH0 S T -CONSERVATIONISTS K AA2 N S ER0 V EY1 SH AH0 N IH0 S T S -CONSERVATISM K AH0 N S ER1 V AH0 T IH2 Z AH0 M -CONSERVATIVE K AH0 N S ER1 V AH0 T IH0 V -CONSERVATIVELY K AH0 N S ER1 V AH0 T IH0 V L IY0 -CONSERVATIVES K AH0 N S ER1 V AH0 T IH0 V Z -CONSERVATIVES' K AH0 N S ER1 V AH0 T IH0 V Z -CONSERVATIVISM K AH0 N S ER1 V AH0 T IH0 V IH2 Z AH0 M -CONSERVATOR K AH0 N S ER1 V AH0 T ER0 -CONSERVATORIES K AH0 N S ER1 V AH0 T AO2 R IY0 Z -CONSERVATORS K AH0 N S ER1 V AH0 T ER0 Z -CONSERVATORSHIP K AH0 N S ER1 V AH0 T ER0 SH IH2 P -CONSERVATORY K AH0 N S ER1 V AH0 T AO0 R IY0 -CONSERVE K AH0 N S ER1 V -CONSERVED K AH0 N S ER1 V D -CONSERVING K AH0 N S ER1 V IH0 NG -CONSHOHOCKEN K AA2 N SH AH0 HH AA1 K AH0 N -CONSIDER K AH0 N S IH1 D ER0 -CONSIDERABLE K AH0 N S IH1 D ER0 AH0 B AH0 L -CONSIDERABLY K AH0 N S IH1 D ER0 AH0 B L IY0 -CONSIDERATE K AH0 N S IH1 D ER0 AH0 T -CONSIDERATION K AH0 N S IH2 D ER0 EY1 SH AH0 N -CONSIDERATIONS K AH0 N S IH2 D ER0 EY1 SH AH0 N Z -CONSIDERED K AH0 N S IH1 D ER0 D -CONSIDERING K AH0 N S IH1 D ER0 IH0 NG -CONSIDERS K AH0 N S IH1 D ER0 Z -CONSIDINE K AA1 N S IH0 D AY2 N -CONSIGLIO K AA0 N S IY1 G L IY0 OW0 -CONSIGN K AH0 N S AY1 N -CONSIGNED K AH0 N S AY1 N D -CONSIGNMENT K AH0 N S AY1 N M AH0 N T -CONSIS K AA1 N S IH0 S -CONSIST K AH0 N S IH1 S T -CONSISTED K AH0 N S IH1 S T AH0 D -CONSISTED(2) K AH0 N S IH1 S T IH0 D -CONSISTENCE K AH0 N S IH1 S T AH0 N S -CONSISTENCY K AH0 N S IH1 S T AH0 N S IY0 -CONSISTENT K AH0 N S IH1 S T AH0 N T -CONSISTENTLY K AH0 N S IH1 S T AH0 N T L IY0 -CONSISTING K AH0 N S IH1 S T IH0 NG -CONSISTS K AH0 N S IH1 S T S -CONSOB K AA1 N S AA0 B -CONSOL K AA1 N S AA0 L -CONSOL'S K AA1 N S AA0 L Z -CONSOLABLE K AH0 N S OW1 L AH0 B AH0 L -CONSOLABLY K AH0 N S OW1 L AH0 B L IY0 -CONSOLATA K AA0 N S OW0 L AA1 T AH0 -CONSOLATION K AA2 N S AH0 L EY1 SH AH0 N -CONSOLATIONS K AA2 N S AH0 L EY1 SH AH0 N Z -CONSOLE K AA1 N S OW0 L -CONSOLE(2) K AH0 N S OW1 L -CONSOLED K AH0 N S OW1 L D -CONSOLES K AH0 N S OW1 L Z -CONSOLI K AA0 N S OW1 L IY0 -CONSOLIDATE K AH0 N S AA1 L IH0 D EY2 T -CONSOLIDATED K AH0 N S AA1 L AH0 D EY2 T AH0 D -CONSOLIDATED'S K AH0 N S AA1 L IH0 D EY2 T IH0 D Z -CONSOLIDATES K AH0 N S AA1 L IH0 D EY2 T S -CONSOLIDATING K AH0 N S AA1 L AH0 D EY2 T IH0 NG -CONSOLIDATION K AH0 N S AA2 L AH0 D EY1 SH AH0 N -CONSOLIDATIONS K AH0 N S AA2 L IH0 D EY1 SH AH0 N Z -CONSOLIDATOR K AH0 N S AA1 L IH0 D EY2 T ER0 -CONSOLIDATORS K AH0 N S AA1 L IH0 D EY2 T ER0 Z -CONSOLING K AH0 N S OW1 L IH0 NG -CONSOLO K AA0 N S OW1 L OW0 -CONSONANT K AA1 N S AH0 N AH0 N T -CONSONANTAL K AA2 N S AH0 N AA1 N T AH0 L -CONSONANTAL(2) K AA2 N S AH0 N AA1 N AH0 L -CONSONANTS K AA1 N S AH0 N AH0 N T S -CONSORT K AH0 N S AO1 R T -CONSORTIA K AH0 N S AO1 R SH AH0 -CONSORTING K AH0 N S AO1 R T IH0 NG -CONSORTIUM K AH0 N S AO1 R SH IY0 AH0 M -CONSORTIUM(2) K AH0 N S AO1 R SH Y AH0 M -CONSORTIUM'S K AH0 N S AO1 R SH IY0 AH0 M Z -CONSORTIUM'S(2) K AH0 N S AO1 R SH Y AH0 M Z -CONSORTIUMS K AH0 N S AO1 R SH AH0 M Z -CONSORTIUMS(2) K AH0 N S AO1 R SH Y AH0 M Z -CONSPICUOUS K AH0 N S P IH1 K Y UW0 AH0 S -CONSPICUOUSLY K AH0 N S P IH1 K Y UW0 AH0 S L IY0 -CONSPIRACIES K AH0 N S P IH1 R AH0 S IY0 Z -CONSPIRACY K AH0 N S P IH1 R AH0 S IY0 -CONSPIRATOR K AH0 N S P IH1 R AH0 T ER0 -CONSPIRATORIAL K AH0 N S P IH2 R AH0 T AO1 R IY0 AH0 L -CONSPIRATORIALLY K AH0 N S P IH2 R AH0 T AO1 R IY0 AH0 L IY0 -CONSPIRATORIALLY(2) K AH0 N S P IH2 R AH0 T AO1 R Y AH0 L IY0 -CONSPIRATORS K AH0 N S P IH1 R AH0 T ER0 Z -CONSPIRE K AH0 N S P AY1 ER0 -CONSPIRED K AH0 N S P AY1 ER0 D -CONSPIRING K AH0 N S P AY1 R IH0 NG -CONSTABLE K AA1 N S T AH0 B AH0 L -CONSTABLES K AA1 N S T AH0 B AH0 L Z -CONSTABULARY K AH0 N S T AE1 B Y AH0 L EH2 R IY0 -CONSTANCE K AA1 N S T AH0 N S -CONSTANCY K AA1 N S T AH0 N S IY0 -CONSTANT K AA1 N S T AH0 N T -CONSTANT'S K AA1 N S T AH0 N T S -CONSTANTA K AA0 N S T AA1 N T AH0 -CONSTANTIN K AH0 N S T AE1 N T IH0 N -CONSTANTINA K AA0 N S T AA0 N T IY1 N AH0 -CONSTANTINE K AA1 N S T AH0 N T IY2 N -CONSTANTINE(2) K AA1 N S T AH0 N T AY2 N -CONSTANTINIDES K AA0 N S T AH0 N T AH0 N IY1 D AH0 S -CONSTANTINIDES(2) K AA2 N S T AE0 N T AH0 N IY1 D IY0 Z -CONSTANTINO K AA2 N S T AH0 N T IY1 N OW0 -CONSTANTINOPLE K AA2 N S T AE0 N T AH0 N OW1 P AH0 L -CONSTANTINOS K AA2 N S T AH0 N T IY1 N OW0 S -CONSTANTLY K AA1 N S T AH0 N T L IY0 -CONSTANTS K AA1 N S T AH0 N T S -CONSTAR K AA1 N S T AA2 R -CONSTELLATION K AA2 N S T AH0 L EY1 SH AH0 N -CONSTELLATION'S K AA2 N S T AH0 L EY1 SH AH0 N Z -CONSTELLATIONS K AA2 N S T AH0 L EY1 SH AH0 N Z -CONSTERNATION K AA2 N S T ER0 N EY1 SH AH0 N -CONSTIPATE K AA1 N S T AH0 P EY2 T -CONSTIPATED K AA1 N S T AH0 P EY2 T AH0 D -CONSTIPATES K AA1 N S T AH0 P EY2 T S -CONSTIPATION K AA2 N S T AH0 P EY1 SH AH0 N -CONSTITUENCIES K AH0 N S T IH1 CH UW0 AH0 N S IY0 Z -CONSTITUENCY K AH0 N S T IH1 CH UW0 AH0 N S IY0 -CONSTITUENT K AH0 N S T IH1 CH UW0 AH0 N T -CONSTITUENTS K AH0 N S T IH1 CH UW0 AH0 N T S -CONSTITUENTS' K AH0 N S T IH1 CH UW0 AH0 N T S -CONSTITUTE K AA1 N S T AH0 T UW2 T -CONSTITUTED K AA1 N S T AH0 T UW2 T AH0 D -CONSTITUTES K AA1 N S T AH0 T UW2 T S -CONSTITUTING K AA1 N S T AH0 T UW2 T IH0 NG -CONSTITUTION K AA2 N S T AH0 T UW1 SH AH0 N -CONSTITUTION'S K AA2 N S T IH0 T UW1 SH AH0 N Z -CONSTITUTIONAL K AA2 N S T AH0 T UW1 SH AH0 N AH0 L -CONSTITUTIONALITY K AA2 N S T IH0 T UW2 SH AH0 N AE1 L IH0 T IY0 -CONSTITUTIONALLY K AA2 N S T AH0 T UW1 SH AH0 N AH0 L IY0 -CONSTITUTIONIST K AA2 N S T AH0 T UW1 SH AH0 N IH0 S T -CONSTITUTIONISTS K AA2 N S T AH0 T UW1 SH AH0 N IH0 S T S -CONSTITUTIONS K AA2 N S T IH0 T UW1 SH AH0 N Z -CONSTITUTIVE K AA2 N S T IH1 T UW0 T IH2 V -CONSTRAIN K AH0 N S T R EY1 N -CONSTRAINED K AH0 N S T R EY1 N D -CONSTRAINING K AH0 N S T R EY1 N IH0 NG -CONSTRAINS K AH0 N S T R EY1 N Z -CONSTRAINT K AH0 N S T R EY1 N T -CONSTRAINTS K AH0 N S T R EY1 N T S -CONSTRICT K AH0 N S T R IH1 K T -CONSTRICTED K AH0 N S T R IH1 K T AH0 D -CONSTRICTING K AH0 N S T R IH1 K T IH0 NG -CONSTRICTION K AH0 N S T R IH1 K SH AH0 N -CONSTRICTIONS K AH0 N S T R IH1 K SH AH0 N Z -CONSTRICTOR K AH0 N S T R IH1 K T ER0 -CONSTRICTORS K AH0 N S T R IH1 K T ER0 Z -CONSTRUCCIONES K AH0 N S T R UW1 CH IY0 OW2 N EY0 Z -CONSTRUCT K AH0 N S T R AH1 K T -CONSTRUCT(2) K AA1 N S T R AH0 K T -CONSTRUCTED K AH0 N S T R AH1 K T AH0 D -CONSTRUCTED(2) K AH0 N S T R AH1 K T IH0 D -CONSTRUCTING K AH0 N S T R AH1 K T IH0 NG -CONSTRUCTION K AH0 N S T R AH1 K SH AH0 N -CONSTRUCTIONIST K AH0 N S T R AH1 K SH AH0 N IH0 S T -CONSTRUCTIONS K AH0 N S T R AH1 K SH AH0 N Z -CONSTRUCTIVE K AH0 N S T R AH1 K T IH0 V -CONSTRUCTIVELY K AH0 N S T R AH1 K T IH0 V L IY0 -CONSTRUCTOR K AH0 N S T R AH1 K T ER0 -CONSTRUCTORS K AH0 N S T R AH1 K T ER0 Z -CONSTRUCTS K AH0 N S T R AH1 K T S -CONSTRUCTS(2) K AA1 N S T R AH0 K T S -CONSTRUE K AH0 N S T R UW1 -CONSTRUED K AH0 N S T R UW1 D -CONSUELA K AH0 N S W EY1 L AH0 -CONSUL K AA1 N S AH0 L -CONSULAR K AA1 N S AH0 L ER0 -CONSULATE K AA1 N S AH0 L AH0 T -CONSULATES K AA1 N S AH0 L AH0 T S -CONSULS K AA1 N S AH0 L Z -CONSULSHIP K AA1 N S AH0 L SH IH2 P -CONSULT K AH0 N S AH1 L T -CONSULTANCY K AH0 N S AH1 L T AH0 N S IY0 -CONSULTANT K AH0 N S AH1 L T AH0 N T -CONSULTANT'S K AH0 N S AH1 L T AH0 N T S -CONSULTANTS K AH0 N S AH1 L T AH0 N T S -CONSULTANTS' K AH0 N S AH1 L T AH2 N T S -CONSULTATION K AA2 N S AH0 L T EY1 SH AH0 N -CONSULTATIONS K AA2 N S AH0 L T EY1 SH AH0 N Z -CONSULTATIVE K AH0 N S AH1 L T AH0 T IH0 V -CONSULTED K AH0 N S AH1 L T AH0 D -CONSULTED(2) K AH0 N S AH1 L T IH0 D -CONSULTING K AH0 N S AH1 L T IH0 NG -CONSULTS K AH0 N S AH1 L T S -CONSUMABLE K AH0 N S UW1 M AH0 B AH0 L -CONSUME K AH0 N S UW1 M -CONSUMED K AH0 N S UW1 M D -CONSUMER K AH0 N S UW1 M ER0 -CONSUMER'S K AH0 N S UW1 M ER0 Z -CONSUMERISM K AH0 N S UW1 M ER0 IH2 Z AH0 M -CONSUMERIST K AH0 N S UW1 M ER0 IH0 S T -CONSUMERISTS K AH0 N S UW1 M ER0 IH0 S T S -CONSUMERS K AH0 N S UW1 M ER0 Z -CONSUMERS' K AH0 N S UW1 M ER0 Z -CONSUMES K AH0 N S UW1 M Z -CONSUMING K AH0 N S UW1 M IH0 NG -CONSUMMATE K AA1 N S AH0 M AH0 T -CONSUMMATE(2) K AA1 N S AH0 M EY2 T -CONSUMMATED K AA1 N S AH0 M EY2 T AH0 D -CONSUMMATES K AA1 N S AH0 M EY2 T S -CONSUMMATING K AA1 N S AH0 M EY2 T IH0 NG -CONSUMMATION K AA2 N S AH0 M EY1 SH AH0 N -CONSUMPTION K AH0 N S AH1 M P SH AH0 N -CONSUMPTION(2) K AH0 N S AH1 M SH AH0 N -CONTAC K AA1 N T AE0 K -CONTACT K AA1 N T AE2 K T -CONTACTED K AA1 N T AE2 K T IH0 D -CONTACTING K AA1 N T AE2 K T IH0 NG -CONTACTLESS K AA1 N T AE0 K T L EH2 S -CONTACTS K AA1 N T AE2 K T S -CONTACTS(2) K AA1 N T AE2 K S -CONTADORA K AA2 N T AH0 D AO1 R AH0 -CONTAGION K AH0 N T EY1 JH AH0 N -CONTAGIOUS K AH0 N T EY1 JH AH0 S -CONTAGIOUSNESS K AH0 N T EY1 JH AH0 S N AH0 S -CONTAIN K AH0 N T EY1 N -CONTAINED K AH0 N T EY1 N D -CONTAINER K AH0 N T EY1 N ER0 -CONTAINER'S K AH0 N T EY1 N ER0 Z -CONTAINERBOARD K AH0 N T EY1 N ER0 B AO2 R D -CONTAINERIZE K AH0 N T EY1 N ER0 AY2 Z -CONTAINERIZED K AH0 N T EY1 N ER0 AY2 Z D -CONTAINERS K AH0 N T EY1 N ER0 Z -CONTAINERS' K AH0 N T EY1 N ER0 Z -CONTAINING K AH0 N T EY1 N IH0 NG -CONTAINMENT K AH0 N T EY1 N M AH0 N T -CONTAINS K AH0 N T EY1 N Z -CONTAMINANT K AH0 N T AE1 M AH0 N AH0 N T -CONTAMINANTS K AH0 N T AE1 M AH0 N AH0 N T S -CONTAMINATE K AH0 N T AE1 M AH0 N EY2 T -CONTAMINATED K AH0 N T AE1 M AH0 N EY2 T IH0 D -CONTAMINATES K AH0 N T AE1 M AH0 N EY2 T S -CONTAMINATING K AH0 N T AE1 M AH0 N EY2 T IH0 NG -CONTAMINATION K AH0 N T AE2 M AH0 N EY1 SH AH0 N -CONTANT K AA1 N T AH0 N T -CONTE K AO1 N T -CONTE(2) K AO1 N T EY0 -CONTEL K AA1 N T EH2 L -CONTEMN K AH2 N T EH1 M -CONTEMPLATE K AA1 N T AH0 M P L EY2 T -CONTEMPLATED K AA1 N T AH0 M P L EY2 T IH0 D -CONTEMPLATES K AA1 N T AH0 M P L EY2 T S -CONTEMPLATING K AA1 N T AH0 M P L EY2 T IH0 NG -CONTEMPLATION K AA2 N T AH0 M P L EY1 SH AH0 N -CONTEMPLATIVE K AH0 N T EH1 M P L AH0 T IH0 V -CONTEMPO K AA2 N T EH1 M P OW0 -CONTEMPORANEOUS K AH0 N T EH2 M P ER0 EY1 N IY0 AH0 S -CONTEMPORANEOUSLY K AH0 N T EH2 M P ER0 EY1 N IY0 AH0 S L IY0 -CONTEMPORARIES K AH0 N T EH1 M P ER0 EH2 R IY0 Z -CONTEMPORARY K AH0 N T EH1 M P ER0 EH2 R IY0 -CONTEMPT K AH0 N T EH1 M P T -CONTEMPTIBLE K AH0 N T EH1 M P T AH0 B AH0 L -CONTEMPTUOUS K AH0 N T EH1 M P CH UW0 AH0 S -CONTEMPTUOUSLY K AH0 N T EH1 M P CH W AH0 S L IY0 -CONTEND K AH0 N T EH1 N D -CONTENDED K AH0 N T EH1 N D IH0 D -CONTENDER K AH0 N T EH1 N D ER0 -CONTENDERS K AH0 N T EH1 N D ER0 Z -CONTENDING K AH0 N T EH1 N D IH0 NG -CONTENDS K AH0 N T EH1 N D Z -CONTENDS(2) K AH0 N T EH1 N Z -CONTENT K AA1 N T EH0 N T -CONTENT(2) K AH0 N T EH1 N T -CONTENTED K AH0 N T EH1 N T AH0 D -CONTENTED(2) K AH0 N T EH1 N T IH0 D -CONTENTEDLY K AH0 N T EH1 N T AH0 D L IY0 -CONTENTION K AH0 N T EH1 N SH AH0 N -CONTENTIONED K AH0 N T EH1 N SH AH0 N D -CONTENTIONS K AH0 N T EH1 N SH AH0 N Z -CONTENTIOUS K AH0 N T EH1 N SH AH0 S -CONTENTIOUSNESS K AH0 N T EH1 N SH AH0 S N AH0 S -CONTENTMENT K AH0 N T EH1 N T M AH0 N T -CONTENTO K AH0 N T EH1 N T OW0 -CONTENTS K AA1 N T EH0 N T S -CONTENTS(2) K AH0 N T EH1 N T S -CONTEST K AA1 N T EH0 S T -CONTEST(2) K AH0 N T EH1 S T -CONTEST'S K AA1 N T EH0 S T S -CONTESTABLE K AH0 N T EH1 S T AH0 B AH0 L -CONTESTANT K AH0 N T EH1 S T AH0 N T -CONTESTANTS K AH0 N T EH1 S T AH0 N T S -CONTESTED K AH0 N T EH1 S T AH0 D -CONTESTING K AH0 N T EH1 S T IH0 NG -CONTESTS K AA1 N T EH0 S T S -CONTESTS(2) K AH0 N T EH1 S T S -CONTESTS(3) K AA1 N T EH0 S -CONTESTS(4) K AH0 N T EH1 S -CONTEXT K AA1 N T EH0 K S T -CONTEXTS K AA1 N T EH2 K S T S -CONTEXTUAL K AA2 N T EH1 K S CH UW2 AH0 L -CONTI K AA1 N T IY0 -CONTIBEL K AA1 N T IH0 B AH0 L -CONTICOMMODITY K AA2 N T IH0 K AH0 M AA1 D IH0 T IY0 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R AH0 L -CORALIE K AO1 R AH0 L IY0 -CORALINE K AO1 R AH0 L AY2 N -CORALLO K AO2 R AE1 L OW0 -CORALS K AO1 R AH0 L Z -CORAM K AO1 R AE2 M -CORATH K AO1 R AE1 TH -CORAZON K AO1 R AH0 Z OW2 N -CORBAN K AO1 R B AH0 N -CORBEIL K AO1 R B AY2 L -CORBEL K AO1 R B AH0 L -CORBELL K AO1 R B EH0 L -CORBELLED K AO1 R B AH0 L D -CORBELLO K ER0 B EH1 L OW0 -CORBET K AO1 R B IH0 T -CORBETT K AO1 R B IH0 T -CORBIN K AO1 R B IH0 N -CORBIT K AO1 R B IH0 T -CORBITT K AO1 R B IH0 T -CORBO K AO1 R B OW0 -CORBRIDGE K AO1 R B R IH0 JH -CORBU K AO2 R B UW1 -CORBUSIER K AO2 R B Y UW1 S Y ER0 -CORBY K AO1 R B IY0 -CORCAP K AO1 R K AE2 P -CORCORAN K AO1 R K ER0 AH0 N -CORD K AO1 R D -CORDA K AO1 R D AH0 -CORDAGE K AO1 R D AH0 JH -CORDARO K ER0 D AA1 R OW0 -CORDASCO K ER0 D AA1 S K OW0 -CORDAY K AO1 R D EY2 -CORDED K AO1 R D AH0 D -CORDEIRO K ER0 D EH1 R OW0 -CORDELIA K AO2 R D IY1 L IY2 AH0 -CORDELL K AO2 R D EH1 L -CORDER K AO1 R D ER0 -CORDERO K ER0 D EH1 R OW0 -CORDES K AO1 R D Z -CORDESMAN K AO1 R D AH0 S M AH0 N -CORDIAL K AO1 R JH AH0 L -CORDIALLY K AO1 R JH AH0 L IY0 -CORDIALS K AO1 R JH AH0 L Z -CORDIANT K AO1 R D IY0 AH0 N T -CORDIANT'S K AO1 R D IY0 AH0 N T S -CORDIE K AO1 R D IY0 -CORDIER K AO1 R D IY0 ER0 -CORDILL K AO1 R D AH0 L -CORDING K AO1 R D IH0 NG -CORDIS K AO1 R D IH0 S -CORDLE K AO1 R D AH0 L -CORDLESS K AO1 R D L AH0 S -CORDNER K AO1 R D N ER0 -CORDOBA K AO2 R D OW1 B AH0 -CORDOBA'S K AO2 R D OW1 B AH0 Z -CORDOBAS K AO2 R D OW1 B AH0 Z -CORDON K AO1 R D AH0 N -CORDONE K AO1 R D AH0 N -CORDONED K AO1 R D AH0 N D -CORDOVA K AO2 R D OW1 V AH0 -CORDOVER K AO1 R D OW2 V ER0 -CORDOVEZ K AO2 R D OW1 V EH0 Z -CORDRAY K AO1 R D R EY2 -CORDREY K AO1 R D R IY0 -CORDRY K AO1 R D ER0 IY0 -CORDS K AO1 R D Z -CORDTS K AO1 R D T S -CORDUROY K AO1 R D ER0 OY2 -CORDY K AO1 R D IY0 -CORE K AO1 R -COREA K AO0 R IY1 AH0 -COREL K AO2 R EH1 L -CORELL K AO2 R EH1 L -CORELLA K ER0 EH1 L AH0 -COREN K AO1 R AH0 N -CORES K AO1 R Z -CORESPONDENT K AO2 R AH0 S P AA1 N D AH0 N T -CORESTATE K AO1 R S T EY2 T -CORESTATES K AO1 R S T EY2 T S -CORESTATES' K AO1 R AH0 S T EY2 T S -CORETECH K AO1 R T EH2 K -CORETTA K AO0 R EH1 T AH0 -CORETTE K AO1 R EH1 T -COREXIT K AO0 R EH1 K S IH0 T S -COREY K AO1 R IY0 -CORGI K AO1 R G IY0 -CORIA K AO1 R IY0 AH0 -CORIANDER K AO2 R IY0 AE1 N D ER0 -CORIELL K AO1 R IY0 L -CORIER K AO1 R IY0 ER0 -CORIGLIANO K ER0 IY0 G L IY0 AA1 N OW0 -CORINA K AO2 R IY1 N AH0 -CORINN K AO1 R IH0 N -CORINN'S K AO1 R IH0 N Z -CORINNA K AO2 R IH1 N AH0 -CORINNE K ER0 IY1 N -CORINTH K AO1 R AH0 N TH -CORINTHIAN K ER0 IH1 N TH IY0 AH0 N -CORINTHIANS K ER0 IH1 N TH IY0 AH0 N Z -CORINTO K AO2 R IH1 N T OW0 -CORIO K AO1 R IY0 OW0 -CORISA K ER0 IY1 S AH0 -CORISSA K AO2 R IH1 S AH0 -CORK K AO1 R K -CORKEN K AO1 R K AH0 N -CORKER K AO1 R K ER0 -CORKERN K AO1 R K ER0 N -CORKERY K AO1 R K ER0 IY0 -CORKILL K AO1 R K IH2 L -CORKING K AO1 R K IH0 NG -CORKINS K AO1 R K IH0 N Z -CORKRAN K AO1 R K R AH0 N -CORKS K AO1 R K S -CORKSCREW K AO1 R K S K R UW2 -CORKUM K AO1 R K AH0 M 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N EH1 T -CORONETS K AO1 R AH0 N EH1 T S -CORP K AO1 R P -CORP(2) K AO1 R P ER0 EY1 SH AH0 N -CORP'S K AO1 R P S -CORP'S(2) K AO1 R P ER0 EY1 SH AH0 N Z -CORP. K AO1 R P -CORP.(2) K AO1 R P ER0 EY1 SH AH0 N -CORP.'S K AO1 R P S -CORP.'S(2) K AO1 R P ER0 EY1 SH AH0 N Z -CORPENING K AO1 R P AH0 N IH0 NG -CORPORA K AO1 R P ER0 AH0 -CORPORACION K AO2 R P ER0 AA2 S IY0 OW1 N -CORPORAL K AO1 R P ER0 AH0 L -CORPORAL(2) K AO1 R P R AH0 L -CORPORATE K AO1 R P ER0 AH0 T -CORPORATE(2) K AO1 R P R AH0 T -CORPORATES K AO1 R P ER0 AH0 T S -CORPORATEWATCH K AO1 R P R AH0 T W AA2 CH -CORPORATEWIDE K AO1 R P ER0 IH0 T W AY2 D -CORPORATION K AO2 R P ER0 EY1 SH AH0 N -CORPORATION'S K AO2 R P ER0 EY1 SH AH0 N Z -CORPORATIONS K AO2 R P ER0 EY1 SH AH0 N Z -CORPORATIONS' K AO2 R P ER0 EY1 SH AH0 N Z -CORPORATISM K AO1 R P ER0 AH0 T IH2 Z AH0 M -CORPORATIST K AO1 R P ER0 AH0 T IH0 S T -CORPS K AO1 R -CORPS(2) K AO1 R Z -CORPS' K AO1 R Z -CORPSE K AO1 R P S -CORPSES K AO1 R P S AH0 Z -CORPSES(2) K AO1 R P S IH0 Z -CORPSMAN K AO1 R M AH0 N -CORPULANT K AO1 R P Y AH0 L AH0 N T -CORPUS K AO1 R P AH0 S -CORPUS-CHRISTI K AO1 R P AH0 S K R IH1 S T IY0 -CORPUZ K AO1 R P UW0 Z -CORR K AO1 R -CORRADI K ER0 AA1 D IY0 -CORRADINO K ER0 AA0 D IY1 N OW0 -CORRADO K ER0 AA1 D OW0 -CORRAL K ER0 AE1 L -CORRALES K ER0 AA1 L EH0 S -CORRALLED K ER0 AE1 L D -CORRALLING K ER0 AE1 L IH0 NG -CORRAO K AO1 R AW0 -CORREA K AO1 R IY0 AH0 -CORREALE K AO1 R IY0 AH0 L -CORRECT K ER0 EH1 K T -CORRECTED K ER0 EH1 K T AH0 D -CORRECTED(2) K ER0 EH1 K T IH0 D -CORRECTING K ER0 EH1 K T IH0 NG -CORRECTION K ER0 EH1 K SH AH0 N -CORRECTIONAL K ER0 EH1 K SH AH0 N AH0 L -CORRECTIONS K ER0 EH1 K SH AH0 N Z -CORRECTIVE K ER0 EH1 K T IH0 V -CORRECTLY K ER0 EH1 K T L IY0 -CORRECTNESS K ER0 EH1 K T N AH0 S -CORRECTS K ER0 EH1 K T S -CORREGIDOR K ER0 EH1 G AH0 D AO0 R -CORREIA K ER0 EY1 IY0 AH0 -CORREIRA K ER0 EH1 R AH0 -CORRELATE K AO1 R AH0 L EY2 T -CORRELATE(2) K AO1 R AH0 L AH0 T -CORRELATED K AO1 R AH0 L EY2 T AH0 D -CORRELATES K AO1 R AH0 L AH0 T S -CORRELATES(2) K AO1 R AH0 L EY0 T S -CORRELATING K AO1 R AH0 L EY0 T IH0 NG -CORRELATION K AO2 R AH0 L EY1 SH AH0 N -CORRELATIONS K AO2 R AH0 L EY1 SH AH0 N Z -CORRELL K ER0 EY1 L -CORRENE K AO1 R IY0 N -CORRENTE K ER0 EH1 N T IY0 -CORRENTI K ER0 EH1 N T IY0 -CORRESPOND K AO2 R AH0 S P AA1 N D -CORRESPONDED K AO2 R AH0 S P AA1 N D IH0 D -CORRESPONDENCE K AO2 R AH0 S P AA1 N D AH0 N S -CORRESPONDENT K AO2 R AH0 S P AA1 N D AH0 N T -CORRESPONDENTS K AO2 R AH0 S P AA1 N D AH0 N T S -CORRESPONDENTS' K AO2 R AH0 S P AA1 N D AH0 N T S -CORRESPONDING K AO2 R AH0 S P AA1 N D IH0 NG -CORRESPONDINGLY K AO2 R AH0 S P AA1 N D IH0 NG L IY0 -CORRESPONDS K AO2 R AH0 S P AA1 N D Z -CORRIB K AO1 R IH0 B -CORRICK K AO1 R IH0 K -CORRIDOR K AO1 R AH0 D ER0 -CORRIDOR(2) K AO1 R IH0 D ER0 -CORRIDORS K AO1 R IH0 D ER0 Z -CORRIDORS(2) K AO1 R AH0 D ER0 Z -CORRIE K AO1 R IY0 -CORRIERE K AO2 R Y EH1 R -CORRIGAN K AO1 R IH0 G AH0 N -CORRIGAN'S K AO1 R IH0 G AH0 N Z -CORRIGIBLE K AO1 R AH0 JH AH0 B AH0 L -CORRIHER K AO1 R IH0 HH ER0 -CORRIN K AO1 R IH0 N -CORRINA K ER0 IY1 N AH0 -CORRINGTON K AO1 R IH0 NG T AH0 N -CORRIVEAU K AO1 R IH0 V OW0 -CORROBORATE K ER0 AA1 B ER0 EY2 T -CORROBORATED K ER0 AA1 B ER0 EY2 T IH0 D -CORROBORATES K ER0 AO1 B ER0 EY2 T S -CORROBORATING K ER0 AA1 B ER0 EY2 T IH0 NG -CORROBORATION K ER0 AO2 B ER0 EY1 SH AH0 N -CORROBORATIVE K ER0 AA1 B ER0 AH0 T IH2 V -CORROBORATIVE(2) K ER0 AA1 B R AH0 T IH2 V -CORRODE K ER0 OW1 D -CORRODED K ER0 OW1 D IH0 D -CORRODES K ER0 OW1 D Z -CORRON K ER0 AO1 N -CORROON K AO0 R UW1 N -CORROSION K ER0 OW1 ZH AH0 N -CORROSIVE K ER0 OW1 S IH0 V -CORROW K AO1 R OW0 -CORRUGATE K AO1 R AH0 G EY2 T -CORRUGATED K AO1 R AH0 G EY2 T IH0 D -CORRUPT K ER0 AH1 P T -CORRUPTED K ER0 AH1 P T IH0 D -CORRUPTIBLE K ER0 AH1 P T IH0 B AH0 L -CORRUPTING K ER0 AH1 P T IH0 NG -CORRUPTION K ER0 AH1 P SH AH0 N -CORRUPTIONS K ER0 AH1 P SH AH0 N Z -CORRUPTIVE K ER0 AH1 P T IH0 V -CORRUPTLY K ER0 AH1 P T L IY0 -CORRUPTS K ER0 AH1 P T S -CORRY K AO1 R IY0 -CORSA K AO1 S AH0 -CORSAGE K AO0 R S AA1 ZH -CORSAIR K AO1 R S EH0 R -CORSARO K ER0 S AA1 R OW0 -CORSE K AO1 R S -CORSELLO K ER0 S EH1 L OW0 -CORSENTINO K 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R EH1 S T IH0 NG -CRESTMONT K R EH1 S T M AA2 N T -CRESTS K R EH1 S T S -CRESWELL K R EH1 S W EH2 L -CRETACEOUS K R IH0 T EY1 SH IH0 S -CRETCHEN K R EH1 CH AH0 N -CRETE K R IY1 T -CRETELLA K R EH0 T EH1 L AH0 -CRETIEN K R IY1 SH Y AH0 N -CRETIN K R IY1 T AH0 N -CRETSINGER K R EH1 T S IH0 N JH ER0 -CREUTZFELDT K R UH1 T S F EH0 L D -CREUTZFELDT(2) K R UH1 T S F EH0 L T -CREVASSE K R AH0 V AE1 S -CREVELING K R EH1 V AH0 L IH0 NG -CREVELING(2) K R EH1 V L IH0 NG -CREVICE K R EH1 V AH0 S -CREVICES K R EH1 V AH0 S IH0 Z -CREVIER K R IY1 V IY0 ER0 -CREVISTON K R EH1 V IH0 S T AA0 N -CREW K R UW1 -CREW'S K R UW1 Z -CREWE K R UW1 -CREWEL K R UW1 AH0 L -CREWES K R UW1 Z -CREWMAN K R UW1 M AH0 N -CREWMEN K R UW1 M IH0 N -CREWS K R UW1 Z -CRIB K R IH1 B -CRIBARI K R IY0 B AA1 R IY0 -CRIBB K R IH1 B -CRIBBAGE K R IH1 B IH0 JH -CRIBBING K R IH1 B IH0 NG -CRIBBS K R IH1 B Z -CRIBS K R IH1 B Z -CRICHLOW K R IH1 K L OW0 -CRICHTON K R IH1 CH T AH0 N -CRICK K R IH1 K -CRICKET K R IH1 K AH0 T -CRICKET(2) K R IH1 K IH0 T -CRICKET'S K R IH1 K AH0 T S -CRICKETS K R IH1 K AH0 T S -CRIDDLE K R IH1 D AH0 L -CRIDER K R AY1 D ER0 -CRIED K R AY1 D -CRIER K R AY1 ER0 -CRIES K R AY1 Z -CRIGER K R AY1 JH ER0 -CRIGGER K R IH1 G ER0 -CRIGLER K R IH1 G L ER0 -CRIHFIELD K R IH1 F IY0 L D -CRILL K R IH1 L -CRILLEY K R IH1 L IY0 -CRILLY K R IH1 L IY0 -CRIM K R IH1 M -CRIME K R AY1 M -CRIME'S K R AY1 M Z -CRIMEA K R AY0 M IY1 AH0 -CRIMEAN K R IH0 M IY1 N -CRIMEAN(2) K R AY2 M IY1 AH0 N -CRIMES K R AY1 M Z -CRIMI K R IY1 M IY0 -CRIMINAL K R IH1 M AH0 N AH0 L -CRIMINAL'S K R IH1 M AH0 N AH0 L Z -CRIMINALIST K R IH1 M AH0 N AH0 L IH0 S T -CRIMINALIST'S K R IH1 M AH0 N AH0 L IH0 S T S -CRIMINALISTICS K R IH2 M AH0 N AH0 L IH1 S T IH0 K S -CRIMINALISTS K R IH1 M AH0 N AH0 L IH0 S T S -CRIMINALITY K R IH2 M AH0 N AE1 L IH0 T IY0 -CRIMINALIZATION K R IH2 M AH0 N AH0 L AH0 Z EY1 SH AH0 N -CRIMINALIZE K R IH1 M AH0 N AH0 L AY2 Z -CRIMINALIZED K R IH1 M AH0 N AH0 L AY2 Z D -CRIMINALIZING K R IH1 M AH0 N AH0 L AY2 Z IH0 NG -CRIMINALLY K R IH1 M AH0 N AH0 L IY0 -CRIMINALS K R IH1 M AH0 N AH0 L Z -CRIMINALS' K R IH1 M AH0 N AH0 L Z -CRIMINOLOGIST K R IH2 M IH0 N AA1 L AH0 JH IH0 S T -CRIMINOLOGISTS K R IH2 M IH0 N AA1 L AH0 JH IH0 S T S -CRIMINOLOGY K R IH2 M IH0 N AA1 L AH0 JH IY0 -CRIMM K R IH1 M -CRIMMINS K R IH1 M IH0 N Z -CRIMP K R IH1 M P -CRIMPED K R IH1 M P T -CRIMPING K R IH1 M P IH0 NG -CRIMPS K R IH1 M P S -CRIMSON K R IH1 M Z AH0 N -CRINER K R AY1 N ER0 -CRINGE K R IH1 N JH -CRINGED K R IH1 N JH D -CRINGES K R IH1 N JH IH0 Z -CRINGING K R IH1 N JH IH0 NG -CRIP K R IH1 P -CRIPE K R AY1 P -CRIPPEN K R IH1 P AH0 N -CRIPPLE K R IH1 P AH0 L -CRIPPLED K R IH1 P AH0 L D -CRIPPLES K R IH1 P AH0 L Z -CRIPPLING K R IH1 P AH0 L IH0 NG -CRIPPLING(2) K R IH1 P L IH0 NG -CRIPPS K R IH1 P S -CRIPS K R IH1 P S -CRIS K R IH1 S -CRISAFI K R IY0 S AA1 F IY0 -CRISAFULLI K R IY0 S AA0 F UW1 L IY0 -CRISANTI K R IH0 S AE1 N T IY0 -CRISCI K R IH1 S IY0 -CRISCIONE K R IY0 S CH OW1 N IY0 -CRISCO K R IY1 S K OW0 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R IH1 S T AH0 B EH0 L -CRISTAL K R IH1 S T AH0 L -CRISTIANI K R IH2 S T IY0 AA1 N IY0 -CRISTIANI(2) K R IH2 S CH IY0 AA1 N IY0 -CRISTIANI'S K R IH2 S T IY0 AA1 N IY0 Z -CRISTIANI'S(2) K R IH2 S CH IY0 AA1 N IY0 Z -CRISTIANO K R IY0 S T IY0 AA1 N OW0 -CRISTINA K R IH0 S T IY1 N AH0 -CRISTO K R IH1 S T OW2 -CRISTO'S K R IH1 S T OW2 Z -CRISTOBAL K R IH1 S T AH0 B AH0 L -CRISTOBEL K R IH1 S T AH0 B EH0 L -CRISTOBEL'S K R IH1 S T AH0 B EH0 L Z -CRISWELL K R IH1 S W EH0 L -CRITCHER K R IH1 CH ER0 -CRITCHFIELD K R IH1 CH F IY0 L D -CRITCHLEY K R IH1 CH L IY0 -CRITCHLOW K R IH1 CH L OW0 -CRITELLI K R IH0 T EH1 L IY0 -CRITERIA K R AY0 T IH1 R IY0 AH0 -CRITERION K R AY0 T IH1 R IY0 AH0 N -CRITES K R AY1 T S -CRITIC K R IH1 T IH0 K -CRITIC'S K R IH1 T IH0 K S -CRITICAL K R IH1 T IH0 K AH0 L -CRITICALITY K R IH2 T AH0 K AE1 L AH0 T IY0 -CRITICALLY K R IH1 T IH0 K AH0 L IY0 -CRITICALLY(2) K R IH1 T IH0 K L IY0 -CRITICISM K R IH1 T IH0 S IH2 Z AH0 M -CRITICISMS K R IH1 T IH0 S IH2 Z AH0 M Z -CRITICIZE K R IH1 T IH0 S AY2 Z -CRITICIZED K R IH1 T AH0 S AY2 Z D -CRITICIZES K R IH1 T IH0 S AY2 Z IH0 Z -CRITICIZING K R IH1 T IH0 S AY2 Z IH0 NG -CRITICS K R IH1 T IH0 K S -CRITICS' K R IH1 T IH0 K S -CRITIQUE K R AH0 T IY1 K -CRITIQUE(2) K R IH0 T IY1 K -CRITIQUED K R IH0 T IY1 K T -CRITIQUES K R IH0 T IY1 K S -CRITIQUING K R IH0 T IY1 K IH0 NG -CRITON K R AY1 T AH0 N -CRITSER K R IH1 T S ER0 -CRITTENDEN K R IH0 T EH1 N D AH0 N -CRITTENDON K R IH1 T AH0 N D IH0 N -CRITTER K R IH1 T ER0 -CRITTERS K R IH1 T ER0 Z -CRITZ K R IH1 T S -CRITZER K R IH1 T Z ER0 -CRIVARO K R IH0 V AE1 R OW0 -CRIVELLI K R IH0 V EH1 L IY0 -CRIVELLO K R IH0 V EH1 L OW0 -CRIVEN K R IH1 V AH0 N -CRM S IY1 AA1 R EH1 M -CRNKOVICH S ER1 N AH0 V IH2 CH -CRO K R OW1 -CROAK K R OW1 K -CROAKER K R OW1 K ER0 -CROAKING K R OW1 K IH0 NG -CROAT K R OW1 AA2 T -CROAT'S K R OW1 AA2 T S -CROATIA K R OW0 EY1 SH AH0 -CROATIA'S K R OW0 EY1 SH AH0 Z -CROATIAN K R OW0 EY1 SH AH0 N -CROATIAN'S K R OW0 EY1 SH AH0 N Z -CROATIANS K R OW0 EY1 SH AH0 N Z -CROATIANS' K R OW0 EY1 SH AH0 N Z -CROATS K R OW1 AA2 T S -CROC K R AA1 K -CROCCO K R AA1 K OW0 -CROCE K R OW1 CH IY0 -CROCHET K R OW0 SH EY1 -CROCHETED K R OW0 SH EY1 D -CROCHETIERE K R OW2 SH AH0 T IH1 R -CROCHETING K R OW0 SH EY1 IH0 NG -CROCK K R AA1 K -CROCKER K R AA1 K ER0 -CROCKER'S K R AA1 K ER0 Z -CROCKERY K R AA1 K ER0 IY0 -CROCKETT K R AA1 K IH0 T -CROCKFORD K R AA1 K F ER0 D -CROCODILE K R AA1 K AH0 D AY2 L -CROCODILES K R AA1 K AH0 D AY2 L Z -CROCODILIAN K R AA2 K AH0 D IH1 L Y AH0 N -CROCUS K R OW1 K AH0 S -CROCUSES K R OW1 K Y UW0 S IH0 Z -CROFF K R AO1 F -CROFFORD K R AA1 F ER0 D -CROFOOT K R OW1 F UH0 T -CROFT K R AO1 F T -CROFTON K R AA1 F T AH0 N -CROFTS K R AO1 F T S -CROGAN K R OW1 G AH0 N -CROGHAN K R AA1 G AH0 N -CROISSANT K W AA2 S AA1 N T -CROISSANT(2) K R AH0 S AA1 N T -CROISSANTS K W AA2 S AA1 N T S -CROISSANTS(2) K R AH0 S AA1 N T S -CROITZER K R OY1 T S ER0 -CROIX K R OY1 -CROIX'S K R OY1 Z -CROKE K R OW1 K -CROKER K R OW1 K ER0 -CROLEY K R OW1 L IY0 -CROLL K R OW1 L -CROM K R AA1 M -CROMARTIE K R AA1 M AA0 R T IY0 -CROMBIE K R AA1 M B IY0 -CROMER K R OW1 M ER0 -CROMER'S K R OW1 M ER0 Z -CROMIE K R AA1 M IY0 -CROMLEY K R AA1 M L IY0 -CROMPTON K R AA1 M P T AH0 N -CROMWELL K R AA1 M W AH0 L -CROMWELL'S K R AA1 M W IH0 L Z -CRON K R AA1 N -CRONAN K R OW1 N AH0 N -CRONAUER K R AA1 N AW0 R -CRONCE K R AA1 N S -CRONE K R OW1 N -CRONEN K R OW1 N AH0 N -CRONER K R OW1 N ER0 -CRONEY K R OW1 N IY0 -CRONIES K R OW1 N IY0 Z -CRONIN K R OW1 N IH0 N -CRONK K R AA1 NG K -CRONKHITE K R AA1 NG K HH AY2 T -CRONKITE K R AA1 NG K AY2 T -CRONKRIGHT K R AA1 NG K R AY2 T -CRONQUIST K R AA1 N K W IH2 S T -CRONUS K R OW1 N AH0 S -CRONY K R OW1 N IY0 -CRONYISM K R OW1 N IY0 IH2 Z AH0 M -CROOK K R UH1 K -CROOKE K R UH1 K -CROOKED K R UH1 K AH0 D -CROOKED-LEG K R UH1 K AH0 D L EH2 G -CROOKEDNESS K R UH1 K AH0 D N AH0 S -CROOKER K R UH1 K ER0 -CROOKLYN K R UH1 K L IH2 N -CROOKLYN'S K R UH1 K L IH2 N Z -CROOKS K R UH1 K S -CROOKSHANK K R UH1 K SH AE2 NG K -CROOKSHANKS K R UH1 K SH AE2 NG K S -CROOKSTON K R UH1 K S T AH0 N -CROOM K R UW1 M -CROOMS K R UW1 M Z -CROON K R UW1 N -CROONER K R UW1 N ER0 -CROONING K R UW1 N IH0 NG -CROONS K R UW1 N Z -CROP K R AA1 P -CROP'S K R AA1 P S -CROPLAND K R AA1 P L AE2 N D -CROPLEY K R AA1 P L IY0 -CROPP K R AA1 P -CROPPED K R AA1 P T -CROPPER K R AA1 P ER0 -CROPPING K R AA1 P IH0 NG -CROPS K R AA1 P S -CROPSEY K R AA1 P S IY0 -CROQUET K R OW0 K EY1 -CROS K R AO1 S -CROSBEY K R AA1 S B IY0 -CROSBIE K R AO1 Z B IY0 -CROSBY K R AO1 Z B IY0 -CROSBY'S K R AO1 Z B IY0 Z -CROSE K R OW1 Z -CROSHAW K R AA1 SH AO0 -CROSKEY K R AA1 S K IY0 -CROSLAND K R AA1 S L AH0 N D -CROSLEY K R AA1 S L IY0 -CROSLIN K R AA1 S L IH0 N -CROSON K R OW1 S AH0 N -CROSS K R AO1 S -CROSS'S K R AO1 S IH0 Z -CROSS-COUNTRY K R AO1 S K AH2 N T R IY0 -CROSS-POLLINATE K R AO1 S P AA2 L AH0 N EY2 T -CROSS-REFERENCE K R AO1 S R EH2 F ER0 AH0 N S -CROSSAN K R AA1 S AH0 N -CROSSBILL K R AO1 S B IH2 L -CROSSBILLS K R AO1 S B IH2 L Z -CROSSBONES K R AO1 S B OW2 N Z -CROSSBORDER K R AO1 S B AO2 R D ER0 -CROSSBOW K R AO1 S B OW2 -CROSSBOWS K R AO1 S B OW2 Z -CROSSCURRENT K R AO1 S K ER2 AH0 N T -CROSSCURRENTS K R AO1 S K ER2 AH0 N T S -CROSSE K R AA1 S -CROSSED K R AO1 S T -CROSSEN K R AO1 S AH0 N -CROSSER K R AO1 S ER0 -CROSSES K R AO1 S IH0 Z -CROSSETT K R AA1 S IH0 T -CROSSFIELD K R AO1 S F IY2 L D -CROSSFIRE K R AO1 S F AY0 R -CROSSFIRE(2) K R AO1 S F AY2 R -CROSSFIRE'S K R AO1 S F AY0 R Z -CROSSIN K R AA1 S IH0 N -CROSSING K R AO1 S IH0 NG -CROSSINGS K R AO1 S IH0 NG Z -CROSSLAND K R AO1 S L AE2 N D -CROSSLEY K R AA1 S L IY0 -CROSSLIN K R AA1 S L IH0 N -CROSSMAN K R AO1 S M AH0 N -CROSSNINE K R AO1 S N AY2 N -CROSSNO K R OW1 S N OW0 -CROSSON K R AA1 S AH0 N -CROSSOVER K R AO1 S OW2 V ER0 -CROSSPIECE K R AO1 S P IY2 S -CROSSPIECES K R AO1 S P IY2 S AH0 Z -CROSSPIECES(2) K R AO1 S P IY2 S IH0 Z -CROSSROAD K R AO1 S R OW2 D -CROSSROADS K R AO1 S R OW2 D Z -CROSSTALK K R AO1 S T AO1 K -CROSSTOWN K R AO1 S T 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Z -CROW K R OW1 -CROW'S K R OW1 Z -CROWBAR K R OW1 B AA2 R -CROWBOROUGH K R OW1 B ER0 OW0 -CROWD K R AW1 D -CROWD'S K R AW1 D Z -CROWDED K R AW1 D AH0 D -CROWDED(2) K R AW1 D IH0 D -CROWDEN K R AW1 D AH0 N -CROWDER K R AW1 D ER0 -CROWDING K R AW1 D IH0 NG -CROWDS K R AW1 D Z -CROWE K R OW1 -CROWED K R OW1 D -CROWELL K R OW1 AH0 L -CROWING K R OW1 IH0 NG -CROWKEEPER K R OW1 K IY2 P ER0 -CROWKEEPERS K R OW1 K IY2 P ER0 Z -CROWL K R AW1 L -CROWLE K R AW1 L -CROWLEY K R AW1 L IY0 -CROWLEY'S K R AW1 L IY0 Z -CROWN K R AW1 N -CROWN'S K R AW1 N Z -CROWNE K R AW1 N -CROWNED K R AW1 N D -CROWNER K R AW1 N ER0 -CROWNING K R AW1 N IH0 NG -CROWNLIKE K R AW1 N L AY2 K -CROWNOVER K R AW1 N OW2 V ER0 -CROWNS K R AW1 N Z -CROWNX K R AW1 N EH2 K S -CROWS K R OW1 Z -CROWSON K R AW1 S AH0 N -CROWTHER K R AW1 DH ER0 -CROWTHER'S K R OW1 TH ER0 Z -CROWTHERS K R OW1 TH ER0 Z -CROXTON K R AA1 K S T AH0 N -CROY K R OY1 -CROYLE K R OY1 L -CROZIER K R OW1 ZH ER0 -CRUCE K R UW1 S -CRUCES K R UW1 S IY0 Z -CRUCIAL 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D IY2 S IY1 -D.S D IY1 Z -DA D AA1 -DA(2) D IY1 EY1 -DA'S D IY1 EY1 Z -DAANE D AA1 N -DAARMSTADT D AA1 R M S T AA0 D -DAAS D AA1 S -DAB D AE1 B -DABAH D AE1 B AH0 -DABAH(2) D AH0 B AA1 -DABBING D AE1 B IH0 NG -DABBLE D AE1 B AH0 L -DABBLED D AE1 B AH0 L D -DABBLES D AE1 B AH0 L Z -DABBLING D AE1 B AH0 L IH0 NG -DABBLING(2) D AE1 B L IH0 NG -DABBS D AE1 B Z -DABCHICK D AE1 B CH IH0 K -DABHOL D AE1 B OW0 L -DABKOWSKI D AH0 B K AO1 F S K IY0 -DABNEY D AE1 B N IY0 -DABROWSKI D AH0 B R AO1 F S K IY0 -DAC D AE1 K -DACE D EY1 S -DACEY D EY1 S IY0 -DACHA D AA1 CH AH0 -DACHAU D AE1 K AW0 -DACHSHUND D AA1 K S HH UH2 N D -DACHSHUNDS D AA1 K S HH UH2 N T S -DACIA D EY1 SH IY0 AH0 -DACK D AE1 K -DACOSTA D AA0 K OW1 S T AH0 -DACQUISTO D AE1 K W IH0 S T OW0 -DACRON D AE1 K R AA2 N -DACRUZ D AA1 K R UW0 Z -DACS D AE1 K S -DACTYL D AE1 K T IH0 L -DACUNHA D AH0 K AH1 N HH AH0 -DACUS D AE1 K IH0 S -DACY D EY1 S IY0 -DAD D AE1 D -DAD'S D AE1 D Z -DADA D AA1 D AA2 -DADAMO D AA0 D AA1 M OW0 -DADDARIO D AA0 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AA1 N AA2 -DIGIOVANNI D IH2 JH OW0 V AA1 N IY2 -DIGIROLAMO D IH2 JH IH0 R OW0 L AA1 M OW2 -DIGIT D IH1 JH AH0 T -DIGIT(2) D IH1 JH IH0 T -DIGITAL D IH1 JH AH0 T AH0 L -DIGITAL(2) D IH1 JH IH0 T AH0 L -DIGITAL'S D IH1 JH AH0 T AH0 L Z -DIGITAL'S(2) D IH1 JH IH0 T AH0 L Z -DIGITALIS D IH2 JH AH0 T AE1 L AH0 S -DIGITALLY D IH1 JH AH0 T AH0 L IY0 -DIGITECH D IH1 JH AH0 T EH2 K -DIGITI D IH1 JH AH0 T AY2 -DIGITI(2) D IH1 JH AH0 T IY2 -DIGITIZE D IH1 JH AH0 T AY2 Z -DIGITIZED D IH1 JH AH0 T AY2 Z D -DIGITIZING D IH1 JH AH0 T AY2 Z IH0 NG -DIGITS D IH1 JH AH0 T S -DIGITS(2) D IH1 JH IH0 T S -DIGIULIO D IH2 JH UW1 L IY0 OW2 -DIGIUSEPPE D IY2 JH UW0 S EH1 P IY2 -DIGMAN D IH1 G M AH0 N -DIGNAN D IH1 G N AH0 N -DIGNIFIED D IH1 G N AH0 F AY2 D -DIGNIFY D IH1 G N AH0 F AY2 -DIGNITARIES D IH1 G N AH0 T EH2 R IY0 Z -DIGNITARY D IH1 G N AH0 T EH2 R IY0 -DIGNITY D IH1 G N AH0 T IY0 -DIGRAZIA D IH2 G R AA1 Z IY0 AA2 -DIGREGORIO D IH2 G R EH0 G AO1 R IY0 OW2 -DIGRESS D AY0 G R EH1 S -DIGRESSED D AY0 G R 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M AO1 R F IH0 K -DIMORPHISM D AY0 M AO1 R F IH0 Z AH0 M -DIMPERIO D IH0 M P EH1 R IY0 OW2 -DIMPLE D IH1 M P AH0 L -DIMPLED D IH1 M P AH0 L D -DIMPLES D IH1 M P AH0 L Z -DIMRY D IH1 M R IY0 -DIMS D IH1 M Z -DIMSDALE D IH1 M Z D EY2 L -DIMUNITION D IH0 M Y UW0 N IH1 SH AH0 N -DIMURO D IH0 M UH1 R OW2 -DIMUZIO D IH0 M UW1 Z IY0 OW2 -DIN D IH1 N -DINA D IY1 N AH0 -DINAH D AY1 N AH0 -DINAN D IH1 N AH0 N -DINAPOLI D IH0 N AE1 P AH0 L IY2 -DINAR D IH0 N AA1 R -DINARDO D IH0 N AA1 R D OW2 -DINARS D AY1 N ER0 Z -DINARS(2) D IH2 N AA1 R Z -DINATALE D IH0 N AA0 T AA1 L IY2 -DINATALE(2) D IY0 N AA0 T AA1 L IY0 -DINDA D IH1 N D AH0 -DINE D AY1 N -DINED D AY1 N D -DINEEN D IH0 N IY1 N -DINEHART D AY1 N HH AA2 R T -DINER D AY1 N ER0 -DINERS D AY1 N ER0 Z -DINES D AY1 N Z -DINESH D IH1 N EH0 SH -DING D IH1 NG -DING-DONG D IH1 NG D AO1 NG -DINGEE D IH1 NG G IY0 -DINGEL D IH1 NG G AH0 L -DINGELL D IH1 NG G AH0 L -DINGELL'S D IH1 NG G AH0 L Z -DINGER D IH1 NG ER0 -DINGES D IH1 N JH IH0 Z -DINGESS D IH1 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M AH0 -DIPANJAN D AH0 P AA1 N JH AH2 N -DIPAOLA D IH0 P AA0 OW1 L AH0 -DIPAOLO D IH0 P AA0 OW1 L OW2 -DIPASQUALE D IH0 P AA0 S K W AA1 L IY2 -DIPASQUALE(2) D IY0 P AA0 S K W AA1 L IY0 -DIPERNA D IH0 P EH1 R N AH0 -DIPHTHERIA D IH0 F TH IH1 R IY0 AA0 -DIPHTHONG D IH1 F TH AO2 NG -DIPIAZZA D IH0 P IY0 AA1 T S AA2 -DIPIAZZA(2) D IY0 P IY0 AA1 T S AA2 -DIPIERO D IH0 P IH1 R OW2 -DIPIERRO D IH0 P IH1 R OW2 -DIPIETRO D IH0 P IY1 T R OW2 -DIPINTO D IH0 P IH1 N T OW2 -DIPIRRO D IH0 P IH1 R OW2 -DIPLOMA D IH0 P L OW1 M AA0 -DIPLOMACY D IH0 P L OW1 M AH0 S IY0 -DIPLOMAS D IH0 P L OW1 M AH0 Z -DIPLOMAT D IH1 P L AH0 M AE2 T -DIPLOMAT'S D IH1 P L AH0 M AE2 T S -DIPLOMATIC D IH2 P L AH0 M AE1 T IH0 K -DIPLOMATICALLY D IH2 P L AH0 M AE1 T IH0 K L IY0 -DIPLOMATS D IH1 P L AH0 M AE2 T S -DIPLOMATS' D IH1 P L AH0 M AE2 T S -DIPOLE D AY1 P OW2 L -DIPPED D IH1 P T -DIPPEL D IH1 P AH0 L -DIPPER D IH1 P ER0 -DIPPERS D IH1 P ER0 Z -DIPPING D IH1 P IH0 NG -DIPPLE D IH1 P AH0 L -DIPPOLD D IH1 P OW2 L D -DIPPOLITO D IH0 P OW0 L IY1 T OW2 -DIPPY D IH1 P IY0 -DIPRIMA D IH0 P R IY1 M AH0 -DIPS D IH1 P S -DIPSHIT D IH1 P SH IH2 T -DIPSTICK D IH1 P S T IH2 K -DIRCKS D ER1 K S -DIRE D AY1 R -DIRE(2) D AY1 ER0 -DIRECT D ER0 EH1 K T -DIRECT(2) D AY0 R EH1 K T -DIRECT(3) D IH0 R EH1 K T -DIRECTED D ER0 EH1 K T AH0 D -DIRECTED(2) D ER0 EH1 K T IH0 D -DIRECTED(3) D AY0 R EH1 K T IH0 D -DIRECTED(4) D IH0 R EH1 K T IH0 D -DIRECTING D ER0 EH1 K T IH0 NG -DIRECTING(2) D IY0 R EH1 K T IH0 NG -DIRECTING(3) D AY0 R EH1 K T IH0 NG -DIRECTING(4) D IH0 R EH1 K T IH0 NG -DIRECTION D ER0 EH1 K SH AH0 N -DIRECTION(2) D IY0 R EH1 K SH IH0 N -DIRECTION(3) D AY0 R EH1 K SH IH0 N -DIRECTION(4) D IH0 R EH1 K SH IH0 N -DIRECTIONAL D ER0 EH1 K SH AH0 N AH0 L -DIRECTIONAL(2) D IY0 R EH1 K SH IH0 N AH0 L -DIRECTIONAL(3) D AY0 R EH1 K SH IH0 N AH0 L -DIRECTIONAL(4) D IH0 R EH1 K SH IH0 N AH0 L -DIRECTIONLESS D ER0 EH1 K SH AH0 N L AH0 S -DIRECTIONLESS(2) D IY0 R EH1 K SH IH0 N L AH0 S -DIRECTIONLESS(3) D AY0 R EH1 K SH IH0 N L AH0 S -DIRECTIONLESS(4) D IH0 R EH1 K SH IH0 N L AH0 S -DIRECTIONS D ER0 EH1 K SH AH0 N Z -DIRECTIONS(2) D IY0 R EH1 K SH IH0 N Z -DIRECTIONS(3) D AY0 R EH1 K SH IH0 N Z -DIRECTIONS(4) D IH0 R EH1 K SH IH0 N Z -DIRECTIVE D ER0 EH1 K T IH0 V -DIRECTIVE(2) D IY0 R EH1 K T IH0 V -DIRECTIVE(3) D AY0 R EH1 K T IH0 V -DIRECTIVE(4) D IH0 R EH1 K T IH0 V -DIRECTIVES D AY0 R EH1 K T IH0 V Z -DIRECTIVES(2) D IY0 R EH1 K T IH0 V Z -DIRECTIVES(3) D ER0 EH1 K T IH0 V Z -DIRECTIVES(4) D IH0 R EH1 K T IH0 V Z -DIRECTLY D ER0 EH1 K T L IY0 -DIRECTLY(2) D IY0 R EH1 K L IY0 -DIRECTLY(3) D AY0 R EH1 K L IY0 -DIRECTLY(4) D IH0 R EH1 K L IY0 -DIRECTNESS D ER0 EH1 K T N AH0 S -DIRECTNESS(2) D IY0 R EH1 K N AH0 S -DIRECTNESS(3) D AY0 R EH1 K N AH0 S -DIRECTNESS(4) D IH0 R EH1 K N AH0 S -DIRECTOR D ER0 EH1 K T ER0 -DIRECTOR(2) D AY0 R EH1 K T ER0 -DIRECTOR(3) D IY0 R EH1 K T ER0 -DIRECTOR(4) D IH0 R EH1 K T ER0 -DIRECTOR'S D AY0 R EH1 K T ER0 Z -DIRECTOR'S(2) D ER0 EH1 K T ER0 Z -DIRECTOR'S(3) D IY0 R EH1 K T ER0 Z -DIRECTOR'S(4) D IH0 R EH1 K T ER0 Z -DIRECTORAL D ER0 EH1 K T ER0 AH0 L -DIRECTORATE D ER0 EH1 K T ER0 AH0 T -DIRECTORATE(2) D AY0 R EH1 K T ER0 AH0 T -DIRECTORATE(3) D IY0 R EH1 K T ER0 AH0 T -DIRECTORATE(4) D IH0 R EH1 K T ER0 AH0 T -DIRECTORIAL D ER0 EH0 K T AO1 R IY0 AH0 L -DIRECTORIAL(2) D AY0 R EH0 K T AO1 R IY0 AH0 L -DIRECTORIAL(3) D IY0 R EH0 K T AO1 R IY0 AH0 L -DIRECTORIAL(4) D IH0 R EH0 K T AO1 R IY0 AH0 L -DIRECTORIES D AY0 R EH1 K T ER0 IY0 Z -DIRECTORIES(2) D ER0 EH1 K T ER0 IY0 Z -DIRECTORIES(3) D IY0 R EH1 K T ER0 IY0 Z -DIRECTORIES(4) D IH0 R EH1 K T ER0 IY0 Z -DIRECTORS D ER0 EH1 K T ER0 Z -DIRECTORS(2) D AY0 R EH1 K T ER0 Z -DIRECTORS(3) D IY0 R EH1 K T ER0 Z -DIRECTORS(4) D IH0 R EH1 K T ER0 Z -DIRECTORS' D IH0 R EH1 K T ER0 Z -DIRECTORS'(2) D ER0 EH1 K T ER0 Z -DIRECTORS'(3) D IY0 R EH1 K T ER0 Z -DIRECTORSHIP D ER0 EH1 K T ER0 SH IH2 P -DIRECTORSHIP(2) D AY0 R EH1 K T ER0 SH IH2 P -DIRECTORSHIP(3) D IY0 R EH1 K T ER0 SH IH2 P -DIRECTORSHIP(4) D IH0 R EH1 K T ER0 SH IH2 P -DIRECTORSHIPS D ER0 EH1 K T ER0 SH IH2 P S -DIRECTORSHIPS(2) D AY0 R EH1 K T ER0 SH IH2 P S -DIRECTORSHIPS(3) D IY0 R EH1 K T ER0 SH IH2 P S -DIRECTORSHIPS(4) D IH0 R EH1 K T ER0 SH IH2 P S -DIRECTORY D ER0 EH1 K T ER0 IY0 -DIRECTORY(2) D AY0 R EH1 K T ER0 IY0 -DIRECTORY(3) D IY0 R EH1 K T ER0 IY0 -DIRECTORY(4) D IH0 R EH1 K T ER0 IY0 -DIRECTS D ER0 EH1 K T S -DIRECTS(2) D AY0 R EH1 K T S -DIRECTS(3) D IY0 R EH1 K T S -DIRECTS(4) D IH0 R EH1 K T S -DIRECTV D ER0 EH1 K T IY1 V IY1 -DIRECTV(2) D AY0 R EH1 K T IY1 V IY1 -DIRECTV(3) D IY0 R EH1 K T IY1 V IY1 -DIRECTV(4) D IH0 R EH1 K T IY1 V IY1 -DIRENZO D IH0 R EH1 N Z OW2 -DIREST D AY1 R AH0 S T -DIRGE D ER1 JH -DIRHAMS D ER1 AH0 M Z -DIRICKSON D IH1 R IH2 K S AH0 N -DIRIENZO D IH0 R IY1 N Z OW2 -DIRK D ER1 K -DIRKES D ER1 K S -DIRKS D ER1 K S -DIRKSE D ER1 K S -DIRKSEN D ER1 K S AH0 N -DIRLAM D ER0 L AE1 M -DIROCCO D IH0 R AA1 K OW2 -DIROSA D IH0 R OW1 S AA0 -DIRR D ER1 -DIRT D ER1 T -DIRT'S D ER1 T S 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-DISADVANTAGES(2) D IH2 S AH0 D V AE1 N IH0 JH Z -DISAFFECT D IH2 S AH0 F EH1 K T -DISAFFECTED D IH2 S AH0 F EH1 K T IH0 D -DISAFFECTION D IH2 S AH0 F EH1 K SH AH0 N -DISAGREE D IH0 S AH0 G R IY1 -DISAGREEABLE D IH2 S AH0 G R IY1 AH0 B AH0 L -DISAGREED D IH0 S AH0 G R IY1 D -DISAGREEING D IH0 S AH0 G R IY1 IH0 NG -DISAGREEMENT D IH0 S AH0 G R IY1 M AH0 N T -DISAGREEMENTS D IH2 S AH0 G R IY1 M AH0 N T S -DISAGREES D IH0 S AH0 G R IY1 Z -DISALLOW D IH2 S AH0 L AW1 -DISALLOWANCE D IH2 S AH0 L AW1 AH0 N S -DISALLOWANCES D IH2 S AH0 L AW1 AH0 N S IH0 Z -DISALLOWED D IH2 S AH0 L AW1 D -DISALLOWING D IH0 S AH0 L AW1 IH0 NG -DISALVO D IH0 S AA1 L V OW2 -DISANO D IH0 S AA1 N OW0 -DISANTI D IH0 S AE1 N T IY2 -DISANTIS D IH0 S AA1 N T IH0 S -DISANTO D IH0 S AE1 N T OW2 -DISAPPEAR D IH2 S AH0 P IH1 R -DISAPPEAR(2) D IH2 S AH0 P IY1 R -DISAPPEARANCE D IH2 S AH0 P IH1 R AH0 N S -DISAPPEARANCE(2) D IH2 S AH0 P IY1 R AH0 N S -DISAPPEARANCES D IH2 S AH0 P IH1 R AH0 N S IH0 Z -DISAPPEARANCES(2) D IH2 S AH0 P IY1 R AH0 N S IH0 Z -DISAPPEARED D IH2 S AH0 P IH1 R D -DISAPPEARED(2) D IH2 S AH0 P IY1 R D -DISAPPEARING D IH2 S AH0 P IH1 R IH0 NG -DISAPPEARING(2) D IH2 S AH0 P IY1 R IH0 NG -DISAPPEARS D IH2 S AH0 P IH1 R Z -DISAPPEARS(2) D IH2 S AH0 P IY1 R Z -DISAPPOINT D IH2 S AH0 P OY1 N T -DISAPPOINTED D IH2 S AH0 P OY1 N T IH0 D -DISAPPOINTED(2) D IH2 S AH0 P OY1 N IH0 D -DISAPPOINTING D IH2 S AH0 P OY1 N T IH0 NG -DISAPPOINTING(2) D IH2 S AH0 P OY1 N IH0 NG -DISAPPOINTINGLY D IH0 S AH0 P OY1 N T IH0 NG L IY0 -DISAPPOINTINGLY(2) D IH0 S AH0 P OY1 N IH0 NG L IY0 -DISAPPOINTMENT D IH2 S AH0 P OY1 N T M AH0 N T -DISAPPOINTMENTS D IH0 S AH0 P OY1 N T M AH0 N T S -DISAPPOINTS D IH2 S AH0 P OY1 N T S -DISAPPROVAL D IH0 S AH0 P R UW1 V AH0 L -DISAPPROVE D IH2 S AH0 P R UW1 V -DISAPPROVED D IH2 S AH0 P R UW1 V D -DISAPPROVES D IH0 S AH0 P R UW1 V Z -DISAPPROVING D IH0 S AH0 P R UW1 V IH0 NG -DISARM D IH0 S AA1 R M -DISARMAMENT D IH0 S AA1 R M AH0 M AH0 N T -DISARMED D IH0 S AA1 R M D -DISARMING D IH0 S AA1 R M IH0 NG -DISARMINGLY D IH0 S AA1 R M IH0 NG L IY0 -DISARRAY D IH2 S ER0 EY1 -DISARRAY(2) D IH2 S AH0 R EY1 -DISASSEMBLE D IH2 S AH0 S EH1 M B AH0 L -DISASSEMBLED D IH2 S AH0 S EH1 M B AH0 L D -DISASSOCIATE D IH2 S AH0 S OW1 SH IY0 EY0 T -DISASSOCIATE(2) D IH2 S AH0 S OW1 S IY0 EY0 T -DISASSOCIATED D IH2 S AH0 S OW1 SH IY0 EY0 T AH0 D -DISASSOCIATED(2) D IH2 S AH0 S OW1 S IY0 EY0 T AH0 D -DISASTER D IH0 Z AE1 S T ER0 -DISASTERS D IH0 Z AE1 S T ER0 Z -DISASTROUS D IH0 Z AE1 S T R AH0 S -DISASTROUSLY D IH0 Z AE1 S T R AH0 S L IY0 -DISAVOW D IH2 S AH0 V AW1 -DISAVOWED D IH2 S AH0 V AW1 D -DISAVOWING D IH0 S AH0 V AW1 IH0 NG -DISBAND D IH0 S B AE1 N D -DISBANDED D IH0 S B AE1 N D IH0 D -DISBANDING D IH0 S B AE1 N D IH0 NG -DISBAR D IH2 S B AA1 R -DISBARMENT D IH2 S B AA1 R M AH0 N T -DISBARRED D IH0 S B AA1 R D -DISBELIEF D IH2 S B IH0 L IY1 F -DISBELIEVE D IH0 S B AH0 L IY1 V -DISBELIEVING D IH0 S B AH0 L IY1 V IH0 NG -DISBRO D IH1 S B R OW0 -DISBROW D IH1 S B R AW0 -DISBURSE D IH0 S B ER1 S -DISBURSED D IH0 S B ER1 S T -DISBURSEMENT D IH0 S B ER1 S M AH0 N T -DISBURSEMENTS D IH0 S B ER1 S M AH0 N T S -DISBURSING D IH0 S B ER1 S IH0 NG -DISC D IH1 S K -DISCARD D IH0 S K AA1 R D -DISCARDED D IH0 S K AA1 R D IH0 D -DISCARDING D IH0 S K AA1 R D IH0 NG -DISCARDS D IH0 S K AA1 R D Z -DISCENZA D IH0 S CH EH1 N Z AA0 -DISCERN D IH0 S ER1 N -DISCERNABLY D IH0 S ER1 N AH0 B L IY0 -DISCERNED D IH0 S ER1 N D -DISCERNIBLE D IH0 S ER1 N AH0 B AH0 L -DISCERNING D IH0 S ER1 N IH0 NG -DISCERNMENT D IH0 S ER1 N M AH0 N T -DISCERNS D IH0 S ER1 N Z -DISCH D IH1 SH -DISCHARGE D IH0 S CH AA1 R JH -DISCHARGE(2) D IH1 S CH AA2 R JH -DISCHARGED D IH0 S CH AA1 R JH D -DISCHARGED(2) D IH1 S CH AA2 R JH D -DISCHARGES D IH0 S CH AA1 R JH AH0 Z -DISCHARGES(2) D IH1 S CH AA2 R JH AH0 Z -DISCHARGING D IH0 S CH AA1 R JH IH0 NG -DISCHARGING(2) D IH1 S CH AA2 R JH IH0 NG -DISCHER D IH1 SH ER0 -DISCHINGER D IH1 SH IH0 N JH ER0 -DISCIPLE D IH0 S AY1 P AH0 L -DISCIPLES D IH0 S AY1 P AH0 L Z -DISCIPLINARIAN D IH2 S IH0 P L IH0 N EH1 R IY0 AH0 N -DISCIPLINARIANS D IH2 S IH0 P L IH0 N EH1 R IY0 AH0 N Z -DISCIPLINARY D IH1 S AH0 P L AH0 N EH2 R IY0 -DISCIPLINE D IH1 S AH0 P L AH0 N -DISCIPLINED D IH1 S AH0 P L AH0 N D -DISCIPLINES D IH1 S AH0 P L AH0 N Z -DISCIPLINING D IH1 S AH0 P L AH0 N IH0 NG -DISCLAIM D IH0 S K L EY1 M -DISCLAIMED D IH0 S K L EY1 M D -DISCLAIMER D IH0 S K L EY1 M ER0 -DISCLAIMERS D IH0 S K L EY1 M ER0 Z -DISCLAIMING D IH0 S K L EY1 M IH0 NG -DISCLAIMS D IH0 S K L EY1 M Z -DISCLAND D IH1 S K L AE2 N D -DISCLOSE D IH0 S K L OW1 Z -DISCLOSED D IH0 S K L OW1 Z D -DISCLOSES D IH0 S K L OW1 Z IH0 Z -DISCLOSING D IH0 S K L OW1 Z IH0 NG -DISCLOSURE D IH0 S K L OW1 ZH ER0 -DISCLOSURES D IH0 S K L OW1 ZH ER0 Z -DISCO D IH1 S K OW0 -DISCOGRAPHY D IH0 S K AO1 G R AH0 F IY0 -DISCOLOR D IH0 S K AH1 L ER0 -DISCOLORATION D IH0 S K AH2 L ER0 EY1 SH AH0 N -DISCOLORATIONS D IH0 S K AH2 L ER0 EY1 SH AH0 N Z -DISCOLORED D IH0 S K AH1 L ER0 D -DISCOLORS D IH0 S K AH1 L ER0 Z -DISCOMBOBULATE D IH2 S K AH0 M B AO1 B Y UW0 L EY0 T -DISCOMBOBULATED D IH2 S K AH0 M B AO1 B Y UW0 L EY0 T AH0 D -DISCOMBOBULATES D IH2 S K AH0 M B AO1 B Y UW0 L EY0 T S -DISCOMBOBULATING D IH2 S K AH0 M B AO1 B Y UW0 L EY0 T IH0 NG -DISCOMFORT D IH0 S K AH1 M F ER0 T -DISCONCERT D IH2 S K AH0 N S ER1 T -DISCONCERTING D IH2 S K AH0 N S ER1 T IH0 NG -DISCONNECT D IH0 S K AH0 N EH1 K T -DISCONNECTED D IH2 S K AH0 N EH1 K T IH0 D -DISCONNECTING D IH2 S K AH0 N EH1 K T IH0 NG -DISCONNECTION D IH2 S K AH0 N EH1 K SH AH0 N -DISCONNECTS D IH0 S K AH0 N EH1 K T S -DISCONTENT D IH0 S K AH0 N T EH1 N T -DISCONTENTED D IH2 S K AH0 N T EH1 N T IH0 D -DISCONTENTEDLY D IH2 S K AH0 N T EH1 N T IH0 D L IY0 -DISCONTENTEDNESS D IH2 S K AH0 N T EH1 N T IH0 D N AH0 S -DISCONTENTS D IH2 S K AH0 N T EH1 N T S -DISCONTINUANCE D IH2 S K AH0 N T IH1 N Y UW0 AH0 N S -DISCONTINUATION D IH2 S K AH0 N T IH2 N Y UW0 EY1 SH AH0 N -DISCONTINUE D IH0 S K AH0 N T IH1 N Y UW0 -DISCONTINUED D IH0 S K AH0 N T IH1 N Y UW0 D -DISCONTINUING D IH0 S K AH0 N T IH1 N Y UW0 IH0 NG -DISCONTINUITY D IH0 S K AA2 N T IH0 N UW1 IH0 T IY0 -DISCORD D IH1 S K AO0 R D -DISCORDANT D IH0 S K AO1 R D AH0 N T -DISCOS D IH1 S K OW0 Z -DISCOTHEQUE D IH1 S K OW0 T EH2 K -DISCOTHEQUES D IH1 S K OW0 T EH2 K S -DISCOUNT D IH0 S K AW1 N T -DISCOUNT(2) D IH1 S K AW0 N T -DISCOUNTABLE D IH1 S K AW2 N T AH0 B AH0 L -DISCOUNTED D IH1 S K AW2 N T IH0 D -DISCOUNTED(2) D IH1 S K AW2 N IH0 D -DISCOUNTER D IH1 S K AW2 N T ER0 -DISCOUNTERS D IH0 S K AW1 N T ER0 Z -DISCOUNTERS(2) D IH1 S K AW2 N ER0 R Z -DISCOUNTING D IH1 S K AW2 N T IH0 NG -DISCOUNTING(2) D IH1 S K AW2 N IH0 NG -DISCOUNTS D IH0 S K AW1 N T S -DISCOUNTS(2) D IH1 S K AW2 N T S -DISCOURAGE D IH0 S K ER1 IH0 JH -DISCOURAGED D IH0 S K ER1 AH0 JH D -DISCOURAGED(2) D IH0 S K ER1 IH0 JH D -DISCOURAGEMENT D IH0 S K ER1 IH0 JH M AH0 N T -DISCOURAGES D IH0 S K ER1 IH0 JH IH0 Z -DISCOURAGING D IH0 S K ER1 AH0 JH IH0 NG -DISCOURAGING(2) D IH0 S K ER1 IH0 JH IH0 NG -DISCOURSE D IH1 S K AO0 R S -DISCOURSES D IH0 S K AO1 R S IH0 Z -DISCOURSES(2) D IH1 S K AO0 R S IH0 Z -DISCOVER D IH0 S K AH1 V ER0 -DISCOVERABLE D IH0 S K AH1 V ER0 AH0 B AH0 L -DISCOVERABLE(2) D IH0 S K AH1 V R AH0 B AH0 L -DISCOVERED D IH0 S K AH1 V ER0 D -DISCOVERER D IH0 S K AH1 V ER0 ER0 -DISCOVERERS D IH0 S K AH1 V AH0 R ER0 Z -DISCOVERIES D IH0 S K AH1 V ER0 IY0 Z -DISCOVERING D IH0 S K AH1 V ER0 IH0 NG -DISCOVERS D IH0 S K AH1 V ER0 Z -DISCOVERY D IH0 S K AH1 V ER0 IY0 -DISCOVERY(2) D IH0 S K AH1 V R IY0 -DISCOVERY(3) D IH2 S K AH1 V R IY0 -DISCOVERY'S D IH0 S K AH1 V ER0 IY0 Z -DISCOVERY'S(2) D IH0 S K AH1 V R IY0 Z -DISCREDIT D IH0 S K R EH1 D AH0 T -DISCREDITED D IH0 S K R EH1 D IH0 T IH0 D -DISCREDITING D IH0 S K R EH1 D AH0 T IH0 NG -DISCREDITS D IH0 S K R EH1 D AH0 T S -DISCREET D IH0 S K R IY1 T -DISCREETLY D IH0 S K R IY1 T L IY0 -DISCREPANCIES D IH0 S K R EH1 P AH0 N S IY0 Z -DISCREPANCY D IH0 S K R EH1 P AH0 N S IY0 -DISCRETE D IH0 S K R IY1 T -DISCRETION D IH0 S K R EH1 SH AH0 N -DISCRETIONARY D IH0 S K R EH1 SH AH0 N EH2 R IY0 -DISCRETIONS D IH0 S K R EH1 SH AH0 N Z -DISCRIMINATE D IH0 S K R IH1 M AH0 N EY2 T -DISCRIMINATED D IH0 S K R IH1 M AH0 N EY2 T IH0 D -DISCRIMINATES D IH0 S K R IH1 M AH0 N EY2 T S -DISCRIMINATING D IH0 S K R IH1 M AH0 N EY2 T IH0 NG -DISCRIMINATION D IH0 S K R IH2 M AH0 N EY1 SH AH0 N -DISCRIMINATIVE D IH0 S K R IH1 M AH0 N AH0 T IH0 V -DISCRIMINATORY D IH0 S K R IH1 M AH0 N AH0 T AO2 R IY0 -DISCS D IH1 S K S -DISCURSIVE D IH0 S K ER1 S IH0 V -DISCUS D IH1 S K AH0 S -DISCUSS D IH0 S K AH1 S -DISCUSSED D IH0 S K AH1 S T -DISCUSSES D IH0 S K AH1 S AH0 Z -DISCUSSES(2) D IH0 S K AH1 S IH0 Z -DISCUSSING D IH0 S K AH1 S IH0 NG -DISCUSSION D IH0 S K AH1 SH AH0 N -DISCUSSIONS D IH0 S K AH1 SH AH0 N Z -DISDAIN D IH0 S D EY1 N -DISDAINED D IH0 S D EY1 N D -DISDAINFUL D IH0 S D EY1 N F AH0 L -DISDAINING D IH0 S D EY1 N IH0 NG -DISDAINS D IH0 S D EY1 N Z -DISE D AY1 S -DISE(2) D AY1 Z -DISEASE D IH0 Z IY1 Z -DISEASE'S D IH0 Z IY1 Z IH0 Z -DISEASED D IH0 Z IY1 Z D -DISEASES D IH0 Z IY1 Z AH0 Z -DISEASES(2) D IH0 Z IY1 Z IH0 Z -DISEMBARK D IH0 S EH0 M B AA1 R K -DISEMBARKATION D IH0 S EH2 M B AA0 R K EY1 SH AH0 N -DISEMBODIED D IH0 S IH0 B AA1 D IY0 D -DISEMBODY D IH0 S IH0 B AA1 D IY0 -DISENCHANTED D IH0 S IH0 N CH AE1 N T IH0 D -DISENCHANTED(2) D IH0 S IH0 N CH AE1 N IH0 D -DISENCHANTMENT D IH0 S IH0 N CH AE1 N T M AH0 N T -DISENFRANCHISE D IH0 S IH0 N F R AE1 N CH AY2 Z -DISENFRANCHISED D IH0 S IH0 N F R AE1 N CH AY2 Z D -DISENFRANCHISEMENT D IH0 S IH0 N F R AE1 N CH AY2 Z M AH0 N T -DISENGAGE D IH0 S IH0 N G EY1 JH -DISENGAGED D IH0 S IH0 N G EY1 JH D -DISENGAGEMENT D IH0 S IH0 N G EY1 JH M AH0 N T -DISENGAGING D IH0 S IH0 N G EY1 JH IH0 NG -DISENTANGLE D IH2 S AH0 N T AE1 NG G AH0 L -DISEQUILIBRIUM D IH0 S IY2 K W AH0 L IH1 B R IY0 AH0 M -DISESTABLISHMENT D IH0 S IH0 S T AE1 B L IH0 SH M AH0 N T -DISFAVOR D IH0 S F EY1 V ER0 -DISFAVORING D IH0 S F EY1 V ER0 IH0 NG -DISFIGURE D IH0 S F IH1 G Y ER0 -DISFIGURED D IH0 S F IH1 G Y ER0 D -DISFIGUREMENT D IH0 S F IH1 G Y ER0 M EH0 N T -DISFIGURING D IH0 S F IH1 G Y ER0 IH0 NG -DISGORGE D IH0 S G AO1 R JH -DISGORGED D IH0 S G AO1 R JH D -DISGORGEMENT D IH0 S G AO1 R JH M AH0 N T -DISGRACE D IH0 S G R EY1 S -DISGRACED D IH0 S G R EY1 S T -DISGRACEFUL D IH0 S G R EY1 S F AH0 L -DISGRUNTLED D IH0 S G R AH1 N T AH0 L D -DISGRUNTLED(2) D IH0 S G R AH1 N AH0 L D -DISGRUNTLEMENT D IH0 S G R AH1 N T AH0 L M AH0 N T -DISGRUNTLING D IH0 S G R AH1 N T AH0 L IH0 NG -DISGRUNTLING(2) D IH0 S G R AH1 N T L IH0 NG -DISGUISE D IH0 S G AY1 Z -DISGUISED D IH0 S G AY1 Z D -DISGUISES D IH0 S G AY1 Z IH0 Z -DISGUISING D IH0 S G AY1 Z IH0 NG -DISGUST D IH0 S G AH1 S T -DISGUSTED D IH0 S G AH1 S T AH0 D -DISGUSTED(2) D IH0 S G AH1 S T IH0 D -DISGUSTING D IH0 S G AH1 S T IH0 NG -DISGUSTS D IH0 S G AH1 S T S -DISH D IH1 SH -DISHARMONY D IH0 S HH AA1 R M AH0 N IY0 -DISHAROON D IH0 SH ER0 UW1 N -DISHAW D IH1 SH AO2 -DISHEARTENED D IH0 S HH AA1 R T AH0 N D -DISHEARTENING D IH0 S HH AA1 R T AH0 N IH0 NG -DISHEARTENING(2) D IH2 S HH AA1 R T N IH0 NG -DISHED D IH1 SH T -DISHER D IH1 SH ER0 -DISHES D IH1 SH AH0 Z -DISHES(2) D IH1 SH IH0 Z -DISHEVEL D IH0 SH EH1 V AH0 L -DISHEVELED D IH0 SH EH1 V AH0 L D -DISHING D IH1 SH IH0 NG -DISHMAN D IH1 SH M AH0 N -DISHNER D IH1 SH N ER0 -DISHON D IH1 S AH0 N -DISHONEST D IH0 S AA1 N AH0 S T -DISHONESTLY D IH0 S AO1 N AH0 S T L IY0 -DISHONESTY D IH0 S AA1 N AH0 S T IY0 -DISHONG D IH1 S AO0 NG -DISHONOR D IH0 S AA1 N ER0 -DISHONORABLE D IH0 S AA1 N ER0 AH0 B AH0 L -DISHONORED D IH0 S AA1 N ER0 D -DISHWASHER D IH1 SH W AA2 SH ER0 -DISHWASHERS D IH1 SH W AA2 SH ER0 Z -DISHWASHING D IH1 SH W AA2 SH IH0 NG -DISILLUSION D IH2 S IH0 L UW1 ZH AH0 N -DISILLUSIONED D IH2 S IH0 L UW1 ZH AH0 N D -DISILLUSIONING D IH2 S IH0 L UW1 ZH AH0 N IH0 NG -DISILLUSIONMENT D IH2 S IH0 L UW1 ZH AH0 N M AH0 N T -DISILVESTRO D IH0 S IY0 L V EH1 S T R OW0 -DISIMONE D IH0 S IY0 M OW1 N IY0 -DISINCENTIVE D IH2 S IH0 N S EH1 N T IH0 V -DISINCENTIVES D IH2 S IH0 N S EH1 N T IH0 V Z -DISINCLINATION D IH0 S IH0 N K L AH0 N EY1 SH AH0 N -DISINCLINE D IH2 S IH0 N K L AY1 N -DISINCLINED D IH2 S IH0 N K L AY1 N D -DISINFECT D IH0 S IH0 N F EH1 K T -DISINFECTANT D IH0 S IH0 N F EH1 K T AH0 N T -DISINFECTANTS D IH0 S IH0 N F EH1 K T AH0 N T S -DISINFECTION D IH0 S IH0 N F EH1 K SH AH0 N -DISINFLATE D IH2 S IH0 N F L EY1 T -DISINFLATION D IH2 S IH0 N F L EY1 SH AH0 N -DISINFLATIONARY D IH2 S IH0 N F L EY1 SH AH0 N EH2 R IY0 -DISINFORM D IH0 S IH0 N F AO1 R M -DISINFORMATION D IH0 Z IH2 N F ER0 M EY1 SH AH0 N -DISINGENUOUS D IH0 S IH0 N JH EH1 N Y UW0 AH0 S -DISINGENUOUSNESS D IH2 S IH0 N JH EH1 N Y UW0 AH0 S N AH0 S -DISINTEGRATE D IH0 S IH1 N T AH0 G R EY2 T -DISINTEGRATED D IH0 S IH1 N T AH0 G R EY2 T IH0 D -DISINTEGRATES D IH0 S IH1 N T AH0 G R EY2 T S -DISINTEGRATING D IH0 S IH1 N T AH0 G R EY2 T IH0 NG -DISINTEGRATION D IH0 S IH2 N T AH0 G R EY1 SH AH0 N -DISINTEGRATION(2) D IH0 S IH2 N AH0 G R EY1 SH AH0 N -DISINTEREST D IH0 S IH1 N T ER0 AH0 S T -DISINTERESTED D IH0 S IH1 N T R IH0 S T IH0 D -DISINTERESTED(2) D IH0 S IH1 N R IH0 S T IH0 D -DISINVESTMENT D IH0 S IH0 N V EH1 S T M AH0 N T -DISINVESTMENT(2) D IH0 S IH0 N V EH1 S M AH0 N T -DISINVITE D IH0 S IH0 N V AY1 T -DISINVITED D IH2 S IH2 N V AY1 T IH0 D -DISJOINT D IH0 S JH OY1 N T -DISJOINTED D IH0 S JH OY1 N T IH0 D -DISK D IH1 S K -DISKETTE D IH0 S K EH1 T -DISKETTES D IH0 S K EH1 T S -DISKIN D IH1 S K IH0 N -DISKLESS D IH1 S K L AH0 S -DISKS D IH1 S K S -DISLIKE D IH0 S L AY1 K -DISLIKED D IH0 S L AY1 K T -DISLIKES D IH0 S L AY1 K S -DISLIKING D IH0 S L AY1 K IH0 NG -DISLOCATE D IH1 S L OW0 K EY0 T -DISLOCATED D IH1 S L OW0 K EY0 T IH0 D -DISLOCATION D IH0 S L OW1 K EY1 SH AH0 N -DISLOCATIONS D IH0 S L OW1 K EY1 SH AH0 N Z -DISLODGE D IH0 S L AA1 JH -DISLODGED D IH0 S L AA1 JH D -DISLODGING D IH0 S L AA1 JH IH0 NG -DISLOYAL D IH0 S L OY1 AH0 L -DISLOYALTY D IH0 S L OY1 AH0 L T IY0 -DISMAL D IH1 Z M AH0 L -DISMALLY D IH1 Z M AH0 L IY0 -DISMANTLE D IH0 S M AE1 N T AH0 L -DISMANTLE(2) D IH0 S M AE1 N AH0 L -DISMANTLED D IH0 S M AE1 N T AH0 L D -DISMANTLED(2) D IH0 S M AE1 N AH0 L D -DISMANTLEMENT D IH0 S M AE1 N T AH0 L M AH0 N T -DISMANTLES D IH0 S M AE1 N T AH0 L Z -DISMANTLING D IH0 S M AE1 N T AH0 L IH0 NG -DISMANTLING(2) D IH0 S M AE1 N AH0 L IH0 NG -DISMANTLING(3) D IH2 S M AE1 N T L IH0 NG -DISMAY D IH0 S M EY1 -DISMAYED D IH0 S M EY1 D -DISMAYING D IH0 S M EY1 IH0 NG -DISMAYS D IH0 S M EY1 Z -DISMEMBER D IH0 S M EH1 M B ER0 -DISMEMBERED D IH0 S M EH1 M B ER0 D -DISMEMBERING D IH0 S M EH1 M B ER0 IH0 NG -DISMEMBERMENT D IH0 S M EH1 M B ER0 M AH0 N T -DISMISS D IH0 S M IH1 S -DISMISSAL D IH0 S M IH1 S AH0 L -DISMISSALS D IH0 S M IH1 S AH0 L Z -DISMISSED D IH0 S M IH1 S T -DISMISSES D IH0 S M IH1 S IH0 Z -DISMISSING D IH0 S M IH1 S IH0 NG -DISMISSIVE D IH0 S M IH1 S IH0 V -DISMORE D IH1 S M AO0 R -DISMUKE D IH1 S M Y UW0 K -DISMUKES D IH1 S M Y UW0 K S -DISMUTASE D IH2 S M Y UW0 T EY1 Z -DISNEY D IH1 Z N IY0 -DISNEY'S D IH1 Z N IY0 Z 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S P EH1 R IH0 JH IH0 Z -DISPARAGING D IH0 S P EH1 R IH0 JH IH0 NG -DISPARAGINGLY D IH0 S P EH1 R IH0 JH IH0 NG L IY0 -DISPARATE D IH1 S P ER0 IH0 T -DISPARATE(2) D IH0 S P EH1 R IH0 T -DISPARITIES D IH0 S P EH1 R AH0 T IY0 Z -DISPARITY D IH0 S P EH1 R AH0 T IY0 -DISPASSIONATE D IH0 S P AE1 SH AH0 N AH0 T -DISPASSIONATELY D IH0 S P AE1 SH AH0 N AH0 T L IY0 -DISPATCH D IH0 S P AE1 CH -DISPATCHED D IH0 S P AE1 CH T -DISPATCHER D IH0 S P AE1 CH ER0 -DISPATCHER(2) D IH1 S P AE2 CH ER0 -DISPATCHER'S D IH0 S P AE1 CH ER0 Z -DISPATCHER'S(2) D IH1 S P AE2 CH ER0 Z -DISPATCHERS D IH0 S P AE1 CH ER0 Z -DISPATCHES D IH0 S P AE1 CH IH0 Z -DISPATCHING D IH0 S P AE1 CH IH0 NG -DISPEL D IH0 S P EH1 L -DISPELL D IH0 S P EH1 L -DISPELLED D IH0 S P EH1 L D -DISPELLING D IH0 S P EH1 L IH0 NG -DISPELS D IH0 S P EH1 L Z -DISPENSABLE D IH0 S P EH1 N S AH0 B AH0 L -DISPENSARY D IH0 S P EH1 N S ER0 IY0 -DISPENSARY(2) D IH1 S P EH0 N S EH2 R IY0 -DISPENSATION D IH2 S P AH0 N S EY1 SH AH0 N -DISPENSE D IH0 S P EH1 N S -DISPENSED D IH0 S P EH1 N S T -DISPENSER D IH0 S P EH1 N S ER0 -DISPENSERS D IH0 S P EH1 N S ER0 Z -DISPENSES D IH0 S P EH1 N S IH0 Z -DISPENSING D IH0 S P EH1 N S IH0 NG -DISPENZA D IH0 S P EH1 N Z AH0 -DISPERSAL D IH0 S P ER1 S AH0 L -DISPERSANT D IH2 S P ER1 S AH0 N T -DISPERSANTS D IH2 S P ER1 S AH0 N T S -DISPERSE D IH0 S P ER1 S -DISPERSED D IH0 S P ER1 S T -DISPERSING D IH0 S P ER1 S IH0 NG -DISPERSION D IH0 S P ER1 ZH AH0 N -DISPERSIVE D IH0 S P ER1 S IH0 V -DISPIRITED D IH0 S P IH1 R AH0 T IH0 D -DISPIRITING D IH0 S P IH1 R IH0 T IH0 NG -DISPLACE D IH0 S P L EY1 S -DISPLACED D IH0 S P L EY1 S T -DISPLACEMENT D IH0 S P L EY1 S M AH0 N T -DISPLACEMENTS D IH0 S P L EY1 S M AH0 N T S -DISPLACES D IH0 S P L EY1 S IH0 Z -DISPLACING D IH0 S P L EY1 S IH0 NG -DISPLAY D IH0 S P L EY1 -DISPLAYED D IH0 S P L EY1 D -DISPLAYING D IH0 S P L EY1 IH0 NG -DISPLAYS D IH0 S P L EY1 Z -DISPLAYWRITE D IH0 S P L EY1 R AY2 T -DISPLAYWRITER D IH0 S P L EY1 R AY2 T ER0 -DISPLEASE D IH0 S P L IY1 Z -DISPLEASED D IH0 S P L IY1 Z D -DISPLEASURE D IH0 S P L EH1 ZH ER0 -DISPOSABLE D IH0 S P OW1 Z AH0 B AH0 L -DISPOSABLES D IH0 S P OW1 Z AH0 B AH0 L Z -DISPOSAL D IH0 S P OW1 Z AH0 L -DISPOSALS D IH0 S P OW1 Z AH0 L Z -DISPOSE D IH0 S P OW1 Z -DISPOSED D IH0 S P OW1 Z D -DISPOSER D IH0 S P OW1 Z ER0 -DISPOSES D IH0 S P OW1 Z IH0 Z -DISPOSING D IH0 S P OW1 Z IH0 NG -DISPOSITION D IH2 S P AH0 Z IH1 SH AH0 N -DISPOSITIONS D IH2 S P AH0 Z IH1 SH AH0 N Z -DISPOSITIVE D IH2 S P AA1 Z AH0 T IH0 V -DISPOSSESS D IH2 S P AH0 Z EH1 S -DISPOSSESSED D IH2 S P AH0 Z EH1 S T -DISPROPORTIONATE D IH2 S P R AH0 P AO1 R SH AH0 N IH0 T -DISPROPORTIONATELY D IH2 S P R AH0 P AO1 R SH AH0 N AH0 T L IY0 -DISPROVE D IH0 S P R UW1 V -DISPROVED D IH0 S P R UW1 V D -DISPROVEN D IH0 S P R UW1 V IH0 N -DISPROVES D IH0 S P R UW1 V Z -DISPUTABLE D IH0 S P Y UW1 T AH0 B AH0 L -DISPUTATION D IH0 S P Y UW1 T EY1 SH AH0 N -DISPUTE D IH0 S P Y UW1 T -DISPUTED D IH0 S P Y UW1 T AH0 D -DISPUTED(2) D IH0 S P Y UW1 T IH0 D -DISPUTES D IH0 S P Y UW1 T S -DISPUTING D IH0 S P Y UW1 T IH0 NG -DISQUALIFICATION D IH0 S K W AA2 L AH0 F AH0 K EY1 SH AH0 N -DISQUALIFIED D IH0 S K W AA1 L AH0 F AY2 D -DISQUALIFIES D IH0 S K W AA1 L AH0 F AY2 Z -DISQUALIFY D IH0 S K W AA1 L AH0 F AY2 -DISQUALIFYING D IH0 S K W AA1 L AH0 F AY2 IH0 NG -DISQUE D IH1 S K -DISQUIET D IH0 S K W AY1 AH0 T -DISQUIETING D IH0 S K W AY1 AH0 T IH0 NG -DISRAELI D IH0 Z R EY1 L IY0 -DISREGARD D IH2 S R IH0 G AA1 R D -DISREGARDED D IH2 S R IH0 G AA1 R D IH0 D -DISREGARDING D IH2 S R IH0 G AA1 R D IH0 NG -DISREGARDS D IH2 S R IH0 G AA1 R D Z -DISREPAIR D IH2 S R IH0 P EH1 R -DISREPUTABLE D IH0 S R EH1 P Y AH0 T AH0 B AH0 L -DISREPUTE D IH2 S R IH0 P Y UW1 T -DISRESPECT D IH2 S R IH0 S P EH1 K T -DISRESPECTED D IH2 S R IH0 S P EH1 K T IH0 D -DISRESPECTFUL D IH2 S R IH0 S P EH1 K T F AH0 L -DISRESPECTING D IH2 S R IH0 S P EH1 K T IH0 NG -DISRESPECTS D IH2 S R IH0 S P EH1 K T S -DISRUPT D IH0 S R AH1 P T -DISRUPTED D IH0 S R AH1 P T IH0 D -DISRUPTING D IH0 S R AH1 P T IH0 NG -DISRUPTION D IH0 S R AH1 P SH AH0 N -DISRUPTIONS D IH0 S R AH1 P SH AH0 N Z -DISRUPTIVE D IH0 S R AH1 P T IH0 V -DISRUPTS D IH0 S R AH1 P T S -DISS D IH1 S -DISSATISFACTION D IH2 S AE0 T IH0 S F AE1 K SH AH0 N -DISSATISFIED D IH0 S AE1 T AH0 S F AY2 D -DISSATISFY D IH0 S AE1 T AH0 S F AY2 -DISSECT D AY0 S EH1 K T -DISSECTED D AY0 S EH1 K T AH0 D -DISSECTING D AY0 S EH1 K T IH0 NG -DISSECTION D AY0 S EH1 K SH AH0 N -DISSECTION(2) D AY1 S EH0 K SH AH0 N -DISSECTIONS D AY0 S EH1 K SH AH0 N Z -DISSECTIONS(2) D AY1 S EH0 K SH AH0 N Z -DISSECTS D AY0 S EH1 K T S -DISSECTS(2) D AY0 S EH1 K S -DISSEMBLE D IH0 S EH1 M B AH0 L -DISSEMBLING D IH0 S EH1 M B L IH0 NG -DISSEMINATE D IH0 S EH1 M AH0 N EY2 T -DISSEMINATED D IH0 S EH1 M AH0 N EY2 T AH0 D -DISSEMINATES D IH0 S EH1 M AH0 N EY2 T S -DISSEMINATING D IH0 S EH1 M AH0 N EY2 T IH0 NG -DISSEMINATION D IH0 S EH2 M AH0 N EY1 SH AH0 N -DISSENSION D IH0 S EH1 N SH AH0 N -DISSENT D IH0 S EH1 N T -DISSENTED D IH0 S EH1 N T IH0 D -DISSENTED(2) D IH0 S EH1 N IH0 D -DISSENTER D IH0 S EH1 N T ER0 -DISSENTERS D IH0 S EH1 N T ER0 Z -DISSENTERS(2) D IH0 S EH1 N ER0 Z -DISSENTING D IH0 S EH1 N T IH0 NG -DISSENTING(2) D IH0 S EH1 N IH0 NG -DISSENTS D IH0 S EH1 N T S -DISSERTATION D IH2 S ER0 T EY1 SH AH0 N -DISSERVICE D IH0 S ER1 V AH0 S -DISSIDENCE D IH1 S AH0 D AH0 N S -DISSIDENT D IH1 S AH0 D AH0 N T -DISSIDENTS D IH1 S AH0 D AH0 N T S -DISSIDENTS' D IH1 S AH0 D AH0 N T S -DISSIMILAR D IH0 S IH1 M AH0 L ER0 -DISSIMILARITY D IH2 S IH2 M AH0 L AE1 R AH0 T IY0 -DISSING D IH1 S IH0 NG -DISSINGER D IH1 S IH0 N JH ER0 -DISSIPATE D IH1 S AH0 P EY2 T -DISSIPATED D IH1 S AH0 P EY2 T IH0 D -DISSIPATES D IH1 S AH0 P EY2 T S -DISSIPATING D IH1 S AH0 P EY2 T IH0 NG -DISSIPATION D IH2 S IH0 P EY1 SH AH0 N -DISSIPATIVE D IH1 S AH0 P EY2 T IH0 V -DISSOCIATE D IH0 S OW1 S IY0 EY0 T -DISSOCIATION D IH0 S OW2 S IY0 EY1 SH AH0 N -DISSOLUTION D IH2 S AH0 L UW1 SH AH0 N -DISSOLVE D IH0 Z AA1 L V -DISSOLVED D IH0 Z AA1 L V D -DISSOLVER D IH0 Z AA1 L V ER0 -DISSOLVERS D IH0 Z AA1 L V ER0 Z -DISSOLVES D IH0 Z AA1 L V Z -DISSOLVING D IH0 Z AO1 L V IH0 NG -DISSONANCE D IH1 S AH0 N AH0 N S -DISSONANT D IH1 S AH0 N AH0 N T -DISSUADE D IH0 S W EY1 D -DISSUADED D IH0 S W EY1 D IH0 D -DISSYMMETRIC D IH2 S IH0 M EH1 T R IH0 K -DISSYMMETRY D IH0 S IH1 M AH0 T R IY0 -DISTAD D IH1 S T AH0 D -DISTAL D IH1 S T AH0 L -DISTANCE D IH1 S T AH0 N S -DISTANCED D IH1 S T AH0 N S T -DISTANCES D IH1 S T AH0 N S AH0 Z -DISTANCES(2) D IH1 S T AH0 N S IH0 Z -DISTANCING D IH1 S T AH0 N S IH0 NG -DISTANT D IH1 S T AH0 N T -DISTASI D IH0 S T AA1 S IY0 -DISTASIO D IH0 S T AA1 S IY0 OW0 -DISTASTE D IH0 S T EY1 S T -DISTASTEFUL D IH0 S T EY1 S T F AH0 L -DISTEFANO D IH0 S T EH0 F AA1 N OW0 -DISTEL D IH1 S T AH0 L -DISTEMPER D IH0 S T EH1 M P ER0 -DISTEND D IH0 S T EH1 N D -DISTENDED D IH0 S T EH1 N D IH0 D -DISTIL D IH0 S T IH1 L -DISTILL D IH0 S T IH1 L -DISTILLATE D IH1 S T AH0 L EY2 T -DISTILLATE(2) D IH1 S T AH0 L AH0 T -DISTILLATES 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D R AA1 G AH0 N -DRAGE D R EY1 JH -DRAGER D R EY1 G ER0 -DRAGGED D R AE1 G D -DRAGGING D R AE1 G IH0 NG -DRAGGY D R AE1 G IY0 -DRAGLINE D R AE1 G L AY2 N -DRAGNET D R AE1 G N EH2 T -DRAGNETS D R AE1 G N EH2 T S -DRAGO D R AA1 G OW0 -DRAGON D R AE1 G AH0 N -DRAGON'S D R AE1 G AH0 N Z -DRAGONAIR D R AE1 G AH0 N EH2 R -DRAGONE D R AH0 G OW1 N -DRAGONHEAD D R AE1 G AH0 N HH EH2 D -DRAGONHEART D R AE1 G AH0 N HH AA2 R T -DRAGONS D R AE1 G AH0 N Z -DRAGOO D R AA1 G UW0 -DRAGOVICH D R AE1 G AH0 V IH0 CH -DRAGS D R AE1 G Z -DRAHEIM D R AE1 HH AY0 M -DRAHOS D R EY1 HH OW0 Z -DRAHUSCHAK D R AE1 HH AH0 SH AE0 K -DRAHUSCHAK(2) D R AH0 HH UW1 SH AH0 K -DRAIN D R EY1 N -DRAINAGE D R EY1 N AH0 JH -DRAINAGE(2) D R EY1 N IH0 JH -DRAINE D R EY1 N -DRAINED D R EY1 N D -DRAINER D R EY1 N ER0 -DRAINERS D R EY1 N ER0 Z -DRAINING D R EY1 N IH0 NG -DRAINS D R EY1 N Z -DRAKE D R EY1 K -DRAKEFORD D R AE1 K F AO0 R D -DRAKES D R EY1 K S -DRAKOS D R EY1 K OW0 Z -DRALLE D R EY1 L -DRAM D R AE1 M -DRAM(2) D IY1 R AE2 M -DRAMA D R AA1 M AH0 -DRAMAMINE D R AE1 M AH0 M IY2 N -DRAMAS D R AA1 M AH0 Z -DRAMATIC D R AH0 M AE1 T IH0 K -DRAMATICALLY D R AH0 M AE1 T IH0 K L IY0 -DRAMATICALLY(2) D R AH0 M AE1 T IH0 K AH0 L IY0 -DRAMATICS D R AH0 M AE1 T IH0 K S -DRAMATIST D R AA1 M AH0 T IH0 S T -DRAMATIZATION D R AE2 M AH0 T AH0 Z EY1 SH AH0 N -DRAMATIZATIONS D R AE2 M AH0 T AH0 Z EY1 SH AH0 N Z -DRAMATIZE D R AA1 M AH0 T AY2 Z -DRAMATIZE(2) D R AE1 M AH0 T AY2 Z -DRAMATIZED D R AE1 M AH0 T AY2 Z D -DRAMATIZES D R AE1 M AH0 T AY2 Z IH0 Z -DRAMATIZING D R AE1 M AH0 T AY2 Z IH0 NG -DRAMATURGY D R AA1 M AH0 T ER0 JH IY0 -DRAMS D R AE1 M Z -DRAMS(2) D IY1 R AE2 M Z -DRANE D R EY1 N -DRANEY D R EY1 N IY0 -DRANG D R AE1 NG -DRANK D R AE1 NG K -DRANSFIELD D R AE1 N S F IY2 L D -DRAPE D R EY1 P -DRAPEAU D R AH0 P OW1 -DRAPED D R EY1 P T -DRAPER D R EY1 P ER0 -DRAPERIES D R EY1 P ER0 IY0 Z -DRAPERY D R EY1 P ER0 IY0 -DRAPES D R EY1 P S -DRAPING D R EY1 P IH0 NG -DRAPKIN D R AE1 P K IH0 N -DRASNER D R AE1 S N ER0 -DRASTIC D R AE1 S T IH0 K -DRASTICALLY D R AE1 S T IH0 K L IY0 -DRAUGHN D R AO1 N -DRAUGHON D R AO1 AH0 N -DRAUGHT D R AE1 F T -DRAUGHTS D R AE1 F T S -DRAUS D R AO1 Z -DRAVECKY D R AH0 V EH1 K IY0 -DRAVES D R EY1 V Z -DRAVIS D R AE1 V IH0 S -DRAVO D R AE1 V OW0 -DRAVO'S D R AE1 V OW0 Z -DRAW D R AO1 -DRAWBACK D R AO1 B AE2 K -DRAWBACKS D R AO1 B AE2 K S -DRAWBAUGH D R AO1 B AO2 -DRAWBRIDGE D R AO1 B R IH2 JH -DRAWDOWN D R AO1 D AW2 N -DRAWDOWNS D R AO1 D AW2 N Z -DRAWDY D R AO1 D IY0 -DRAWER D R AO1 R -DRAWERS D R AO1 R Z -DRAWING D R AO1 IH0 NG -DRAWINGS D R AO1 IH0 NG Z -DRAWL D R AO1 L -DRAWLED D R AO1 L D -DRAWLS D R AO1 L Z -DRAWN D R AO1 N -DRAWS D R AO1 Z -DRAY D R EY1 -DRAYER D R EY1 ER0 -DRAYTON D R EY1 T AH0 N -DRAYTON'S D R EY1 T AH0 N Z -DREAD D R EH1 D -DREADED D R EH1 D IH0 D -DREADFUL D R EH1 D F AH0 L -DREADFULLY D R EH1 D F AH0 L IY0 -DREADING D R EH1 D IH0 NG -DREADNOUGHT D R EH1 D N AO2 T -DREADS D R EH1 D Z -DREAM D R IY1 M -DREAMED D R IY1 M D -DREAMER D R IY1 M ER0 -DREAMERS D R IY1 M ER0 Z -DREAMING D R IY1 M IH0 NG -DREAMLAND D R IY1 M L AE2 N D -DREAMLIKE D R IY1 M L AY2 K -DREAMS D R IY1 M Z -DREAMT D R EH1 M T -DREAMWORKS D R IY1 M W ER2 K S -DREAMWORKS' D R IY1 M W ER2 K S -DREAMWORLD D R IY1 M W ER2 L D -DREAMY D R IY1 M IY0 -DREARINESS D R IY1 R IY0 N AH0 S -DREARY D R IH1 R IY0 -DREBSKY D R EH1 B S K IY0 -DRECHSEL D R EH1 K S AH0 L -DRECHSLER D R EH1 K S AH0 L ER0 -DRECHSLER(2) D R EH1 K S L ER0 -DRED D R EH1 D -DREDD D R EH1 D -DREDGE D R EH1 JH -DREDGED D R EH1 JH D -DREDGES D R EH1 JH AH0 Z -DREDGES(2) D R EH1 JH IH0 Z -DREDGING D R EH1 JH IH0 NG -DREES D R IY1 Z -DREESE D R IY1 Z -DREESSEN D R IY1 S AH0 N -DREGER D R EH1 G ER0 -DREGS D R EH1 G Z -DREHER D R EH1 R -DREIBELBIS D R AY1 B IH0 L B IH0 S -DREIER D R AY1 ER0 -DREIGHTON D R AY1 T IH0 N -DREILING D R AY1 L IH0 NG -DREIS D R IY1 Z -DREISBACH D R AY1 S B AA2 K -DREMAN D R IY1 M AH0 N -DRENCH D R EH1 N CH -DRENCHED D R EH1 N CH T -DRENCHES D R EH1 N CH AH0 Z -DRENCHING D R EH1 N CH IH0 NG -DRENNAN D R EH1 N AH0 N -DRENNEN D R EH1 N AH0 N -DRENNING D R EH1 N IH0 NG -DRENNON D R EH1 N AH0 N -DRENTH D R EH1 N TH -DREPUNG D R EY1 P AH2 NG -DREPUNG(2) D R EY2 P AO1 NG -DRESBACH D R EH1 S B AA2 K -DRESCH D R EH1 SH -DRESCHER D R EH1 SH ER0 -DRESDEN D R EH1 Z D IH0 N -DRESDNER D R EH1 Z D N ER0 -DRESDNER'S D R EH1 Z D N ER0 Z -DRESEN D R IY1 Z AH0 N -DRESHER D R EH1 SH ER0 -DRESNER D R EH1 Z N ER0 -DRESS D R EH1 S -DRESSAGE D R EH0 S AA1 ZH -DRESSED D R EH1 S T -DRESSEL D R EH1 S AH0 L -DRESSEN D R EH1 S AH0 N -DRESSER D R EH1 S ER0 -DRESSER'S D R EH1 S ER0 Z -DRESSERS D R EH1 S ER0 Z -DRESSES D R EH1 S AH0 Z -DRESSES(2) D R EH1 S IH0 Z -DRESSIER D R EH1 S IY0 ER0 -DRESSING D R EH1 S IH0 NG -DRESSINGS D R EH1 S IH0 NG Z -DRESSLER D R EH1 S L ER0 -DRESSMAKER D R EH1 S M EY2 K ER0 -DRESSMAKERS D R EH1 S M EY2 K ER0 Z -DRESSMAKING D R EH1 S M EY2 K IH0 NG -DRESSY D R EH1 S IY0 -DREW D R UW1 -DREWERY D R UW1 ER0 IY0 -DREWES D R UW1 Z -DREWETT D R UW1 IH0 T -DREWRY D R UW1 R IY0 -DREWS D R UW1 Z -DREXEL D R EH1 K S AH0 L -DREXEL'S D R EH1 K S AH0 L Z -DREXLER D R EH1 K S L ER0 -DREY D R EY1 -DREYER D R EY1 ER0 -DREYFUS D R AY1 F AH0 S -DREYFUS(2) D R EY1 F AH0 S -DREYFUS'S D R EY1 F AH0 S IH0 Z -DREYFUSS D R EY1 F AH0 S -DRIBBED D R IH1 B D -DRIBBLE D R IH1 B AH0 L -DRIBBLED D R IH1 B AH0 L D -DRIBBLES D R IH1 B AH0 L Z -DRIBBLING D R IH1 B AH0 L IH0 NG -DRIBBLING(2) D R IH1 B L IH0 NG -DRIBS D R IH1 B Z -DRIED D R AY1 D -DRIEHAUS D R IY1 HH AW2 S -DRIER D R AY1 ER0 -DRIES D R AY1 Z -DRIESSEN D R IY1 S AH0 N -DRIEST D R AY1 AH0 S T -DRIEVER D R IY1 V ER0 -DRIFT D R IH1 F T -DRIFTA D R IH1 F T AH0 -DRIFTED D R IH1 F T AH0 D -DRIFTED(2) D R IH1 F T IH0 D -DRIFTER D R IH1 F T ER0 -DRIFTERS D R IH1 F T ER0 Z -DRIFTING D R IH1 F T IH0 NG -DRIFTNET D R IH1 F T N EH2 T -DRIFTS D R IH1 F T S -DRIFTWOOD D R IH1 F T W UH2 D -DRIGGERS D R IH1 G ER0 Z -DRIGGS D R IH1 G Z -DRILL D R IH1 L -DRILLBIT D R IH1 L B IH2 T -DRILLED D R IH1 L D -DRILLER D R IH1 L ER0 -DRILLERS D R IH1 L ER0 Z -DRILLING D R IH1 L IH0 NG -DRILLING'S D R IH1 L IH0 NG Z -DRILLS D R IH1 L Z -DRINA D IY1 N AH0 -DRINA'S D IY1 N AH0 Z -DRINAS D IY1 N AH0 Z -DRING D R IH1 NG -DRINK D R IH1 NG K -DRINKABLE D R IH1 N K AH0 B AH0 L -DRINKARD D R IH1 NG K ER0 D -DRINKER D R IH1 NG K ER0 -DRINKER'S D R IH1 NG K ER0 Z -DRINKERS D R IH1 NG K ER0 Z -DRINKING D R IH1 NG K IH0 NG -DRINKS D R IH1 NG K S -DRINKWATER D R IH1 NG K W AO2 T ER0 -DRINKWINE D R IH1 NG K W AY2 N -DRINNON D R IH1 N AH0 N -DRIP D R IH1 P -DRIP-DRY D R IH1 P D R AY1 -DRIPPED D R IH1 P T -DRIPPING D R IH1 P IH0 NG -DRIPPS D R IH1 P S -DRIPS D R IH1 P S -DRISCOLL D R IH1 S K AH0 L -DRISKELL D R IH1 S K AH0 L -DRISKILL D R IH1 S K IH0 L -DRIVABLE D R AY1 V AH0 B AH0 L -DRIVE D R AY1 V -DRIVE'S D R AY1 V Z -DRIVE-BY D R AY1 V B AY1 -DRIVE-THRU D R AY1 V TH R UW1 -DRIVE-THRUS D R AY1 V TH R UW1 Z -DRIVEL D R IH1 V AH0 L -DRIVEN D R IH1 V AH0 N -DRIVER D R AY1 V ER0 -DRIVER'S D R AY1 V ER0 Z -DRIVERLESS D R AY1 V ER0 L IH0 S -DRIVERS D R AY1 V ER0 Z -DRIVERS' D R AY1 V ER0 Z -DRIVES D R AY1 V Z -DRIVEWAY D R AY1 V W EY2 -DRIVEWAYS D R AY1 V W EY2 Z -DRIVING D R AY1 V IH0 NG -DRIZZLE D R IH1 Z AH0 L -DRIZZLING D R IH1 Z AH0 L IH0 NG -DRIZZLING(2) D R IH1 Z L IH0 NG -DRIZZLY D R IH1 Z L IY0 -DROBKOV D R AO1 B K AO0 V -DROBKOV(2) D R AO1 B K AO0 F -DROBNY D R AA1 B N IY0 -DRODDY D R AA1 D IY0 -DROEGE D R OW1 JH -DROESSLER D R OW1 S AH0 L ER0 -DROESSLER(2) D R OW1 S L ER0 -DROGE D R OW1 JH -DROGOUL D R OW0 G UW1 L -DROGOUL'S D R OW0 G UW1 L Z -DROGUE D R OW1 G -DROHAN D R OW1 AH0 N -DROID D R OY1 D -DROIDS D R OY1 D Z -DROKE D R OW1 K -DROLET D R OW1 L IH0 T -DROLL D R OW1 L -DROLLINGER D R OW1 L IH0 NG ER0 -DROMEDARY D R AA1 M AH0 D EH2 R IY0 -DROMER D R OW1 M ER0 -DROMEY D R OW1 M IY0 -DROMGOOLE D R AA1 M G UW2 L -DROMGOOLES D R AA0 M G UW1 L Z -DROMI D R AA1 M IY0 -DROMOMANIA D R OW2 M OW0 M EY1 N IY0 AH0 -DROMOMANIA(2) D R OW2 M OW0 M EY1 N Y AH0 -DROMON D R OW1 M AH0 N -DRONE D R OW1 N -DRONED D R OW1 N D -DRONES D R OW1 N Z -DRONET D R OW1 N IH0 T -DRONEY D R OW1 N IY0 -DRONING D R OW1 N IH0 NG -DROOL D R UW1 L -DROOLED D R UW1 L D -DROOLING D R UW1 L IH0 NG -DROOP D R UW1 P -DROOPED D R UW1 P T -DROOPING D R UW1 P IH0 NG -DROOPY D R UW1 P IY0 -DROP D R AA1 P -DROP(2) D R AO1 P -DROP-OFF D R AA1 P AO2 F -DROP-OFFS D R AA1 P AO2 F S -DROPBOX D R AA1 P B AA2 K S -DROPKIN D R AA1 P K IH0 N -DROPLET D R AA1 P L AH0 T -DROPLETS D R AA1 P L AH0 T S -DROPOFF D R AA1 P AO2 F -DROPOFFS D R AA1 P AO2 F S -DROPOUT D R AA1 P AW2 T -DROPOUTS D R AA1 P AW2 T S -DROPPED D R AA1 P T -DROPPER D R AA1 P ER0 -DROPPERS D R AA1 P ER0 Z -DROPPING D R AA1 P IH0 NG -DROPPINGS D R AA1 P IH0 NG Z -DROPS D R AA1 P S -DROPSY D R AA1 P S IY0 -DROSER D R OW1 Z ER0 -DROSS D R AO1 S -DROST D R AA1 S T -DROSTE D R OW1 S T -DROSTE(2) D R AA1 S T -DROSTE(3) D R AA1 S T AH0 -DROTAR D R OW1 T ER0 -DROUGHT D R AW1 T -DROUGHT'S D R AW1 T S -DROUGHTS D R AW1 T S -DROUILLARD D R W IY0 L AA1 R D -DROUIN D R W IY1 N 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G Z -DRUGSTORE D R AH1 G S T AO2 R -DRUGSTORES D R AH1 G S T AO2 R Z -DRUID D R UW1 IH0 D -DRUIDISM D R UW1 AH0 D IH2 Z AH0 M -DRUIDS D R UW1 IH0 D Z -DRUISILLA D R IH0 S IH1 L AH0 -DRUM D R AH1 M -DRUMBEAT D R AH1 M B IY2 T -DRUMHEAD D R AH1 M HH EH2 D -DRUMHELLER D R AH1 M HH EH2 L ER0 -DRUMLIN'S D R AH1 M L IH0 N Z -DRUMM D R AH1 M -DRUMMED D R AH1 M D -DRUMMER D R AH1 M ER0 -DRUMMERS D R AH1 M ER0 Z -DRUMMEY D R AH1 M IY0 -DRUMMING D R AH1 M IH0 NG -DRUMMOND D R AH1 M AH0 N D -DRUMMONDS D R AH1 M AH0 N D Z -DRUMRIGHT D R AH1 M R AY2 T -DRUMS D R AH1 M Z -DRUMSTICK D R AH1 M S T IH0 K -DRUMWRIGHT D R AH1 M R AY2 T -DRUNK D R AH1 NG K -DRUNKARD D R AH1 NG K ER0 D -DRUNKARDS D R AH1 NG K ER0 D Z -DRUNKEN D R AH1 NG K AH0 N -DRUNKENNESS D R AH1 NG K AH0 N N AH0 S -DRUNKS D R AH1 NG K S -DRUPE D R UW1 P -DRUPES D R UW1 P S -DRURY D R UW1 R IY0 -DRUSA D R UW1 S AH0 -DRUSE D R UW1 Z -DRUSIE D R AH1 S IY0 -DRUSILLA D R UW2 S IH1 L AH0 -DRUTHERS D R AH1 DH ER0 Z -DRUZE D R UW1 Z -DRY D R AY1 -DRYCLEAN D R AY1 K L IY2 N -DRYCLEANER D R AY1 K L IY2 N ER0 -DRYCLEANING D R AY1 K L IY2 N IH0 NG -DRYDEN D R AY1 D AH0 N -DRYE D R AY1 -DRYER D R AY1 ER0 -DRYERS D R AY1 ER0 Z -DRYING D R AY1 IH0 NG -DRYLY D R AY1 L IY0 -DRYNESS D R AY1 N AH0 S -DRYPERS D R AY1 P ER0 Z -DRYPOINT D R AY1 P OY2 N T -DRYSDALE D R AY1 Z D EY2 L -DRYSER D R AY1 S ER0 -DRYWALL D R AY1 W AA2 L -DRZEWIECKI JH UW0 IY1 T S K IY0 -DSOUZA D AH0 S UW1 Z AH0 -DSS D IY1 EH1 S EH1 S -DSV D IY1 EH1 S V IY1 -DU D UW1 -DU(2) D AH0 -DU-BOIS D UW1 B OY1 S -DUAL D UW1 AH0 L -DUAL(2) D UW1 L -DUALISM D UW1 AH0 L IH2 Z AH0 M -DUALISMS D UW1 AH0 L IH2 Z AH0 M Z -DUALIST D UW1 AH0 L IH0 S T -DUALISTIC D UW2 AH0 L IH1 S T IH0 K -DUALITY D UW0 AE1 L AH0 T IY0 -DUALS D UW1 AH0 L Z -DUAN D W AE1 N -DUAN(2) D W EY1 N -DUANA D UW0 AE1 N AH0 -DUANE D W EY1 N -DUARTE D W AA1 R T EY2 -DUARTE'S D W AA1 R T EY2 Z -DUB D AH1 B -DUBA D UW1 B AH0 -DUBACH D AH1 B AA0 K -DUBAI D UW0 B AY1 -DUBARRY D UW1 B EH2 R IY0 -DUBAS D UW1 B AH0 Z 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-DUBREUIL D AH1 B R UW0 L -DUBROC D AH1 B R AH0 K -DUBROFF D UW1 B R AO0 F -DUBROVNIK D UW0 B R AA1 V N IH0 K -DUBROVNIK'S D UW0 B R AA1 V N IH0 K Z -DUBROVNIKS D UW0 B R AA1 V N IH0 K Z -DUBROVSKY D UW0 B R AO1 F S K IY0 -DUBROVSKY(2) D UW0 B R AO1 V S K IY0 -DUBROW D AH1 B R OW2 -DUBS D AH1 B Z -DUBUC D UW1 B AH0 K -DUBUISSON D AH1 B IH0 S AH0 N -DUBUQUE D AH0 B Y UW1 K -DUBUQUE'S D AH0 B Y UW1 K S -DUBY D UW1 B IY0 -DUC D AH1 K -DUCA D UW1 K AH0 -DUCAL D UW1 K AH0 L -DUCE D UW1 S -DUCEY D AH1 S IY0 -DUCH D AH1 CH -DUCHAINE D AH0 SH EY1 N -DUCHAMP D UW0 SH AA1 M P -DUCHAMP'S D UW0 SH AA1 M P S -DUCHARME D AH0 SH AA1 R M -DUCHEMIN D AH1 SH IH0 M AE0 N -DUCHENE D AH1 K IY0 N -DUCHENNE D UW0 SH EH1 N -DUCHESNEAU D AH1 SH IH0 S N OW0 -DUCHESS D AH1 CH AH0 S -DUCHIN D UW1 CH IH0 N -DUCHON D AH1 CH AH0 N -DUCHOSSOIS D UW0 CH AO1 S W AA2 -DUCHOW D AH1 CH OW0 -DUCHY D AH1 CH IY0 -DUCK D AH1 K -DUCK'S D AH1 K S -DUCKED D AH1 K T -DUCKER D AH1 K ER0 -DUCKETT D AH1 K IH0 T -DUCKIES D AH1 K IH2 Z -DUCKING D AH1 K IH0 NG -DUCKLING D AH1 K L IH0 NG -DUCKLINGS D AH1 K L IH0 NG Z -DUCKS D AH1 K S -DUCKS' D AH1 K S -DUCKSWORTH D AH1 K S W ER2 TH -DUCKWALL D AH1 K W AO2 L -DUCKWEED D AH1 K W IY2 D -DUCKWORTH D AH1 K W ER2 TH -DUCKY D AH1 K IY2 -DUCLOS D AH0 K L OW1 Z -DUCOMMUN D UW0 K AA1 M AH0 N -DUCOMMUN(2) D UW0 K AA1 M UW1 N -DUCOTE D AH0 K OW1 T -DUCT D AH1 K T -DUCTILE D AH1 K T AH0 L -DUCTILITY D AH0 K T IH1 L AH0 T IY0 -DUCTLESS D AH1 K T L AH0 S -DUCTS D AH1 K T S -DUD D AH1 D -DUDA D UW1 D AH0 -DUDACK D UW1 D AE0 K -DUDAR D UW1 D ER0 -DUDAS D UW1 D AH0 Z -DUDASH D AH1 D AH0 SH -DUDAYEV D UW0 D AY1 EH2 V -DUDAYEV'S D UW0 D AY1 EH2 V Z -DUDD D AH1 D -DUDDING D AH1 D IH0 NG -DUDDY D AH1 D IY0 -DUDE D UW1 D -DUDECK D UW1 D EH0 K -DUDECK'S D UW1 D EH0 K S -DUDEK D UW1 D IH0 K -DUDEN D UW1 D AH0 N -DUDENHOEFFER D AH1 D IH0 N HH OW0 F ER0 -DUDES D Y UW1 D Z -DUDGEON D AH1 JH AH0 N -DUDIK D UW1 D IH0 K -DUDLEY D AH1 D L IY0 -DUDMAN D AH1 D M AH0 N -DUDNEY D AH1 D N IY0 -DUDS D AH1 D Z -DUDZIAK D AH1 JH IY0 AE0 K -DUDZIK D AH1 D Z IH0 K -DUDZINSKI D AH0 JH IH1 N S K IY0 -DUE D UW1 -DUE(2) D Y UW1 -DUECKER D UW1 K ER0 -DUEITT D UW1 AH0 T -DUEKER D UW1 K ER0 -DUEL D UW1 AH0 L -DUELED D UW1 AH0 L D -DUELING D UW1 L IH0 NG -DUELIST D UW1 AH0 L IH0 S T -DUELL JH UW1 L -DUELL(2) D UW1 L -DUELS D UW1 AH0 L Z -DUENA D UW0 EH1 N AH0 -DUENA(2) D W EY1 N Y AH0 -DUENAS D UW0 EH1 N AH0 S -DUENAS(2) D W EY1 N Y AH0 S -DUENEZ D W EH0 N EH1 Z -DUENOW D UW1 N OW0 -DUENSING D UH1 N S IH0 NG -DUER D UW1 ER0 -DUERKSEN D UH1 R K S AH0 N -DUERR D UH1 R -DUERSON D UH1 R S AH0 N -DUERST D UH1 R S T -DUES D UW1 Z -DUESBERG D UW1 Z B ER0 G -DUESING D UW1 S IH0 NG -DUESLER D UW1 S AH0 L ER0 -DUESLER(2) D UW1 S L ER0 -DUESSELDORF D UW1 S AH0 L D AO2 R F -DUET D UW0 EH1 T -DUET(2) D Y UW0 EH1 T -DUETS D UW0 EH1 T S -DUETS(2) D Y UW0 EH1 T S -DUEY D UW1 IY0 -DUEY(2) D Y UW1 IY0 -DUFAULT D AH0 F OW1 -DUFEK D UW1 F IH0 K -DUFF D AH1 F -DUFFALA D AH0 F AE1 L AH0 -DUFFEE D AH1 F IY0 -DUFFEK D 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N -DUSTS D AH1 S T S -DUSTY D AH1 S T IY0 -DUSZA D AH1 SH AH0 -DUSZYNSKI D AH0 SH IH1 N S K IY0 -DUTCH D AH1 CH -DUTCHER D AH1 CH ER0 -DUTCHMAN D AH1 CH M AH0 N -DUTHIE D AH1 TH IY0 -DUTIES D UW1 T IY0 Z -DUTIFUL D UW1 T IY0 F AH0 L -DUTIFULLY D UW1 T IY0 F AH0 L IY0 -DUTIL D AH0 T IH1 L -DUTKA D AH1 T K AH0 -DUTKIEWICZ D AH1 T K AH0 V IH0 CH -DUTKO D AH1 T K OW0 -DUTOIT D UW0 T OY1 T -DUTRA D UW1 T R AH0 -DUTRO D AH1 T R OW0 -DUTROW D AH1 T R OW0 -DUTSON D AH1 T S AH0 N -DUTT D AH1 T -DUTTER D AH1 T ER0 -DUTTON D AH1 T AH0 N -DUTY D UW1 T IY0 -DUTY(2) D Y UW1 T IY0 -DUVA D UW1 V AH0 -DUVAL D UW0 V AE1 L -DUVALIER D UW0 V AE1 L Y ER0 -DUVALIERS D UW0 V EY1 L Y ER0 Z -DUVALL D UW0 V AA1 L -DUVE D UW1 V -DUVERNAY D AH0 V ER1 N EY0 -DUVREES D UW0 V R IY1 Z -DUWAYNE D UW0 W EY1 N -DUWE D UW1 W -DUX D AH1 K S -DUZAN D UW1 Z AH0 N -DVD D IY2 V IY2 D IY1 -DVDS D IY2 V IY2 D IY1 Z -DVORACEK D V AO1 R AH0 CH EH0 K -DVORAK D V AO1 R AH0 K -DVORSKY D V AO1 R S K IY0 -DWAN D W AA1 N -DWANA D W AA1 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AO1 R T AH0 B AH0 L -EXPORTED IH0 K S P AO1 R T AH0 D -EXPORTER IH0 K S P AO1 R T ER0 -EXPORTERS IH0 K S P AO1 R T ER0 Z -EXPORTERS' EH2 K S P AO1 R T ER0 Z -EXPORTING IH0 K S P AO1 R T IH0 NG -EXPORTS EH1 K S P AO0 R T S -EXPOS EH1 K S P OW0 Z -EXPOSE IH0 K S P OW1 Z -EXPOSED IH0 K S P OW1 Z D -EXPOSES IH0 K S P OW1 Z IH0 Z -EXPOSING IH0 K S P OW1 Z IH0 NG -EXPOSITION EH2 K S P AH0 Z IH1 SH AH0 N -EXPOSITIONS EH2 K S P AH0 Z IH1 SH AH0 N Z -EXPOSITO EH0 K S P AH0 S AY1 T OW0 -EXPOSITO(2) EH0 K S P AH0 Z IY1 T OW0 -EXPOSURE IH0 K S P OW1 ZH ER0 -EXPOSURES IH0 K S P OW1 ZH ER0 Z -EXPOUND IH0 K S P AW1 N D -EXPOUNDED IH0 K S P AW1 N D AH0 D -EXPOUNDING IH0 K S P AW1 N D IH0 NG -EXPOUNDS IH0 K S P AW1 N D Z -EXPRESS IH0 K S P R EH1 S -EXPRESS' IH0 K S P R EH1 S -EXPRESS'S IH0 K S P R EH1 S IH0 Z -EXPRESSED IH0 K S P R EH1 S T -EXPRESSES IH0 K S P R EH1 S AH0 Z -EXPRESSES(2) IH0 K S P R EH1 S IH0 Z -EXPRESSING IH0 K S P R EH1 S IH0 NG -EXPRESSION IH0 K S P R EH1 SH AH0 N -EXPRESSIONISM IH0 K S P R EH1 SH AH0 N IH2 Z AH0 M -EXPRESSIONIST IH0 K S P R EH1 SH AH0 N AH0 S T -EXPRESSIONISTIC IH0 K S P R EH2 SH AH0 N IH1 S T IH0 K -EXPRESSIONLESS IH0 K S P R EH2 SH AH0 N L IH0 S -EXPRESSIONS IH0 K S P R EH1 SH AH0 N Z -EXPRESSIVE IH0 K S P R EH1 S IH0 V -EXPRESSIVITY EH2 K S P R AH0 S IH1 V IH0 T IY0 -EXPRESSLY EH0 K S P R EH1 S L IY0 -EXPRESSO IH0 K S P EH1 S OW0 -EXPRESSO(2) EH0 K S P EH1 S OW0 -EXPRESSWAY IH0 K S P R EH1 S W EY2 -EXPROPRIATE EH0 K S P R OW1 P R IY0 EY2 T -EXPROPRIATED EH0 K S P R OW1 P R IY0 EY2 T IH0 D -EXPROPRIATION EH2 K S P R OW2 P R IY0 EY1 SH AH0 N -EXPROPRIATIONS EH2 K S P R OW2 P R IY0 EY1 SH AH0 N Z -EXPULSION IH0 K S P AH1 L SH AH0 N -EXPULSIONS IH0 K S P AH1 L SH AH0 N Z -EXPUNGE IH0 K S P AH1 N JH -EXPUNGED IH0 K S P AH1 N JH D -EXQUISITE EH1 K S K W AH0 Z AH0 T -EXQUISITELY EH2 K S K W IH1 Z IH0 T L IY0 -EXTANT EH1 K S T AH0 N T -EXTEL EH1 K S T EH2 L -EXTEND IH0 K S T EH1 N D -EXTENDABLE EH2 K S T EH1 N D AH0 B AH0 L -EXTENDED IH0 K S T EH1 N D AH0 D -EXTENDED(2) IH0 K S T EH1 N D IH0 D -EXTENDER EH1 K S T EH2 N D ER0 -EXTENDERS EH1 K S T EH2 N D ER0 Z -EXTENDIBLE EH2 K S T EH1 N D IH0 B AH0 L -EXTENDING IH0 K S T EH1 N D IH0 NG -EXTENDS IH0 K S T EH1 N D Z -EXTENSION IH0 K S T EH1 N SH AH0 N -EXTENSIONS IH0 K S T EH1 N SH AH0 N Z -EXTENSIVE IH0 K S T EH1 N S IH0 V -EXTENSIVELY IH0 K S T EH1 N S IH0 V L IY0 -EXTENT IH0 K S T EH1 N T -EXTENUATE IH0 K S T EH1 N Y UW0 EY2 T -EXTENUATING IH0 K S T EH1 N Y UW0 EY2 T IH0 NG -EXTERIOR IH0 K S T IH1 R IY0 ER0 -EXTERIORS EH0 K S T IH1 R IY0 ER0 Z -EXTERMINATE IH0 K S T ER1 M AH0 N EY2 T -EXTERMINATED IH0 K S T ER1 M AH0 N EY2 T IH0 D -EXTERMINATES IH0 K S T ER1 M AH0 N EY2 T S -EXTERMINATING IH0 K S T ER1 M AH0 N EY2 T IH0 NG -EXTERMINATION IH0 K S T ER2 M AH0 N EY1 SH AH0 N -EXTERMINATOR IH0 K S T ER1 M AH0 N EY2 T ER0 -EXTERMINATORS IH0 K S T ER1 M AH0 N EY2 T ER0 Z -EXTERNAL IH0 K S T ER1 N AH0 L -EXTERNALLY IH0 K S T ER1 N AH0 L IY0 -EXTINCT IH0 K S T IH1 NG K T -EXTINCTION IH0 K 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JH IY1 -G.'S JH IY1 Z -G.S JH IY1 Z -GA G AA1 -GA(2) JH IY1 EY1 -GA(3) JH AO1 R JH AH0 -GAAL G AA1 L -GAAR G AA1 R -GAARDER G AA1 R D ER0 -GAB G AE1 B -GABA G AA1 B AH0 -GABALDON G AA0 B AA0 L D AO1 N -GABARDINE G AE1 B ER0 D IY2 N -GABARDINES G AE1 B ER0 D IY2 N Z -GABAY G AE1 B EY0 -GABBARD G AH0 B AA1 R D -GABBERT G AE1 B ER0 T -GABBING G AE1 B IH0 NG -GABBRO G AE1 B R OW0 -GABBROIC G AE0 B R OW1 IH0 K -GABBY G AE1 B IY0 -GABE G EY1 B -GABEHART G EY1 B HH AA2 R T -GABEIRA G AH0 B EY1 R AH0 -GABEL G AH0 B EH1 L -GABELE G AH0 B EH1 L EY0 -GABELLI G AH0 B EH1 L IY0 -GABER G EY1 B ER0 -GABERT G AE1 B ER0 T -GABHART G AE1 B HH AA2 R T -GABIE G AE1 B IY0 -GABLE G EY1 B AH0 L -GABLED G EY1 B AH0 L D -GABLER G EY1 B AH0 L ER0 -GABLER(2) G EY1 B L ER0 -GABLES G EY1 B AH0 L Z -GABON G AH0 B AA1 N -GABOR G AH0 B AO1 R -GABORIAULT G AE1 B ER0 IY0 OW0 -GABOURY G AE1 B UH0 R IY0 -GABRALL G EY1 B R AH0 L -GABRALL'S G EY1 B R AH0 L Z -GABRIEL G EY1 B R IY0 AH0 L -GABRIEL'S G EY1 B R IY0 AH0 L Z -GABRIELA G AA2 B R IY0 EH1 L AA2 -GABRIELE G AA0 B R IY0 EH1 L -GABRIELLA G AA2 B R IY0 EH1 L AA2 -GABRIELLE G AE1 B R IY0 EH0 L -GABRIELLI G AA2 B R IY0 EH1 L IY2 -GABRIELSEN G AE1 B R IY0 L S AH0 N -GABRIELSEN(2) G EY1 B R IY0 EH0 L S AH0 N -GABRIELSON G AE1 B R IY0 L S AH0 N -GABRIELSON(2) G EY1 B R IY0 EH0 L S AH0 N -GABROWNY G AH0 B R AW1 N IY0 -GABRYS G AE1 B ER0 IY0 Z -GABY G AE1 B IY0 -GACCIONE G AA0 K CH OW1 N IY2 -GACEK G AA1 CH EH2 K -GACH G AE1 CH -GACY G EY1 S IY2 -GACY'S G EY1 S IY2 Z -GAD G AE1 D -GADBERRY G AE1 D B EH2 R IY2 -GADBOIS G AE1 D B W AA2 -GADD G AE1 D -GADDIE G AE1 D IY0 -GADDING G AE1 D IH0 NG -GADDIS G AE1 D IH0 S -GADDUM G AE1 D AH0 M -GADDY G AE1 D IY0 -GADE G EY1 D -GADFLIES G AE1 D F L AY2 Z -GADFLY G AE1 D F L AY2 -GADGET G AE1 JH AH0 T -GADGET'S G AE1 JH AH0 T S -GADGETRY G AE1 JH AH0 T R IY0 -GADGETS G AE1 JH AH0 T S -GADHAFI G AH0 D AA1 F IY0 -GADHAFI(2) G AH0 D HH AA1 F IY0 -GADHAFI'S G AH0 D AA1 F IY0 Z -GADHAFI'S(2) G AH0 D HH AA1 F IY0 Z 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IY0 OW0 -GAGLIONE G AA0 G L IY0 OW1 N IY0 -GAGLIONE(2) G AE0 G L IY0 OW1 N IY0 -GAGNE G EY1 N IY0 -GAGNE(2) G AE1 G N IY0 -GAGNER G AE1 G N ER0 -GAGNIER G AE1 G N IY0 ER0 -GAGNON G AE1 N Y AH0 N -GAGS G AE1 G Z -GAHAGAN G AA0 HH AA1 G AA0 N -GAHAN G AE1 HH AH0 N -GAHLI G AA1 L IY0 -GAHM G AE1 M -GAHN G AE1 N -GAHR G AA1 R -GAI G EY1 -GAIA G AY1 AH0 -GAIDAR G AY1 D AA2 R -GAIDAR'S G AY1 D AA2 R Z -GAIER G EY1 ER0 -GAIGE G EY1 JH -GAIL G EY1 L -GAIL'S G EY1 L Z -GAILE G EY1 L -GAILEY G EY1 L IY0 -GAILLARD G EY1 L ER0 D -GAILY G EY1 L IY0 -GAIN G EY1 N -GAINED G EY1 N D -GAINER G EY1 N ER0 -GAINERS G EY1 N ER0 Z -GAINES G EY1 N Z -GAINESVILLE G EY1 N Z V IH2 L -GAINEY G EY1 N IY0 -GAINFUL G EY1 N F AH0 L -GAINFULLY G EY1 N F AH0 L IY0 -GAINING G EY1 N IH0 NG -GAINOR G EY1 N ER0 -GAINOUS G EY1 N AH0 S -GAINS G EY1 N Z -GAINSAY G EY1 N S EY2 -GAINSBORO G EY1 N Z B ER0 OW0 -GAINSCO G EY1 N S K OW0 -GAIR G EH1 R -GAISER G EY1 Z ER0 -GAIT G EY1 T -GAITAN G AY2 T AA1 N -GAITER G EY1 T ER0 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AE1 L AH0 K S IY0 Z -GALBAN G AE1 L B AH0 N -GALBO G AE1 L B OW0 -GALBRAITH G AE1 L B R EY2 TH -GALBREATH G AE1 L B R EH2 TH -GALE G EY1 L -GALE'S G EY1 L Z -GALEA G EY1 L IY0 AH0 -GALEANO G AA0 L IY1 N OW0 -GALEB G AE1 L AH0 B -GALEB(2) G EY1 L AH0 B -GALEBS G AE1 L AH0 B Z -GALEBS(2) G EY1 L AH0 B Z -GALEF G AE1 L AH0 F -GALEN G EY1 L AH0 N -GALEN'S G EY1 L AH0 N Z -GALENA G AH0 L IY1 N AH0 -GALENICAL G AH0 L EH1 N IH0 K AH0 L -GALENTINE G AA0 L EH0 N T IY1 N IY0 -GALER G EY1 L ER0 -GALERIA G AE2 L ER0 IY1 AH0 -GALERIAS G AE2 L ER0 IY1 AH0 Z -GALERIES G AE1 L ER0 IY0 Z -GALERNTER G AH0 L ER1 N T ER0 -GALES G EY1 L Z -GALESBURG G EY1 L Z B ER0 G -GALESI G AH0 L EH1 S IY0 -GALEY G EY1 L IY0 -GALFORD G AE1 L F ER0 D -GALGANO G AA0 L G AA1 N OW0 -GALI G AA1 L IY0 -GALI'S G AA1 L IY0 Z -GALIANO G AA0 L IY0 AA1 N OW0 -GALICIA G AH0 L IH1 SH IY0 AH0 -GALIE G EY1 L IY0 -GALIENA G AA0 L IY1 N AH0 -GALIK G AE1 L IH0 K -GALILEAN G AE2 L AH0 L IY1 AH0 N -GALILEE G AE1 L AH0 L IY2 -GALILEO G AE2 L AH0 L IY1 OW0 -GALILEO(2) G AE2 L AH0 L EY1 OW0 -GALILEO'S G AE2 L AH0 L IY1 OW0 Z -GALILEO'S(2) G AE2 L AH0 L EY1 OW0 Z -GALIN G AE1 L IH0 N -GALINA G AH0 L IY1 N AH0 -GALINDO G AA0 L IY1 N D OW0 -GALINSKI G AH0 L IH1 N S K IY0 -GALINSKY G AH0 L IH1 N S K IY0 -GALIOTO G AA0 L IY0 OW1 T OW0 -GALIPAULT G AE1 L IH0 P AO2 L T -GALIPAULT(2) G AE1 L IH0 P OW2 -GALIPEAU G AE1 L IH0 P OW2 -GALITSIN G AH0 L IH1 T S IH0 N -GALKA G AE1 L K AH0 -GALL G AO1 L -GALLA G AE1 L AH0 -GALLACHER G AE1 L AH0 K ER0 -GALLAGER G AO1 L IH0 JH ER0 -GALLAGHER G AE1 L AH0 G ER0 -GALLAHAN G AE1 L AH0 HH AE0 N -GALLAHER G AE1 L AH0 HH ER0 -GALLAMORE G AA0 L AA1 M AO0 R -GALLAND G AE1 L AH0 N D -GALLANT G AE1 L AH0 N T -GALLANTLY G AE1 L AH0 N T L IY0 -GALLANTRY G AE1 L AH0 N T R IY0 -GALLARD G AH0 L AA1 R D -GALLARDO G AA0 L AA1 R D OW0 -GALLAS G AE1 L AH0 Z -GALLATIN G AE1 L AH0 T IH0 N -GALLAUDET G AE1 L AH0 D EH0 T -GALLAWAY G AO1 L AH0 W EY2 -GALLBLADDER G AO1 L B L AE2 D ER0 -GALLE G EY1 L -GALLE(2) G AE2 L EY1 -GALLEGO G AA0 L EH1 G OW0 -GALLEGOS G AE1 L IH0 G OW0 Z -GALLEMORE G AA0 L EH1 M AO0 R -GALLEN G AO1 L AH0 N -GALLENTINE G AA0 L EH0 N T IY1 N IY0 -GALLEON G AE1 L IY0 AH0 N -GALLEONS G AE1 L IY0 AH0 N Z -GALLER G AO1 L ER0 -GALLERANI G AA0 L ER0 AA1 N IY0 -GALLERIA G AE2 L ER0 IY1 AH0 -GALLERIES G AE1 L ER0 IY0 Z -GALLERY G AE1 L ER0 IY0 -GALLERY'S G AE1 L ER0 IY0 Z -GALLES G EY1 L Z -GALLET G AE1 L IH0 T -GALLETTI G AA0 L EH1 T IY0 -GALLEY G AE1 L IY0 -GALLEYS G AE1 L IY0 Z -GALLI G AE1 L IY0 -GALLIA G AE1 L Y AH0 -GALLIANO G AA0 L IY0 AA1 N OW0 -GALLIC G AE1 L IH0 K -GALLICK G AE1 L IH0 K -GALLIEN G AH0 L IY1 N -GALLIER G AE1 L IY0 ER0 -GALLIGAN G AE1 L IH0 G AH0 N -GALLIHER G AE1 L IH0 HH ER0 -GALLIK G AE1 L IH0 K -GALLIMARD G AE1 L IH0 M ER0 D -GALLIMORE G AA0 L IY1 M AO0 R -GALLINA G AA0 L IY1 N AH0 -GALLING G AO1 L IH0 NG -GALLINGER G AO1 L IH0 NG ER0 -GALLINSKY G AH0 L IH1 N S K IY0 -GALLION G AE1 L Y AH0 N -GALLIUM G AE1 L IY0 AH0 M -GALLIVAN G AE1 L IH0 V AH0 N -GALLMAN G AO1 L M AH0 N -GALLO G AE1 L OW0 -GALLO'S G AE1 L OW0 Z -GALLOGLY G AE1 L AH0 G L IY0 -GALLON G AE1 L AH0 N -GALLONS G AE1 L AH0 N Z -GALLOON G AH0 L UW1 N -GALLOP G AE1 L AH0 P -GALLOPED G AE1 L AH0 P T -GALLOPING G AE1 L AH0 P IH0 NG -GALLOS G AE1 L OW0 Z -GALLOW G AE1 L OW0 -GALLOWAY G AE1 L OW0 W EY2 -GALLOWAYS G AE1 L OW0 W EY2 Z -GALLOWS G AE1 L OW0 Z -GALLS G AO1 L Z -GALLSTONE G AO1 L S T OW2 N -GALLSTONES G AA1 L S T OW2 N Z -GALLSTONES(2) G AO1 L S T OW2 N Z -GALLUCCI G AA0 L UW1 CH IY0 -GALLUCCIO G AA0 L UW1 CH IY0 OW0 -GALLUP G AE1 L AH0 P -GALLUS G AE1 L AH0 S -GALLUZZO G AA0 L UW1 Z OW0 -GALLWAY G AO1 L W EY2 -GALOOB G AH0 L UW1 B -GALOOB'S G AH0 L UW1 B Z -GALORE G AH0 L AO1 R -GALOSH G AH0 L AA1 SH -GALOSHES G AH0 L AA1 SH AH0 Z -GALOSHES(2) G AH0 L AA1 SH IH0 Z -GALOTTI G AH0 L AA1 T IY0 -GALPIN G AE1 L P IH0 N -GALS G AE1 L Z -GALSTER G AE1 L S T ER0 -GALSWORTHY G AE1 L Z W ER2 DH IY0 -GALT G AO1 L T -GALTON G AE1 L T AH0 N -GALUDET G AE2 L Y UW0 D EH1 T -GALUS G AE1 L IH0 S -GALUSHA G AE1 L AH0 SH AH0 -GALUSKA G AH0 L AH1 S K AH0 -GALVAN G AA0 L V AA1 N -GALVANIC G AE0 L V AE1 N IH0 K -GALVANIZE G AE1 L V AH0 N AY2 Z -GALVANIZED G AE1 L V AH0 N AY2 Z D -GALVANIZES G AE1 L V AH0 N AY2 Z AH0 Z -GALVANIZING G AE1 L V AH0 N AY2 Z IH0 NG -GALVEN G AA1 L V AH0 N -GALVESTON G AE1 L V AH0 S T AH0 N -GALVEZ G AA0 L V EH1 Z -GALVIN G AE1 L V IH0 N -GALVIN'S G AE1 L V IH0 N Z -GALWAY G AA1 L W EY0 -GALYEAN G AE1 L IY0 AH0 N -GALYEN G AE1 L IY0 EH0 N -GALYON G AE1 L IY0 AA0 N -GAMA G AA1 M AH0 -GAMACHE G AA1 M EY0 K -GAMAGE G AE1 M IH0 JH -GAMAL G AH0 M AA1 L -GAMBA G AE1 M B AH0 -GAMBALE G AA0 M B AA1 L IY0 -GAMBARDELLA G AA0 M B AA0 R D EH1 L AH0 -GAMBER G AE1 M B ER0 -GAMBIA G AE1 M B IY0 AH0 -GAMBILL G AH0 M B IH1 L -GAMBINO G AE0 M B IY1 N OW0 -GAMBIT G AE1 M B IH0 T -GAMBITS G AE1 M B AH0 T S -GAMBLE G AE1 M B AH0 L -GAMBLE'S G AE1 M B AH0 L Z -GAMBLED G AE1 M B AH0 L D -GAMBLER G AE1 M B L ER0 -GAMBLER'S G AE1 M B L ER0 Z -GAMBLERS G AE1 M B L ER0 Z -GAMBLES G AE1 M B AH0 L Z -GAMBLIN G AE1 M B L IH0 N -GAMBLING G AE1 M B AH0 L IH0 NG -GAMBLING(2) G AE1 M B L IH0 NG -GAMBLING'S G AE1 M B L IH0 NG Z -GAMBOA G AA0 M B OW1 AH0 -GAMBONE G AA1 M B OW0 N -GAMBREL G AE1 M B R AH0 L -GAMBRELL G AE1 M B R AH0 L -GAMBRILL G AE1 M B R IH0 L -GAMBRO G AE1 M B R OW0 -GAMCO G AE1 M K OW0 -GAME G EY1 M -GAME'S G EY1 M Z -GAMEBOY G EY1 M B OY2 -GAMECOCK G EY1 M K AO2 K -GAMECOCKS G EY1 M K AO2 K S -GAMEKEEPER G EY1 M K IY2 P ER0 -GAMEKEEPERS G EY1 M K IY2 P ER0 S -GAMEL G AA1 M AH0 L -GAMELIN G AE1 M IH0 L IH0 N -GAMELIN(2) G AE1 M L IH0 N -GAMELLO G AH0 M EH1 L OW0 -GAMELY G EY1 M L IY0 -GAMEPLAY G EY1 M P L EY2 -GAMER G EY1 M ER0 -GAMERS G EY1 M ER0 Z -GAMES G EY1 M Z -GAMES' G EY1 M Z -GAMESHOW G EY1 M SH OW2 -GAMESHOWS G EY1 M SH OW2 Z -GAMESMAN G EY1 M Z M AE2 N -GAMESMANSHIP G EY1 M Z M AH0 N SH IH2 P -GAMEST G EY1 M AH0 S T -GAMET G AE1 M IH0 T -GAMETANGIA G AE2 M AH0 T AE1 N JH IY0 AH0 -GAMETE G AE1 M IY0 T -GAMETE(2) G AH0 M IY1 T -GAMETOPHYTE G AH0 M IY1 T AH0 F AY2 T -GAMEZ G AA0 M EH1 Z -GAMING G EY1 M IH0 NG -GAMING'S G EY1 M IH0 NG Z -GAMINO G AA0 M IY1 N OW0 -GAMM G AE1 M -GAMMA G AE1 M AH0 -GAMMAGE G AE1 M IH0 JH -GAMMAL G AH0 M AA1 L -GAMMEL G AE1 M AH0 L -GAMMELL G AE1 M AH0 L -GAMMILL G AE1 M IH0 L -GAMMON G AE1 M AH0 N -GAMMONS G AE1 M AH0 N Z -GAMONS G AE1 M AH0 N Z -GAMP G AE1 M P -GAMPER G AE1 M P ER0 -GAMSAKHURDIA G AE0 M S AH0 K ER1 D IY0 AH0 -GAMSAKHURDIA(2) G AE0 M S AH0 K ER1 D Y AH0 -GAMUNDE G AH0 M UW1 N D -GAMUNDE'S G AH0 M UW1 N D Z -GAMUT G AE1 M AH0 T -GAN G AE1 N -GANANOQUE G AH0 N AE1 N AH0 K -GANAS G AE1 N AH0 Z -GANATIEUGANAUF G AH0 N EY1 SH AH0 G AE2 N AH0 L F -GANAWAY G AE1 N AH0 W EY0 -GANCARZ G AA1 N K AA0 R Z -GANCI G AE1 N S IY0 -GANDA G AE1 N D AH0 -GANDALF G AE1 N D AO0 L F -GANDALF'S G AE1 N D AO0 L F S -GANDARA G AA0 N D AA1 R AH0 -GANDEE G AE1 N D IY0 -GANDER G AE1 N D ER0 -GANDERS G AE1 N D ER0 Z -GANDHI G AA1 N D IY0 -GANDHI'S G AA1 N D IY0 Z -GANDOLFI G AA0 N D OW1 L F IY0 -GANDOLFO G AA0 N D OW1 L F OW0 -GANDY G AE1 N D IY0 -GANEM G AE1 N IH0 M -GANES G EY1 N Z -GANEY G EY1 N IY0 -GANG G AE1 NG -GANG'S G AE1 NG Z -GANGBANGER G AE1 NG B AE0 NG G ER0 -GANGBANGERS G AE1 NG B AE0 NG G ER0 Z -GANGBUSTER G AE1 NG B AH2 S T ER0 -GANGBUSTERS G AE1 NG B AH2 S T ER0 Z -GANGE G AE1 N JH -GANGEMI G AA0 NG G EH1 M IY0 -GANGER G AE1 NG ER0 -GANGES G AE1 N JH IY0 Z -GANGI G AE1 N JH IY0 -GANGING G AE1 NG IH0 NG -GANGL G AE1 NG G AH0 L -GANGLIA G AE1 NG G L IY0 AA0 -GANGLION G AE1 NG G L IY0 AA0 N -GANGLIONIC G AE2 NG G L IY0 AA1 N IH0 K -GANGLOFF G AE1 NG G L AO0 F -GANGLY G AE1 NG L IY0 -GANGPLANK G AE1 NG P L AE2 NG K -GANGRENE G AE1 N G R IY0 N -GANGS G AE1 NG Z -GANGSTA G AE1 NG S T AH0 -GANGSTER G AE1 NG S T ER0 -GANGSTERS G AE1 NG S T ER0 Z -GANGWER G AE1 NG W ER0 -GANIC G AE1 N IH0 K -GANIM G AE1 N IH0 M -GANIS G AE1 N IH0 S -GANLEY G AE1 N L IY0 -GANN G AE1 N -GANNAWAY G AE1 N AH0 W EY0 -GANNETT G AE1 N IH0 T -GANNETT'S G AE1 N AH0 T S -GANNON G AE1 N AH0 N -GANO G AA1 N OW0 -GANOE G AE1 N OW0 -GANONG G AE1 N AO0 NG -GANS G AE1 N Z -GANSEN G AE1 N S AH0 N -GANSER G AE1 N S ER0 -GANSKE G AE1 N S K -GANSON G AE1 N S AH0 N -GANSTER G AE1 N S T ER0 -GANT G AE1 N T -GANTENBEIN G AE1 N T IH0 N B AY0 N -GANTER G AE1 N T ER0 -GANTLET G AO1 N T L AH0 T -GANTNER G AE1 N T N ER0 -GANTOS G AE1 N T OW0 S -GANTRY G AE1 N T R IY0 -GANTT G AE1 N T -GANTZ G AE1 N T S -GANUS G EY1 N IH0 S -GANYMEDE G AE1 N AH0 M IY2 D -GANZ G AE1 N Z -GANZEL G AE1 N Z AH0 L -GANZER G AE1 N Z ER0 -GAO G AW1 -GAONA G AA0 OW1 N AH0 -GAP G AE1 P -GAP'S G AE1 P S -GAPE G EY1 P -GAPING G EY1 P IH0 NG -GAPINSKI G AH0 P IH1 N S K IY0 -GAPP G AE1 P -GAPPA G AE1 P AH0 -GAPS G AE1 P S -GAR G AA1 R -GARA G AE1 R AH0 -GARABEDIAN G AE2 R AH0 B IY1 D IY0 AH0 N -GARAFALO G AA0 R AA0 F AA1 L OW0 -GARAFOLA G AA0 R AA0 F OW1 L AH0 -GARAGE G ER0 AA1 ZH -GARAGES G ER0 AA1 ZH IH0 Z -GARAGIOLA G ER0 AE2 JH IY0 OW1 L AH0 -GARAGIOLA(2) G EH2 R AH0 JH IY0 OW1 L AH0 -GARAJDA G ER0 AA1 ZH D AH0 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AA1 R L IH0 NG ER0 -GARLINGHOUSE G AA1 R L IH0 NG HH AW2 S -GARLINGTON G AA1 R L IH0 NG T AH0 N -GARLITZ G AA1 R L IH0 T S -GARLOCK G AA1 R L AH0 K -GARLOW G AA1 R L OW0 -GARMAN G AA1 R M AH0 N -GARMANY G ER0 M AO1 N IY0 -GARMENT G AA1 R M AH0 N T -GARMENTS G AA1 R M AH0 N T S -GARMON G AA1 R M AH0 N -GARMOND G AA1 R M AH0 N D -GARMS G AA1 R M Z -GARMUND G AA1 R M AH0 N D -GARN G AA1 R N -GARNEAU G AA0 R N OW1 -GARNELL G AA0 R N EH1 L -GARNER G AA1 R N ER0 -GARNERED G AA1 R N ER0 D -GARNERING G AA1 R N ER0 IH0 NG -GARNERS G AA1 R N ER0 Z -GARNES G AA1 R N Z -GARNET G AA1 R N AH0 T -GARNETT G AA1 R N IH0 T -GARNETTE G AA0 R N EH1 T -GARNEY G AA1 R N IY0 -GARNICA G AA0 R N IY1 K AH0 -GARNIER G AA1 R N IY0 ER0 -GARNISH G AA1 R N IH0 SH -GARNISHED G AA1 R N IH0 SH T -GARNISHES G AA1 R N IH0 SH AH0 Z -GARNISHMENT G AA1 R N IH0 SH M AH0 N T -GARNO G AA1 R N OW0 -GARNOCK G AA1 R N AH0 K -GARNSEY G AA1 R N S IY0 -GARO G EH1 R OW0 -GAROFALO G AA0 R OW0 F AA1 L OW0 -GAROFANO G AA0 R OW0 F AA1 N 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G AE1 R IY0 AH0 T -GARRIS G AE1 R IH0 S -GARRISON G AE1 R IH0 S AH0 N -GARRISONED G AE1 R AH0 S AH0 N D -GARRITANO G AA0 R IY0 T AA1 N OW0 -GARRITT G AE1 R IH0 T -GARRITY G EH1 R IH0 T IY0 -GARRO G AA1 R OW0 -GARROD G AE1 R AH0 D -GARROL G AE1 R AH0 L -GARROLS G AE1 R AH0 L Z -GARRON G AE1 R AH0 N -GARRON'S G EH1 R AH0 N Z -GARROS G EH1 R OW0 S -GARROTT G AE1 R AH0 T -GARROW G EH1 R OW0 -GARROWAY G AE1 R OW0 W EY2 -GARROZ G AE1 R AH0 Z -GARRULOUS G EH1 R AH0 L AH0 S -GARRY G AE1 R IY0 -GARRY(2) G EH1 R IY0 -GARS G AA1 R Z -GARSIDE G AA1 R S AY2 D -GARSKE G AA1 R S K -GARSON G AA1 R S AH0 N -GARST G AA1 R S T -GARSTEN G AA1 R S T EH0 N -GARSTEN'S G AA1 R S T EH0 N Z -GARSTIN G AA1 R S T AH0 N -GARSTKA G AA1 R S T K AH0 -GARSTON G AA1 R S T AH0 N -GARTEN G AA1 R T AH0 N -GARTENBERG G AA1 R T AH0 N B ER0 G -GARTER G AA1 R T ER0 -GARTERS G AA1 R T ER0 Z -GARTH G AA1 R TH -GARTHWAITE G AA1 R TH W EY2 T -GARTIN G AA1 R T IH0 N -GARTLAND G AA1 R T L AH0 N D -GARTLEY G AA1 R T L IY0 -GARTMAN G AA1 R T M AH0 N -GARTMORE G AA1 R T M AO2 R -GARTNER G AA1 R T N ER0 -GARTON G AA1 R T AH0 N -GARTRELL G AA1 R T R AH0 L -GARTSIDE G AA1 R T S AY2 D -GARTZKE G AA1 R T S K -GARTZKE(2) G AA1 R T S K IY0 -GARUDA G AH0 R UW1 D AH0 -GARUDA'S G AH0 R UW1 D AH0 Z -GARVER G AA1 R V ER0 -GARVER'S G AA1 R V ER0 Z -GARVERICK G AA1 R V ER0 IH0 K -GARVEY G AA1 R V IY0 -GARVIE G AA1 R V IY0 -GARVIN G AA1 R V IH0 N -GARWIN G AA1 R W IH0 N -GARWOOD G AA1 R W UH2 D -GARY G EH1 R IY0 -GARY(2) G AE1 R IY0 -GARY'S G EH1 R IY0 Z -GARY'S(2) G AE1 R IY0 Z -GARZA G AA1 R Z AH0 -GARZARELLI G AA1 R Z ER0 EH2 L IY0 -GARZON G AA1 R Z AH0 N -GAS G AE1 S -GAS'S G AE1 S IH0 Z -GASAWAY G AE1 S AH0 W EY2 -GASBARRO G AA0 S B AA1 R OW0 -GASCA G AA1 S K AH0 -GASCON G AE1 S K AH0 N -GASCONS G AE1 S K AH0 N Z -GASE G EY1 Z -GASEOUS G AE1 S IY0 AH0 S -GASES G AE1 S AH0 Z -GASES(2) G AE1 S IH0 Z -GASH G AE1 SH -GASHED G AE1 SH T -GASHES G AE1 SH AH0 Z -GASICH G EY1 Z IH0 K -GASIFICATION G AE2 S AH0 F AH0 K EY1 SH AH0 N 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S K OW0 -GLASER G L EY1 Z ER0 -GLASGOW G L AE1 S K OW2 -GLASGOW(2) G L AE1 S G OW2 -GLASHEEN G L AH0 SH IY1 N -GLASHOW G L AE1 SH AW0 -GLASNER G L AE1 S N ER0 -GLASNOST G L AE1 S N AA0 S T -GLASNOST(2) G L AO1 S T N OW2 S T -GLASNOST(3) G L AO1 S N OW2 S T -GLASOW G L EY1 Z OW0 -GLASPER G L AE1 S P ER0 -GLASPIE G L AE1 S P IY0 -GLASPY G L AE1 S P IY0 -GLASS G L AE1 S -GLASS'S G L AE1 S IH0 Z -GLASSBERG G L AE1 S B ER0 G -GLASSBLOWER G L AE1 S B L OW2 ER0 -GLASSBLOWERS G L AE1 S B L OW2 ER0 Z -GLASSBLOWING G L AE1 S B L OW2 IH0 NG -GLASSBURN G L AE1 S B ER2 N -GLASSCO G L AE1 S K OW0 -GLASSCOCK G L AE1 S K AA2 K -GLASSED G L AE1 S T -GLASSER G L AE1 S ER0 -GLASSES G L AE1 S AH0 Z -GLASSES(2) G L AE1 S IH0 Z -GLASSEY G L AE1 S IY0 -GLASSFORD G L AE1 S F AO0 R D -GLASSLIKE G L AE1 S L AY2 K -GLASSMAKER G L AE1 S M EY2 K ER0 -GLASSMAKING G L AE1 S M EY2 K IH0 NG -GLASSMAN G L AE1 S M AE2 N -GLASSMAN(2) G L AE1 S M AH0 N -GLASSMEYER G L AE1 S M AY0 ER0 -GLASSNER G L AE1 S N ER0 -GLASSON G L AE1 S AH0 N -GLASSWARE G L AE1 S W EH2 R -GLASSY G L AE1 S IY0 -GLASTETTER G L AE1 S T IH0 T ER0 -GLATFELTER G L AE1 T F IH0 L T ER0 -GLATT G L AE1 T -GLATZ G L AE1 T S -GLATZER G L EY1 T Z ER0 -GLAUB G L AO1 B -GLAUBER G L AW1 B ER0 -GLAUCOMA G L AO0 K OW1 M AH0 -GLAUDE G L AO1 D -GLAUS G L AO1 Z -GLAUSER G L AW1 S ER0 -GLAVAN G L EY1 V AH0 N -GLAVIN G L AE1 V IH0 N -GLAVINE G L AE0 V IH1 N -GLAVINE(2) G L AE0 V IY1 N -GLAWE G L AO1 -GLAXO G L AE1 K S OW0 -GLAXO'S G L AE1 K S OW0 Z -GLAZA G L AA1 Z AH0 -GLAZE G L EY1 Z -GLAZEBROOK G L EY1 Z B R UH2 K -GLAZED G L EY1 Z D -GLAZENER G L AE1 Z IY0 N ER0 -GLAZER G L EY1 Z ER0 -GLAZES G L EY1 Z AH0 Z -GLAZES(2) G L EY1 Z IH0 Z -GLAZIER G L EY1 Z IY0 ER0 -GLAZING G L EY1 Z IH0 NG -GLAZNER G L AE1 Z N ER0 -GLAZUNOV G L AE1 Z UW0 N AA0 V -GLEACHER G L IY1 CH ER0 -GLEAM G L IY1 M -GLEAMED G L IY1 M D -GLEAMING G L IY1 M IH0 NG -GLEAMS G L IY1 M Z -GLEAN G L IY1 N -GLEANED G L IY1 N D -GLEASON G L IY1 S AH0 N -GLEASON'S G L IY1 S AH0 N Z 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G L AH0 N AO1 R AH0 -GLENS G L EH1 N Z -GLENVIEW G L EH1 N V Y UW2 -GLENVILLE G L EH1 N V IH0 L -GLENWOOD G L EH1 N W UH2 D -GLENWOOD'S G L EH1 N W UH2 D Z -GLESS G L EH1 S -GLESSNER G L EH1 S N ER0 -GLEW G L UW1 -GLIB G L IH1 B -GLIBLY G L IH1 B L IY0 -GLICK G L IH1 K -GLICKENHAUS G L IH1 K AH0 N HH AW2 S -GLICKMAN G L IH1 K M AH0 N -GLICKMAN'S G L IH1 K S M AH0 N Z -GLICKSMAN G L IH1 K S M AH0 N -GLICKSTEIN G L IH1 K S T IY2 N -GLICKSTEIN(2) G L IH1 K S T AY2 N -GLIDDEN G L IH1 D AH0 N -GLIDE G L AY1 D -GLIDED G L AY1 D IH0 D -GLIDER G L AY1 D ER0 -GLIDERS G L AY1 D ER0 Z -GLIDES G L AY1 D Z -GLIDEWELL G L AY1 D W EH2 L -GLIDING G L AY1 D IH0 NG -GLIMCHER G L IH1 M CH ER0 -GLIMMER G L IH1 M ER0 -GLIMMERING G L IH1 M ER0 IH0 NG -GLIMMERS G L IH1 M ER0 Z -GLIMPSE G L IH1 M P S -GLIMPSED G L IH1 M P S T -GLIMPSES G L IH1 M P S IH0 Z -GLINES G L AY1 N Z -GLINKA G L IH1 NG K AH0 -GLINSKI G L IH1 N S K IY0 -GLINT G L IH1 N T -GLINTING G L IH1 N T IH0 NG -GLISSANDI G L AH0 S AA1 N D IY0 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B Z -GLOBULAR G L AA1 B Y AH0 L ER0 -GLOBULIN G L AA1 B Y AH0 L IH0 N -GLOBULINS G L AA1 B Y AH0 L IH0 N Z -GLOBUS G L OW1 B AH0 S -GLOCESTER G L AW1 S T ER0 -GLOCK G L AA1 K -GLOCKENSPIEL G L AA1 K AH0 N S P IY2 L -GLOCKNER G L AA1 K N ER0 -GLOD G L AA1 D -GLODOWSKI G L AH0 D AO1 F S K IY0 -GLOE G L OW1 -GLOECKNER G L OW1 K N ER0 -GLOEDE G L OW1 D -GLOGOWSKI G L AH0 G AO1 F S K IY0 -GLOMAR G L OW1 M AA0 R -GLOMB G L AA1 M -GLOMSKI G L AA1 M S K IY2 -GLONASS G L AA1 N AH0 S -GLOOM G L UW1 M -GLOOMIER G L UW1 M IY0 ER0 -GLOOMILY G L UW1 M AH0 L IY0 -GLOOMY G L UW1 M IY0 -GLOOR G L UH1 R -GLOP G L AA1 P -GLOPPY G L AA1 P IY0 -GLOR G L AO1 R -GLORE G L AO1 R -GLORI G L AO1 R IY0 -GLORIA G L AO1 R IY0 AH0 -GLORIANA G L AO2 R IY0 AE1 N AH0 -GLORIANE G L AO2 R IY0 AE1 N -GLORIES G L AO1 R IY0 Z -GLORIFICATION G L AO2 R AH0 F IH0 K EY1 SH AH0 N -GLORIFICATION(2) G L AO2 R IH0 F IH0 K EY1 SH AH0 N -GLORIFIED G L AO1 R AH0 F AY2 D -GLORIFIES G L AO1 R AH0 F AY2 Z -GLORIFY G L AO1 R AH0 F AY2 -GLORIFYING G L AO1 R AH0 F AY2 IH0 NG -GLORIOSO G L AO0 R IY0 OW1 S OW0 -GLORIOUS G L AO1 R IY0 AH0 S -GLORIOUSLY G L AO1 R IY0 AH0 S L IY0 -GLORY G L AO1 R IY0 -GLOSS G L AO1 S -GLOSSARY G L AO1 S ER0 IY0 -GLOSSED G L AO1 S T -GLOSSER G L AO1 S ER0 -GLOSSES G L AO1 S IH0 Z -GLOSSIER G L AO1 S IY0 ER0 -GLOSSMAN G L AO1 S M AH0 N -GLOSSON G L AA1 S AH0 N -GLOSSY G L AO1 S IY0 -GLOSTER G L AA1 S T ER0 -GLOTFELTY G L AA1 T F IH0 L T IY0 -GLOTTAL G L AA1 T AH0 L -GLOTTIS G L AA1 T AH0 S -GLOTZBACH G L AA1 T S B AA0 K -GLOUCESTER G L AO1 S T ER0 -GLOVE G L AH1 V -GLOVED G L AH1 V D -GLOVER G L AH1 V ER0 -GLOVES G L AH1 V Z -GLOW G L OW1 -GLOWACKI G L AW0 AA1 T S K IY0 -GLOWED G L OW1 D -GLOWER G L AW1 ER0 -GLOWERED G L AW1 ER0 D -GLOWERING G L AW1 ER0 IH0 NG -GLOWING G L OW1 IH0 NG -GLOWINGLY G L OW1 IH0 NG L IY0 -GLOWS G L OW1 Z -GLOYD G L OY1 D -GLUCK G L AH1 K -GLUCK'S G L AH1 K S -GLUCKMAN G L AH1 K M AH0 N -GLUCKSMAN G L AH1 K S M AH0 N -GLUCOSAMATE G L UW0 K AA1 S AH0 M EY2 T 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R AE1 F T IH0 NG -GRAFTON G R AE1 F T AH0 N -GRAFTS G R AE1 F T S -GRAGE G R EY1 JH -GRAGERT G R EY1 G ER0 T -GRAGG G R AE1 G -GRAHAM G R EY1 AH0 M -GRAHAM(2) G R AE1 M -GRAHAM'S G R EY1 AH0 M Z -GRAHAM'S(2) G R AE1 M Z -GRAHAMS G R AE1 M Z -GRAHAMS(2) G R EY1 AH0 M Z -GRAHEK G R AE1 HH IH0 K -GRAHL G R AA1 L -GRAHN G R AE1 N -GRAICHEN G R AY1 K AH0 N -GRAIL G R EY1 L -GRAIN G R EY1 N -GRAINED G R EY1 N D -GRAINERY G R EY1 N ER0 IY0 -GRAINGER G R EY1 NG JH ER0 -GRAINS G R EY1 N Z -GRAINY G R EY1 N IY0 -GRAJEDA G R AY0 IY1 D AH0 -GRALEY G R AE1 L IY0 -GRALL G R AO1 L -GRAM G R AE1 M -GRAMA G R AE1 M AH0 -GRAMAPHONE G R AE1 M AH0 F OW2 N -GRAMBLING G R AE1 M B L IH0 NG -GRAMBLING(2) G R AE1 M B AH0 L IH0 NG -GRAMER G R EY1 M ER0 -GRAMERCY G R AH0 M ER1 S IY0 -GRAMERSEY G R AH0 M ER1 S IY0 -GRAMES G R EY1 M Z -GRAMLEY G R AE1 M L IY0 -GRAMLICH G R AE1 M L IH0 K -GRAMLING G R AE1 M L IH0 NG -GRAMM G R AE1 M -GRAMM'S G R AE1 M Z -GRAMMAR G R AE1 M ER0 -GRAMMATICAL G R AH0 M AE1 T AH0 K AH0 L -GRAMMATICAL(2) G R AH0 M AE1 T IH0 K AH0 L -GRAMMATICO G R AA0 M AA0 T IY1 K OW0 -GRAMMER G R AE1 M ER0 -GRAMMIES G R AE1 M IY2 Z -GRAMMOPHON G R AE1 M AH0 F AA0 N -GRAMMS G R AE1 M Z -GRAMMY G R AE1 M IY0 -GRAMMY'S G R AE1 M IY0 Z -GRAMMYS G R AE1 M IY0 Z -GRAMS G R AE1 M Z -GRAMZA G R AE1 M Z AH0 -GRAN G R AE1 N -GRANA G R AE1 N AH0 -GRANADA G R AH0 N AA1 D AH0 -GRANADA'S G R AH0 N AA1 D AH0 Z -GRANADE G R AH0 N EY1 D -GRANADO G R AA0 N AA1 D OW0 -GRANADOS G R AA0 N AA1 D OW0 Z -GRANAHAN G R AE1 N AH0 HH AE0 N -GRANAT G R AE1 N AH0 T -GRANATA G R AA0 N AA1 T AH0 -GRANATO G R AA0 N AA1 T OW0 -GRANBERG G R AE1 N B ER0 G -GRANBERRY G R AE1 N B EH2 R IY0 -GRANCARE G R AE1 N K EH2 R -GRAND G R AE1 N D -GRAND'S G R AE1 N D Z -GRAND-DAUGHTER G R AE1 N D AO2 T ER0 -GRANDA G R AE1 N D AH0 -GRANDAD G R AE1 N D AE2 D -GRANDAD'S G R AE1 N D AE2 D Z -GRANDBABIES G R AE1 N D B EY2 B IY0 Z -GRANDBABIES(2) G R AE1 N B EY2 B IY0 Z -GRANDBABY G R AE1 N D B EY2 B IY0 -GRANDBABY(2) G R AE1 N B EY2 B IY0 -GRANDBERRY G R AE1 N D B EH2 R IY0 -GRANDCHAMP G R AE1 N D CH AE2 M P -GRANDCHILD G R AE1 N D CH AY2 L D -GRANDCHILD(2) G R AE1 N CH AY2 L D -GRANDCHILDREN G R AE1 N CH IH2 L D R AH0 N -GRANDCHILDREN(2) G R AE1 N D CH IH2 L D R AH0 N -GRANDCHILDREN'S G R AE1 N CH IH2 L D R AH0 N Z -GRANDCHILDREN'S(2) G R AE1 N D CH IH2 L D R AH0 N Z -GRANDDAD G R AE1 N D AE2 D -GRANDDADDY G R AE1 N D AE2 D IY0 -GRANDDAUGHTER G R AE1 N D AO2 T ER0 -GRANDDAUGHTER'S G R AE1 N D AO2 T ER0 Z -GRANDDAUGHTERS G R AE1 N D AO2 T ER0 Z -GRANDE G R AE1 N D -GRANDER G R AE1 N D ER0 -GRANDERSON G R AE1 N D ER0 S AH0 N -GRANDEST G R AE1 N D AH0 S T -GRANDEUR G R AE0 N D UW1 R -GRANDFATHER G R AE1 N D F AA2 DH ER0 -GRANDFATHER(2) G R AE1 N F AA2 DH ER0 -GRANDFATHER'S G R AE1 N D F AA2 DH ER0 Z -GRANDFATHER'S(2) G R AE1 N F AA2 DH ER0 Z -GRANDFATHERED G R AE1 N D F AA2 DH ER0 D -GRANDFATHERED(2) G R AE1 N F AA2 DH ER0 D -GRANDFATHERING G R AE1 N D F AA2 DH ER0 IH0 NG -GRANDFATHERING(2) G R AE1 N F AA2 DH ER0 IH0 NG -GRANDFATHERLY G R AE1 N D F AA2 DH ER0 L IY0 -GRANDFATHERLY(2) G R AE1 N F AA2 DH ER0 L IY0 -GRANDFATHERS G R AE1 N D F AA2 DH ER0 Z -GRANDFATHERS(2) G R AE1 N F AA2 DH ER0 Z -GRANDFIELD G R AE1 N D F IY2 L D -GRANDI G R AE1 N D IY0 -GRANDILLO G R AE0 N D IH1 L OW0 -GRANDILOQUENT G R AE0 N D IH1 L AH0 K W AH0 N T -GRANDIN G R AE1 N D IH2 N -GRANDINETTI G R AE0 N D IY0 N EH1 T IY0 -GRANDIOSE G R AE2 N D IY0 OW1 S -GRANDIOSE(2) G R AE1 N D IY0 OW2 S -GRANDIS G R AE1 N D IH0 S -GRANDISON G R AE1 N D IH0 S AH0 N -GRANDKID G R AE1 N D K IH2 D -GRANDKID(2) G R AE1 N K IH2 D -GRANDKIDS G R AE1 N D K IH2 D Z -GRANDKIDS(2) G R AE1 N K IH2 D Z -GRANDLY G R AE1 N D L IY0 -GRANDMA G R AE1 N D M AA0 -GRANDMA'S G R AE1 N D M AA2 Z -GRANDMAISON G R AE1 N D M AY2 Z AA1 N -GRANDMAISON(2) G R AE1 N D M EY1 S AH0 N -GRANDMAS G R AE1 N D M AA2 Z -GRANDMASTER G R AE1 N D M AE1 S T ER0 -GRANDMASTER(2) G R AE1 N M AE1 S T ER0 -GRANDMET G R AE1 N D M EH2 T -GRANDMOM G R AE1 N D M AA2 M -GRANDMOTHER G R AE1 N D M AH2 DH ER0 -GRANDMOTHER'S G R AE1 N D M AH2 DH ER0 Z -GRANDMOTHERLY G R AE1 N D M AH2 DH ER0 L IY0 -GRANDMOTHERS G R AE1 N D M AH2 DH ER0 Z -GRANDNEPHEW G R AE1 N D N EH1 F Y UW0 -GRANDNEPHEWS G R AE1 N D N EH1 F Y UW0 Z -GRANDON G R AE1 N D AA0 N -GRANDPA G R AE1 N D P AA2 -GRANDPA'S G R AE1 N P AA2 Z -GRANDPARENT G R AE1 N D P EH2 R AH0 N T -GRANDPARENTS G R AE1 N D P EH2 R AH0 N T S -GRANDPARENTS' G R AE1 N D P EH2 R AH0 N T S -GRANDPAS G R AE1 N D P AA2 Z -GRANDPRE G R AE1 N D P R EY2 -GRANDS G R AE1 N D Z -GRANDSON G R AE1 N D S AH2 N -GRANDSON'S G R AE1 N D S AH2 N Z -GRANDSONS G R AE1 N D S AH2 N Z -GRANDSTAFF G R AE1 N D S T AE2 F -GRANDSTAND G R AE1 N D S T AE2 N D -GRANDSTANDING G R AE1 N D S T AE2 N D IH0 NG -GRANDT G R AE1 N T -GRANDUNCLE G R AE1 N D AH1 NG K AH0 L -GRANDUNCLES G R AE1 N D AH1 NG K AH0 L Z -GRANDVIEW G R AE1 N D V Y UW2 -GRANDY G R AE1 N D IY0 -GRANER G R EY1 N ER0 -GRANESE G R AE1 N IY0 Z -GRANEY G R EY1 N IY0 -GRANFIELD G R AE1 N F IY2 L D -GRANGE G R EY1 N JH -GRANGER G R EY1 N JH ER0 -GRANGERS G R EY1 N JH ER0 Z -GRANGES G R EY1 N JH IH0 Z -GRANHOLM G R AE1 N HH OW2 L M -GRANIER G R EY1 N IY0 ER0 -GRANIERI G R AA0 N IH1 R IY0 -GRANILLO G R AH0 N IH1 L OW0 -GRANINGEVERKEN G R AE2 N IH0 NG G EH1 V ER0 K AH0 N -GRANINGEVERKEN'S G R AE2 N IH0 NG G EH1 V ER0 K AH0 N Z -GRANITE G R AE1 N AH0 T -GRANITE(2) G R AE1 N IH0 T -GRANITIC G R AH0 N IH1 T IH0 K -GRANITO G R AA0 N IY1 T OW0 -GRANLUND G R AE1 N L AH0 N D -GRANNAN G R AE1 N AH0 N -GRANNIES G R AE1 N IY0 Z -GRANNIS G R AE1 N IH0 S -GRANNY G R AE1 N IY0 -GRANO G R AA1 N OW0 -GRANOFF G R AE1 N AO0 F -GRANOLA G R AH0 N OW1 L AH0 -GRANQUIST G R AE1 N K W IH2 S T -GRANSTROM G R AE1 N S T R AH0 M -GRANT G R AE1 N T -GRANT'S G R AE1 N T S -GRANTED G R AE1 N T AH0 D -GRANTED(2) G R AE1 N T IH0 D -GRANTED(3) G R AE1 N AH0 D -GRANTED(4) G R AE1 N IH0 D -GRANTHAM G R AE1 N TH AH0 M -GRANTING G R AE1 N T IH0 NG -GRANTING(2) G R AE1 N IH0 NG -GRANTLAND G R AE1 N T L AH0 N D -GRANTOR G R AE1 N T ER0 -GRANTORS G R AE1 N T ER0 Z -GRANTREE G R AE1 N T R IY2 -GRANTREE'S G R AE1 N T R IY2 Z -GRANTS G R AE1 N T S -GRANTZ G R AE1 N T S -GRANULAR G R AE1 N Y AH0 L ER0 -GRANULATION G R AE2 N Y AH0 L EY1 SH AH0 N -GRANULE G R AE1 N Y AH0 L -GRANULES G R AE1 N Y AH0 L Z -GRANULOCYTE G R AH0 N UW1 L OW0 S AY2 T -GRANUM G R AE1 N AH0 M -GRANVILLE G R AE1 N V IH0 L -GRANVILLE'S G R AE1 N V IH0 L Z -GRANZ G R AE1 N T S -GRANZOW G R AE1 N Z OW0 -GRAPAGE G R EY1 P AH0 JH -GRAPE G R EY1 P -GRAPEFRUIT G R EY1 P F R UW2 T -GRAPEFRUITS G R EY1 P F R UW2 T S -GRAPER G R EY1 P ER0 -GRAPES G R EY1 P S -GRAPESHOT G R EY1 P SH AA2 T -GRAPEVINE G R EY1 P V AY2 N -GRAPEVINES G R EY1 P V AY2 N Z -GRAPH G R AE1 F -GRAPHIC G R AE1 F IH0 K -GRAPHICAL G R AE1 F IH0 K AH0 L -GRAPHICALLY G R AE1 F IH0 K L IY0 -GRAPHICS G R AE1 F IH0 K S -GRAPHICS' G R AE1 F IH0 K S -GRAPHITE G R AE1 F AY2 T -GRAPHOLOGY G R AH0 F AA1 L AH0 JH IY0 -GRAPHS G R AE1 F S -GRAPPLE G R AE1 P AH0 L -GRAPPLED G R AE1 P AH0 L D -GRAPPLES G R AE1 P AH0 L Z -GRAPPLING G R AE1 P L IH0 NG -GRAPPLING(2) G R AE1 P AH0 L IH0 NG -GRAS G R AE1 S -GRAS(2) G R AA1 -GRASER G R EY1 Z ER0 -GRASMICK G R AE1 Z M IH0 K -GRASP G R AE1 S P -GRASPED G R AE1 S P T -GRASPING G R AE1 S P IH0 NG -GRASPS G R AE1 S P S -GRASS G R AE1 S -GRASSE G R AE1 S -GRASSED G R AE1 S T -GRASSEL G R AE1 S AH0 L -GRASSER G R AE1 S ER0 -GRASSERS G R AE1 S ER0 Z -GRASSES G R AE1 S AH0 Z -GRASSES(2) G R AE1 S IH0 Z -GRASSFIELD G R AE1 S F IY2 L D -GRASSFIELD'S G R AE1 S F IY2 L D Z -GRASSGREEN G R AE1 S G R IY2 N -GRASSHOPPER G R AE1 S HH AA2 P ER0 -GRASSHOPPERS G R AE1 S HH AA2 P ER0 Z -GRASSI G R AE1 S IY0 -GRASSIA G R AA1 S IY0 AH0 -GRASSINGS G R AE1 S IH0 NG Z -GRASSL G R AE1 S AH0 L -GRASSLAND G R AE1 S L AE2 N D -GRASSLANDS G R AE1 S L AE2 N D Z -GRASSLEY G R AE1 S L IY0 -GRASSLIKE G R AE1 S L AY2 K -GRASSMAN G R AE1 S M AH0 N -GRASSO G R AE1 S OW0 -GRASSROOT G R AE1 S R UW1 T -GRASSROOTS G R AE1 S R UW1 T S -GRASSY G R AE1 S IY0 -GRASTY G R AE1 S T IY0 -GRATA G R AA1 T AH0 -GRATE G R EY1 T -GRATED G R EY1 T IH0 D -GRATEFUL G R EY1 T F AH0 L -GRATEFULLY G R EY1 T F AH0 L IY0 -GRATER G R EY1 T ER0 -GRATES G R EY1 T S -GRATHWOHL G R AE1 TH W OW2 L -GRATIFICATION G R AE2 T AH0 F AH0 K EY1 SH AH0 N -GRATIFIED G R AE1 T AH0 F AY2 D -GRATIFIES G R AE1 T AH0 F AY2 Z -GRATIFY G R AE1 T AH0 F AY2 -GRATIFYING G R AE1 T AH0 F AY2 IH0 NG -GRATING G R EY1 T IH0 NG -GRATIS G R AE1 T AH0 S -GRATITUDE G R AE1 T AH0 T UW2 D -GRATTAN G R AE1 T AH0 N -GRATTON G R AE1 T AH0 N -GRATUITIES G R AH0 T UW1 IH0 T IY0 Z -GRATUITOUS G R AH0 T UW1 AH0 T AH0 S -GRATUITOUSLY G R AH0 T UW1 AH0 T AH0 S L IY0 -GRATUITY G R AH0 T UW1 IH0 T IY0 -GRATZ G R AE1 T S -GRATZER G R EY1 T Z ER0 -GRAU G R AW1 -GRAUBERGER G R AW1 B ER0 G ER0 -GRAUE G R AW1 -GRAUE(2) G R UW1 -GRAUEL G R AW1 AH0 L -GRAUEL(2) G R UW1 AH0 L -GRAUER G R AW1 ER0 -GRAUER(2) G R UW1 ER0 -GRAUL G R AO1 L -GRAUMAN G R AO1 M AH0 N -GRAUMANN G R AO1 M AH0 N -GRAUNKE G R AO1 NG K -GRAVAMEN G R AA0 V EY1 M AH0 N -GRAVANO G R AH0 V AA1 N OW0 -GRAVANO(2) G R AH0 V AE1 N OW0 -GRAVATT G R AE1 V AH0 T -GRAVE G R EY1 V -GRAVEL G R AE1 V AH0 L -GRAVELINE G R EY1 V L AY2 N -GRAVELL G R AE1 V AH0 L -GRAVELLE G R AH0 V EH1 L -GRAVELLY G R AE1 V AH0 L IY0 -GRAVELY G R EY1 V L IY0 -GRAVEN G R EY1 V AH0 N -GRAVER G R EY1 V ER0 -GRAVES G R EY1 V Z -GRAVES'S G R EY1 V Z IH0 Z -GRAVESIDE G R EY1 V S AY2 D -GRAVESITE G R EY1 V S AY2 T -GRAVEST G R AE1 V AH0 S T -GRAVESTONE G R EY1 V S T OW2 N -GRAVESTONES G R EY1 V S T OW2 N Z -GRAVETT G R AE1 V IH0 T -GRAVETTE G R AH0 V EH1 T -GRAVEYARD G R EY1 V Y AA2 R D -GRAVEYARDS G R EY1 V Y AA2 R D Z -GRAVIES G R EY1 V IY0 Z -GRAVIMETER G R AE1 V AH0 M IY2 T ER0 -GRAVIMETRIC G R AE2 V AH0 M EH1 T R IH0 K -GRAVINA G R AA0 V IY1 N AH0 -GRAVINO G R AA0 V IY1 N OW0 -GRAVITAS G R AE1 V AH0 T AH0 S -GRAVITATE G R AE1 V IH0 T EY2 T -GRAVITATED G R AE1 V AH0 T EY2 T IH0 D -GRAVITATES G R AE1 V IH0 T EY2 T S -GRAVITATING G R AE1 V IH0 T EY2 T IH0 NG 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EY1 CH -H.'S EY1 CH IH0 Z -HA HH AA1 -HA'ARETZ HH AA1 R EH0 T S -HA'ARETZ(2) HH AH0 AA1 R EH0 T S -HA'ETZNI HH AH0 EH1 T S N IY0 -HA-HA HH AA2 HH AA1 -HA-HA-HA HH AA2 HH AA2 HH AA1 -HAAB HH AA1 B -HAACK HH AA1 K -HAACKE HH AA1 K -HAAF HH AA1 F -HAAG HH AA1 G -HAAGEN HH AA1 G AH0 N -HAAGENSON HH AA1 G IH0 N S AH0 N -HAAK HH AA1 K -HAAKE HH AA1 K -HAAKENSON HH AA1 K IH0 N S AH0 N -HAALAND HH AA1 L AH0 N D -HAAN HH AA1 N -HAAPALA HH AA2 P AA1 L AH0 -HAAR HH AA1 R -HAAS HH AA1 S -HAASE HH AA1 S -HAASS HH AA1 S -HAAVELMO HH AA2 V EH1 L M OW0 -HABBEN HH AE1 B AH0 N -HABEAS HH AE1 B IY0 AH0 S -HABECK HH AA1 B EH0 K -HABECKER HH AE1 B EH0 K ER0 -HABEEB HH AE1 B IY0 B -HABEGGER HH AE1 B IH0 G ER0 -HABEL HH AE1 B AH0 L -HABENICHT HH AE1 B IH0 N IH0 K T -HABER HH EY1 B ER0 -HABERDASHERY HH AE1 B ER0 D AE2 SH ER0 IY0 -HABERER HH AE1 B ER0 ER0 -HABERKORN HH AE1 B ER0 K ER0 N -HABERL HH AE1 B ER0 L -HABERLAND HH AE1 B ER0 L AH0 N D -HABERLE HH AE1 B ER0 AH0 L -HABERMAN HH EY1 B ER0 M AH0 N -HABERMANN HH EY1 B ER0 M AH0 N -HABERMAS HH AA1 B ER0 M AA2 S -HABERMEHL HH AE1 B ER0 M AH0 L -HABERSON HH EY1 B ER0 S IH0 N -HABERSON(2) HH AE1 B ER0 S IH0 N -HABERSTROH HH AA0 B EH1 R S T R OW0 -HABIB HH AH0 B IY1 B -HABIBI HH AH0 B IY1 B IY0 -HABIBIE HH AH0 B IY1 B IY0 -HABICH HH AE1 B IH0 K -HABICHT HH AE1 B IH0 K T -HABIG HH AE1 B IH0 G -HABIGER HH AE1 B IH0 G ER0 -HABIT HH AE1 B AH0 T -HABITABLE HH AE1 B AH0 T AH0 B AH0 L -HABITAT HH AE1 B AH0 T AE2 T -HABITAT'S HH AE1 B AH0 T AE2 T S -HABITATION HH AE2 B AH0 T EY1 SH AH0 N -HABITATS HH AE1 B AH0 T AE2 T S -HABITS HH AE1 B AH0 T S -HABITUAL HH AH0 B IH1 CH UW0 AH0 L -HABITUALLY HH AH0 B IH1 CH UW0 AH0 L IY0 -HABITUALLY(2) HH AH0 B IH1 CH UW0 L IY0 -HABITUES HH AE1 B IH0 CH UW0 Z -HABLE HH EY1 B AH0 L -HABS HH AE1 B Z -HABSBURG HH AE1 P S B ER0 G -HABY HH EY1 B IY0 -HABYARIMANA HH AE2 B IY0 ER0 IY0 M AA1 N AA2 -HABYARIMANA(2) HH AE2 B IY0 AA0 R IY0 M AA1 N AH0 -HACH HH AE1 CH -HACHETTE HH AH0 SH EH1 T -HACHEY HH AE1 CH IY0 -HACHTEL HH AE1 K T AH0 L -HACIENDA HH AE2 S IY0 EH1 N D AH0 -HACK HH AE1 K -HACKATHON HH AE1 K AH0 TH AO2 N -HACKATHORN HH AE1 K AH0 TH ER2 N -HACKBART HH AE1 K B AA2 R T -HACKBARTH HH AE1 K B AA2 R TH -HACKBERRY HH AE1 K B EH2 R IY0 -HACKE HH AE1 K -HACKED HH AE1 K T -HACKEL HH AE1 K AH0 L -HACKENBERG HH AE1 K AH0 N B ER0 G -HACKENSACK HH AE1 K AH0 N S AE2 K -HACKER HH AE1 K ER0 -HACKER'S HH AE1 K ER0 Z -HACKERS HH AE1 K ER0 Z -HACKERT HH AE1 K ER0 T -HACKETT HH AE1 K IH0 T -HACKFORD HH AE1 K F ER0 D -HACKING HH AE1 K IH0 NG -HACKL HH AE1 K AH0 L -HACKLE HH AE1 K AH0 L -HACKLEMAN HH AE1 K AH0 L M AH0 N -HACKLER HH AE1 K AH0 L ER0 -HACKLER(2) HH AE1 K L ER0 -HACKLES HH AE1 K AH0 L Z -HACKLEY HH AE1 K L IY0 -HACKMAN HH AE1 K M AE2 N -HACKMAN(2) HH AE1 K M AH0 N -HACKMANN HH AE1 K M AH0 N -HACKMATACK HH AE1 K M AH0 T AE2 K -HACKNEY HH AE1 K N IY0 -HACKNEYED HH AE1 K N IY0 D -HACKS HH AE1 K S -HACKSAW HH AE1 K S AO2 -HACKSTAFF HH AE1 K S T AE2 F -HACKWORTH HH AE1 K W ER2 TH -HAD HH AE1 D -HADA HH AA1 D AH0 -HADAD HH AE1 D AH0 D -HADAR HH AE1 D ER2 -HADAWAY HH AA1 D AH0 W EY0 -HADD HH AE1 D -HADDAD HH AE1 D AH0 D -HADDAN HH AE1 D AH0 N -HADDAWAY HH AE1 D AH0 W EY0 -HADDEN HH AE1 D AH0 N -HADDOCK HH AE1 D AH0 K -HADDON HH AE1 D AH0 N -HADDONFIELD HH AE1 D AH0 N F IY0 L D -HADDOW HH AE1 D OW0 -HADE HH EY1 D -HADEN HH EY1 D AH0 N -HADER HH EY1 D ER0 -HADES HH EY1 D IY0 Z -HADES(2) HH EY1 D Z -HADFIELD HH AE1 D F IY2 L D -HADID HH AA0 D IY1 D -HADLEIGH HH AE1 D L AH0 -HADLER HH EY1 D AH0 L ER0 -HADLER(2) HH EY1 D L ER0 -HADLEY HH AE1 D L IY0 -HADLINE HH AE1 D L AY0 N -HADLOCK HH AE1 D L AH0 K -HADN'T HH AE1 D AH0 N T -HADN'T(2) HH AE1 D AH0 N -HADNOT HH AE1 D N AH0 T -HADOOP HH AE1 D UW1 P -HADOYA HH AH0 D OY1 AH0 -HADRIA HH AE1 D R IY0 AH0 -HADRIAN HH AE1 D R IY0 AH0 N -HADRIAN(2) HH EY1 D R IY0 AH0 N -HADRIAN'S HH EY1 D R IY0 AH0 N Z -HADRON HH AE1 D R AO2 N -HADSALL HH AE1 D S AH0 L -HADSELL HH AE1 D S AH0 L -HADSON HH AE1 D S AH0 N -HADWIN HH AE1 D W IH0 N -HAEBERLE HH EH1 B ER0 AH0 L -HAECKER HH EH1 K ER0 -HAEFELE HH EH1 F AH0 L -HAEFFNER HH EH1 F N ER0 -HAEFNER HH EH1 F N ER0 -HAEGELE HH EH1 G AH0 L -HAEGER HH EH1 G ER0 -HAEN HH IY1 N -HAENEL HH EH1 N AH0 L -HAERING HH AA1 ER0 IH0 NG -HAERTEL HH EH1 R T AH0 L -HAESE HH IY1 S -HAESSLY HH AE1 S L IY0 -HAEUSSLER HH AW1 S L ER0 -HAFELE HH AE1 F AH0 L -HAFEMAN HH EY1 F M AH0 N -HAFEN HH AE1 F AH0 N -HAFER HH EY1 F ER0 -HAFEY HH EY1 F IY0 -HAFEZ HH AA1 F EH0 Z -HAFF HH AE1 F -HAFFEY HH AE1 F IY0 -HAFFEZ HH AA1 F EH0 Z -HAFFNER HH AE1 F N ER0 -HAFFORD HH AE1 F ER0 D -HAFIF HH AA0 F IY1 F -HAFIF'S HH AA0 F IY1 F S -HAFLEY HH AE1 F L IY0 -HAFNER HH AE1 F N ER0 -HAFNIA HH AE1 F N IY0 AH0 -HAFNIUM HH AE1 F N IY0 AH0 M -HAFT HH AE1 F T -HAFT'S HH AE1 F T S -HAFT'S(2) HH AE1 F S -HAFTA HH AE1 F T AH0 -HAFTS HH AE1 F T S -HAFTS(2) HH AE1 F S -HAFTS' HH AE1 F T S -HAFTS'(2) HH AE1 F S -HAG HH AE1 G -HAGA HH AA1 G AH0 -HAGADORN HH AE1 G AH0 D AO0 R N -HAGAMAN HH AE1 G AH0 M AH0 N -HAGAN HH EY1 G AH0 N -HAGANS HH EY1 G AH0 N Z -HAGAR HH EY1 G AA0 R -HAGAR(2) HH EY1 G ER0 -HAGARTY HH AE1 G AA0 R T IY0 -HAGBERG HH AE1 G B ER0 G -HAGE HH EY1 JH -HAGEDORN HH AE1 G IH0 D ER0 N -HAGEE HH AE1 JH IY0 -HAGEL HH AE1 G AH0 L -HAGELIN HH AE1 G IH0 L IH0 N -HAGEMAN HH EY1 JH M AH0 N -HAGEMANN HH EY1 JH M AH0 N -HAGEMEIER HH AE1 G IH0 M AY0 ER0 -HAGEMEISTER HH AE1 G IH0 M AY0 S T ER0 -HAGEMEYER HH AE1 G IH0 M AY0 ER0 -HAGEN HH EY1 G AH0 N -HAGENBUCH HH AE1 G IH0 N B AH0 K -HAGENLOCKER HH EY1 G AH0 N L AA2 K ER0 -HAGENOW HH AE1 JH IH0 N OW0 -HAGENS HH EY1 G AH0 N Z -HAGER HH EY1 G ER0 -HAGERMAN HH EY1 G ER0 M AH0 N -HAGERSTOWN HH EY1 G ER0 Z T AW2 N -HAGERTY HH AE1 JH ER0 T IY0 -HAGEWOOD HH EY1 JH W UH0 D -HAGEY HH AE1 JH IY0 -HAGFISH HH AE1 G F IH0 SH -HAGG HH AE1 G -HAGGADAH HH AH0 G AH1 D AH0 -HAGGAN HH AE1 G AH0 N -HAGGAR HH AE1 G ER0 -HAGGARD HH AE1 G ER0 D -HAGGART HH AE1 G ER0 T -HAGGARTY HH AE1 G AA2 R T IY0 -HAGGE HH AE1 G -HAGGERTY HH AE1 G ER0 T IY0 -HAGGETT HH AE1 G IH0 T -HAGGINS HH AE1 G IH0 N Z -HAGGLE HH AE1 G AH0 L -HAGGLED HH AE1 G AH0 L D -HAGGLING HH AE1 G AH0 L IH0 NG -HAGGLING(2) HH AE1 G L IH0 NG -HAGGLUND HH AE1 G L AH0 N D -HAGGSTROM HH AE1 G S T R AH0 M -HAGIN HH AE1 JH IH0 N -HAGIN(2) HH AE1 G IH2 N -HAGINS HH AE1 G IH0 N Z -HAGINS(2) HH AE1 JH IH2 N Z -HAGIOGRAPHY HH AE2 G IY0 AA1 G R AH0 F IY0 -HAGIWARA HH AA2 G IH0 W AA1 R AH0 -HAGLE HH EY1 G AH0 L -HAGLER HH AE1 G L ER0 -HAGLEY HH AE1 G L IY0 -HAGLUND HH AE1 G L AH0 N D -HAGMAN HH AE1 G M AH0 N -HAGMANN HH AE1 G M AH0 N -HAGNER HH AE1 G N ER0 -HAGOOD HH AE1 G UH0 D -HAGOPIAN HH AH0 G OW1 P IY0 AH0 N -HAGSTROM HH AE1 G S T R AH0 M -HAGUE HH EY1 G -HAGWOOD HH AE1 G W UH2 D -HAGY HH EY1 G IY0 -HAH HH AA1 -HAHL HH AA1 L -HAHM HH AE1 M -HAHN HH AA1 N -HAHNE HH EY1 N -HAHNER HH AA1 N ER0 -HAHS HH AA1 S -HAID HH EY1 D -HAIDA HH AY1 D AH0 -HAIDEE HH EY1 D IY0 -HAIDER HH EY1 D ER0 -HAIDET HH EY1 D IH0 T -HAIFA HH AY1 F AH0 -HAIFONG HH AY1 F AO0 NG -HAIG HH EY1 G -HAIG'S HH EY1 G Z -HAIGH HH EY1 -HAIGHT HH EY1 T -HAIGLER HH EY1 G L ER0 -HAIK HH EY1 K -HAIKU HH AY1 K UW0 -HAIKUS HH AY1 K UW0 Z -HAIL HH EY1 L -HAILAND HH EY1 L AH0 N D -HAILE HH EY1 L -HAILE(2) HH AY1 L IY0 -HAILED HH EY1 L D -HAILES HH EY1 L Z -HAILES(2) HH AY1 L IY0 Z -HAILEY HH EY1 L IY0 -HAILING HH EY1 L IH0 NG -HAILS HH EY1 L Z -HAILSTONE HH EY1 L S T OW2 N -HAILSTONES HH EY1 L S T OW2 N Z -HAILSTORM HH EY1 L S T AO2 R M -HAIM HH AY1 M -HAIM(2) HH EY1 M -HAIMES HH EY1 M Z -HAIMOVITCH HH EY1 M AH0 V IH0 CH -HAIMOVITZ HH EY1 M AH0 V IH0 T S -HAIMOWITZ HH AY1 M AH0 W IH0 T S -HAIN HH EY1 N -HAINAN HH EY1 N AH0 N -HAINER HH EY1 N ER0 -HAINES HH EY1 N Z -HAINEY HH EY1 N IY0 -HAINLEY HH EY1 N L IY0 -HAINLINE HH EY1 N L AY2 N -HAINS HH EY1 N Z -HAINSWORTH HH EY1 N S W ER0 TH -HAIR HH EH1 R -HAIR'S HH EH1 R Z -HAIRCUT HH EH1 R K AH2 T -HAIRCUTS HH EH1 R K AH2 T S -HAIRDO HH EH1 R D UW2 -HAIRDOS HH EH1 R D UW2 Z -HAIRDRESSER HH EH1 R D R EH2 S ER0 -HAIRDRESSERS HH EH1 R D R EH2 S ER0 Z -HAIRDRESSING HH EH1 R D R EH2 S IH0 NG -HAIRE HH EH1 R -HAIRED HH EH1 R D -HAIRFIELD HH EH1 R F IY2 L D -HAIRGROVE HH AY1 R G R AH0 V -HAIRINESS HH EH1 R IY0 N AH0 S -HAIRLESS HH EH1 R L AH0 S -HAIRLINE HH EH1 R L AY2 N -HAIRR HH EH1 R -HAIRS HH EH1 R Z -HAIRSPRAY HH EH1 R S P R EY2 -HAIRSTON HH AY1 R S T AH0 N -HAIRSTYLE HH EH1 R S T AY2 L -HAIRY HH EH1 R IY0 -HAISLEY HH EY1 Z L IY0 -HAISLIP HH AY1 S L IH0 P -HAIST HH AA1 IH0 S T -HAIT HH EY1 T -HAITH HH EY1 TH -HAITHCOCK HH EY1 TH K AA2 K -HAITI HH EY1 T IY0 -HAITI'S HH EY1 T IY0 Z -HAITIAN HH EY1 SH AH0 N -HAITIANS HH EY1 SH AH0 N Z -HAITIEN HH EY1 SH AH0 N -HAITIENS HH EY1 SH AH0 N Z -HAITIS HH EY1 T IY0 Z -HAIZLIP HH EY1 Z L IH0 P -HAJDU HH AA1 Y D UW0 -HAJDUK HH AY1 D AH0 K -HAJEK HH AY1 EH0 K -HAJIME HH AA0 JH IY1 M IY0 -HAJJ HH AE1 JH -HAJJAR HH AA0 Y AA1 R -HAKALA HH AH0 K AA1 L AH0 -HAKAN HH EY1 K AH0 N -HAKAN(2) HH AA1 K AH0 N -HAKANSON HH AE1 K AH0 N S AH0 N -HAKE HH EY1 K -HAKEEM HH AA0 K IY1 M -HAKEEM'S HH AA0 K IY1 M Z -HAKES HH EY1 K S -HAKIM HH AA0 K IY1 M -HAKIM(2) AA0 K IY1 M -HAKIM'S HH AA0 K IY1 M Z -HAKIM'S(2) AA0 K IY1 M Z -HAKKI HH AE1 K IY0 -HAKKO HH AE1 K OW0 -HAKON HH AE1 K AH0 N -HAKUHODO HH AA2 K UW2 HH OW1 D OW0 -HAL HH AE1 L -HAL'S HH AE1 L Z -HALAMA HH AA0 L AA1 M AH0 -HALAMANDARIS HH AE2 L AH0 M AE1 N D ER0 IH0 S -HALAS HH AA1 L AH0 Z -HALASZ HH AA1 L AH0 SH -HALBACH HH AE1 L B AA0 K -HALBERG HH AE1 L B ER0 G -HALBERSTADT HH AE1 L B ER0 SH T AE0 T -HALBERSTAM HH AE1 L B ER0 S T AE0 M -HALBERT HH AE1 L B ER0 T -HALBIG HH AE1 L B IH0 G -HALBROOK HH AE1 L B R UH0 K -HALBROOKS HH AE1 L B R UH0 K S -HALBUR HH AE1 L B ER0 -HALBUTOGULLARI HH AE0 L B UW2 T OW2 G UW0 L AA1 R IY0 -HALCION HH AE1 L S IY0 AH0 N -HALCION'S HH AE1 L S IY0 AH0 N Z -HALCOMB HH AE1 L K AH0 M -HALCYON HH AE1 L S IY0 AH0 N -HALCYONE HH AE1 L S IY0 OW0 N -HALD HH AO1 L D -HALDAN HH AE1 L D AH0 N -HALDANA HH AH0 L D AE1 N AH0 -HALDEMAN HH AA1 L D M AH0 N -HALDEMAN(2) HH AA1 L D AH0 M AH0 N -HALDEMAN'S HH AA1 L D M AH0 N Z -HALDEMAN'S(2) HH AA1 L D AH0 M AH0 N Z -HALDEN HH AO1 L D AH0 N -HALDER HH AO1 L D ER0 -HALDERMAN HH AO1 L D ER0 M AH0 N -HALE HH EY1 L -HALE'S HH EY1 L Z -HALEBOPP HH AY0 L B AO1 P -HALEN HH EY1 L EH0 N -HALES HH EY1 L Z -HALEVI HH AH0 L EY1 V IY0 -HALEVI(2) HH AH0 L IY1 V IY0 -HALEY HH EY1 L IY0 -HALEY'S HH EY1 L IY0 Z -HALF HH AE1 F -HALF'S HH AE1 F S -HALF-BAKED HH AE1 F B EY2 K T -HALF-COCK HH AE1 F K AA2 K -HALF-COCKED HH AE1 F K AA2 K T -HALF-DOZEN HH AE1 F D AH2 Z AH0 N -HALF-HEARTED HH AE1 F HH AA2 R T IH0 D -HALF-HOUR HH AE1 F AW2 ER0 -HALF-TIME HH AE1 F T AY2 M -HALFACRE HH AE1 F EY2 K ER0 -HALFBACK HH AE1 F B AE2 K -HALFBACKS HH AE1 F B AE2 K S -HALFDAN HH AE1 L F D AH0 N -HALFERTY HH AE1 F ER0 T IY0 -HALFHEARTED HH AE1 F HH AA2 R T IH0 D -HALFHILL HH AE1 F HH IH2 L -HALFMAN HH AE1 F M AH0 N -HALFMANN HH AE1 F M AH0 N -HALFORD HH AE1 F ER0 D -HALFRIDA HH AE1 F R IH0 D AH0 -HALFTIME HH AE1 F T AY2 M -HALFTIMES HH AE1 F T AY2 M Z -HALFTONE HH AE1 F T OW2 N -HALFTONES HH AE1 F T OW2 N Z -HALFWAY HH AE1 F W EY1 -HALFWIT HH AE1 F W IH0 T -HALFWITS HH AE1 F W IH0 T S -HALGREN HH AE1 L G R EH0 N -HALIBURTON HH AE1 L IH0 B ER2 T AH0 N -HALIBUT HH AE1 L AH0 B AH0 T -HALIDE HH AE1 L AY2 D -HALIFA HH AH0 L IY1 F AH0 -HALIFAX HH AE1 L IH0 F AE2 K S -HALIK HH AE1 L IH0 K -HALIMA HH AH0 L IY1 M AH0 -HALIMEDA HH AA0 L IY0 M EY1 D AH0 -HALITE HH AE1 L AY0 T -HALKO HH AE1 L K OW0 -HALL HH AO1 L -HALL'S HH AO1 L Z -HALLA HH AE1 L AH0 -HALLADAY HH AE1 L AH0 D EY2 -HALLAHAN HH AE1 L AH0 HH AE0 N -HALLAM HH AE1 L AH0 M -HALLANAN HH AE1 L AH0 N AH0 N -HALLANDALE HH AE1 L AH0 N D EY2 L -HALLAS HH AE1 L AH0 Z -HALLAUER HH AE1 L AW0 ER0 -HALLBAUER HH AO1 L B AW2 R -HALLBERG HH AO1 L B ER0 G -HALLE HH AE1 L -HALLE(2) HH AE1 L IY0 -HALLECK HH AE1 L IH0 K -HALLELUJAH HH AE2 L AH0 L UW1 Y AH0 -HALLEN HH AO1 L AH0 N -HALLENBECK HH AO1 L AH0 N B EH2 K -HALLER HH AO1 L ER0 -HALLERAN HH AE1 L ER0 AE0 N -HALLET HH AE1 L IH0 T -HALLETT HH AE1 L IH0 T -HALLEY HH AE1 L IY0 -HALLEY(2) HH EY1 L IY0 -HALLFORD HH AE1 L F ER0 D -HALLGARTEN HH AO1 L G AA2 R T AH0 N -HALLGREN HH AE1 L G R EH0 N -HALLIBURTON HH AE1 L IH0 B ER2 T AH0 N -HALLIBURTON'S HH AE1 L IH0 B ER2 T AH0 N Z -HALLICIFORN HH AH0 L IH1 S IH0 F AO0 R N -HALLIDAY HH AE1 L IH0 D EY2 -HALLIE HH AO1 L IY0 -HALLIGAN HH AE1 L IH0 G AH0 N -HALLIN HH AE1 L IH0 N -HALLINAN HH AE1 L IH0 N AH0 N -HALLING HH AO1 L IH0 NG -HALLINGBY HH AO1 L IH0 NG B IY0 -HALLISEY HH AE1 L IH0 S IY0 -HALLMAN HH AO1 L M AH0 N -HALLMARK HH AA1 L M AA2 R K -HALLMARK'S HH AO1 L M AA2 R K S -HALLMARKS HH AO1 L M AA2 R K S -HALLOCK HH AE1 L AH0 K -HALLORAN HH AE1 L ER0 AH0 N -HALLOW HH AE1 L OW0 -HALLOWAY HH AE1 L OW0 W EY2 -HALLOWE'EN HH AE2 L AH0 W IY1 N -HALLOWED HH AE1 L OW0 D -HALLOWEEN HH AE2 L AH0 W IY1 N -HALLOWEEN'S HH AE2 L AH0 W IY1 N Z -HALLOWELL HH AE1 L AH0 W EH0 L -HALLOWS HH AE1 L OW0 Z -HALLQUIST HH AE1 L K W IH0 S T -HALLS HH AO1 L Z -HALLSTROM HH AE1 L S T R AH0 M -HALLUCINATE HH AH0 L UW1 S AH0 N EY0 T -HALLUCINATED HH AH0 L UW1 S AH0 N EY0 T IH0 D -HALLUCINATES HH AH0 L UW1 S AH0 N EY0 T S -HALLUCINATING HH AH0 L UW1 S AH0 N EY0 T IH0 NG -HALLUCINATING(2) HH AH0 L UW1 S IH0 N EY0 T IH0 NG -HALLUCINATION HH AH0 L UW2 S AH0 N EY1 SH AH0 N -HALLUCINATIONS HH AH0 L UW2 S AH0 N EY1 SH AH0 N Z -HALLUCINATORY HH AH0 L UW1 S AH0 N AH0 T AO2 R IY0 -HALLUCINOGENIC HH AH0 L UW2 S AH0 N AH0 JH EH1 N IH0 K -HALLUM HH AE1 L AH0 M -HALLUMS HH AE1 L AH0 M Z -HALLWARD HH AO1 L W ER0 D -HALLWAY HH AO1 L W EY2 -HALLWAYS HH AO1 L W EY2 Z -HALLWOOD HH AO1 L W UH2 D -HALLY HH AE1 L IY0 -HALM HH AA1 M -HALMI HH AO1 L M IY0 -HALMOS HH AO1 L M OW0 S -HALMSTAD HH AA1 L M S T AE2 D -HALO HH EY1 L OW0 -HALOGEN HH AE1 L AH0 JH AH0 N -HALOGENATE HH AE1 L AH0 JH AH0 N EY2 T -HALOGENATED HH AE1 L AH0 JH AH0 N EY2 T IH0 D -HALON HH EY1 L AA2 N -HALOPHYTIC HH AE2 L AH0 F IH1 T IH0 K -HALOS HH EY1 L OW0 Z -HALPER HH AE1 L P ER0 -HALPERIN HH AE1 L P ER0 IH0 N -HALPERIN(2) HH AE1 L P R IH0 N -HALPERN HH AE1 L P ER0 N -HALPERSTANT HH AE1 L P ER0 S T AH0 N T -HALPERT HH AE1 L P ER0 T -HALPIN HH AE1 L P IH0 N -HALPRIN HH AE1 L P R IH0 N -HALSELL HH AE1 L S AH0 L -HALSETH HH AE1 L S IH0 TH -HALSEY HH AE1 L S IY0 -HALSTEAD HH AE1 L S T EH0 D -HALSTED HH AE1 L S T IH0 D -HALSTON HH AO1 L S T AH0 N -HALSTON'S HH AO1 L S T AH0 N Z -HALT HH AO1 L T -HALTED HH AO1 L T AH0 D -HALTED(2) HH AO1 L T IH0 D -HALTEMAN HH EY1 L T M AH0 N -HALTER HH AO1 L T ER0 -HALTER'S HH AO1 L T ER0 Z -HALTERMAN HH AO1 L T ER0 M AH0 N -HALTERS HH AO1 L T ER0 Z -HALTING HH AO1 L T IH0 NG -HALTINGLY HH AO1 L T IH0 NG L IY0 -HALTIWANGER HH AE1 L T IH0 W AH0 NG ER0 -HALTOM HH AE1 L T AH0 M -HALTON HH AE1 L T AH0 N -HALTS HH AO1 L T S -HALUSKA HH AH0 L AH1 S K AH0 -HALVE HH AE1 V -HALVED HH AE1 V D -HALVERSON HH AE1 L V ER0 S AH0 N -HALVES HH AE1 V Z -HALVING HH AE1 V IH0 NG -HALVORSEN HH AE0 L V AO1 R S AH0 N -HALVORSON HH AE1 L V ER0 S AH0 N -HAM HH AE1 M -HAMA HH AA1 M AH0 -HAMACHER HH AE1 M AH0 K ER0 -HAMAD HH AE1 M AH0 D -HAMADA HH AA0 M AA1 D AH0 -HAMADEI HH AE1 M AH0 D EY2 -HAMADI HH AH0 M AA1 D IY0 -HAMAKER HH AA1 M EY0 K ER0 -HAMAL HH EY1 M AH0 L -HAMAMOTO HH AA0 M AA0 M OW1 T OW0 -HAMAN HH EY1 M AH0 N -HAMANAKA HH AH0 M AH0 N AA1 K AH0 -HAMANN HH AA1 M AH0 N -HAMAR HH AH0 M AA1 R -HAMAS HH AA2 M AA1 S -HAMAS' HH AA2 M AA1 S -HAMASAKI HH AA2 M AA0 S AA1 K IY0 -HAMBELTON HH AH0 M B EH1 L T AH0 N -HAMBERG HH AE1 M B ER0 G -HAMBERGER HH AE1 M B ER0 G ER0 -HAMBLEN HH AE1 M B AH0 L AH0 N -HAMBLET HH AE1 M B L IH0 T -HAMBLETON HH AE1 M B AH0 L T AA0 N -HAMBLEY HH AE1 M B L IY0 -HAMBLIN HH AE1 M B L IH0 N -HAMBLY HH AE1 M B L IY0 -HAMBRECHT HH AE1 M B R EH2 K T -HAMBRICK HH AE1 M B R IH2 K -HAMBRIGHT HH AE1 M B R AY2 T -HAMBRO HH AE1 M B R OW0 -HAMBROS HH AE1 M B R OW0 S -HAMBURG HH AE1 M B ER0 G -HAMBURGER HH AE1 M B ER0 G ER0 -HAMBURGERS HH AE1 M B ER0 G ER0 Z -HAMBY HH AE1 M B IY0 -HAMDAN HH AE1 M D AH0 N -HAMDOON HH AE0 M D UW1 N -HAMDUN HH AE1 M D AH0 N -HAMED HH AE1 M D -HAMEISTER HH AE1 M AY0 S T ER0 -HAMEL HH AE1 M AH0 L -HAMELIN HH AE1 M AH0 L IH0 N -HAMELIN(2) HH AE1 M L IH2 N -HAMER HH AE1 M ER0 -HAMES HH EY1 M Z -HAMID HH AH0 M IY1 D -HAMIEL HH AE1 M IY0 L -HAMIL HH AE1 M AH0 L -HAMILL HH AE1 M AH0 L -HAMILTON HH AE1 M AH0 L T AH0 N -HAMILTON'S HH AE1 M AH0 L T AH0 N Z -HAMILTONIAN HH AE1 M AH0 L T OW2 N Y AH0 N -HAMILTONS HH AE1 M AH0 L T AH0 N Z -HAMISH HH AE1 M IH0 SH -HAMITER HH AE1 M AY0 T ER0 -HAMITIC HH AE0 M IH1 T IH0 K -HAMLER HH AE1 M AH0 L ER0 -HAMLER(2) HH AE1 M L ER0 -HAMLET HH AE1 M L AH0 T -HAMLET(2) HH AE1 M L IH0 T -HAMLETS HH AE1 M L AH0 T S -HAMLETT HH AE1 M L IH0 T -HAMLEY HH AE1 M L IY0 -HAMLEY'S HH AE1 M L IY0 Z -HAMLIN HH AE1 M L IH0 N -HAMLING HH AE1 M L IH0 NG -HAMLISCH HH AE1 M L IH0 SH -HAMLISCH'S HH AE1 M L IH0 SH AH0 Z -HAMM HH AE1 M -HAMMAC HH AE1 M AH0 K -HAMMACHER HH AE1 M AA2 K ER0 -HAMMACK HH AE1 M AH0 K -HAMMAKER HH AE1 M EY2 K ER0 -HAMMAN HH AE1 M AH0 N -HAMMANN HH AE1 M AH0 N -HAMMAR HH AE1 M ER0 -HAMMAS HH AH0 M AA1 S -HAMMAS' HH AH0 M AA1 S -HAMMAS'S HH AH0 M AA1 S IH0 S -HAMME HH AE1 M -HAMMEL HH AE1 M AH0 L -HAMMELL HH AE1 M AH0 L -HAMMEN HH AE1 M AH0 N -HAMMER HH AE1 M ER0 -HAMMER'S HH AE1 M ER0 Z -HAMMERED HH AE1 M ER0 D -HAMMERING HH AE1 M ER0 IH0 NG -HAMMERLE HH AE1 M ER0 AH0 L -HAMMERLOCK HH AE1 M ER0 L AA2 K -HAMMERMAN HH AE1 M ER0 M AH0 N -HAMMERMEISTER HH AE1 M ER0 M AY2 S T ER0 -HAMMERMILL HH AE1 M ER0 M IH2 L -HAMMERS HH AE1 M ER0 Z -HAMMERSCHMIDT HH AE1 M ER0 SH M IH2 T -HAMMERSLEY HH AE1 M ER0 S L IY0 -HAMMERSMITH HH AE1 M ER0 S M IH2 TH -HAMMERSON HH AE1 M ER0 S AH0 N -HAMMERSTEIN HH AE1 M ER0 S T IY2 N -HAMMERSTEIN(2) HH AE1 M ER0 S T AY2 N -HAMMERSTEIN'S HH AE1 M ER0 S T IY2 N Z -HAMMERSTEIN'S(2) HH AE1 M ER0 S T AY2 N Z -HAMMERSTROM HH AE1 M ER0 S T R AH0 M -HAMMES HH AE1 M Z -HAMMETT HH AE1 M IH0 T -HAMMILL HH AE1 M AH0 L -HAMMITT HH AE1 M IH0 T -HAMMOCK HH AE1 M AH0 K -HAMMOCKS HH AE1 M AH0 K S -HAMMON HH AE1 M AH0 N -HAMMOND HH AE1 M AH0 N D -HAMMONDS HH AE1 M AH0 N D Z -HAMMONS HH AE1 M AH0 N Z -HAMMONTREE HH AE0 M AH0 N T R IY1 -HAMMURABI HH AE1 M AH0 R AA2 B IY2 -HAMNER HH AE1 M N ER0 -HAMON HH AE1 M AH0 N -HAMOR HH AE1 M ER0 -HAMP HH AE1 M P -HAMPE HH AE1 M P -HAMPEL HH AE1 M P AH0 L -HAMPER HH AE1 M P ER0 -HAMPERED HH AE1 M P ER0 D -HAMPERING HH AE1 M P ER0 IH0 NG -HAMPERS HH AE1 M P ER0 Z -HAMPLE HH AE1 M P AH0 L -HAMPSHIRE HH AE1 M P SH ER0 -HAMPSHIRE(2) HH AE1 M SH ER0 -HAMPSHIRE(3) HH AE1 M P SH AY0 ER0 -HAMPSHIRE(4) HH AE1 M SH AY0 ER0 -HAMPSHIRE'S HH AE1 M P SH ER0 Z -HAMPSHIRE'S(2) HH AE1 M SH ER0 Z -HAMPSHIRE'S(3) HH AE1 M P SH AY0 ER0 Z -HAMPSHIRE'S(4) HH AE1 M SH AY0 ER0 Z -HAMPSHIRITES HH AE1 M P SH ER0 AY2 T S -HAMPSON HH AE1 M P S AH0 N -HAMPSTEAD HH AE1 M P S T EH2 D -HAMPTON HH AE1 M P T AH0 N -HAMPTON'S HH AE1 M P T AH0 N Z -HAMPTONS HH AE1 M P T AH0 N Z -HAMPTONSHIRE HH AE1 M P T AH0 N SH ER0 -HAMPTONSHIRE'S HH AE1 M P T AH0 N SH ER0 Z -HAMRE HH AE1 M ER0 -HAMRIC HH AE1 M R IH0 K -HAMRICK HH AE1 M R IH0 K -HAMROCK HH AE1 M R AA2 K -HAMS HH AE1 M Z -HAMSHER HH AE1 M SH ER0 -HAMSON HH AE1 M S AH0 N -HAMSPHIRE HH AE1 M S F AY2 R -HAMSTER HH AE1 M S T ER0 -HAMSTERS HH AE1 M S T ER0 Z -HAMSTRA HH AE1 M S T R AH0 -HAMSTRING HH AE1 M S T R IH2 NG -HAMSTRINGS HH AE1 M S T R IH2 NG Z -HAMSTRUNG HH AE1 M S T R AH0 NG -HAMTRAMCK HH AE0 M T R AE1 M IH0 K -HAN HH AA1 N -HAN(2) HH AE1 N -HAN'S HH AA1 N Z -HAN'S(2) HH AE1 N Z -HANA HH AE1 N AH0 -HANAFIN HH AE1 N AH0 F IH0 N -HANAGAN HH AA0 N AA1 G AA0 N -HANAHAN HH AE1 N AH0 HH AE0 N -HANAK HH AA1 N AH0 K -HANAN HH EY1 N AH0 N -HANAS HH AE1 N AH0 Z -HANAUER HH AE1 N AW0 ER0 -HANAWALT HH AE1 N AH0 W AO2 L T -HANAWAY HH AE1 N AH0 W EY0 -HANBACK HH AE1 N B AE2 K -HANBERRY HH AE1 N B EH2 R IY0 -HANBO HH AE1 N B OW0 -HANBURY HH AE1 N B EH2 R IY0 -HANBY HH AE1 N B IY0 -HANCE HH AE1 N S -HANCHER HH AE1 N CH ER0 -HANCHETT HH AE1 N CH IH0 T -HANCHEY HH AE1 N CH IY0 -HANCOCK HH AE1 N K AA2 K -HANCOCK'S HH AE1 N K AA2 K S -HANCOX HH AE1 N K AA0 K S -HAND HH AE1 N D -HAND'S HH AE1 N D Z -HAND-HELD HH AE1 N D HH EH2 L D -HANDA HH AE1 N D AH0 -HANDBAG HH AE1 N D B AE2 G -HANDBAGS HH AE1 N D B AE2 G Z -HANDBALL HH AE1 N D B AO2 L -HANDBALLS HH AE1 N D B AO2 L Z -HANDBILL HH AE1 N D B IH2 L -HANDBILLS HH AE1 N D B IH2 L Z -HANDBOOK HH AE1 N D B UH2 K -HANDBOOKS HH AE1 N D B UH2 K S -HANDCLASP HH AE1 N D K L AE2 S P -HANDCRAFT HH AE1 N D K R AE2 F T -HANDCRAFTED HH AE1 N D K R AE2 F T IH0 D -HANDCRAFTS HH AE1 N D K R AE2 F T S -HANDCUFF HH AE1 N D K AH2 F -HANDCUFFED HH AE1 N D K AH2 F T -HANDCUFFING HH AE1 N D K AH2 F IH0 NG -HANDCUFFS HH AE1 N D K AH2 F S -HANDED HH AE1 N D AH0 D -HANDED(2) HH AE1 N D IH0 D -HANDEDLY HH AE1 N D IH0 D L IY0 -HANDEDNESS HH AE1 N D AH0 D N AH0 S -HANDEL HH AE1 N D AH0 L -HANDEL'S HH AE1 N D AH0 L Z -HANDELAND HH AE1 N D IH0 L AH0 N D -HANDELMAN HH AE1 N D AH0 L M AH0 N -HANDELS HH AE1 N D AH0 L Z -HANDELSBANK HH AE1 N D AH0 L Z B AE2 NG K -HANDELSBANKEN HH AE2 N D AH0 L S B AE1 NG K AH0 N -HANDELSMAN HH AE1 N D IH0 L S M AH0 N -HANDER HH AE1 N D ER0 -HANDERS HH AE1 N D ER0 Z -HANDFORD HH AE1 N D F ER0 D -HANDFUL HH AE1 N D F UH2 L -HANDFULS HH AE1 N D F UH2 L Z -HANDGUN HH AE1 N D G AH2 N -HANDGUNS HH AE1 N D G AH2 N Z -HANDHELD HH AE1 N D HH EH1 L D -HANDHOLD HH AE1 N D HH OW2 L D -HANDHOLDING HH AE1 N D HH OW2 L D IH0 NG -HANDICAP HH AE1 N D IY0 K AE2 P -HANDICAPPED HH AE1 N D IY0 K AE2 P T -HANDICAPPER HH AE1 N D IY0 K AE2 P ER0 -HANDICAPPERS HH AE1 N D IY0 K AE2 P ER0 Z -HANDICAPPING HH AE1 N D IY0 K AE2 P IH0 NG -HANDICAPS HH AE1 N D IY0 K AE2 P S -HANDICRAFT HH AE1 N D IY0 K R AE2 F T -HANDICRAFTS HH AE1 N D IY0 K R AE2 F T S -HANDIER HH AE1 N D IY0 ER0 -HANDIEST HH AE1 N D IY0 AH0 S T -HANDILY HH AE1 N D AH0 L IY0 -HANDING HH AE1 N D IH0 NG -HANDIWORK HH AE1 N D IY0 W ER2 K -HANDKE HH AE1 N D K IY0 -HANDKERCHIEF HH AE1 NG K ER0 CH IH0 F -HANDKERCHIEF(2) HH AE1 NG K ER0 CH IY0 F -HANDKERCHIEFS HH AE1 NG K ER0 CH AH0 F S -HANDKERCHIEFS(2) HH AE1 NG K ER0 CH IY0 F S -HANDLE HH AE1 N D AH0 L -HANDLEBAR HH AE1 N D AH0 L B AA2 R -HANDLEBARS HH AE1 N D AH0 L B AA2 R Z -HANDLED HH AE1 N D AH0 L D -HANDLER HH AE1 N D L ER0 -HANDLER(2) HH AE1 N D AH0 L ER0 -HANDLERS HH AE1 N D L ER0 Z -HANDLERS(2) HH AE1 N D AH0 L ER0 Z -HANDLES HH AE1 N D AH0 L Z -HANDLEY HH AE1 N D L IY0 -HANDLIN HH AE1 N D L IH0 N -HANDLING HH AE1 N D L IH0 NG -HANDLING(2) HH AE1 N D AH0 L IH0 NG -HANDLON HH AE1 N D L AH0 N -HANDLOOM HH AE1 N D L UW2 M -HANDLOOMS HH AE1 N D L UW2 M Z -HANDLY HH AE1 N D L IY0 -HANDMADE HH AE1 N D M EY1 D -HANDMADE(2) HH AE1 N M EY1 D -HANDOUT HH AE1 N D AW2 T -HANDOUTS HH AE1 N D AW2 T S -HANDOVER HH AE1 N D OW0 V ER0 -HANDPICK HH AE1 N D P IH1 K -HANDPICKED HH AE1 N D P IH1 K T -HANDRAIL HH AE1 N D R EY2 L -HANDRAILS HH AE1 N D R EY2 L Z -HANDRICH HH AE1 N D R IH0 K -HANDROS HH AE1 N D R OW0 S -HANDS HH AE1 N D Z -HANDS(2) HH AE1 N Z -HANDS-ON HH AE2 N Z AO1 N -HANDSAW HH AE1 N D S AO2 -HANDSAWS HH AE1 N D S AO2 Z -HANDSET HH AE1 N D S EH2 T -HANDSETS HH AE1 N D S EH2 T S -HANDSHAKE HH AE1 N D SH EY2 K -HANDSHAKES HH AE1 N D SH EY2 K S -HANDSHAKING HH AE1 N D SH EY2 K IH0 NG -HANDSOME HH AE1 N S AH0 M -HANDSOMELY HH AE1 N S AH0 M L IY0 -HANDSTAND HH AE1 N D S T AE2 N D -HANDSTANDS HH AE1 N D S T AE2 N D Z -HANDWERK HH AE1 N D W ER0 K -HANDWERKER HH AE1 N D W ER0 K ER0 -HANDWOVEN HH AE1 N D W OW1 V AH0 N -HANDWOVEN(2) HH AE1 N W OW1 V AH0 N -HANDWRITING HH AE1 N D R AY2 T IH0 NG -HANDWRITTEN HH AE1 N D R IH2 T AH0 N -HANDY HH AE1 N D IY0 -HANDYMAN HH AE1 N D IY0 M AE2 N -HANDYMEN HH AE1 N D IY0 M EH1 N -HANE HH EY1 N -HANEDA HH AH0 N EY1 D AH0 -HANEL HH AE1 N AH0 L -HANELINE HH AE1 N IH0 L AY2 N -HANEMANN HH EY1 N M AH0 N -HANER HH EY1 N ER0 -HANES HH EY1 N Z -HANEY HH EY1 N IY0 -HANF HH AE1 N F -HANFORD HH AE1 N F ER0 D -HANFT HH AE1 N F T -HANG HH AE1 NG -HANGAR HH AE1 NG ER0 -HANGARS HH AE1 NG ER0 Z -HANGARTNER HH AE1 NG G AA0 R T N ER0 -HANGED HH AE1 NG D -HANGEN HH AE1 NG AH0 N -HANGER HH AE1 NG ER0 -HANGERS HH AE1 NG ER0 Z -HANGIN' HH AE1 NG G IH0 N -HANGING HH AE1 NG IH0 NG -HANGING(2) HH AE1 NG G IH0 NG -HANGINGS HH AE1 NG G IH0 NG Z -HANGMAN HH AE1 NG M AH0 N -HANGOUT HH AE1 NG AW2 T -HANGOUTS HH AE1 NG AW2 T S -HANGOVER HH AE1 NG OW2 V ER0 -HANGOVERS HH AE1 NG OW2 V ER0 Z -HANGS HH AE1 NG Z -HANGSANG HH AE1 NG S AE2 NG -HANGSANG'S HH AE1 NG S AE2 NG Z -HANGUL HH AE1 N G UH2 L -HANGUP HH AE1 NG G AH2 P -HANGUPS HH AE1 NG G AH2 P S -HANI HH AE1 N IY0 -HANI'S HH AE1 N IY0 Z -HANIFEN HH AE1 N IH0 F AH0 N -HANIFIN HH AE1 N IH0 F IH0 N -HANIGAN HH AE1 N IH0 G AH0 N -HANING HH EY1 N IH0 NG -HANISCH HH AE1 N IH0 SH -HANISEE HH AE1 N IH0 S IY0 -HANISH HH AE1 N IH0 SH -HANJIN HH AE1 N JH IH0 N -HANK HH AE1 NG K -HANK'S HH AE1 N K S -HANKE HH AE1 NG K -HANKEL HH AE1 NG K AH0 L -HANKEN HH AE1 NG K AH0 N -HANKER HH AE1 NG K ER0 -HANKERING HH AE1 NG K ER0 IH0 NG -HANKERSON HH AE1 NG K ER0 S AH0 N -HANKES HH AE1 NG K S -HANKEY HH AE1 NG K IY0 -HANKIN HH AE1 NG K IH0 N -HANKINS HH AE1 NG K IH0 N Z -HANKINSON HH AE1 NG K IH0 N S AH0 N -HANKLA HH AE1 NG K L AH0 -HANKO HH AE1 NG K OW0 -HANKS HH AE1 NG K S -HANKY HH AE1 NG K IY0 -HANLEY HH AE1 N L IY0 -HANLEY'S HH AE1 N L IY0 Z -HANLIN HH AE1 N L IH0 N -HANLON HH AE1 N L AH0 N -HANLY HH AE1 N L IY0 -HANMER HH AE1 N M ER0 -HANN HH AE1 N -HANNA HH AE1 N AH0 -HANNA'S HH AE1 N AH0 Z -HANNAFORD HH AE1 N AH0 F ER0 D -HANNAGAN HH AE1 N AH0 G AE0 N -HANNAH HH AE1 N AH0 -HANNAHS HH AE1 N AH0 Z -HANNAM HH AE1 N AH0 M -HANNAMAN HH AE1 N AH0 M AH0 N -HANNAN HH AE1 N AH0 N -HANNAY HH AE1 N EY0 -HANNEKEN HH AE1 N IH0 K AH0 N -HANNEMAN HH AE1 N M AH0 N -HANNEMANN HH AE1 N M AH0 N -HANNEN HH AE1 N AH0 N -HANNER HH AE1 N ER0 -HANNERS HH AE1 N ER0 Z -HANNES HH AE1 N Z -HANNESSON HH AE1 N AH0 S AH0 N -HANNEY HH AE1 N IY0 -HANNI HH AE1 N IY0 -HANNIBAL HH AE1 N IH0 B AH0 L -HANNIE HH AE1 N IY0 -HANNIFIN HH AE1 N IH0 F IH0 N -HANNIG HH AE1 N IH0 G -HANNIGAN HH AE1 N IH0 G AH0 N -HANNING HH AE1 N IH0 NG -HANNIS HH AE1 N IH0 S -HANNITY HH AE1 N IH0 T IY0 -HANNOCH HH AE1 N AH0 K -HANNOLD HH AE1 N OW2 L D -HANNON HH AE1 N AH0 N -HANNULA HH AE1 N UW0 L AH0 -HANNUM HH AE1 N AH0 M -HANNY HH AE1 N IY0 -HANO HH AA1 N OW0 -HANOI HH AE1 N OY0 -HANOI'S HH AH0 N OY1 Z -HANOLD HH AE1 N OW0 L D -HANOVER HH AE1 N OW0 V ER0 -HANOVER'S HH AE1 N OW0 V ER0 Z -HANOVERIAN HH AE2 N OW0 V IH1 R IY0 AH0 N -HANRAHAN HH AE1 N R AH0 HH AE0 N -HANRATTY HH AE1 N R AH0 T IY0 -HANS HH AA1 N S -HANS(2) HH AE1 N Z -HANSA HH AE1 N S AH0 -HANSARD HH AE1 N S ER0 D -HANSBERGER HH AE1 N S B ER0 G ER0 -HANSBERRY HH AE1 N S B EH2 R IY0 -HANSBROUGH HH AE1 N S B R AW0 -HANSBURY HH AE1 N S B EH0 R IY0 -HANSCHE HH AE1 N SH IY0 -HANSCOM HH AE1 N S K AH0 M -HANSEATIC HH AE2 N S IY0 AE1 T IH0 K -HANSEL HH AE1 N S AH0 L -HANSELL HH AE1 N S AH0 L -HANSELMAN HH AE1 N S AH0 L M AH0 N -HANSEN HH AE1 N S AH0 N -HANSEN'S HH AE1 N S AH0 N Z -HANSER HH AA1 N S ER0 -HANSFORD HH AE1 N S F ER0 D -HANSHAW HH AE1 N SH AO2 -HANSHEW HH AE1 N SH UW0 -HANSHIN HH AE1 N SH IH0 N -HANSLEY HH AE1 N S L IY0 -HANSMA HH AE1 N S M AH0 -HANSMAN HH AE1 N S M AH0 N -HANSMANN HH AE1 N S M AH0 N -HANSOM HH AE1 N S AH0 M -HANSON HH AE1 N S AH0 N -HANSON'S HH AE1 N S AH0 N Z -HANSSEN HH AE1 N S AH0 N -HANSSON HH AE1 N S AH0 N -HANTA HH AE1 N T AH0 -HANTA(2) HH AA1 N T AH0 -HANTAVIRUS HH AE1 N T AH0 V AY2 R AH0 S -HANTEN HH AE1 N T AH0 N -HANTHORN HH AE1 N TH ER0 N -HANTMAN HH AE1 N T M AH0 N -HANTZ HH AE1 N T S -HANUKKAH HH AA1 N AH0 K AH0 -HANUKKAH'S HH AA1 N AH0 K AH0 Z -HANUKKAHS HH AA1 N AH0 K AH0 Z -HANUS HH EY1 N IH0 S -HANVEY HH AE1 N V IY0 -HANWA HH AE1 N W AH0 -HANWA'S HH AE1 N W AH0 Z -HANWAY HH AE1 N W EY2 -HANY HH EY1 N IY0 -HANY'S HH EY1 N IY0 Z -HANZEL HH AE1 N Z AH0 L -HANZLIK HH AE1 N Z L IH0 K -HAO HH AW1 -HAO-CHI HH AW1 CH IY1 -HAP HH AE1 P -HAPAG HH EY1 P AE2 G -HAPEMAN HH EY1 P M AH0 N -HAPGOOD HH AE1 P G UH2 D -HAPHAZARD HH AE0 P HH AE1 Z ER0 D -HAPHAZARDLY HH AE1 F AH0 Z ER0 D L IY0 -HAPHAZARDLY(2) HH AE0 P HH AE1 Z ER0 D L IY0 -HAPKE HH EY1 P K IY0 -HAPLESS HH AE1 P L AH0 S -HAPLOID HH AE1 P L OY0 D -HAPNER HH AE1 P N ER0 -HAPOALIM HH AH0 P OW1 L IH0 M -HAPOALIM(2) HH AH0 P OW2 AH0 L IY1 M -HAPP HH AE1 P -HAPPE HH AE1 P -HAPPEL HH AE1 P AH0 L -HAPPEN HH AE1 P AH0 N -HAPPENED HH AE1 P AH0 N D -HAPPENING HH AE1 P AH0 N IH0 NG -HAPPENING(2) HH AE1 P N IH0 NG -HAPPENINGS HH AE1 P AH0 N IH0 NG Z -HAPPENINGS(2) HH AE1 P N IH0 NG Z -HAPPENS HH AE1 P AH0 N Z -HAPPENSTANCE HH AE1 P AH0 N S T AE2 N S -HAPPIER HH AE1 P IY0 ER0 -HAPPIEST HH AE1 P IY0 AH0 S T -HAPPILY HH AE1 P AH0 L IY0 -HAPPINESS HH AE1 P IY0 N AH0 S -HAPPY HH AE1 P IY0 -HAPSBURG HH AE1 P S B ER0 G -HAQ HH AE1 K -HAQ'S HH AE1 K S -HAQUE HH AE1 K -HAR HH AA1 R -HARA HH EH1 R AH0 -HARA-KIRI HH AA1 R IH0 K IH1 R IY0 -HARADA HH AA0 R AA1 D AH0 -HARADIM HH AH0 R AE1 D IH2 M -HARADIM(2) HH AH0 R AE2 D IY1 M -HARAHAN HH AE1 R AH0 HH AE2 N -HARALD HH AA1 R AH0 L D -HARALDA HH AA0 R AA1 L D AH0 -HARALSON HH AE1 R AH0 L S AH0 N -HARAN HH AE1 R AH0 N -HARANGUE HH ER0 AE1 NG -HARANGUED HH ER0 AE1 NG D -HARANGUES HH ER0 AE1 NG Z -HARANGUING HH ER0 AE1 NG IH0 NG -HARARE HH ER0 AA1 R IY0 -HARASS HH ER0 AE1 S -HARASSED HH ER0 AE1 S T -HARASSER HH ER0 AE1 S ER0 -HARASSERS HH ER0 AE1 S ER0 Z -HARASSES HH ER0 AE1 S IH0 Z -HARASSING HH ER0 AE1 S IH0 NG -HARASSMENT HH ER0 AE1 S M AH0 N T -HARASZTI HH ER0 AE1 S T IY0 -HARAWAY HH AA1 R AH0 W EY0 -HARB HH AA1 R B -HARBACH HH AA1 R B AA2 K -HARBAUGH HH AA1 R B AO2 -HARBECK HH AA1 R B EH2 K -HARBER HH AA1 R B ER0 -HARBERT HH AA1 R B ER0 T -HARBERTS HH AA1 R B ER0 T S -HARBESON HH AA1 R B IH0 S AH0 N -HARBIN HH AA1 R B IH0 N -HARBINGER HH AA1 R B IH0 N JH ER0 -HARBINGERS HH AA1 R B IH0 NG ER0 Z -HARBINSON HH AA1 R B IH0 N S AH0 N -HARBISON HH AA1 R B IH0 S AH0 N -HARBOLD HH AA1 R B OW2 L D -HARBOR HH AA1 R B ER0 -HARBOR'S HH AA1 R B ER0 Z -HARBORED HH AA1 R B ER0 D -HARBORING HH AA1 R B ER0 IH0 NG -HARBORS HH AA1 R B ER0 Z -HARBORSIDE HH AA1 R B ER0 S AY2 D -HARBORY HH AA1 R B AH0 R IY0 -HARBOUR HH AA1 R B ER0 -HARBUCK HH AA1 R B AH0 K -HARBURY HH AA1 R B ER0 IY0 -HARC HH AA1 R K -HARCLERODE HH AA1 R K AH0 L ER0 OW0 D -HARCLERODE(2) HH AA1 R K L ER0 OW0 D -HARCOURT HH AA1 R K AO2 R T -HARCOURT'S HH AA1 R K ER0 T S -HARCROW HH AA1 R K R OW0 -HARCUM HH AA1 R K AH0 M -HARD HH AA1 R D -HARD-ASS HH AA2 R D AE2 S -HARD-WORKING HH AA2 R D HH W ER1 K IH0 NG -HARDACRE HH AA1 R D EY2 K ER0 -HARDAGE HH AA1 R D IH0 JH -HARDART HH AA1 R D AA2 R T -HARDAWAY HH AA1 R D AH0 W EY2 -HARDBACK HH AA1 R D B AE2 K -HARDBALL HH AA1 R D B AO2 L -HARDBOARD HH AA1 R D B AO2 R D -HARDCASTLE HH AA1 R D K AE2 S AH0 L -HARDCASTLE'S HH AA1 R D K AE2 S AH0 L Z -HARDCORE HH AA1 R D K AO1 R -HARDCOVER HH AA1 R D K AH2 V ER0 -HARDCOVERS HH AA1 R D K AH2 V ER0 Z -HARDEBECK HH AA1 R D B EH0 K -HARDEE HH AA1 R D IY1 -HARDEE'S HH AA1 R D IY1 Z -HARDEGREE HH AA0 R D IH0 G R IY1 -HARDEMAN HH AA1 R D M AH0 N -HARDEN HH AA1 R D AH0 N -HARDEN'S HH AA1 R D AH0 N Z -HARDENBROOK HH AA1 R D AH0 N B R UH2 K -HARDENED HH AA1 R D AH0 N D -HARDENER HH AA1 R D AH0 N ER0 -HARDENERS HH AA1 R D AH0 N ER0 Z -HARDENING HH AA1 R D AH0 N IH0 NG -HARDENING(2) HH AA1 R D N IH0 NG -HARDENS HH AA1 R D AH0 N Z -HARDER HH AA1 R D ER0 -HARDERS HH AA1 R D ER0 Z -HARDEST HH AA1 R D AH0 S T -HARDESTY HH AA1 R D AH0 S T IY0 -HARDEY HH AA1 R D IY0 -HARDGOOD HH AA1 R D G UH2 D -HARDGOODS HH AA1 R D G UH2 D Z -HARDGRAVE HH AA1 R D G R EY2 V -HARDGROVE HH AA1 R D G R OW2 V -HARDHEAD HH AA1 R D HH EH2 D -HARDHEADED HH AA1 R D HH EH2 D IH0 D -HARDICK HH AA1 R D IH0 K -HARDIE HH AA1 R D IY0 -HARDIER HH AA1 R D IY0 ER0 -HARDIEST HH AA1 R D IY0 AH0 S T -HARDIGREE HH AA0 R D IH0 G R IY1 -HARDIMAN HH AA1 R D IH0 M AH0 N -HARDIMON HH AA1 R D IH0 M AA0 N -HARDIN HH AA1 R D IH0 N -HARDING HH AA1 R D IH0 NG -HARDING'S HH AA1 R D IH0 NG S -HARDINGER HH AA1 R D IH0 NG ER0 -HARDISON HH AA1 R D IH0 S AH0 N -HARDISTER HH AA1 R D IH0 S T ER0 -HARDISTY HH AA1 R D IH0 S T IY0 -HARDLINE HH AA1 R D L AY2 N -HARDLINER HH AA1 R D L AY2 N ER0 -HARDLINERS HH AA1 R D L AY2 N ER0 Z -HARDLY HH AA1 R D L IY0 -HARDMAN HH AA1 R D M AH0 N -HARDNESS HH AA1 R D N AH0 S -HARDNETT HH AA1 R D N IH0 T -HARDPRESSED HH AA1 R D P R EH2 S T -HARDRICK HH AA1 R D R IH0 K -HARDS HH AA1 R D Z -HARDSCRABBLE HH AA1 R D S K R AE2 B AH0 L -HARDSHIP HH AA1 R D SH IH0 P -HARDSHIPS HH AA1 R D SH IH0 P S -HARDT HH AA1 R T -HARDTKE HH AA1 R D K IY0 -HARDWARE HH AA1 R D W EH2 R -HARDWAY HH AA1 R D W EY2 -HARDWICK HH AA1 R D W IH2 K -HARDWICKE HH AA1 R D W IH0 K -HARDWIN HH AA1 R D W IH0 N -HARDWIRED HH AA2 R D W AY1 ER0 D -HARDWOOD HH AA1 R D W UH2 D -HARDWOODS HH AA1 R D W UH2 D Z -HARDWORK HH AA1 R D W ER2 K -HARDWORKING HH AA1 R D W ER2 K IH0 NG -HARDY HH AA1 R D IY0 -HARDY'S HH AA1 R D IY0 Z -HARDYMON HH AA1 R D IY0 M AA2 N -HARE HH EH1 R -HARE'S HH EH1 R Z -HAREBRAINED HH EH1 R B R EY2 N D -HARELSON HH AE1 R IH0 L S AH0 N -HAREM HH EH1 R AH0 M -HAREN HH EH1 R AH0 N -HARER HH EH1 R ER0 -HARES HH EH1 R Z -HAREWOOD HH EH1 R W UH2 D -HARFF HH AA1 R F -HARFORD HH AA1 R F ER0 D -HARGADON HH AA0 R G AA0 D AO1 N -HARGAN HH AA1 R G AH0 N -HARGARTEN HH AA1 R G AA0 R T AH0 N -HARGENS HH AA1 R G AH0 N Z -HARGER HH AA1 R G ER0 -HARGETT HH AA1 R JH IH0 T -HARGIS HH AA1 R G IH0 S -HARGRAVE HH AA1 R G R EY2 V -HARGRAVES HH AA1 R G R EY2 V Z -HARGREAVES HH AA1 R G R IY2 V Z -HARGROVE HH AA1 R G R OW2 V -HARGUS HH AA1 R G AH0 S -HARI HH AA1 R IY0 -HARIG HH AE1 R IH0 G -HARIMA HH EH0 R IY1 M AH0 -HARING HH EH1 R IH0 NG -HARIRI HH ER0 IH1 R IY0 -HARIS HH AA1 R IY0 S -HARIS(2) HH EH1 R IH0 S -HARITOS HH EH0 R IY1 T OW0 S -HARIZ HH EH1 R IH0 Z -HARJO HH AA1 R JH OW0 -HARJU HH AA1 R Y UW0 -HARK HH AA1 R K -HARKAVY HH AA1 R K AH0 V IY0 -HARKE HH AA1 R K -HARKEN HH AA1 R K AH0 N -HARKENS HH AA1 R K AH0 N Z -HARKER HH AA1 R K ER0 -HARKEY HH AA1 R K IY0 -HARKIN HH AA1 R K IH0 N -HARKIN'S HH AA1 R K IH0 N Z -HARKING HH AA1 R K IH0 NG -HARKINS HH AA1 R K IH0 N Z -HARKLEROAD HH AA1 R K L ER0 OW0 D -HARKLESS HH AA1 R K L AH0 S -HARKNESS HH AA1 R K N AH0 S -HARKRADER HH AA1 R K R AH0 D ER0 -HARKRIDER HH AA1 R K R AY2 D ER0 -HARKS HH AA1 R K S -HARL HH AA1 R L -HARLACHER HH AA1 R L AH0 K ER0 -HARLAN HH AA1 R L AH0 N -HARLAND HH AA1 R L AH0 N D -HARLE HH AA1 R AH0 L -HARLEM HH AA1 R L AH0 M -HARLEM'S HH AA1 R L AH0 M Z -HARLEMAN HH AA1 R AH0 L M AH0 N -HARLEQUIN HH AA1 R L AH0 K W AH0 N -HARLESS HH AA1 R L IH0 S -HARLEY HH AA1 R L IY0 -HARLEY'S HH AA1 R L IY0 Z -HARLEYSVILLE HH AA1 R L IY0 Z V IH2 L -HARLIN HH AA1 R L IH0 N -HARLIN'S HH AA1 R L IH0 N Z -HARLING HH AA1 R L IH0 NG -HARLINGEN HH AA1 R L IH0 NG AH0 N -HARLISON HH AA1 R L IH0 S AH0 N -HARLOFF HH AA1 R L AO0 F -HARLOT HH AA1 R L AH0 T -HARLOW HH AA1 R L OW0 -HARM HH AA1 R M -HARM'S HH AA1 R M Z -HARMAN HH AA1 R M AH0 N -HARMATA HH AA0 R M AA1 T AH0 -HARMATTAN HH AA2 R M AH0 T AE1 N -HARMED HH AA1 R M D -HARMEL HH AA1 R M AH0 L -HARMEN HH AA1 R M AH0 N -HARMENING HH AA1 R M AH0 N IH0 NG -HARMER HH AA1 R M ER0 -HARMES HH AA1 R M Z -HARMETZ HH AA1 R M EH2 T S -HARMEYER HH AA1 R M AY2 ER0 -HARMFUL HH AA1 R M F AH0 L -HARMFULLY HH AA1 R M F AH0 L IY0 -HARMFULNESS HH AA1 R M F AH0 L N AH0 S -HARMING HH AA1 R M IH0 NG -HARMISON HH AA1 R M IH0 S AH0 N -HARMLESS HH AA1 R M L AH0 S -HARMLESSLY HH AA1 R M L AH0 S L IY0 -HARMON HH AA1 R M AH0 N -HARMON'S HH AA1 R M AH0 N Z -HARMONIA HH AA0 R M OW1 N IY0 AH0 -HARMONIC HH AA0 R M AA1 N IH0 K -HARMONICA HH AA0 R M AA1 N IH0 K AH0 -HARMONICS HH AA0 R M AA1 N IH0 K S -HARMONIE HH AA1 R M AH0 N IY0 -HARMONIES HH AA1 R M AH0 N IY0 Z -HARMONIOUS HH AA0 R M OW1 N IY0 AH0 S -HARMONIOUSLY HH AA0 R M OW1 N IY0 AH0 S L IY0 -HARMONIUM HH AA0 R M OW1 N IY0 AH0 M -HARMONIZATION HH AA2 R M AH0 N IH0 Z EY1 SH AH0 N -HARMONIZE HH AA1 R M AH0 N AY2 Z -HARMONIZED HH AA1 R M AH0 N AY2 Z D -HARMONIZES HH AA1 R M AH0 N AY2 Z IH0 Z -HARMONIZING HH AA1 R M AH0 N AY2 Z IH0 NG -HARMONY HH AA1 R M AH0 N IY0 -HARMS HH AA1 R M Z -HARMSEN HH AA1 R M S AH0 N -HARN HH AA1 R N -HARNACK HH AA1 R N AH0 K -HARNAGE HH AA1 R N IH0 JH -HARNDEN HH AA1 R N D AH0 N -HARNE HH AA1 R N -HARNED HH AA1 R N D -HARNER HH AA1 R N ER0 -HARNESS HH AA1 R N AH0 S -HARNESS(2) HH AA1 R N IH0 S -HARNESSED HH AA1 R N AH0 S T -HARNESSES HH AA1 R N AH0 S AH0 Z -HARNESSES(2) HH AA1 R N AH0 S IH0 Z -HARNESSING HH AA1 R N AH0 S IH0 NG -HARNETT HH AA1 R N IH0 T -HARNEY HH AA1 R N IY0 -HARNISCH HH AA1 R N IH0 SH -HARNISCHFEGER HH AA1 R N IH0 SH F EH2 G ER0 -HARNISCHFEGER'S HH AA1 R N IH0 SH F EH2 G ER0 Z -HARNISH HH AA1 R N IH0 SH -HARNOIS HH AA0 R N W AA1 -HARO HH AA1 R OW0 -HAROLD HH EH1 R AH0 L D -HAROLD'S HH EH1 R AH0 L D Z -HAROLDSON HH AE1 R OW0 L D S AH0 N -HARP HH AA1 R P -HARPE HH AA1 R P -HARPED HH AA1 R P T -HARPEL HH AA0 R P EH1 L -HARPENAU HH AA1 R P IH0 N OW0 -HARPER HH AA1 R P ER0 -HARPER'S HH AA1 R P ER0 Z -HARPERCOLLINS HH AA1 R P ER0 K AO1 L IH0 N Z -HARPERS HH AA1 R P ER0 Z -HARPHAM HH AA1 R F AH0 M -HARPIN HH AA0 R P AE1 N -HARPING HH AA1 R P IH0 NG -HARPIST HH AA1 R P IH0 S T -HARPISTS HH AA1 R P IH0 S T S -HARPLEY HH AA1 R P L IY0 -HARPO HH AA1 R P OW0 -HARPOLD HH AA1 R P OW2 L D -HARPOLE HH AA1 R P OW2 L -HARPOON HH AA0 R P UW1 N -HARPOONS HH AA0 R P UW1 N Z -HARPOOTLIAN HH AA0 R P UW1 T L IY0 AH0 N -HARPS HH AA1 R P S -HARPSICHORD HH AA1 R P S AH0 K AO2 R D -HARPST HH AA1 R P S T -HARPSTER HH AA1 R P S T ER0 -HARQUEBUS HH AA1 R K W AH0 B AH0 S -HARR HH AE1 R -HARRA HH AA1 R AH0 -HARRAH HH AE1 R AH0 -HARRAH'S HH EH1 R AH0 Z -HARRAL HH AE1 R AH0 L -HARRALSON HH AE1 R AH0 L S AH0 N -HARRE HH AE1 R -HARREL HH AE1 R AH0 L -HARRELD HH AE1 R IH0 L D -HARRELL HH EH1 R AH0 L -HARRELL'S HH AE1 R AH0 L Z -HARRELSON HH EH1 R IH0 L S AH0 N -HARREN HH AE1 R AH0 N -HARRER HH AA1 R ER0 -HARRIED HH EH1 R IY0 D -HARRIER HH EH1 R IY0 ER0 -HARRIES HH EH1 R IY0 Z -HARRIET HH EH1 R IY0 AH0 T -HARRIETTE HH AE1 R IY0 EH0 T -HARRIGAN HH EH1 R IH0 G AH0 N -HARRIGER HH AE1 R IH0 G ER0 -HARRILL HH AE1 R AH0 L -HARRIMAN HH EH1 R IH0 M AH0 N -HARRING HH AE1 R IH0 NG -HARRINGTON HH EH1 R IH0 NG T AH0 N -HARRIOTT HH AE1 R IY0 AH0 T -HARRIS HH EH1 R IH0 S -HARRIS' HH EH1 R IH0 S -HARRIS'S HH EH1 R IH0 S IH0 Z -HARRISBURG HH AE1 R IH0 S B ER0 G -HARRISBURG(2) HH EH1 R IH0 S B ER0 G -HARRISBURG'S HH AE1 R IH0 S B ER0 G Z -HARRISBURG'S(2) HH EH1 R IH0 S B ER0 G Z -HARRISBURGH HH AE1 R IH0 S B ER0 G -HARRISBURGH(2) HH EH1 R IH0 S B ER0 G -HARRISBURGH'S HH AE1 R IH0 S B ER0 G Z -HARRISBURGH'S(2) HH EH1 R IH0 S B ER0 G Z -HARRISES HH AE1 R IH0 S IH0 Z -HARRISON HH EH1 R IH0 S AH0 N -HARRISON'S HH EH1 R IH0 S AH0 N Z -HARRISS HH AE1 R IH0 S -HARRITY HH AE1 R IH0 T IY0 -HARROD HH EH1 R AH0 D -HARROD'S HH EH1 R AH0 D Z -HARRODS HH EH1 R AH0 D Z -HARROLD HH EH1 R AH0 L D -HARRON HH AE1 R AH0 N -HARROP HH EH1 R AH0 P -HARROUN HH ER0 UW1 N -HARROW HH AE1 R OW0 -HARROWER HH AE1 R OW0 W ER0 -HARROWING HH EH1 R OW0 IH0 NG -HARRY HH EH1 R IY0 -HARRY'S HH EH1 R IY0 Z -HARRYMAN HH AE1 R IY0 M AH0 N -HARSCH HH AA1 R SH -HARSCO HH AA1 R S K OW0 -HARSH HH AA1 R SH -HARSHA HH AA1 R SH AH0 -HARSHAM HH AA1 R SH AH0 M -HARSHAW HH AA1 R SH AO2 -HARSHBARGER HH AA1 R SH B AA2 R G ER0 -HARSHBERGER HH AA1 R SH B ER0 G ER0 -HARSHER HH AA1 R SH ER0 -HARSHEST HH AA1 R SH AH0 S T -HARSHFIELD HH AA1 R SH F IY2 L D -HARSHLY HH AA1 R SH L IY0 -HARSHMAN HH AA1 R SH M AH0 N -HARSHNESS HH AA1 R SH N AH0 S -HARSTAD HH AA1 R S T AH0 D -HARSTON HH AA1 R S T AH0 N -HART HH AA1 R T -HART'S HH AA1 R T S -HARTE HH AA1 R T -HARTEL HH AA1 R T AH0 L -HARTELL HH AA1 R T AH0 L -HARTEN HH AA1 R T AH0 N -HARTENSTEIN HH AA1 R T AH0 N S T AY2 N -HARTENSTEIN(2) HH AA1 R T AH0 N S T IY2 N -HARTER HH AA1 R T ER0 -HARTFIEL HH AA1 R T F IY2 L -HARTFIELD HH AA1 R T F IY2 L D -HARTFORD HH AA1 R T F ER0 D -HARTFORD'S HH AA1 R T F ER0 D Z -HARTGRAVES HH AA1 R T G R EY2 V Z -HARTGROVE HH AA1 R T G R OW2 V -HARTH HH AA1 R TH -HARTIG HH AA1 R T IH0 G -HARTIGAN HH AA1 R T IH0 G AH0 N -HARTIN HH AA1 R T IH0 N -HARTING HH AA1 R T IH0 NG -HARTINGER HH AA1 R T IH0 NG ER0 -HARTIS HH AA1 R T IH0 S -HARTJE HH AA1 R T JH EY0 -HARTKE HH AA1 R T K IY0 -HARTKOPF HH AA1 R T K AO0 F -HARTL HH AA1 R T AH0 L -HARTLAGE HH AA1 R T L IH0 JH -HARTLAND HH AA1 R T L AH0 N D -HARTLAUB HH AA1 R T L AW2 B -HARTLE HH AA1 R T AH0 L -HARTLESS HH AA1 R T L AH0 S -HARTLEY HH AA1 R T L IY0 -HARTLIEB HH AA1 R T L IY2 B -HARTLINE HH AA1 R T L AY2 N -HARTLING HH AA1 R T L IH0 NG -HARTMAN HH AA1 R T M AH0 N -HARTMANN HH AA1 R T M AH0 N -HARTMARX HH AA1 R T M AA2 R K S -HARTNELL HH AA1 R T N AH0 L -HARTNER HH AA1 R T N ER0 -HARTNESS HH AA1 R T N AH0 S -HARTNETT HH AA1 R T N IH0 T -HARTNEY HH AA1 R T N IY0 -HARTOG HH AA1 R T AH0 G -HARTON HH AA1 R T AH0 N -HARTONG HH AA1 R T AO0 NG -HARTRANFT HH AA1 R T R AH2 N F T -HARTS HH AA1 R T S -HARTSELL HH AA1 R T S AH0 L -HARTSFIELD HH AA1 R T S F IY2 L D -HARTSHORN HH AA1 R T S HH AO2 R N -HARTSHORNE HH AA1 R CH ER0 N -HARTSOCK HH AA1 R T S AH0 K -HARTSOE HH AA1 R T S OW0 -HARTSON HH AA1 R T S AH0 N -HARTSOOK HH AA1 R T S UH0 K -HARTSOUGH HH AA1 R T S AW0 -HARTSTEIN HH AA1 R T S T AY2 N -HARTSTEIN(2) HH AA1 R T S T IY2 N -HARTSVILLE HH AA1 R T S V IH0 L -HARTT HH AA1 R T -HARTTER HH AA1 R T ER0 -HARTUNG HH AA1 R T AH0 NG -HARTWELL HH AA1 R T W EH2 L -HARTWICK HH AA1 R T W IH2 K -HARTWIG HH AA1 R T W IH0 K -HARTWOOD HH AA1 R T W UH2 D -HARTY HH AA1 R T IY0 -HARTZ HH AA1 R T S -HARTZEL HH AA1 R T Z AH0 L -HARTZELL HH AA1 R T Z AH0 L -HARTZLER HH AA1 R T S L ER0 -HARTZOG HH AA1 R T Z AH0 G -HARUO HH AA0 R UW1 OW0 -HARV HH AA1 R V -HARVARD HH AA1 R V ER0 D -HARVARD'S HH AA1 R V ER0 D Z -HARVATH HH AA1 R V AH0 TH -HARVE HH AA1 R V -HARVEL HH AA1 R V AH0 L -HARVELL HH AA1 R V AH0 L -HARVEST HH AA1 R V AH0 S T -HARVESTABLE HH AA1 R V AH0 S T AH0 B AH0 L -HARVESTED HH AA1 R V AH0 S T IH0 D -HARVESTER HH AA1 R V AH0 S T ER0 -HARVESTERS HH AA1 R V AH0 S T ER0 Z -HARVESTING HH AA1 R V AH0 S T IH0 NG -HARVESTS HH AA1 R V AH0 S T S -HARVEY HH AA1 R V IY0 -HARVEY'S HH AA1 R V IY0 Z -HARVIE HH AA1 R V IY0 -HARVILL HH AA1 R V IH0 L -HARVILLE HH AA1 R V IH2 L -HARVIN HH AA1 R V IH0 N -HARVISON HH AA1 R V IH0 S AH0 N -HARWARD HH AA1 R W ER0 D -HARWELL HH AA1 R W EH2 L -HARWICK HH AA1 R W IH0 K -HARWOOD HH AA1 R W UH2 D -HARYANA HH EH0 R Y AA1 N AH0 -HAS HH AE1 Z -HAS(2) HH AH0 Z -HAS-BEEN HH AE1 Z B IH2 N -HAS-BEENS HH AE1 Z B IH2 N Z -HASAN HH EY1 Z AH0 N -HASBRO HH AE1 Z B R OW0 -HASBRO'S HH AE1 Z B R OW2 Z -HASBROOK HH AE1 S B R UH0 K -HASBROUCK HH AE1 Z B R UH2 K -HASCALL HH AE1 S K AH0 L -HASCH HH AE1 SH -HASCHKE HH AE1 SH K -HASE HH EY1 Z -HASEGAWA HH AA2 S EY0 G AA1 W AH0 -HASEK HH AA1 S EH0 K -HASELDEN HH AE1 S IH0 L D AH0 N -HASELEY HH AE1 S IH0 L IY0 -HASELHORST HH AE1 S IH0 L HH AO0 R S T -HASELTINE HH AE1 S IH0 L T IY0 N -HASELTON HH AH0 S EH1 L T AH0 N -HASEMAN HH EY1 S M AH0 N -HASENAUER HH AE1 S IH0 N AW0 ER0 -HASENFUS HH EY1 Z AH0 N F AH2 S -HASER HH EH1 Z ER0 -HASH HH AE1 SH -HASHAGEN HH AE1 SH AH0 G AH0 N -HASHED HH AE1 SH T -HASHEM HH AE1 SH IH0 M -HASHEMI HH AH0 SH EY1 M IY0 -HASHEMITE HH AE1 SH AH0 M AY2 T -HASHER HH AE1 SH ER0 -HASHER'S HH AE1 SH ER0 Z -HASHERS HH AE1 SH ER0 Z -HASHES HH AE1 SH AH0 Z -HASHI HH AE1 SH IY2 -HASHIM HH AE1 SH IH0 M -HASHIMOTO HH AA0 SH IY0 M OW1 T OW0 -HASHING HH AE1 SH IH0 NG -HASHISH HH AE1 SH IH0 SH -HASHISH(2) HH AH0 SH IY1 SH -HASHMAN HH AE1 SH M AH0 N -HASHMARK HH AE1 SH M AA2 R K -HASHMI HH AE1 SH M IY0 -HASHMI(2) HH AA1 SH M IY0 -HASHTAG HH AE1 SH T AE2 G -HASID HH AH0 S IH1 D -HASIDIC HH AH0 S IH1 D IH0 K -HASIDIC(2) HH AH0 S IY1 D IH0 K -HASIDIM HH AH0 S IH1 D IH0 M -HASIDIM(2) HH AH0 S IY1 D IH0 M -HASIDS HH AH0 S IH1 D Z -HASKE HH EY1 S K -HASKELL HH AE1 S K AH0 L -HASKETT HH AE1 S K IH0 T -HASKEW HH AE1 S K Y UW0 -HASKIN HH AE1 S K IH0 N -HASKINS HH AE1 S K IH0 N Z -HASLAM HH AE1 S L AH0 M -HASLER HH AE1 S AH0 L ER0 -HASLER(2) HH AE1 S L ER0 -HASLETT HH AE1 S L IH0 T -HASLEY HH AE1 Z L IY0 -HASMAN HH AE1 S M AH0 N -HASN'T HH AE1 Z AH0 N T -HASO HH AA1 S OW2 -HASO'S HH AA1 S OW2 Z -HASPEL HH AE1 S P AH0 L -HASS HH AE1 S -HASSAN HH AH0 S AA1 N -HASSE HH AA1 S -HASSEBROCK HH AE1 S IH0 B R AA1 K -HASSEL HH AE1 S AH0 L -HASSELBACH HH AE1 S IH0 L B AA0 K -HASSELBRING HH AE1 S IH0 L B R IH0 NG -HASSELL HH AE1 S AH0 L -HASSELMAN HH AE1 S AH0 L M AH0 N -HASSEN HH AE1 S AH0 N -HASSENBERG HH AE1 S AH0 N B ER0 G -HASSENFELD HH AE1 S AH0 N F EH2 L D -HASSETT HH AE1 S IH0 T -HASSEY HH AE1 S IY0 -HASSIG HH AE1 S IH0 G -HASSING HH AE1 S IH0 NG -HASSINGER HH AE1 S IH0 N JH ER0 -HASSLE HH AE1 S AH0 L -HASSLED HH AE1 S AH0 L D -HASSLER HH AE1 S L ER0 -HASSLES HH AE1 S AH0 L Z -HASSLING HH AE1 S AH0 L IH0 NG -HASSLING(2) HH AE1 S L IH0 NG -HASSMAN HH AE1 S M AH0 N -HASSON HH AE1 S AH0 N -HAST HH AE1 S T -HASTA HH AE1 S T AH0 -HASTA(2) AA1 S T AH0 -HASTE HH EY1 S T -HASTEN HH EY1 S AH0 N -HASTENED HH EY1 S AH0 N D -HASTENING HH EY1 S AH0 N IH0 NG -HASTENING(2) HH EY1 S N IH0 NG -HASTENS HH EY1 S AH0 N Z -HASTERT HH AE1 S T ER0 T -HASTEY HH EY1 S T IY0 -HASTIE HH EY1 S T IY0 -HASTILY HH EY1 S T AH0 L IY0 -HASTING HH EY1 S T IH0 NG -HASTINGS HH EY1 S T IH0 NG Z -HASTON HH AE1 S T AH0 N -HASTY HH EY1 S T IY0 -HASWELL HH AE1 S W EH0 L -HASZ HH AA1 SH -HAT HH AE1 T -HAT'S HH AE1 T S -HATA HH AA1 T AH0 -HATALA HH AE1 T AH0 L AH0 -HATAWAY HH AE1 T AH0 W EY2 -HATCH HH AE1 CH -HATCH'S HH AE1 CH IH0 Z -HATCHBACK HH AE1 CH B AE2 K -HATCHED HH AE1 CH T -HATCHEL HH AE1 CH AH0 L -HATCHELL HH AE1 CH AH0 L -HATCHER HH AE1 CH ER0 -HATCHERIES HH AE1 CH ER0 IY0 Z -HATCHERY HH AE1 CH ER0 IY0 -HATCHES HH AE1 CH IH0 Z -HATCHET HH AE1 CH AH0 T -HATCHETS HH AE1 CH AH0 T S -HATCHETT HH AE1 CH IH0 T -HATCHING HH AE1 CH IH0 NG -HATE HH EY1 T -HATED HH EY1 T AH0 D -HATED(2) HH EY1 T IH0 D -HATEFUL HH EY1 T F AH0 L -HATEM HH AE1 T IH0 M -HATER HH EY1 T ER0 -HATERS HH EY1 T ER0 Z -HATES HH EY1 T S -HATFIELD HH AE1 T F IY2 L D -HATFIELD'S HH AE1 T F IY2 L D Z -HATH HH AE1 TH -HATHAWAY HH AE1 TH AH0 W EY2 -HATHAWAY'S HH AE1 TH AH0 W EY2 Z -HATHCOAT HH AE1 TH K OW2 T -HATHCOCK HH AE1 TH K AH0 K -HATHEWAY HH EY1 DH W EY0 -HATHORN HH AA1 TH AO0 R N -HATHORNE HH AE1 TH ER0 N -HATING HH EY1 T IH0 NG -HATLER HH EY1 T AH0 L ER0 -HATLER(2) HH EY1 T L ER0 -HATLESTAD HH AE1 T AH0 L S T AH0 D -HATLEY HH AE1 T L IY0 -HATMAKER HH AE1 T M EY2 K ER0 -HATON HH AE1 T AO0 N -HATON(2) AE2 T AO1 N -HATRED HH EY1 T R AH0 D -HATREDS HH EY1 T R AH0 D Z -HATRICK HH AE1 T R IH0 K -HATS HH AE1 T S -HATT HH AE1 T -HATTABAUGH HH AE1 T AH0 B AO0 -HATTAN HH AE1 T AH0 N -HATTAWAY HH AE1 T AH0 W EY0 -HATTEN HH AE1 T AH0 N -HATTENDORF HH AE1 T IH0 N D AO0 R F -HATTER HH AE1 T ER0 -HATTERAS HH AE1 T ER0 AH0 S -HATTERSLEY HH AE1 T ER0 Z L IY0 -HATTERY HH AE1 T ER0 IY0 -HATTIE HH AE1 T IY0 -HATTON HH AE1 T AH0 N -HATTORI HH AA0 T AO1 R IY0 -HATTUSAS HH AH0 T UW1 S AH0 S -HATTY HH AE1 T IY0 -HATZ HH AE1 T S -HAU HH AW1 -HAUB HH AO1 B -HAUBER HH AW1 B ER0 -HAUBERT HH AW1 B ER0 T -HAUBNER HH AW1 B N ER0 -HAUBRICH HH AW1 B R IH0 K -HAUCH HH AO1 CH -HAUCK HH AO1 K -HAUENSTEIN HH AW1 AH0 N S T AY0 N -HAUENSTEIN(2) HH AW1 AH0 N S T IY0 N -HAUER HH AW1 ER0 -HAUETER HH AW1 T ER0 -HAUF HH AO1 F -HAUFER HH AO1 F ER0 -HAUFF HH AO1 F -HAUG HH AO1 G -HAUGAN HH AO1 G AH0 N -HAUGE HH AO1 JH -HAUGEN HH AW1 G AH0 N -HAUGER HH AW1 G ER0 -HAUGH HH AO1 -HAUGHEY HH AO1 K IY0 -HAUGHN HH AO1 N -HAUGHT HH AO1 T -HAUGHTILY HH AO1 T IH0 L IY0 -HAUGHTON HH AO1 T AH0 N -HAUGHTY HH AO1 T IY0 -HAUGLAND HH AO1 G L AH0 N D -HAUK HH AO1 K -HAUKE HH AO1 K -HAUL HH AO1 L -HAULAGE HH AO1 L AH0 JH -HAULED HH AO1 L D -HAULER HH AO1 L ER0 -HAULERS HH AO1 L ER0 Z -HAULING HH AO1 L IH0 NG -HAULS HH AO1 L Z -HAULSEY HH AO1 L S IY0 -HAUN HH AO1 N -HAUNT HH AO1 N T -HAUNTED HH AO1 N T IH0 D -HAUNTING HH AO1 N T IH0 NG -HAUNTINGLY HH AO1 N T IH0 NG L IY0 -HAUNTS HH AO1 N T S -HAUPERT HH AW1 P ER0 T -HAUPPAUGE HH AW1 P AO0 JH -HAUPT HH AO1 P T -HAUPTFUHRER HH AW1 P T F Y UH2 R ER0 -HAUPTMAN HH AW1 P T M AH0 N -HAUPTMANN HH AW1 P T M AH0 N -HAURY HH AO1 R IY0 -HAUS HH AW1 S -HAUSA HH AW1 S AH0 -HAUSAUER HH AW1 S AW0 ER0 -HAUSCH HH AW1 SH -HAUSCHILD HH AW1 S CH AY2 L D -HAUSCHILDT HH AW1 SH IH0 L T -HAUSE HH AW1 S -HAUSEN HH AW1 Z AH0 N -HAUSER HH AW1 Z ER0 -HAUSERMAN HH AW1 Z ER0 M AH0 N -HAUSFELD HH AW1 S F EH2 L D -HAUSKY HH AO1 S K IY0 -HAUSLER HH AW1 S AH0 L ER0 -HAUSLER(2) HH AW1 S L ER0 -HAUSMAN HH AW1 S M AH0 N -HAUSMANN HH AW1 S M AH0 N -HAUSNER HH AW1 S N ER0 -HAUSS HH AW1 S -HAUSSER HH AW1 S ER0 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IY2 K -HEALTHWISE HH EH1 L TH W AY2 Z -HEALTHWORK HH EH1 L TH W ER0 K -HEALTHWORKS HH EH1 L TH W ER0 K S -HEALTHY HH EH1 L TH IY0 -HEALY HH IY1 L IY0 -HEALY'S HH IY1 L IY0 Z -HEANEY HH IY1 N IY0 -HEAP HH IY1 P -HEAPE HH IY1 P -HEAPED HH IY1 P T -HEAPHY HH IY1 F IY0 -HEAPING HH IY1 P IH0 NG -HEAPS HH IY1 P S -HEAR HH IY1 R -HEARD HH ER1 D -HEARER HH IY1 R ER0 -HEARERS HH IY1 R ER0 Z -HEARIN HH IH1 R IH0 N -HEARING HH IY1 R IH0 NG -HEARING'S HH IY1 R IH0 NG Z -HEARINGS HH IY1 R IH0 NG Z -HEARKENS HH AA1 R K AH0 N Z -HEARL HH ER1 L -HEARN HH ER1 N -HEARNE HH ER1 N -HEARNS HH ER1 N Z -HEARON HH IH1 R AH0 N -HEARRON HH AO1 R AH0 N -HEARS HH IY1 R Z -HEARSAY HH IY1 R S EY2 -HEARSE HH ER1 S -HEARST HH ER1 S T -HEARST'S HH ER1 S T S -HEART HH AA1 R T -HEART'S HH AA1 R T S -HEARTACHE HH AA1 R T EY2 K -HEARTBEAT HH AA1 R T B IY2 T -HEARTBEATS HH AA1 R T B IY2 T S -HEARTBREAK HH AA1 R T B R EY2 K -HEARTBREAKING HH AA1 R T B R EY2 K IH0 NG -HEARTBREAKS HH AA1 R T B R EY2 K S -HEARTBROKEN HH AA1 R T B R OW2 K AH0 N -HEARTBURN HH AA1 R T B ER2 N -HEARTED HH AA1 R T AH0 D -HEARTED(2) HH AA1 R T IH0 D -HEARTEDLY HH AA1 R T IH0 D L IY0 -HEARTEN HH AA1 R T AH0 N -HEARTENED HH AA1 R T AH0 N D -HEARTENING HH AA1 R T AH0 N IH0 NG -HEARTENING(2) HH AA1 R T N IH0 NG -HEARTFELT HH AA1 R T F EH2 L T -HEARTH HH AA1 R TH -HEARTHS HH AA1 R TH S -HEARTIER HH AA1 R T IY0 ER0 -HEARTIEST HH AA1 R T IY0 AH0 S T -HEARTILY HH AA1 R T AH0 L IY0 -HEARTLAND HH AA1 R T L AE2 N D -HEARTLESS HH AA1 R T L AH0 S -HEARTS HH AA1 R T S -HEARTSCAN HH AA1 R T S K AE2 N -HEARTTHROB HH AA1 R T TH R AA2 B -HEARTTHROBS HH AA1 R T TH R AA2 B Z -HEARTWARMING HH AA1 R T W AO2 R M IH0 NG -HEARTWISE HH AA1 R T W AY2 Z -HEARTWOOD HH AA1 R T W UH2 D -HEARTY HH AA1 R T IY0 -HEASLEY HH IY1 Z L IY0 -HEASLIP HH IY1 S L IH0 P -HEASTON HH IY1 S T AH0 N -HEAT HH IY1 T -HEAT'S HH IY1 T S -HEATED HH IY1 T AH0 D -HEATED(2) HH IY1 T IH0 D -HEATEDLY HH IY1 T IH0 D L IY0 -HEATER HH IY1 T ER0 -HEATERS HH IY1 T ER0 Z -HEATH HH IY1 TH -HEATH'S HH IY1 TH S -HEATHCLIFF HH EH1 TH K L IH0 F -HEATHCOCK HH EH1 TH K AH0 K -HEATHCOTE HH EH1 TH K AH0 T -HEATHEN HH IY1 DH AH0 N -HEATHER HH EH1 DH ER0 -HEATHER'S HH EH1 DH ER0 Z -HEATHERINGTON HH EH1 DH ER0 IH0 NG T AH0 N -HEATHERLY HH EH1 DH ER0 L IY0 -HEATHERS HH EH1 DH ER0 Z -HEATHMAN HH IY1 TH M AH0 N -HEATHROW HH IY1 TH R OW0 -HEATHROW'S HH IY1 TH R OW0 Z -HEATHWOOD HH IY1 TH W UH2 D -HEATING HH IY1 T IH0 NG -HEATLEY HH IY1 T L IY0 -HEATON HH IY1 T AH0 N -HEATS HH IY1 T S -HEATWOLE HH IY1 T W OW2 L -HEAVE HH IY1 V -HEAVED HH IY1 V D -HEAVEN HH EH1 V AH0 N -HEAVEN'S HH EH1 V AH0 N Z -HEAVENER HH EH1 V AH0 N ER0 -HEAVENLY HH EH1 V AH0 N L IY0 -HEAVENS HH EH1 V AH0 N Z -HEAVES HH IY1 V Z -HEAVEY HH IY1 V IY0 -HEAVIER HH EH1 V IY0 ER0 -HEAVIES HH EH1 V IY0 Z -HEAVIEST HH EH1 V IY0 AH0 S T -HEAVILY HH EH1 V AH0 L IY0 -HEAVIN HH EH1 V IH0 N -HEAVING HH IY1 V IH0 NG -HEAVNER HH IY1 V N ER0 -HEAVRIN HH IY1 V R IH0 N -HEAVY HH EH1 V IY0 -HEAVYHANDED HH EH1 V IY0 HH AE2 N D IH0 D -HEAVYSET HH EH1 V IY0 S EH2 T -HEAVYWEIGHT HH EH1 V IY0 W EY2 T -HEAVYWEIGHTS HH EH1 V IY0 W EY2 T S -HEBARD HH EH1 B ER0 D -HEBB HH EH1 B -HEBDA HH EH1 B D AH0 -HEBDING HH EH1 B D IH0 NG -HEBE HH IY1 B -HEBEI HH IY1 B IY0 -HEBEL HH EH1 B AH0 L -HEBELER HH EH1 B AH0 L ER0 -HEBENSTREIT HH EH1 B IH0 N S T R AY0 T -HEBER HH IY1 B ER0 -HEBERER HH EH1 B ER0 ER0 -HEBERLE HH EH1 B ER0 AH0 L -HEBERLEIN HH EH1 B ER0 L AY2 N -HEBERLING HH EH1 B ER0 L IH0 NG -HEBERT HH EH1 B ER0 T -HEBNER HH EH1 B N ER0 -HEBREW HH IY1 B R UW0 -HEBREWS HH IY1 B R UW0 Z -HEBRIDES HH EH1 B R IH0 D IY0 Z -HEBRON HH EH1 B R AH0 N -HEBRON(2) HH EH1 B R AO2 N -HECCO HH EH1 K OW0 -HECHINGER HH EH1 K IH0 N JH ER0 -HECHLER HH EH1 K L ER0 -HECHT HH EH1 K T -HECHT'S HH EH1 K T S -HECHTMAN HH EH1 K T M AH0 N -HECK HH EH1 K -HECK'S HH EH1 K S -HECKAMAN HH EH1 K AH0 M AH0 N -HECKARD HH EH1 K ER0 D -HECKART HH EH1 K ER0 T -HECKATHORN HH EH1 K AH0 TH ER0 N -HECKBERT HH EH1 K B ER0 T -HECKEL HH EH1 K AH0 L -HECKENDORN HH EH1 K EH0 N D AO0 R N -HECKER HH EH1 K ER0 -HECKERT HH EH1 K ER0 T -HECKLE HH EH1 K AH0 L -HECKLED HH EH1 K AH0 L D -HECKLER HH EH1 K L ER0 -HECKLERS HH EH1 K L ER0 Z -HECKLING HH EH1 K L IH0 NG -HECKMAN HH EH1 K M AH0 N -HECKMANN HH EH1 K M AH0 N -HECKUVA HH EH0 K Y UW1 V AH0 -HECKUVA(2) HH EH1 K AH0 V AH0 -HECLA HH EH1 K L AH0 -HECLA'S HH EH1 K L AH0 Z -HECOX HH EH1 K AA0 K S -HECTARE HH EH1 K T AA2 R -HECTARES HH EH1 K T AA2 R Z -HECTIC HH EH1 K T IH0 K -HECTOGRAPH HH EH1 K T AH0 G R AE2 F -HECTOR HH EH1 K T ER0 -HECTOR'S HH EH1 K T ER0 Z -HECTORING HH EH1 K T ER0 IH0 NG -HECUBA HH EH1 K Y AH0 B AH0 -HECUBA(2) HH EH1 K Y UW0 B AH0 -HEDA HH EY1 D AH0 -HEDBERG HH EH1 D B ER0 G -HEDDA HH EH1 D AH0 -HEDDEN HH EH1 D AH0 N -HEDDING HH EH1 D IH0 NG -HEDDY HH EH1 D IY0 -HEDEEN HH EH1 D IY0 N -HEDGE HH EH1 JH -HEDGECOCK HH EH1 JH K AA2 K -HEDGED HH EH1 JH D -HEDGEHOG HH EH1 JH HH AA2 G -HEDGEHOGS HH EH1 JH HH AA2 G Z -HEDGEPATH HH EH1 JH P AE2 TH -HEDGEPETH HH EH1 JH IH0 P EH0 TH -HEDGER HH EH1 JH ER0 -HEDGERS HH EH1 JH ER0 Z -HEDGES HH EH1 JH IH0 Z -HEDGING HH EH1 JH IH0 NG -HEDGLIN HH EH1 JH L IH0 N -HEDGPETH HH EH1 JH P IH0 TH -HEDI HH EH1 D IY0 -HEDIGER HH EH1 D IH0 G ER0 -HEDIN HH EH1 D IH0 N -HEDINGER HH EH1 D IH0 N G ER0 -HEDINGER(2) HH EH1 D IH0 N JH ER0 -HEDLEY HH EH1 D L IY0 -HEDLUND HH EH1 D L AH0 N D -HEDMAN HH EH1 D M AH0 N -HEDONIC HH AH0 D AA1 N IH0 K -HEDONISM HH IY1 D AH0 N IH2 Z AH0 M -HEDONISTIC HH IY2 D AH0 N IH1 S T IH0 K -HEDQUIST HH EH1 D K W IH2 S T -HEDRICH HH EH1 D R IH0 K -HEDRICK HH EH1 D R IH0 K -HEDSTROM HH EH1 D S T R AH0 M -HEDTKE HH EH1 D K IY0 -HEDWIG HH EH1 D W IH0 G -HEDWIGA HH EH1 D W IH0 G AH0 -HEDY HH IY1 D IY0 -HEE HH IY1 -HEEB HH IY1 B -HEEBNER HH IY1 B N ER0 -HEED HH IY1 D -HEEDED HH IY1 D AH0 D -HEEDED(2) HH IY1 D IH0 D -HEEDING HH IY1 D IH0 NG -HEEDS HH IY1 D Z -HEEFNER HH IY1 F N ER0 -HEEG HH IY1 G -HEEHAW HH IY1 HH AW2 -HEEKE HH IY1 K -HEEKIN HH IY1 K IH0 N -HEEL HH IY1 L -HEELAN HH IY1 L AH0 N -HEELED HH IY1 L D -HEELING HH IY1 L IH0 NG -HEELS HH IY1 L Z -HEEMSTRA HH IY1 M S T R AH0 -HEENAN HH IY1 N AH0 N -HEENEY HH IY1 N IY0 -HEER HH IY1 ER0 -HEEREN HH IH1 R AH0 N -HEERMANN HH IH1 R M AH0 N -HEES HH IY1 Z -HEESCH HH IY1 SH -HEESE HH IY1 Z -HEETER HH IY1 T ER0 -HEFEI HH AH0 F EY1 -HEFFEL HH EH1 F AH0 L -HEFFELFINGER HH EH1 F IH0 L F IH0 NG ER0 -HEFFERAN HH EH1 F ER0 AH0 N -HEFFERMAN HH EH1 F ER0 M AH0 N -HEFFERN HH EH1 F ER0 N -HEFFERNAN HH EH1 F ER0 N AH0 N -HEFFERON HH EH1 F ER0 AH0 N -HEFFINGTON HH EH1 F IH0 NG T AH0 N -HEFFLER HH EH1 F L ER0 -HEFFLEY HH EH1 F L IY0 -HEFFNER HH EH1 F N ER0 -HEFFRON HH EH1 F R AH0 N -HEFLER HH EH1 F L ER0 -HEFLEY HH EH1 F L IY0 -HEFLIN HH EH1 F L IH0 N -HEFNER HH EH1 F N ER0 -HEFNER'S HH EH1 F N ER0 Z -HEFT HH EH1 F T -HEFTER HH EH1 F T ER0 -HEFTI HH EH1 F T IY0 -HEFTIER HH EH1 F T IY0 ER0 -HEFTIEST HH EH1 F T IY0 AH0 S T -HEFTY HH EH1 F T IY0 -HEGADORN HH EH1 G AH0 D AO2 R N -HEGARTY HH EH1 G AA0 R T IY0 -HEGE HH IY1 JH -HEGEDUS HH EH1 G IH0 D IH0 S -HEGEL HH EH1 G AH0 L -HEGELIAN HH IY0 JH IY1 L IY0 AH0 N -HEGEMAN HH IY1 G M AH0 N -HEGEMONIC HH EH2 G AH0 M AA1 N IH0 K -HEGEMONY HH IY0 JH EH1 M AH0 N IY0 -HEGENNA HH EH0 G EH1 N AH0 -HEGER HH IY1 G ER0 -HEGG HH EH1 G -HEGGE HH EH1 G -HEGGEN HH EH1 G AH0 N -HEGGIE HH EH1 G IY0 -HEGLAND HH EH1 G L AH0 N D -HEGLER HH EH1 G L ER0 -HEGLUND HH EH1 G L AH0 N D -HEGNA HH EH1 G N AH0 -HEGNER HH EH1 G N ER0 -HEGSTROM HH EH1 G S T R AH0 M -HEGWOOD HH EH1 G W UH2 D -HEGYI HH EY1 G Y IY0 -HEH HH EH1 -HEHIR HH EH1 HH IH0 R -HEHL HH EH1 L -HEHMAN HH EH1 M AH0 N -HEHMEYER HH EH1 M AY2 R -HEHN HH EH1 N -HEHR HH EH1 R -HEIBEL HH AY1 B AH0 L -HEIBERG HH AY1 B ER0 G -HEIBERGER HH AY1 B ER0 G ER0 -HEICHEL HH AY1 K AH0 L -HEICHELBECH HH AY1 K IH0 L B IH0 K -HEICK HH AY1 K -HEICO HH AY1 K OW0 -HEICO'S HH AY1 K OW0 Z -HEID HH AY1 D -HEIDBREDER HH AY1 D B R IH0 D ER0 -HEIDBRINK HH AY1 D B R IH0 NG K -HEIDE HH AY1 D -HEIDECKER HH AY1 D IH0 K ER0 -HEIDEGGER HH AY1 D IH0 G ER0 -HEIDEL HH AY1 D AH0 L -HEIDELBERG HH AY1 D AH0 L B ER0 G -HEIDELBERG'S HH AY1 D AH0 L B ER0 G Z -HEIDELBERGER HH AY1 D AH0 L B ER0 G ER0 -HEIDEMAN HH AY1 D M AH0 N -HEIDEMANN HH AY1 D M AH0 N -HEIDEN HH AY1 D AH0 N -HEIDENREICH HH AY1 D IH0 N R AY0 K -HEIDER HH AY1 D ER0 -HEIDI HH AY1 D IY0 -HEIDI'S HH AY1 D IY0 S -HEIDINGER HH AY1 D IH0 NG ER0 -HEIDIWEAR HH AY1 D IY0 W EH0 R -HEIDLER HH AY1 D AH0 L ER0 -HEIDLER(2) HH AY1 D L ER0 -HEIDORN HH AY1 D ER0 N -HEIDRICH HH AY1 D R IH0 K -HEIDRICK HH AY1 D R IH0 K -HEIDSTRA HH AY1 D S T R AH0 -HEIDT HH AY1 D T -HEIER HH AY1 ER0 -HEIFER HH AY1 F ER0 -HEIFER(2) HH EH1 F ER0 -HEIFERS HH EH1 F ER0 Z -HEIFERS(2) HH AY1 F ER0 Z -HEIFETZ HH AY1 F IH0 T S -HEIFNER HH IY1 F N ER0 -HEIGES HH AY1 JH IH0 Z -HEIGHT HH AY1 T -HEIGHTEN HH AY1 T AH0 N -HEIGHTENED HH AY1 T AH0 N D -HEIGHTENING HH AY1 T AH0 N IH0 NG -HEIGHTENING(2) HH AY1 T N IH0 NG -HEIGHTENS HH AY1 T AH0 N Z -HEIGHTH HH AY1 TH -HEIGHTS HH AY1 T S -HEIGL HH AY1 G AH0 L -HEIKEN HH AY1 K AH0 N -HEIKES HH AY1 K AH0 Z -HEIKKILA HH AY1 K IH0 L AH0 -HEIKKINEN HH AY1 K IH0 N AH0 N -HEIKO HH AY1 K OW0 -HEIKO(2) HH EY1 K OW0 -HEIL HH AY1 L -HEILAND HH AY1 L AH0 N D -HEILBRUN HH AY1 L B R AH0 N -HEILEMAN HH AY1 L M AH0 N -HEILEMAN'S HH AY1 L M AH0 N Z -HEILER HH AY1 L ER0 -HEILIG HH AY1 L IH0 G -HEILMAN HH AY1 L M AH0 N -HEILMANN HH AY1 L M AH0 N -HEIM HH AY1 M -HEIMAN HH AY1 M AH0 N -HEIMANN HH AY1 M AH0 N -HEIMBACH HH AY1 M B AA2 K -HEIMBERGER HH AY1 M B ER0 G ER0 -HEIMBIGNER HH AY1 M B AY0 G N ER0 -HEIMBUCH HH AY1 M B AH0 K -HEIMBURGER HH AY1 M B ER0 G ER0 -HEIMER HH AY1 M ER0 -HEIMERL HH AY1 M ER0 L -HEIMLICH HH AY1 M L IH0 K -HEIMS HH AY1 M Z -HEIMSOTH HH AY1 M S AH0 TH -HEIN HH AY1 N -HEINBACH HH AY1 N B AA2 K -HEINBAUGH HH AY1 N B AW0 -HEINBURGER HH AY1 N B ER0 G ER0 -HEINDEL HH AY1 N D AH0 L -HEINDL HH AY1 N D AH0 L -HEINE HH AY1 N -HEINECKE HH AY1 N IH0 K -HEINEKEN HH AY1 N AH0 K AH0 N -HEINEMAN HH AY1 N M AH0 N -HEINEMANN HH AY1 N M AH0 N -HEINEN HH AY1 N AH0 N -HEINER HH AY1 N ER0 -HEINES HH AY1 N Z -HEINEY HH AY1 N IY0 -HEINI HH AY1 N IY0 -HEINICKE HH AY1 N IH0 K -HEINIG HH AY1 N IH0 G -HEININGER HH AY1 N IH0 NG ER0 -HEINISCH HH AY1 N IH0 SH -HEINKE HH AY1 NG K -HEINKEL HH AY1 NG K AH0 L -HEINL HH AY1 N AH0 L -HEINLE HH AY1 N AH0 L -HEINLEIN HH AY1 N L AY2 N -HEINLEN HH AY1 N AH0 L AH0 N -HEINLY HH AY1 N L IY0 -HEINO HH AY1 N OW0 -HEINOLD HH AY1 N OW0 L D -HEINONEN HH AY1 N AH0 N AH0 N -HEINOUS HH EY1 N AH0 S -HEINRICH HH AY1 N R IH0 K -HEINRICHS HH AY1 N R IH0 K S -HEINS HH AY1 N Z -HEINSOHN HH AY1 N S AH0 N -HEINTZ HH AY1 N T S -HEINTZE HH AY1 N T S -HEINTZELMAN HH AY1 N T Z AH0 L M AH0 N -HEINTZMAN HH AY1 N T S M AH0 N -HEINY HH AY1 N IY0 -HEINZ HH AY1 N Z -HEINZ'S HH AY1 N Z IH0 Z -HEINZE HH AY1 N Z -HEINZEL HH AY1 N Z AH0 L -HEINZELMAN HH AY1 N Z AH0 L M AH0 N -HEINZEN HH AY1 N Z AH0 N -HEINZMAN HH AY1 N Z M AH0 N -HEINZMANN HH AY1 N Z M AH0 N -HEIPLE HH AY1 P AH0 L -HEIR EH1 R -HEIRESS EH1 R AH0 S -HEIRESSES EH1 R AH0 S IH0 Z -HEIRLOOM EH1 R L UW2 M -HEIRLOOMS EH1 R L UW2 M Z -HEIRONIMUS EH1 R AA0 N IH0 M UW0 S -HEIRONIMUS(2) HH AY0 R AA1 N IH0 M AH0 S -HEIRS EH1 R Z -HEISBOURG HH AY1 S B AO2 R G -HEISE HH AY1 S -HEISEL HH AY1 S AH0 L -HEISER HH AY1 S ER0 -HEISERMAN HH AY1 S ER0 M AH0 N -HEISEY HH AY1 S IY0 -HEISHMAN HH IY1 IH0 SH M AH0 N -HEISINGER HH AY1 S IH0 N JH ER0 -HEISKELL HH AY1 S K AH0 L -HEISLER HH AY1 S AH0 L ER0 -HEISLER(2) HH AY1 S L ER0 -HEISMAN HH AY1 S M AH0 N -HEISMAN'S HH AY1 S M AH0 N Z -HEISNER HH AY1 S N ER0 -HEISS HH AY1 S -HEIST HH AY1 S T -HEISTAND HH AY1 S T AH0 N D -HEISTER HH AY1 S T ER0 -HEIT HH AY1 T -HEITKAMP HH AY1 T K AE2 M P -HEITMAN HH AY1 T M AH0 N -HEITMANN HH AY1 T M AH0 N -HEITMEYER HH AY1 T M AY0 ER0 -HEITNER HH AY1 T N ER0 -HEITZ HH AY1 T S -HEITZENRATER HH AY1 T Z IH0 N R EY0 T ER0 -HEITZMAN HH AY1 T S M AH0 N -HEIWA HH AY1 W AH0 -HEIZER HH AY1 Z ER0 -HEJL HH EH1 JH L -HEJNA HH EH1 JH N AH0 -HEKKER HH EH1 K ER0 -HEKMATYAR HH EH2 K M AH0 T Y AA1 R -HEKMATYAR'S HH EH2 K M AH0 T Y AA1 R Z -HELABA HH EH0 L AA1 B AH0 -HELANDER HH EH1 L AH0 N D ER0 -HELANE HH AH0 L EY1 N -HELBER HH EH1 L B ER0 -HELBERG HH EH1 L B ER0 G -HELBERT HH EH1 L B ER0 T -HELBIG HH EH1 L B IH0 G -HELBING HH EH1 L B IH0 NG -HELBLING HH EH1 L B AH0 L IH0 NG -HELBLING(2) HH EH1 L B L IH0 NG -HELD HH EH1 L D -HELDENBRAND HH EH1 L D IH0 N B R AH0 N D -HELDENBRAND(2) HH EH1 L D IH0 N B R AE0 N D -HELDENTENOR HH EH1 L D EH0 N T EH2 N ER0 -HELDER HH EH1 L D ER0 -HELDERMAN HH EH1 L D ER0 M AH0 N -HELDMAN HH EH1 L D M AH0 N -HELDOR HH EH1 L D ER0 -HELDRETH HH EH1 L D R IH0 TH -HELDRING HH EH1 L D R IH0 NG -HELDS HH EH1 L D Z -HELDT HH EH1 L T -HELEN HH EH1 L AH0 N -HELEN'S HH EH1 L IH0 N Z -HELENA HH EH1 L AH0 N AH0 -HELENA'S HH EH1 L IH0 N AH0 Z -HELENE HH AH0 L IY1 N -HELENS HH EH1 L AH0 N Z -HELF HH EH1 L F -HELFAND HH EH1 L F AH0 N D -HELFER HH EH1 L F ER0 -HELFGOTT HH EH1 L F G AA2 T -HELFMAN HH EH1 L F M AH0 N -HELFRICH HH EH1 L F R IH0 K -HELGA HH EH1 L G AH0 -HELGERSON HH EH1 L G ER0 S AH0 N -HELGESEN HH EH1 L G IY0 Z AH0 N -HELGESON HH EH1 L G IH0 S AH0 N -HELGET HH EH1 L G IH0 T -HELGREN HH EH1 L G R EH0 N -HELICAL HH EH1 L IH0 K AH0 L -HELICE HH EH1 L IH0 S -HELICON HH EH1 L IH0 K AA2 N -HELICONS HH EH1 L IH0 K AA2 N Z -HELICOPTER HH EH1 L IH0 K AA2 P T ER0 -HELICOPTER'S HH EH1 L AH0 K AA2 P T ER0 Z -HELICOPTERS HH EH1 L IH0 K AA2 P T ER0 Z -HELIE HH EH1 L IY0 -HELIN HH EH1 L IH0 N -HELING HH IY1 L IH0 NG -HELINSKI HH IH0 L IH1 N S K IY0 -HELIONETIC HH IY2 L IY0 OW0 N EH1 T IH0 K -HELIONETICS HH IY2 L IY0 OW0 N EH1 T IH0 K S -HELIOPOLIS HH IY2 L IY0 AA1 P AH0 L AH0 S -HELIOS HH IY1 L IY0 AA2 S -HELIOTROPE HH IY1 L IY0 AH0 T R OW2 P -HELIUM HH IY1 L IY0 AH0 M -HELIX HH IY1 L IH0 K S -HELKE HH EH1 L K -HELL HH EH1 L -HELL'S HH EH1 L Z -HELLACIOUS HH EH2 L EY1 SH AH0 S -HELLACIOUSLY HH EH2 L EY1 SH AH0 S L IY0 -HELLACIOUSNESS HH EH2 L EY1 SH AH0 S N AH0 S -HELLAMS HH EH1 L AH0 M Z -HELLAND HH EH1 L AH0 N D -HELLARD HH EH1 L ER0 D -HELLBERG HH EH1 L B ER0 G -HELLBUSCH HH EH1 L B AH0 SH -HELLBUSCH(2) HH EH1 L B UH0 SH -HELLE HH EH1 L -HELLEN HH EH1 L AH0 N -HELLENBRAND HH EH1 L AH0 N B R AE2 N D -HELLENIC HH AH0 L EH1 N IH0 K -HELLENISM HH EH1 L AH0 N IH2 Z AH0 M -HELLENISTIC HH EH2 L AH0 N IH1 S T IH0 K -HELLENIZE HH EH1 L AH0 N AY2 Z -HELLENIZED HH EH1 L AH0 N AY2 Z D -HELLENIZES HH EH1 L AH0 N AY2 Z IH0 Z -HELLENIZING HH EH1 L AH0 N AY2 Z IH0 NG -HELLER HH EH1 L ER0 -HELLER'S HH EH1 L ER0 Z -HELLERMAN HH EH1 L ER0 M AH0 N -HELLFIRE HH EH1 L F AY2 R -HELLICKSON HH EH1 L IH0 K S AH0 N -HELLIER HH EH1 L Y ER0 -HELLING HH EH1 L IH0 NG -HELLINGER HH EH1 L IH0 NG ER0 -HELLISH HH EH1 L IH0 SH -HELLMAN HH EH1 L M AH0 N -HELLMANN HH EH1 L M AH0 N -HELLMER HH EH1 L M ER0 -HELLMUTH HH EH1 L M UW2 TH -HELLNER HH EH1 L N ER0 -HELLO HH AH0 L OW1 -HELLO(2) HH EH0 L OW1 -HELLRAISER HH EH1 L R EY2 Z ER0 -HELLSTROM HH EH1 L S T R AH0 M -HELLUMS HH EH1 L AH0 M Z -HELLUVA HH EH2 L UW1 V AH0 -HELLWIG HH EH1 L W IH0 G -HELLYER HH EH1 L IY0 ER0 -HELM HH EH1 L M -HELMA HH EH1 L M AH0 -HELMAN HH EH1 L M AH0 N -HELMBRECHT HH EH1 L M B R IH0 K T -HELME HH EH1 L M -HELMER HH EH1 L M ER0 -HELMERICH HH EH1 L M ER0 IH0 K -HELMERS HH EH1 L M ER0 Z -HELMES HH EH1 L M Z -HELMET HH EH1 L M AH0 T -HELMETED HH EH1 L M AH0 T IH0 D -HELMETS HH EH1 L M AH0 T S -HELMICH HH EH1 L M IH0 K -HELMICK HH EH1 L M IH0 K -HELMIG HH EH1 L M IH0 G -HELMING HH EH1 L M IH0 NG -HELMINIAK HH EH1 L M IH0 N IY0 AE0 K -HELMINSKI HH IH0 L M IH1 N S K IY0 -HELMINTH HH EH1 L M IH0 N TH -HELMKAMP HH EH1 L M K AE2 M P -HELMKE HH EH1 L M K -HELMONT HH EH1 L M AA2 N T -HELMS HH EH1 L M Z -HELMS' HH EH1 L M Z -HELMS'S HH EH1 L M Z IH0 Z -HELMSBURTON HH EH1 L M Z B ER0 T AH0 N -HELMSLEY HH EH1 L M Z L IY0 -HELMSLEY'S HH EH1 L M Z L IY0 Z -HELMSLEYS HH EH1 L M Z L IY0 Z -HELMSMAN HH EH1 L M Z M AE2 N -HELMSTETTER HH EH1 L M S T IH0 T ER0 -HELMUT HH EH1 L M AH0 T -HELMUTH HH EH1 L M UW2 TH -HELOT HH EH1 L AH0 T -HELOTISM HH EH1 L AH0 T IH2 Z AH0 M -HELOTRY HH EH1 L AH0 T R IY0 -HELOTS HH EH1 L AH0 T S -HELP HH EH1 L P -HELPED HH EH1 L P T -HELPER HH EH1 L P ER0 -HELPERS HH EH1 L P ER0 Z -HELPFUL HH EH1 L P F AH0 L -HELPFULLY HH EH1 L P F AH0 L IY0 -HELPING HH EH1 L P IH0 NG -HELPINGS HH EH1 L P IH0 NG Z -HELPLESS HH EH1 L P L AH0 S -HELPLESSLY HH EH1 L P L AH0 S L IY0 -HELPLESSNESS HH EH1 L P L AH0 S N AH0 S -HELPRIN HH EH1 L P R IH0 N -HELPS HH EH1 L P S -HELSEL HH EH1 L S AH0 L -HELSER HH EH1 L S ER0 -HELSETH HH EH1 L S IH0 TH -HELSINKI HH EH1 L S IH0 NG K IY0 -HELSLEY HH EH1 L S L IY0 -HELSTROM HH EH1 L S T R AH0 M -HELT HH EH1 L T -HELTER HH EH1 L T ER0 -HELTER-SKELTER HH EH2 L T ER0 S K EH1 L T ER0 -HELTON HH EH1 L T AH0 N -HELTSLEY HH EH1 L T S L IY0 -HELTZEL HH EH1 L T Z AH0 L -HELVETICA HH EH2 L V EH1 T IH0 K AH0 -HELVEY HH EH1 L V IY0 -HELVIE HH EH1 L V IY0 -HELWIG HH EH1 L W IH0 G -HELZER HH EH1 L Z ER0 -HEM HH EH1 M -HEMAN HH IY1 M AH0 N -HEMANI HH AH0 M AA1 N IY0 -HEMANN HH EH1 M AH0 N -HEMANT HH EH1 M AH0 N T -HEMATITE HH EH1 M AH0 T AY2 T -HEMATOLOGY HH EH2 M AH0 T AA1 L AH0 JH IY0 -HEMATOLOGY(2) HH IY2 M AH0 T AA1 L AH0 JH IY0 -HEMBERGER HH EH1 M B ER0 G ER0 -HEMBREE HH IH0 M B R IY1 -HEMBRICK HH EH1 M B R IH2 K -HEMBY HH EH1 M B IY0 -HEMDALE HH EH1 M D EY2 L -HEMDALE'S HH EH1 M D EY2 L Z -HEMEL HH EH1 M AH0 L -HEMENWAY HH EH1 M AH0 N W EY2 -HEMIMORPHITE HH EH2 M AH0 M AO1 R F AY2 T -HEMING HH EH1 M IH0 NG -HEMINGER HH EH1 M IH0 NG ER0 -HEMINGWAY HH EH1 M IH0 NG W EY2 -HEMINGWAY'S HH EH1 M IH0 NG W EY2 Z -HEMIPLEGIA HH EH2 M AH0 P L IY1 JH IY0 AH0 -HEMIPLEGIA(2) HH EH2 M AH0 P L IY1 JH Y AH0 -HEMISPHERE HH EH1 M IH0 S F IH2 R -HEMISPHERIC HH EH2 M AH0 S F IH1 R IH0 K -HEMKER HH EH1 M K ER0 -HEMLER HH EH1 M L ER0 -HEMLINE HH EH1 M L AY2 N -HEMLINES HH EH1 M L AY2 N Z -HEMLO HH EH1 M L OW0 -HEMLOCK HH EH1 M L AA2 K -HEMM HH EH1 M -HEMME HH EH1 M -HEMMED HH EH1 M D -HEMMELGARN HH EH1 M IH0 L G AA0 R N -HEMMEN HH EH1 M AH0 N -HEMMER HH EH1 M ER0 -HEMMERICH HH EH1 M ER0 IH0 K -HEMMERLE HH EH1 M ER0 L IY0 -HEMMERLING HH EH1 M ER0 L IH0 NG -HEMMERT HH EH1 M ER0 T -HEMMETER HH EH1 M IH0 T ER0 -HEMMING HH EH1 M IH0 NG -HEMMINGER HH EH1 M IH0 NG ER0 -HEMMINGHAUS HH EH1 M IH0 NG HH AW2 S -HEMMINGS HH EH1 M IH0 NG Z -HEMMINGSEN HH EH1 M IH0 NG S AH0 N -HEMMINGSON HH EH1 M IH0 NG S AH0 N -HEMO HH IY1 M OW0 -HEMOCYANIN HH IY2 M AH0 S AY1 AH0 N AH0 N -HEMODYNAMIC HH EH2 M OW0 D AY0 N AE1 M IH0 K -HEMODYNAMICS HH EH2 M OW0 D AY0 N AE1 M IH0 K S -HEMOGLOBIN HH IY2 M AH0 G L OW1 B AH0 N -HEMOLYTIC HH IY2 M AH0 L IH1 T IH0 K -HEMOND HH EH1 M AH0 N D -HEMOPHILIA HH IY2 M AH0 F IY1 L IY0 AH0 -HEMOPHILIAC HH IY0 M AH0 F IH1 L IY0 AE0 K -HEMOPHILIAC(2) HH IY0 M OW0 F IH1 L IY0 AE0 K -HEMOPHILIAC(3) HH IY0 M AH0 F IH1 L Y AE0 K -HEMOPHILIAC(4) HH IY0 M OW0 F IH1 L Y AE0 K -HEMOPHILIACS HH IY2 M AH0 F IH1 L IY0 AE2 K S -HEMORRHAGE HH EH1 M ER0 IH0 JH -HEMORRHAGE(2) HH EH1 M R AH0 JH -HEMORRHAGED HH EH1 M ER0 IH0 JH D -HEMORRHAGIC HH EH2 M ER0 AE1 G IH0 K -HEMORRHAGING HH EH1 M ER0 IH0 JH IH0 NG -HEMORRHOID HH EH1 M ER0 OY2 D -HEMORRHOIDS HH EH1 M ER0 OY2 D Z -HEMOTEC HH EH1 M OW0 T EH2 K -HEMP HH EH1 M P -HEMPEL HH EH1 M P AH0 L -HEMPEN HH EH1 M P AH0 N -HEMPFLING HH EH1 M P F AH0 L IH0 NG -HEMPFLING(2) HH EH1 M P F L IH0 NG -HEMPHILL HH EH1 M P HH IH2 L -HEMPSTEAD HH EH1 M P S T EH0 D -HEMRIC HH EH1 M R IH0 K -HEMRICK HH EH1 M R IH0 K -HEMRY HH EH1 M R IY0 -HEMS HH EH1 M Z -HEMSLEY HH EH1 M Z L IY0 -HEMSTREET HH EH1 M S T R IY2 T -HEMY HH EH1 M IY0 -HEN HH EH1 N -HEN'S HH EH1 N Z -HENAO HH EY1 N AW0 -HENARD HH EH1 N ER0 D -HENAULT HH EH1 N AW0 L T -HENBANE HH EH1 N B EY2 N -HENCE HH EH1 N S -HENCEFORTH HH EH1 N S F AO1 R TH -HENCH HH EH1 N CH -HENCHMAN HH EH1 N CH M AH0 N -HENCHMEN HH EH1 N CH M AH0 N -HENCKEL HH EH1 N K AH0 L -HENDEE HH EH1 N D IY0 -HENDEL HH EH1 N D AH0 L -HENDERSHOT HH EH1 N D ER0 SH AH0 T -HENDERSHOTT HH EH1 N D ER0 SH AH0 T -HENDERSON HH EH1 N D ER0 S AH0 N -HENDLER HH EH1 N D L ER0 -HENDLEY HH EH1 N D L IY0 -HENDON HH EH1 N D OW0 N -HENDRA HH EH1 N D R AH0 -HENDREN HH EH1 N D ER0 AH0 N -HENDRICH HH EH1 N D R IH0 K -HENDRICK HH EH1 N D R IH0 K -HENDRICKS HH EH1 N D R IH0 K S -HENDRICKSEN HH EH1 N D R IH0 K S AH0 N -HENDRICKSON HH EH1 N D R IH0 K S AH0 N -HENDRIE HH EH1 N D ER0 IY0 -HENDRIK HH EH1 N D R IH0 K -HENDRIKS HH EH1 N D R IH0 K S -HENDRIKSEN HH EH1 N D R IH0 K S AH0 N -HENDRIX HH EH1 N D R IH0 K S -HENDRIXSON HH EH1 N D R IH0 K S AH0 N -HENDRON HH EH1 N D R AH0 N -HENDRY HH EH1 N D R IY0 -HENDRY'S HH EH1 N D R IY0 Z -HENDRYX HH EH1 N D R IH0 K S -HENDY HH EH1 N D IY0 -HENEGAR HH EH1 N IH0 G ER0 -HENEGHAN HH IH0 N EH1 G AH0 N -HENEHAN HH EH1 N IH0 HH AE0 N -HENERY HH EH1 N ER0 IY0 -HENES HH IY1 N Z -HENEY HH EH1 N IY0 -HENG HH EH1 NG -HENGEL HH EH1 NG G AH0 L -HENGST HH EH1 NG G S T -HENHOUSE HH EH1 N HH AW2 S -HENIE HH EH1 N IY0 -HENIGAN HH EH1 N IH0 G AH0 N -HENIN HH EH1 N IH0 N -HENINGER HH EH1 N IH0 NG ER0 -HENION HH EH1 N Y AH0 N -HENK HH EH1 NG K -HENKE HH EH1 NG K -HENKEL HH EH1 NG K AH0 L -HENKELMAN HH EH1 NG K AH0 L M AH0 N -HENKELS HH EH1 NG K AH0 L Z -HENKEN HH EH1 NG K AH0 N -HENKES HH EH1 NG K S -HENKIN HH EH1 NG K IH0 N -HENKLE HH EH1 NG K AH0 L -HENLE HH EH1 N AH0 L -HENLEY HH EH1 N L IY0 -HENLEY'S HH EH1 N L IY0 Z -HENLEYS HH EH1 N L IY0 Z -HENLINE HH EH1 N L AY2 N -HENLY HH EH1 N L IY0 -HENMAN HH EH1 N M AH0 N -HENN HH EH1 N -HENNA HH EH1 N AH0 -HENNAN HH EH1 N AH0 N -HENNE HH EH1 N -HENNE(2) HH EH1 N IY0 -HENNEBERGER HH EH1 N B ER0 G ER0 -HENNEBERRY HH EH1 N B EH0 R IY0 -HENNEKE HH EH1 N IH0 K -HENNELLY HH EH1 N AH0 L IY0 -HENNEMAN HH EH1 N M AH0 N -HENNEN HH EH1 N AH0 N -HENNEPIN HH EH1 N IH0 P IH0 N -HENNER HH EH1 N ER0 -HENNES HH EH1 N Z -HENNESS HH EH1 N IH0 S -HENNESSEE HH EH1 N IH0 S IY0 -HENNESSEY HH EH1 N AH0 S IY0 -HENNESSEY'S HH EH1 N AH0 S IY0 Z -HENNESSY HH EH1 N AH0 S IY0 -HENNEY HH EH1 N IY0 -HENNICK HH EH1 N IH0 K -HENNIG HH EH1 N IH0 G -HENNIGAN HH EH1 N IH0 G AH0 N -HENNIGAR HH EH1 N IH0 G ER0 -HENNING HH EH1 N IH0 NG -HENNINGER HH EH1 N IH0 NG ER0 -HENNINGS HH EH1 N IH0 NG Z -HENNINGSEN HH EH1 N IH0 NG S AH0 N -HENNINGTON HH EH1 N IH0 NG T AH0 N -HENNIS HH EH1 N IH0 S -HENNON HH EH1 N AH0 N -HENPECK HH EH1 N P EH2 K -HENPECKED HH EH1 N P EH2 K T -HENRI HH EH1 N R IY0 -HENRI(2) AO2 R IY1 -HENRI(3) AA2 N R IY1 -HENRICH HH EH1 N R IH0 K -HENRICHS HH EH1 N R IH0 K S -HENRICHSEN HH EH1 N R IH0 K S AH0 N -HENRICK HH EH1 N R IH0 K -HENRICKS HH EH1 N R IH0 K S -HENRICKSEN HH EH1 N R IH0 K S AH0 N -HENRICKSON HH EH1 N R IH0 K S AH0 N -HENRIE HH EH1 N ER0 IY0 -HENRIETTA HH EH2 N R IY0 EH1 T AH0 -HENRIETTE HH EH2 N R IY0 EH1 T -HENRIK HH EH1 N R IH0 K -HENRIKA HH EH1 N R IH0 K AH0 -HENRIKSEN HH EH1 N R IH0 K S AH0 N -HENRIKSON HH EH1 N R IH0 K S AH0 N -HENRIQUE AA0 N R IY1 K -HENRIQUES HH EH0 N R IY1 K EH0 Z -HENRIQUES(2) AA0 N R IY1 K -HENRIQUEZ HH EH0 N R IY1 K EH0 Z -HENRIQUEZ(2) AA0 N R IY1 K -HENRY HH EH1 N R IY0 -HENRY'S HH EH1 N R IY0 Z -HENS HH EH1 N Z -HENSARLING HH EH1 N S AA0 R L IH0 NG -HENSCH HH EH1 N SH -HENSCHEL HH EH1 N SH AH0 L -HENSCHEN HH EH1 N SH AH0 N -HENSE HH EH1 N S -HENSEL HH EH1 N S AH0 L -HENSEN HH EH1 N S AH0 N -HENSHAW HH EH1 N SH AO2 -HENSIL HH EH1 N S AH0 L -HENSKE HH EH1 N S K IY0 -HENSLEE HH EH1 N Z L IY2 -HENSLER HH EH1 N S AH0 L ER0 -HENSLER(2) HH EH1 N S L ER0 -HENSLEY HH EH1 N Z L IY0 -HENSON HH EH1 N S AH0 N -HENTGES HH EH1 N T JH IH0 Z -HENTHORN HH EH1 N TH AO2 R N -HENTHORNE HH EH1 N TH AO2 R N -HENTIC HH EH1 N T IH0 K -HENTOFF HH EH1 N T AO0 F -HENTON HH EH1 N T AH0 N -HENTSCHEL HH EH1 N CH AH0 L -HENTZ HH EH1 N T S -HENWOOD HH EH1 N W UH2 D -HENZE HH EH1 N Z -HENZEL HH EH1 N Z AH0 L -HENZLER HH EH1 N Z L ER0 -HEON HH IY1 AH0 N -HEOROICO HH IY2 AH0 R OW1 AH0 K OW0 -HEP HH EH1 P -HEPARIN HH EH1 P ER0 IH0 N -HEPATIC HH AH0 P AE1 T IH0 K -HEPATITIS HH EH2 P AH0 T AY1 T AH0 S -HEPBURN HH EH1 P B ER0 N -HEPBURN'S HH EH1 P B ER0 N Z -HEPFER HH EH1 P F ER0 -HEPKER HH EH1 P K ER0 -HEPLER HH EH1 P L ER0 -HEPNER HH EH1 P N ER0 -HEPP HH EH1 P -HEPPE HH EH1 P -HEPPER HH EH1 P ER0 -HEPPLER HH EH1 P L ER0 -HEPPNER HH EH1 P N ER0 -HEPTATHLON HH EH0 P T AE1 TH L AA0 N -HEPWORTH HH EH1 P W ER0 TH -HER HH ER1 -HER(2) HH ER0 -HER'S HH ER1 Z -HERA HH IH1 R AH0 -HERADIA HH ER0 AA1 D IY0 AH0 -HERALD HH EH1 R AH0 L D -HERALD'S HH EH1 R AH0 L D Z -HERALDED HH EH1 R AH0 L D IH0 D -HERALDIC HH EH0 R AE1 L D IH0 K -HERALDING HH EH1 R AH0 L D IH0 NG -HERALDRY HH EH1 R AH0 L D R IY0 -HERALDS HH EH1 R AH0 L D Z -HERB ER1 B -HERB(2) HH ER1 B -HERB'S ER1 B Z -HERB'S(2) HH ER1 B Z -HERBACEOUS ER0 B EY1 SH AH0 S -HERBAL ER1 B AH0 L -HERBAL(2) HH ER1 B AH0 L -HERBALIFE HH ER1 B AH0 L AY2 F -HERBALIFE(2) ER1 B AH0 L AY2 F -HERBALIST ER1 B AH0 L AH0 S T -HERBALIST(2) HH ER1 B AH0 L AH0 S T -HERBALIST'S ER1 B AH0 L AH0 S T S -HERBALIST'S(2) HH ER1 B AH0 L AH0 S T S -HERBALISTS ER1 B AH0 L AH0 S T S -HERBALISTS(2) HH ER1 B AH0 L AH0 S T S -HERBALISTS(3) ER1 B AH0 L AH0 S -HERBALISTS(4) HH ER1 B AH0 L AH0 S -HERBALISTS' ER1 B AH0 L AH0 S T S -HERBALISTS'(2) HH ER1 B AH0 L AH0 S T S -HERBARIUM HH ER0 B EH1 R IY2 AH0 M -HERBARIUM(2) ER0 B EH1 R IY2 AH0 M -HERBARIUMS HH ER0 B EH1 R IY2 AH0 M Z -HERBARIUMS(2) ER0 B EH1 R IY2 AH0 M Z -HERBECK HH ER1 B EH0 K -HERBEL HH ER1 B AH0 L -HERBER HH ER1 B ER0 -HERBERG HH ER1 B ER0 G -HERBERGER HH ER1 B ER0 G ER0 -HERBERS HH ER1 B ER0 Z -HERBERT HH ER1 B ER0 T -HERBERT'S HH ER1 B ER0 T S -HERBICIDE HH ER1 B IH0 S AY2 D -HERBICIDE(2) ER1 B IH0 S AY2 D -HERBICIDES ER1 B AH0 S AY2 D Z -HERBICIDES(2) HH ER1 B AH0 S AY2 D Z -HERBIE HH ER1 B IY0 -HERBIG HH ER1 B IH0 G -HERBIN HH ER1 B IH0 N -HERBISON HH ER1 B IH0 S AH0 N -HERBIVORE HH ER1 B IH0 V AO2 R -HERBIVORE(2) ER1 B IH0 V AO2 R -HERBIVOROUS HH ER0 B IH1 V ER0 AH0 S -HERBIVOROUS(2) ER0 B IH1 V ER0 AH0 S -HERBOLD HH ER1 B OW0 L D -HERBS ER1 B Z -HERBST HH ER1 B S T -HERBSTER HH ER1 B S T ER0 -HERCEG HH ER1 S IH0 G -HERCEGOVINA HH EH2 R T S AH0 G OW0 V IY1 N AH0 -HERCEGOVINA(2) HH ER2 R T S AH0 G OW0 V IY1 N AH0 -HERCEGOVINA'S HH EH2 R T S AH0 G OW0 V IY1 N AH0 Z -HERCEGOVINA'S(2) HH ER2 R T S AH0 G OW0 V IY1 N AH0 Z -HERCULEAN HH ER0 K Y UW1 L IY0 AH0 N -HERCULES HH ER1 K Y AH0 L IY2 Z -HERCZEG HH ER1 CH IH0 G -HERD HH ER1 D -HERDA HH EH1 R D AH0 -HERDAL HH EH1 R D AH0 L -HERDED HH ER1 D IH0 D -HERDER HH EH1 R D ER0 -HERDER(2) HH ER1 D ER0 -HERDERS HH ER1 D ER0 Z -HERDING HH ER1 D IH0 NG -HERDMAN HH ER1 D M AH0 N -HERDS HH ER1 D Z -HERDSMEN HH ER1 D Z M IH0 N -HERDT HH ER1 T -HERE HH IY1 R -HERE'S HH IH1 R Z -HEREABOUT HH IH1 R AH0 B AW2 T -HEREABOUTS HH IH1 R AH0 B AW2 T S -HEREAFTER HH IH0 R AE1 F T ER0 -HEREBY HH IH0 R B AY1 -HEREDIA HH ER0 EH1 D IY0 AH0 -HEREDITARY HH ER0 EH1 D AH0 T EH2 R IY0 -HEREDITY HH ER0 EH1 D AH0 T IY0 -HEREFORD HH EH1 R AH0 F ER0 D -HEREIN HH IH0 R IH1 N -HERENDEEN HH IH1 R N D IY0 N -HERESY HH EH1 R AH0 S IY0 -HERETIC HH EH1 R AH0 T IH0 K -HERETICAL HH ER0 EH1 T IH0 K AH0 L -HERETICS HH EH1 R AH0 T IH0 K S -HERETOFORE HH IH2 R T AH0 F AO1 R -HEREWITH HH IH1 R W IH1 TH -HERFORD HH ER1 F ER0 D -HERFURTH HH ER1 F ER0 TH -HERGERT HH ER1 G ER0 T -HERGET HH ER1 G IH0 T -HERGOTT HH ER1 G AH0 T -HERIN HH EH1 R IH0 N -HERING HH ER1 IH0 NG -HERINGER HH EH1 R IH0 N JH ER0 -HERINGTON HH EH1 R IH0 NG T AH0 N -HERITABLE HH EH1 R AH0 T AH0 B AH0 L -HERITAGE HH EH1 R AH0 T AH0 JH -HERITAGE(2) HH EH1 R IH0 T IH0 JH -HERITAGE'S HH EH1 R AH0 T IH0 JH IH0 Z -HERITAGES HH EH1 R IH0 T IH0 JH AH0 Z -HERK HH ER1 K -HERKERT HH ER1 K ER0 T -HERL HH ER1 L -HERLIHY HH ER1 L IH0 HH IY0 -HERLING HH ER1 L IH0 NG -HERLONG HH ER1 L AO0 NG -HERM HH ER1 M -HERMAN HH ER1 M AH0 N -HERMAN'S HH ER1 M AH0 N Z -HERMANCE HH ER1 M AH0 N S -HERMANN HH ER1 M AH0 N -HERMANNS HH ER1 M AA0 N Z -HERMANS HH ER1 M AH0 N Z -HERMANSEN HH ER1 M AH0 N S AH0 N -HERMANSON HH ER1 M AH0 N S AH0 N -HERMAPHRODITE HH ER0 M AE1 F R AH0 D AY2 T -HERMAPHRODITIC HH ER0 M AE2 F R AH0 D IH1 T IH0 K -HERMENIO HH ER0 M EH1 N IY0 OW0 -HERMES HH ER1 M IY0 Z -HERMETICALLY HH ER0 M EH1 T IH0 K AH0 L IY0 -HERMETICALLY(2) HH ER0 M EH1 T IH0 K L IY0 -HERMIA HH ER1 M IY0 AH0 -HERMIAS HH ER1 M IY0 AH0 Z -HERMIDA HH EH0 R M IY1 D AH0 -HERMIE HH ER1 M IY0 -HERMINA HH ER1 M IH0 N AH0 -HERMINA(2) HH ER0 M IY1 N AH0 -HERMINE HH ER1 M IH0 N -HERMINIA HH EH0 R M IY1 N IY0 AH0 -HERMINIE HH ER2 M IH1 N IY0 -HERMIONE HH ER0 M IY0 OW1 N IY0 -HERMIT HH ER1 M AH0 T -HERMITAGE HH ER1 M AH0 T AH0 JH -HERMITS HH ER1 M AH0 T S -HERMON HH ER1 M AH0 N -HERMOSA HH EH0 R M OW1 S AH0 -HERMOSILLO HH ER0 M AH0 S IH1 L OW0 -HERMS HH ER1 M Z -HERMSEN HH ER1 M S AH0 N -HERN HH ER1 N -HERNAN HH ER1 N AH0 N -HERNANDES HH ER1 N IH0 N D Z -HERNANDES(2) HH ER0 N AE1 N D EH0 Z -HERNANDEZ HH ER0 N AE1 N D EH0 Z -HERNANDEZ'S HH ER0 N AE1 N D EH0 Z AH0 Z -HERNANDO HH ER0 N AA1 N D OW0 -HERNDON HH ER1 N D AH0 N -HERNE HH ER1 N -HERNER HH ER1 N ER0 -HERNIA HH ER1 N IY0 AH0 -HERNIATE HH ER1 N IY0 EY2 T -HERNIATES HH ER1 N IY0 EY2 T S -HERNON HH ER1 N AH0 N -HERO HH IH1 R OW0 -HERO(2) HH IY1 R OW0 -HERO'S HH IH1 R OW0 Z -HERO'S(2) HH IY1 R OW0 Z -HEROD HH EH1 R AH0 D -HEROES HH IH1 R OW0 Z -HEROES(2) HH IY1 R OW0 Z -HEROIC HH IH0 R OW1 IH0 K -HEROICALLY HH IH2 R OW1 IH0 K L IY0 -HEROICS HH IH0 R OW1 IH0 K S -HEROIN HH EH1 R OW0 AH0 N -HEROIN'S HH EH1 R OW0 AH0 N Z -HEROINE HH EH1 R OW0 AH0 N -HEROINES HH EH1 R OW2 AH0 N Z -HEROISM HH EH1 R OW0 IH2 Z AH0 M -HEROIZE HH IY1 R OW0 AY2 Z -HEROIZED HH IY1 R OW0 AY2 Z D -HEROLD HH EH1 R AH0 L D -HERON HH EH1 R AH0 N -HERON'S HH EH1 R AH0 N Z -HERONS HH EH1 R AH0 N Z -HEROS HH IH1 R OW0 Z -HEROUX HH ER0 UW1 -HERPES HH ER1 P IY0 Z -HERR HH EH1 R -HERRE HH EH1 R -HERRE(2) HH AH1 R IY0 -HERREID HH EH1 R AY0 D -HERRELL HH EH1 R AH0 L -HERREN HH EH1 R AH0 N -HERRERA HH ER0 EH1 R AH0 -HERRERO HH EH0 R EH1 R OW0 -HERRHAUSEN HH EH1 R HH AW2 Z AH0 N -HERRIAGE HH EH1 R IY0 IH0 JH -HERRICK HH EH1 R IH0 K -HERRIDGE HH EH1 R IH0 JH -HERRIG HH EH1 R IH0 G -HERRIMAN HH EH1 R IH0 M AH0 N -HERRIN HH EH1 R IH0 N -HERRING HH EH1 R IH0 NG -HERRINGS HH EH1 R IH0 NG Z -HERRINGSHAW HH EH1 R IH0 NG SH AO2 -HERRINGTON HH EH1 R IH0 NG T AH0 N -HERRINGTON'S HH EH1 R IH0 NG T AH0 N Z -HERRIOTT HH EH1 R IY0 AA0 T -HERRIOTT(2) HH EH1 R IY0 AH0 T -HERRLE HH EH1 R AH0 L -HERRLINGER HH EH1 R L IH2 NG ER0 -HERRLINGER(2) HH EH1 R L IH2 NG G ER0 -HERRMAN HH EH1 R M AH0 N -HERRMANN HH EH1 R M AH0 N -HERRO HH EH1 R OW0 -HERROD HH EH1 R AH0 D -HERROLD HH EH1 R OW2 L D -HERRON HH EH1 R AH0 N -HERRONIMO HH ER0 AA1 N AH0 M OW0 -HERRONIMO'S HH ER0 AA1 N AH0 M OW0 Z -HERS HH ER0 Z -HERS(2) HH ER1 Z -HERSANT HH ER1 S AH0 N T -HERSCH HH ER1 SH -HERSCHEL HH ER1 SH AH0 L -HERSCHEL'S HH ER1 SH AH0 L Z -HERSCHELL HH ER1 SH AH0 L -HERSCHENSOHN HH ER1 SH AH0 N S AH0 N -HERSCU HH ER0 S K UW1 -HERSELF HH ER0 S EH1 L F -HERSEY HH ER1 S IY0 -HERSH HH ER1 SH -HERSHBERGER HH ER1 SH B ER0 G ER0 -HERSHEY HH ER1 SH IY0 -HERSHEY'S HH ER1 SH IY0 Z -HERSHISER HH ER1 SH AY0 Z ER0 -HERSHKOWITZ HH ER1 SH K AH0 W IH0 T S -HERSHMAN HH ER1 SH M AH0 N -HERSHNER HH ER1 SH N ER0 -HERSKOVITZ HH ER1 S K AH0 V IH0 T S -HERSKOWITZ HH ER1 S K AH0 W IH0 T S -HERSMAN HH ER1 S M AH0 N -HERSOM HH ER1 S AH0 M -HERSON HH ER1 S AH0 N -HERST HH ER1 S T -HERT HH ER1 T -HERTA HH ER1 T AH0 -HERTEL HH ER1 T AH0 L -HERTENSTEIN HH ER1 T AH0 N S T AY0 N -HERTENSTEIN(2) HH ER1 T AH0 N S T IY0 N -HERTER HH ER1 T ER0 -HERTHA HH ER1 TH AH0 -HERTIG HH ER1 T IH0 G -HERTING HH ER1 T IH0 NG -HERTLEIN HH ER1 T L AY0 N -HERTOG HH ER1 T AA0 G -HERTZ HH EH1 R T S -HERTZ(2) HH ER1 T S -HERTZBERG HH ER1 T S B ER0 G -HERTZENLEBEN HH ER1 T AH0 N L EY2 B AH0 N -HERTZENLEBEN'S HH ER1 T AH0 N L EY2 B AH0 N Z -HERTZLER HH ER1 T Z AH0 L ER0 -HERTZLER(2) HH ER1 T Z L ER0 -HERTZOG HH ER1 T Z AH0 G -HERTZOG(2) HH ER1 T Z AA2 G -HERVE HH ER1 V -HERVE(2) HH ER1 V EY0 -HERVEY HH ER0 V EY1 -HERWICK HH ER1 W IH2 K -HERWIG HH ER1 W IH0 G -HERWITZ HH ER1 W IH0 T S -HERYANA HH ER0 Y AE1 N AH0 -HERZ HH ER1 Z -HERZBERG HH ER1 Z B ER0 G -HERZBERGER HH ER1 Z B ER0 G ER0 -HERZEGOVINA HH EH2 R T S AH0 G OW0 V IY1 N AH0 -HERZEGOVINA(2) HH ER2 T S AH0 G OW0 V IY1 N AH0 -HERZEGOVINA'S HH EH2 R T S AH0 G OW0 V IY1 N AH0 Z -HERZEGOVINA'S(2) HH ER2 T S AH0 G OW0 V IY1 N AH0 Z -HERZER HH ER1 Z ER0 -HERZFELD HH ER1 Z F EH0 L D -HERZIG HH ER1 Z IH0 G -HERZING HH ER1 Z IH0 NG -HERZLINGER HH ER1 Z L IH2 NG ER0 -HERZOG HH ER1 Z AA0 G -HESCH HH EH1 SH -HESELTINE HH EH1 S AH0 L T IY2 N -HESELTINE(2) HH EH1 S AH0 L T AY2 N -HESELTON HH IH0 S EH1 L T AH0 N -HESHENG HH EH1 SH EH1 NG -HESIK HH EH1 S IH0 K -HESIK(2) HH EY1 S IH0 K -HESIOD HH IY1 S IY0 AH0 D -HESITANCY HH EH1 Z IH0 T AH0 N S IY0 -HESITANT HH EH1 Z IH0 T AH0 N T -HESITANTLY HH EH1 Z IH0 T AH0 N T L IY0 -HESITATE HH EH1 Z AH0 T EY2 T -HESITATED HH EH1 Z IH0 T EY2 T IH0 D -HESITATES HH EH1 Z AH0 T EY2 T S -HESITATING HH EH1 Z AH0 T EY2 T IH0 NG -HESITATION HH EH2 Z AH0 T EY1 SH AH0 N -HESITATIONS HH EH2 Z AH0 T EY1 SH AH0 N Z -HESKETH HH EH1 S K IH0 TH -HESKETT HH EH1 S K IH0 T -HESLEP HH EH1 S L IH0 P -HESLER HH EH1 S AH0 L ER0 -HESLER(2) HH EH1 S L ER0 -HESLIN HH EH1 Z L IH0 N -HESLIN'S HH EH1 Z L IH0 N Z -HESLOP HH EH1 S L AH0 P -HESPER HH EH1 S P ER0 -HESPERA HH EY0 S P EH1 R AH0 -HESS HH EH1 S -HESSE HH EH1 S -HESSE'S HH EH1 S IH0 Z -HESSEL HH EH1 S AH0 L -HESSELTINE HH EH1 S IH0 L T IY0 N -HESSER HH EH1 S ER0 -HESSIAN HH EH1 SH AH0 N -HESSING HH EH1 S IH0 NG -HESSINGER HH EH1 S IH0 N JH ER0 -HESSION HH EH1 SH IH0 N -HESSITE HH EH1 S AY0 T -HESSLER HH EH1 S L ER0 -HESSLING HH EH1 S AH0 L IH0 NG -HESSLING(2) HH EH1 S L IH0 NG -HESSON HH EH1 S AH0 N -HESSTON HH EH1 S T AH0 N -HESSTON'S HH EH1 S T AH0 N Z -HESTAND HH EH1 S T AH0 N D -HESTER HH EH1 S T ER0 -HESTHER HH EH1 S DH ER0 -HESTIA HH EH1 S T IY0 AH0 -HESTON HH EH1 S T AH0 N -HETEROCERCAL HH EH2 T ER0 OW0 S ER1 K AH0 L -HETERODOX HH EH1 T ER0 AH0 D AA2 K S -HETERODOXY HH EH1 T ER0 AH0 D AA2 K S IY0 -HETERODYNE HH EH1 T ER0 AH0 D AY2 N -HETEROGENEITY HH EH2 T ER0 AH0 JH IH0 N IY1 AH0 T IY0 -HETEROGENEITY(2) HH EH2 T ER0 AH0 JH IH0 N EY1 AH0 T IY0 -HETEROGENEOUS HH EH2 T ER0 AH0 JH IY1 N Y AH0 S -HETEROSEXUAL HH EH2 T ER0 OW0 S EH1 K SH AH0 W AH0 L -HETEROSEXUALITY HH EH2 T ER0 OW0 S EH0 K SH AH0 W AE1 L IH0 T IY0 -HETEROSEXUALS HH EH2 T ER0 OW0 S EH1 K SH AH0 W AH0 L Z -HETEROSIS HH EH2 T ER0 OW1 S AH0 S -HETEROSPOROUS HH EH2 T ER0 AA1 S P ER0 AH0 S -HETEROTROPHIC HH EH2 T ER0 AH0 T R AA1 F IH0 K -HETEROZYGOUS HH EH2 T ER0 AH0 Z AY1 G AH0 S -HETH HH EH1 TH -HETHERINGTON HH EH1 DH ER0 IH0 NG T AH0 N -HETLAND HH EH1 T L AH0 N D -HETMAN HH EH1 T M AH0 N -HETRICK HH EH1 T R IH0 K -HETT HH EH1 T -HETTEL HH EH1 T AH0 L -HETTI HH EH1 T IY0 -HETTICK HH EH1 T IH0 K -HETTIE HH EH1 T IY0 -HETTLER HH EH1 T L ER0 -HETTRICK HH EH1 T R IH0 K -HETTY HH EH1 T IY0 -HETU HH IY1 CH UW0 -HETZ HH EH1 T S -HETZEL HH EH1 T Z AH0 L -HETZER HH EH1 T Z ER0 -HETZLER HH EH1 T S L ER0 -HEUBERGER HH OY1 B ER0 G ER0 -HEUBLEIN HH Y UW1 B L AY2 N -HEUER HH Y UW1 ER0 -HEUER'S HH Y UW1 ER0 Z -HEUERMAN HH OY1 ER0 M AH0 N -HEUERMANN HH OY1 ER0 M AH0 N -HEUMAN HH Y UW1 M AH0 N -HEUMANN HH Y UW1 M AH0 N -HEUN HH Y UW1 N -HEUNG-YEUNG HH UW1 NG Y UW1 NG -HEUPEL HH OY1 P AH0 L -HEURING HH ER1 IH0 NG -HEURISTIC HH Y UH0 R IH1 S T IH0 K -HEURISTICS HH Y UH0 R IH1 S T IH0 K S -HEUSEN HH Y UW1 S AH0 N -HEUSER HH OY1 S ER0 -HEUSSER HH Y UW1 S ER0 -HEVENER HH EH1 V IY0 N ER0 -HEVERLY HH EH1 V ER0 L IY0 -HEVEY HH IH0 V EY1 -HEVIA HH EY1 V IY0 AH0 -HEVNER HH EH1 V N ER0 -HEW HH Y UW1 -HEWARD HH Y UW1 ER0 D -HEWE HH Y UW1 -HEWELL HH EH1 W EH0 L -HEWELL(2) HH Y UW1 W EH0 L -HEWER HH Y UW1 ER0 -HEWES HH Y UW1 Z -HEWETT HH Y UW1 IH0 T -HEWEY HH Y UW1 IY0 -HEWING HH Y UW1 IH0 NG -HEWINS HH Y UW1 IH0 N Z -HEWITT HH Y UW1 IH0 T -HEWITT'S HH Y UW1 IH0 T S -HEWLER HH Y UW1 L ER0 -HEWLER'S HH Y UW1 L ER0 Z -HEWLETT HH Y UW1 L IH0 T -HEWN HH Y UW1 N -HEWS HH Y UW1 Z -HEWSON HH Y UW1 S AH0 N -HEX HH EH1 K S -HEXACHLOROPHENE HH EH2 K S AH0 K L AO1 R AH0 F IY2 N -HEXAGON HH EH1 K S AH0 G AA2 N -HEXAGONAL HH EH0 K S AE1 G AH0 N AH0 L -HEXANE HH EH1 K S EY0 N -HEXCEL HH EH1 K S AH0 L -HEXT HH EH1 K S T -HEY HH EY1 -HEYBOER HH EY1 B OW0 ER0 -HEYD HH EY1 D -HEYDAY HH EY1 D EY2 -HEYDE HH EY1 D -HEYDEN HH EY1 D AH0 N -HEYDON HH EY1 D AH0 N -HEYDT HH EY1 D T -HEYE HH AY1 -HEYEN HH AY1 N -HEYER HH EY1 ER0 -HEYING HH EY1 IH0 NG -HEYL HH EY1 L -HEYMAN HH EY1 M AH0 N -HEYMAN'S HH EY1 M AH0 N Z -HEYMANN HH EY1 M AH0 N -HEYMANN'S HH EY1 M AH0 N Z -HEYN HH EY1 N -HEYNE HH EY1 N -HEYS HH EY1 Z -HEYSE HH EY1 S -HEYSER HH EY1 Z ER0 -HEYWARD HH EY1 W ER0 D -HEYWOOD HH EY1 W UH2 D -HEZBOLLAH HH EH1 Z B AA0 L AH2 -HEZBOLLAH'S HH EH1 Z B AA0 L AH2 Z -HEZBULLAH HH EH2 Z B AA0 L AH1 -HEZBULLAH(2) HH EH1 Z B AA0 L AH2 -HEZBULLAH'S HH EH1 Z B AA0 L AH2 Z -HFDF EY1 CH EH1 F D IY1 EH1 F -HGH EY1 CH JH IY1 EY1 CH -HI HH AY1 -HI-FI HH AY1 F AY1 -HIAA EY1 CH AY1 EY1 EY1 -HIAASEN HH AY1 AA0 S IH0 N -HIALEAH HH AY2 AH0 L IY1 AH0 -HIAM HH AY1 AH0 M -HIATT HH AY1 AH0 T -HIATT'S HH AY1 AH0 T S -HIATUS HH AY0 EY1 T AH0 S -HIAWATHA HH AY2 AH0 W AA1 TH AH0 -HIBAAQ HH AY1 B AE2 K -HIBACHI HH AH0 B AA1 CH IY0 -HIBACHI(2) HH IY0 B AA1 CH IY0 -HIBBARD HH IH1 B ER0 D -HIBBEN HH IH1 B AH0 N -HIBBERD HH IH1 B ER0 D -HIBBERT HH IH1 B ER0 T -HIBBETT HH IH1 B IH0 T -HIBBING HH IH1 B IH0 NG -HIBBITTS HH IH1 B IH0 T S -HIBBLER HH IH1 B L ER0 -HIBBS HH IH1 B Z -HIBDON HH IH1 B D AH0 N -HIBERNATE HH AY1 B ER0 N EY2 T -HIBERNATION HH AY2 B ER0 N EY1 SH AH0 N -HIBERNIA HH AY2 B ER1 N IY0 AH0 -HIBERNIA'S HH AY2 B ER1 N IY0 AH0 Z -HIBLER HH IH1 B L ER0 -HIBMA HH IH1 B M AH0 -HIBNER HH IH1 B N ER0 -HIBOR HH AY1 B ER0 -HIBSHMAN HH IH1 B SH M AH0 N -HICCOUGH HH IH1 K AH0 P -HICCOUGHS HH IH1 K AH0 P S -HICCUP HH IH1 K AH0 P -HICCUPS HH IH1 K AH0 P S -HICE HH AY1 S -HICFA HH IH1 K F AH0 -HICHENS HH IH1 K AH0 N Z -HICK HH IH1 K -HICKAM HH IH1 K AH0 M -HICKCOX HH IH1 K AA0 K S -HICKEL HH IH1 K AH0 L -HICKEN HH IH1 K AH0 N -HICKERSON HH IH1 K ER0 S AH0 N -HICKEY HH IH1 K IY0 -HICKLE HH IH1 K AH0 L -HICKLIN HH IH1 K L IH0 N -HICKLING HH IH1 K L IH0 NG -HICKMAN HH IH1 K M AH0 N -HICKMON HH IH1 K M AH0 N -HICKOK HH IH1 K AH0 K -HICKORIES HH IH1 K ER0 IY0 Z -HICKORY HH IH1 K ER0 IY0 -HICKORY(2) HH IH1 K R IY0 -HICKOX HH IH1 K AA0 K S -HICKS HH IH1 K S -HICKSON HH IH1 K S AH0 N -HICKSVILLE HH IH1 K S V IH2 L -HID HH IH1 D -HIDALGO HH AH0 D AE1 L G OW2 -HIDDEN HH IH1 D AH0 N -HIDE HH AY1 D -HIDEAKI HH IY2 D EY0 AA1 K IY0 -HIDEAWAY HH AY1 D AH0 W EY2 -HIDEBOUND HH AY1 D B AW2 N D -HIDEO HH IH0 D EY1 OW0 -HIDEOUS HH IH1 D IY0 AH0 S -HIDEOUSLY HH IH1 D IY0 AH0 S L IY0 -HIDEOUT HH AY1 D AW2 T -HIDEOUTS HH AY1 D AW2 T S -HIDER HH AY1 D ER0 -HIDES HH AY1 D Z -HIDING HH AY1 D IH0 NG -HIDY HH AY1 D IY0 -HIEB HH IY1 B -HIEBER HH IY1 B ER0 -HIEBERT HH IY1 B ER0 T -HIEGEL HH IY1 G AH0 L -HIEMS HH IY1 M Z -HIEMSTRA HH IY1 M S T R AH0 -HIER HH AY1 ER0 -HIERARCHICAL HH AY2 R AA1 R K AH0 K AH0 L -HIERARCHIES HH AY1 R AA2 R K IY0 Z -HIERARCHY HH AY1 ER0 AA2 R K IY0 -HIERARCHY(2) HH AY1 R AA2 R K IY0 -HIERHOLZER HH AY1 R HH OW0 L Z ER0 -HIEROGLYPH HH AY2 R OW0 G L IH1 F -HIEROGLYPHIC HH AY2 R OW0 G L IH1 F IH0 K -HIEROGLYPHICS HH AY2 R OW0 G L IH1 F IH0 K S -HIEROGLYPHS HH AY2 R OW0 G L IH1 F S -HIERS HH IY1 R Z -HIESTAND HH IY1 S T AH0 N D -HIESTER HH AY1 IH0 S T ER0 -HIETALA HH AY1 T AH0 L AH0 -HIETPAS HH AY1 T P AH0 Z -HIETT HH AY1 T -HIGA HH IY1 G AH0 -HIGASHI HH IY0 G AA1 SH IY0 -HIGBEE HH IH1 G B IY2 -HIGBIE HH IH1 G B IY0 -HIGBY HH IH1 G B IY0 -HIGDON HH IH1 G D AH0 N -HIGGASON HH IH1 G AH0 S AH0 N -HIGGENBOTHAM HH IH1 G IH0 N B AH0 TH AH0 M -HIGGENS HH IH1 G AH0 N Z -HIGGERSON HH IH1 G ER0 S AH0 N -HIGGINBOTHAM HH IH0 G IH0 N B AA1 TH AH0 M -HIGGINBOTTOM HH IH0 G IH0 N B AA1 T AH0 M -HIGGINS HH IH1 G IH0 N Z -HIGGINSON HH IH1 G IH0 N S AH0 N -HIGGS HH IH1 G Z -HIGH HH AY1 -HIGH-END HH AY2 EH1 N D -HIGH-LEVEL HH AY2 L EH1 V AH0 L -HIGH-MINDED HH AY2 M AY1 N D IH0 D -HIGH-POWERED HH AY2 P AW1 ER0 D -HIGH-PROFILE HH AY2 P R OW1 F AY0 L -HIGH-QUALITY HH AY2 K W AA1 L AH0 T IY0 -HIGH-SCHOOL HH AY1 S K UW2 L -HIGH-SPEED HH AY1 S P IY2 D -HIGH-SPIRITED HH AY2 S P IH1 R IH0 D IH0 D -HIGH-SPIRITEDNESS HH AY2 S P IH1 R IH0 D IH0 D N AH0 S -HIGH-TECH HH AY2 T EH1 K -HIGHAM HH AY1 AH0 M -HIGHBERGER HH AY1 B ER2 G ER0 -HIGHBOY HH AY1 B OY2 -HIGHBROW HH AY1 B R AW2 -HIGHBROWS HH AY1 B R AW2 Z -HIGHER HH AY1 ER0 -HIGHEST HH AY1 AH0 S T -HIGHFALUTIN HH AY2 F AH0 L UW1 T IH0 N -HIGHFIELD HH AY1 F IY2 L D -HIGHFILL HH AY1 F IH2 L -HIGHFLIER HH AY1 F L AY2 ER0 -HIGHFLIERS HH AY1 F L AY2 ER0 Z -HIGHFLYING HH AY1 F L AY2 IH0 NG -HIGHLAND HH AY1 L AH0 N D -HIGHLANDER HH AY1 L AE2 N D ER0 -HIGHLANDERS HH AY1 L AE2 N D ER0 Z -HIGHLANDS HH AY1 L AH0 N D Z -HIGHLEY HH AY1 L IY0 -HIGHLIGHT HH AY1 L AY2 T -HIGHLIGHTED HH AY1 L AY2 T IH0 D -HIGHLIGHTING HH AY1 L AY2 T IH0 NG -HIGHLIGHTS HH AY1 L AY2 T S -HIGHLY HH AY1 L IY0 -HIGHMAN HH AY1 M AH0 N -HIGHNESS HH AY1 N AH0 S -HIGHOSIN HH AY1 OW0 S IH0 N -HIGHRISE HH AY1 R AY2 Z -HIGHRISES HH AY1 R AY2 Z IH0 Z -HIGHS HH AY1 Z -HIGHSCHOOL HH AY1 S K UW2 L -HIGHSCHOOLS HH AY1 S K UW2 L Z -HIGHSMITH HH AY1 S M IH2 TH -HIGHSPEED HH AY1 S P IY2 D -HIGHT HH AY1 T -HIGHTECH HH AY1 T EH2 K -HIGHTOWER HH AY1 T AW2 ER0 -HIGHTOWER'S HH AY1 T AW2 ER0 Z -HIGHWAY HH AY1 W EY2 -HIGHWAY'S HH AY1 W EY2 Z -HIGHWAYS HH AY1 W EY2 Z -HIGHYIELD HH AY1 Y IY1 L D -HIGINBOTHAM HH IH1 G IH0 N B AH0 TH AH0 M -HIGLEY HH IH1 G L IY0 -HIGMAN HH IH1 G M AH0 N -HIGNIGHT HH IH1 G N AY2 T -HIGNITE HH IH1 G N AY2 T -HIGUCHI HH IY0 G UW1 CH IY0 -HIGUERA HH IY0 G EH1 R AH0 -HIJACK HH AY1 JH AE2 K -HIJACKED HH AY1 JH AE2 K T -HIJACKER HH AY1 JH AE2 K ER0 -HIJACKERS HH AY1 JH AE2 K ER0 Z -HIJACKING HH AY1 JH AE2 K IH0 NG -HIJACKINGS HH AY1 JH AE2 K IH0 NG Z -HIJINKS HH IH1 JH IH0 NG K S -HIKE HH AY1 K -HIKED HH AY1 K T -HIKER HH AY1 K ER0 -HIKERS HH AY1 K ER0 Z -HIKES HH AY1 K S -HIKING HH AY1 K IH0 NG -HILAND HH IH1 L AH0 N D -HILARIA HH IY0 L AA1 R IY0 AH0 -HILARIO HH IY0 L AA1 R IY0 OW0 -HILARIOUS HH IH0 L EH1 R IY0 AH0 S -HILARIOUSLY HH IH0 L EH1 R IY0 AH0 S L IY0 -HILARITY HH IH0 L EH1 R AH0 T IY0 -HILARY HH IH1 L ER0 IY0 -HILB HH IH1 L B -HILBERG HH IH1 L B ER0 G -HILBERT HH IH1 L B ER0 T -HILBORN HH IH1 L B ER0 N -HILBUN HH IH1 L B AH0 N -HILBURN HH IH1 L B ER0 N -HILD HH IH1 L D -HILDA HH IH1 L D AH0 -HILDE HH IH1 L D -HILDEBRAN HH IH1 L D IH0 B R AH0 N -HILDEBRAND HH IH1 L D IH0 B R AE2 N D -HILDEBRANDT HH IH1 L D IH0 B R AH0 N T -HILDEBRANT HH IH1 L D IH0 B R AH0 N T -HILDEGARD HH IH1 L D AH0 G AA2 R D -HILDEGARDE HH IH1 L D IH0 G AA2 R D -HILDEGARDES HH IH1 L D AH0 G AA2 R D Z -HILDEMAR HH IH1 L D IH0 M ER0 -HILDEN HH AY1 L D AH0 N -HILDENBRAND HH IH1 L D IH0 N B R AH0 N D -HILDENBRAND(2) HH IH1 L D AH0 N B R AE0 N D -HILDER HH IH1 L D ER0 -HILDERBRAND HH IH1 L D ER0 B R AH0 N D -HILDERBRANDT HH IH1 L D ER0 B R AH0 N T -HILDIE HH AY1 L D IY0 -HILDITCH HH IH1 L D IH0 CH -HILDRETH HH IH1 L D R IH0 TH -HILDUM HH IH1 L D AH0 M -HILDY HH IH1 L D IY0 -HILE HH AY1 L -HILEMAN HH AY1 L M AH0 N -HILEMON HH AY1 L M AH0 N -HILEMON'S HH AY1 L M AH0 N Z -HILER HH AY1 L ER0 -HILES HH AY1 L Z -HILEY HH IH1 L IY0 -HILFIGER HH IH1 L F AY0 G ER0 -HILFIGER(2) HH IH1 L F IH0 G ER0 -HILFIKER HH IH1 L F IH0 K ER0 -HILGART HH IH1 L G AA2 R T -HILGEMAN HH IH1 L G M AH0 N -HILGENBERG HH IH1 L G AH0 N B ER0 G -HILGENDORF HH IH1 L G IH0 N D AO0 R F -HILGER HH IH1 L G ER0 -HILGERS HH IH1 L G ER0 Z -HILGERT HH IH1 L G ER0 T -HILINSKI HH IH0 L IH1 N S K IY0 -HILKE HH IH1 L K AH0 -HILKER HH IH1 L K ER0 -HILL HH IH1 L -HILL'S HH IH1 L Z -HILLA HH IH1 L AH0 -HILLARD HH IH1 L ER0 D -HILLARD'S HH IH1 L ER0 D Z -HILLARY HH IH1 L ER0 IY0 -HILLARY'S HH IH1 L ER0 IY0 Z -HILLAS HH IH1 L AH0 S -HILLBILLIES HH IH1 L B IH2 L IY0 Z -HILLBILLY HH IH1 L B IH0 L IY0 -HILLCREST HH IH1 L K R EH0 S T -HILLE HH IH1 L -HILLEARY HH IH1 L ER0 IY0 -HILLEBRAND HH IH1 L IH0 B R AH0 N D -HILLEGAS HH IH1 L IH0 G AH0 Z -HILLEGASS HH IH1 L IH0 G AH0 S -HILLEL HH IH2 L EH1 L -HILLEN HH IH1 L AH0 N -HILLENBRAND HH IH1 L AH0 N B R AE2 N D -HILLENBRAND'S HH IH1 L AH0 N B R AE2 N D Z -HILLENBURG HH IH1 L AH0 N B ER0 G -HILLER HH IH1 L ER0 -HILLERMAN HH IH1 L ER0 M AH0 N -HILLERY HH IH1 L ER0 IY0 -HILLESHEIM HH IH1 L IH0 S HH AY0 M -HILLESTAD HH IH1 L IH0 S T AH0 D -HILLEY HH IH1 L IY0 -HILLHAVEN HH IH1 L HH EY2 V AH0 N -HILLHAVEN'S HH IH1 L HH EY2 V AH0 N Z -HILLHOUSE HH IH1 L HH AW2 S -HILLIAN HH IH1 L Y AH0 N -HILLIANS HH IH1 L Y AH0 N Z -HILLIARD HH IH1 L Y AA0 R D -HILLIER HH IH1 L IY0 ER0 -HILLIGOSS HH IH1 L IH0 G AA0 S -HILLIKER HH IH1 L AY0 K ER0 -HILLIKER(2) HH IH1 L IH0 K ER0 -HILLIN HH IH1 L IH0 N -HILLING HH IH1 L IH0 NG -HILLIS HH IH1 L IH0 S -HILLMAN HH IH1 L M AE2 N -HILLMANN HH IH1 L M AH0 N -HILLMER HH IH1 L M ER0 -HILLOCK HH IH1 L AH0 K -HILLS HH IH1 L Z -HILLS' HH IH1 L Z -HILLSBORO HH IH1 L Z B ER0 OW0 -HILLSBOROUGH HH IH1 L Z B ER0 OW0 -HILLSDALE HH IH1 L Z D EY2 L -HILLSDOWN HH IH1 L Z D AW2 N -HILLSIDE HH IH1 L S AY2 D -HILLSIDES HH IH1 L S AY2 D Z -HILLSMAN HH IH1 L S M AH0 N -HILLSON HH IH1 L S AH0 N -HILLSTROM HH IH1 L S T R AH0 M -HILLTOP HH IH1 L T AA2 P -HILLTOPS HH IH1 L T AA2 P S -HILLY HH IH1 L IY0 -HILLYARD HH IH1 L Y AA2 R D -HILLYER HH IH1 L IY0 ER0 -HILMA HH IH1 L M AH0 -HILMER HH IH1 L M ER0 -HILMES HH IH1 L M Z -HILO HH IY1 L OW0 -HILPERT HH IH1 L P ER0 T -HILSABECK HH IH1 L S AH0 B EH2 K -HILSCHER HH IH1 L SH ER0 -HILSINGER HH IH1 L S IH0 NG ER0 -HILSMAN HH IH1 L Z M AH0 N -HILSON HH IH1 L S AH0 N -HILT HH IH1 L T -HILTNER HH IH1 L T N ER0 -HILTON HH IH1 L T AH0 N -HILTON'S HH IH1 L T AH0 N Z -HILTON-HEAD HH IH1 L T AH0 N HH EH1 D -HILTONS HH IH1 L T AH0 N Z -HILTS HH IH1 L T S -HILTUNEN HH IH1 L T AH0 N AH0 N -HILTY HH IH1 L T IY0 -HILTZ HH IH1 L T S -HILYARD HH AH0 L Y AA1 R D -HILYER HH IH1 L IY0 ER0 -HIM HH IH1 M -HIM(2) IH0 M -HIMALAYA HH IH2 M AH0 L AY1 AH0 -HIMALAYA(2) HH IH2 M AH0 L EY1 AH0 -HIMALAYAN HH IH2 M AH0 L EY1 AH0 N -HIMALAYAN(2) HH IH2 M AH0 L AY1 AH0 N -HIMALAYAS HH IH2 M AH0 L AY1 AH0 S -HIMALAYAS(2) HH IH2 M AH0 L EY1 AH0 S -HIME HH AY1 M -HIMEBAUGH HH IH1 M IH0 B AO0 -HIMEL HH IH1 M AH0 L -HIMES HH AY1 M Z -HIMMEL HH IH1 M AH0 L -HIMMELBERGER HH IH1 M AH0 L B ER0 G ER0 -HIMMELFARB HH IH1 M AH0 L F AA2 R B -HIMMELSBACH HH IH1 M IH0 L S B AA0 K -HIMMELSTEIN HH IH1 M AH0 L S T AY0 N -HIMMELSTEIN(2) HH IH1 M AH0 L S T IY0 N -HIMMLER HH IH1 M L ER0 -HIMONT HH IH1 M AH0 N T -HIMONT(2) HH AY1 M AH0 N T -HIMSELF HH IH0 M S EH1 L F -HINCH HH IH1 N CH -HINCHCLIFF HH IH1 N CH K L IH0 F -HINCHCLIFFE HH IH1 N CH K L IH0 F -HINCHEY HH IH1 N CH IY0 -HINCHLIFFE HH IH1 N K L IH0 F -HINCHMAN HH IH1 NG K M AH0 N -HINCK HH IH1 NG K -HINCKLEY HH IH1 NG K L IY0 -HIND HH AY1 N D -HINDE HH IH1 N D -HINDELONG HH IH1 N D AH0 L AO0 NG -HINDER HH IH1 N D ER0 -HINDERED HH IH1 N D ER0 D -HINDERER HH IH1 N D ER0 ER0 -HINDERING HH IH1 N D ER0 IH0 NG -HINDERLITER HH IH1 N D ER0 L IY0 T ER0 -HINDERMAN HH AY1 N D ER0 M AH0 N -HINDERS HH IH1 N D ER0 Z -HINDES HH IH1 N D Z -HINDI HH IH1 N D IY0 -HINDLE HH IH1 N D AH0 L -HINDLEY HH IH1 N D L IY0 -HINDMAN HH AY1 N D M AH0 N -HINDQUARTER HH AY1 N D K W AO2 R T ER0 -HINDQUARTER(2) HH AY1 N D K AO2 R T ER0 -HINDQUARTERS HH AY1 N D K W AO2 R T ER0 Z -HINDQUARTERS(2) HH AY1 N D K AO2 R T ER0 Z -HINDRANCE HH IH1 N D R AH0 N S -HINDRANCES HH IH1 N D R AH0 N S IH0 Z -HINDS HH AY1 N D Z -HINDSIGHT HH AY1 N D S AY2 T -HINDSIGHT(2) HH AY1 N S AY2 T -HINDSIGHT'S HH AY1 N D S AY2 T S -HINDSIGHT'S(2) HH AY1 N S AY2 T S -HINDU HH IH1 N D UW2 -HINDUISM HH IH1 N JH UW0 IH2 Z AH0 M -HINDUS HH IH1 N D UW2 Z -HINDUSTAN HH IH1 N D UW0 S T AE2 N -HINE HH AY1 N -HINEBAUGH HH IH1 N IH0 B AO0 -HINELINE HH IH1 N IH0 L AY2 N -HINELY HH AY1 N L IY0 -HINEMAN HH AY1 N M AH0 N -HINER HH AY1 N ER0 -HINERMAN HH AY1 N ER0 M AH0 N -HINES HH AY1 N Z -HINESLEY HH IH1 N IH0 S L IY0 -HINESLEY(2) HH AY1 N Z L IY0 -HINEY HH IH1 N IY0 -HING HH IH1 NG -HINGE HH IH1 N JH -HINGED HH IH1 N JH D -HINGER HH IH1 N JH ER0 -HINGES HH IH1 N JH IH0 Z -HINGHAM HH IH1 NG AH0 M -HINGIS HH IH1 NG AH0 S -HINGLE HH IH1 NG G AH0 L -HINGST HH IH1 NG S T -HINGSTON HH IH1 NG S T AH0 N -HINK HH IH1 NG K -HINKEL HH IH1 NG K AH0 L -HINKELMAN HH IH1 NG K AH0 L M AH0 N -HINKLE HH IH1 NG K AH0 L -HINKLEY HH IH1 NG K L IY0 -HINKSON HH IH1 NG K S AH0 N -HINMAN HH IH1 N M AH0 N -HINMEN HH IH1 N M EH0 N -HINN HH IH1 N -HINNANT HH IH1 N AH0 N T -HINNENKAMP HH IH1 N IH0 N K AE0 M P -HINNERS HH IH1 N ER0 Z -HINNY HH IH1 N IY0 -HINO HH IY1 N OW0 -HINOJOS HH IY0 N OW1 Y OW0 Z -HINOJOSA HH IY0 N OW0 JH OW1 S AH0 -HINOTE HH IH0 N OW1 T -HINRICHS HH IH1 N R IH0 K S -HINRICHSEN HH IH1 N R IH0 K S AH0 N -HINSCH HH IH1 N SH -HINSDALE HH IH1 N S D EY2 L -HINSHAW HH IH1 N SH AO2 -HINSLEY HH IH1 N S L IY0 -HINSON HH IH1 N S AH0 N -HINT HH IH1 N T -HINTED HH IH1 N T AH0 D -HINTED(2) HH IH1 N T IH0 D -HINTED(3) HH IH1 N IH0 D -HINTERLAND HH IH1 N T ER0 L AE2 N D -HINTERLANDS HH IH1 N T ER0 L AE2 N D Z -HINTING HH IH1 N T IH0 NG -HINTON HH IH1 N T AH0 N -HINTS HH IH1 N T S -HINTZ HH IH1 N T S -HINTZE HH IH1 N T Z -HINZ HH IH1 N Z -HINZACK HH IH1 N Z AE0 K -HINZE HH IH1 N Z -HINZMAN HH IH1 N Z M AH0 N -HIOTT HH AY1 AH0 T -HIP HH IH1 P -HIP-HOP HH IH1 P HH AO2 P -HIP-POCKET HH IH1 P P AA1 K AH0 T -HIPBONE HH IH1 P B OW1 N -HIPBONES HH IH1 P B OW1 N Z -HIPKINS HH IH1 P K IH0 N Z -HIPOLITO IY1 P OW0 L IY1 T OW0 -HIPP HH IH1 P -HIPPE HH IH1 P -HIPPEN HH IH1 P AH0 N -HIPPENSTEEL HH IH1 P IH0 N S T IY0 L -HIPPER HH IH1 P ER0 -HIPPERT HH IH1 P ER0 T -HIPPEST HH IH1 P AH0 S T -HIPPIE HH IH1 P IY0 -HIPPIES HH IH1 P IY0 Z -HIPPLE HH IH1 P AH0 L -HIPPLER HH IH1 P L ER0 -HIPPO HH IH1 P OW0 -HIPPOCAMPUS HH IH2 P OW0 K AE1 M P AH0 S -HIPPOCRATES HH IH1 P AH0 K R EY2 T S -HIPPOCRATES(2) HH IH0 P AO1 K R AH0 T IY0 Z -HIPPOCRATIC HH IH0 P AH0 K R AE1 T IH0 K -HIPPODROME HH IH1 P AH0 D R OW2 M -HIPPOLYTUS HH AH0 P AA1 L AH0 T AH0 S -HIPPOPOTAMUS HH IH2 P AH0 P AA1 T AH0 M AH0 S -HIPPOPOTAMUSES HH IH2 P AH0 P AA1 T AH0 M AH0 S IH0 Z -HIPPOS HH IH1 P OW0 Z -HIPPS HH IH1 P S -HIPS HH IH1 P S -HIPSHER HH IH1 P SH ER0 -HIPSKIND HH IH1 P S K IH0 N D -HIPSTER HH IH1 P S T ER0 -HIPSTERS HH IH1 P S T ER0 Z -HIPWELL HH IH1 P W EH2 L -HIRABAYASHI HH IH0 R AH0 B AY0 AA1 SH IY0 -HIRADIN HH IH0 R AA1 D IH0 N -HIRAI HH IH0 R AA1 IY0 -HIRAM HH AY1 R AH0 M -HIRANO HH IH0 R AA1 N OW0 -HIRATA HH IH0 R AA1 T AH0 -HIRAYAMA HH IH0 R AA0 Y AA1 M AH0 -HIRD HH ER1 D -HIRE HH AY1 ER0 -HIRE(2) HH AY1 R -HIRED HH AY1 ER0 D -HIRES HH AY1 ER0 Z -HIRES(2) HH AY1 R Z -HIRIART HH IH1 R IY0 AA0 R T -HIRING HH AY1 R IH0 NG -HIRINGS HH AY1 R IH0 NG Z -HIRN HH ER1 N -HIRO HH IH1 R OW0 -HIROAKI HH IH2 R OW0 AA1 K IY0 -HIROHITO HH IH2 R OW0 HH IY1 T OW2 -HIROHITO(2) HH IH2 R AH0 HH IY1 T OW2 -HIROHITO'S HH IH2 R OW0 HH IY1 T OW2 Z -HIROHITO'S(2) HH IH2 R AH0 HH IY1 T OW2 Z -HIROMASA HH IH2 R OW0 M AA1 S AH0 -HIRONS HH AY1 R AH0 N Z -HIROSAKAMOKI HH IH2 R AH0 S AE0 K AH0 M OW1 K IY0 -HIROSAKIMA HH IH2 R AH0 S AH0 K IY1 M AH0 -HIROSE HH IH0 R OW1 S EY0 -HIROSHI HH IH0 R OW1 SH IY0 -HIROSHIMA HH IH2 R OW0 SH IY1 M AH0 -HIROSHIMA(2) HH IH2 R OW1 SH IH0 M AH0 -HIROTA HH IH0 R OW1 T AH0 -HIROYUKI HH IH2 R OW0 Y UW1 K IY0 -HIRSCH HH ER1 SH -HIRSCH'S HH ER1 SH IH0 Z -HIRSCHBERG HH ER1 SH B ER0 G -HIRSCHFELD HH ER1 SH F EH0 L D -HIRSCHFIELD HH ER1 S K F IY0 L D -HIRSCHHORN HH ER1 SH HH ER0 N -HIRSCHI HH IH1 R S K IY0 -HIRSCHMAN HH ER1 SH M AH0 N -HIRSCHMANN HH ER1 SH M AH0 N -HIRSCHY HH ER1 SH IY0 -HIRSH HH ER1 SH -HIRSHBERG HH ER1 SH B ER0 G -HIRSHFIELD HH ER1 SH F IY0 L D -HIRSHHORN HH ER1 SH HH AO2 R N -HIRSHHORN(2) HH ER1 SH AO2 R N -HIRSHMAN HH ER1 SH M AH0 N -HIRST HH ER1 S T -HIRST'S HH ER1 S T S -HIRSUTE HH ER0 S UW1 T -HIRT HH ER1 T -HIRTH HH ER1 TH -HIRTLE HH ER1 T AH0 L -HIRTZ HH ER1 T S -HIRULOG HH IH1 R UW2 L AA2 G -HIRZEL HH ER1 Z AH0 L -HIS HH IH1 Z -HIS(2) HH IH0 Z -HISADA HH IH0 S AA1 D AH0 -HISAO HH IH0 S AA1 OW0 -HISAW HH AY1 S AO0 -HISCOCK HH IH1 S K AH0 K -HISCOX HH IH1 S K AA0 K S -HISE HH AY1 Z -HISEL HH IH1 S AH0 L -HISER HH AY1 Z ER0 -HISEY HH IH1 S IY0 -HISHAM HH IH1 SH AH0 M -HISLE HH AY1 AH0 L -HISLOP HH IH1 S L AH0 P -HISPANIC HH IH0 S P AE1 N IH0 K -HISPANICS HH IH0 S P AE1 N IH0 K S -HISPANIOLA HH IH0 S P AA2 NG OW1 L AH0 -HISPANO HH IH0 S P AA1 N OW0 -HISPANOIL HH IH1 S P AH0 N OY2 L -HISS HH IH1 S -HISS'S HH IH1 S IH0 Z -HISSED HH IH1 S T -HISSELF HH IH2 S EH1 L F -HISSES HH IH1 S IH0 Z -HISSING HH IH1 S IH0 NG -HISSONG HH IH1 S AO2 NG -HISTADRUT HH IH1 S T AH0 D R AH0 T -HISTAMINE HH IH1 S T AH0 M IY2 N -HISTIDINE HH IH1 S T AH0 D IY2 N -HISTOGRAM HH IH1 S T AH0 G R AE2 M -HISTOGRAMS HH IH1 S T AH0 G R AE2 M Z -HISTOLOGY HH IH0 S T AA1 L AH0 JH IY0 -HISTORIAN HH IH0 S T AO1 R IY0 AH0 N -HISTORIANS HH IH0 S T AO1 R IY0 AH0 N Z -HISTORIC HH IH0 S T AO1 R IH0 K -HISTORICAL HH IH0 S T AO1 R IH0 K AH0 L -HISTORICALLY HH IH0 S T AO1 R IH0 K AH0 L IY0 -HISTORICALLY(2) HH IH0 S T AO1 R IH0 K L IY0 -HISTORIES HH IH1 S T ER0 IY0 Z -HISTORIES(2) HH IH1 S T R IY0 Z -HISTORIOGRAPHY HH IH0 S T AO2 R IY0 AA1 G R AH0 F IY0 -HISTORY HH IH1 S T ER0 IY0 -HISTORY(2) HH IH1 S T R IY0 -HISTORY'S HH IH1 S T ER0 IY0 Z -HISTORY'S(2) HH IH1 S T R IY0 Z -HISTRIONIC HH IH2 S T R IY0 AA1 N IH0 K -HISTRIONICS HH IH2 S T R IY0 AA1 N IH0 K S -HIT HH IH1 T -HIT-AND-RUN HH IH1 T AE0 N R AH2 N -HITACHI HH IH0 T AA1 CH IY0 -HITCH HH IH1 CH -HITCHCOCK HH IH1 CH K AA2 K -HITCHCOCK'S HH IH1 CH K AA2 K S -HITCHED HH IH1 CH T -HITCHENS HH IH1 CH AH0 N Z -HITCHES HH IH1 CH IH0 Z -HITCHHIKE HH IH1 CH HH AY2 K -HITCHHIKED HH IH1 CH HH AY2 K T -HITCHHIKER HH IH1 CH HH AY2 K ER0 -HITCHHIKERS HH IH1 CH HH AY2 K ER0 Z -HITCHHIKES HH IH1 CH HH AY2 K S -HITCHHIKING HH IH1 CH HH AY2 K IH0 NG -HITCHING HH IH1 CH IH0 NG -HITCHINGS HH IH1 CH IH0 NG Z -HITCHINS HH IH1 CH IH0 N Z -HITCHMAN HH IH1 CH M AH0 N -HITCHNER HH IH1 CH N ER0 -HITE HH AY1 T -HITES HH AY1 T S -HITHER HH IH1 DH ER0 -HITHERTO HH IH1 DH ER2 T UW1 -HITLER HH IH1 T L ER0 -HITLER'S HH IH1 T L ER0 Z -HITMAN HH IH1 T M AE2 N -HITOSHI HH IH0 T OW1 SH IY0 -HITS HH IH1 T S -HITSCHLER HH IH1 T S L ER0 -HITSCHLER(2) HH IH1 CH L ER0 -HITSMAN HH IH1 T S M AH0 N -HITSON HH IH1 T S AH0 N -HITT HH IH1 T -HITTER HH IH1 T ER0 -HITTER'S HH IH1 T ER0 Z -HITTERS HH IH1 T ER0 Z -HITTING HH IH1 T IH0 NG -HITTITE HH IH1 T AY2 T -HITTLE HH IH1 T AH0 L -HITTNER HH IH1 T N ER0 -HITTY HH IH1 T IY0 -HITZ HH IH1 T S -HITZEMAN HH IH1 T S M AH0 N -HIV EY1 CH AY1 V IY1 # ABBREV -HIVE HH AY1 V -HIVELY HH AY1 V L IY0 -HIVES HH AY1 V Z -HIWAY HH AY1 W EY2 -HIX HH IH1 K S -HIXENBAUGH HH IH0 G Z EH1 N B AO0 -HIXON HH IH1 K S AH0 N -HIXSON HH IH1 K S AH0 N -HIZBOLLAH HH IH0 Z B OW1 L AH0 -HIZER HH AY1 Z ER0 -HJELM HH Y EH1 L M -HJERPE HH Y ER1 P -HJORT HH AH0 Y AO1 R T -HLAD HH L AE1 D -HLADIK HH L AE1 D IH0 K -HLADKY HH L AE1 D K IY0 -HLAVAC HH L AA1 V AH0 K -HLAVACEK HH L AA1 V AH0 CH EH0 K -HLAVATY HH L AH0 V AA1 T IY0 -HM HH M -HMM HH M -HMMM HH M -HMONG M AO1 NG -HMONG(2) HH M AO1 NG -HMONG(3) HH AH0 M AO1 NG -HNAT HH N AE1 T -HO HH OW1 -HO'S HH OW1 Z -HOADLEY HH OW1 D L IY0 -HOAG HH OW1 G -HOAGIE HH OW1 G IY0 -HOAGLAND HH OW1 G L AH0 N D -HOAGLIN HH OW1 G L IH0 N -HOAGLUND HH OW1 G L AH0 N D -HOAGY HH OW1 G IY0 -HOAK HH OW1 K -HOANG HH OW1 NG -HOAR HH AO1 R -HOARD HH AO1 R D -HOARDED HH AO1 R D IH0 D -HOARDING HH AO1 R D IH0 NG -HOARDS HH AO1 R D Z -HOARE HH AO1 R -HOARSE HH AO1 R S -HOARSENESS HH AO1 R S N AH0 S -HOARY HH AO1 R IY0 -HOAX HH OW1 K S -HOAXES HH OW1 K S IH0 Z -HOB HH AA1 B -HOBACK HH OW1 B AE2 K -HOBAN HH OW1 B AH0 N -HOBART HH OW1 B AA2 R T -HOBAUGH HH AA1 B AO0 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-HOCHBRUECKNER HH AA1 K B R AH2 K N ER0 -HOCHENHEIM HH AO1 K AH0 N HH AY2 M -HOCHHALTER HH AA1 K HH AH0 L T ER0 -HOCHHAUSER HH AA1 K HH AW2 Z ER0 -HOCHMAN HH AA1 K M AH0 N -HOCHMUTH HH AA1 K M UW2 TH -HOCHSTATTER HH AA1 K S T AH0 T ER0 -HOCHSTEDLER HH AA1 K S T IH0 D AH0 L ER0 -HOCHSTEDLER(2) HH AA1 K S T EH0 D L ER0 -HOCHSTEIN HH AA1 K S T AY0 N -HOCHSTEIN(2) HH AA1 K S T IY0 N -HOCHSTETLER HH AA1 K S T IH0 T AH0 L ER0 -HOCHSTETLER(2) HH AA1 K S T EH0 T L ER0 -HOCHTIEF HH AA1 K T IY2 F -HOCK HH AA1 K -HOCKADAY HH AA1 K AH0 D EY2 -HOCKBERG HH AA1 K B ER0 G -HOCKENBERRY HH AA1 K AH0 N B EH2 R IY0 -HOCKENBURY HH AA1 K AH0 N B EH2 R IY0 -HOCKENSMITH HH AA1 K AH0 N S M IH2 TH -HOCKER HH AA1 K ER0 -HOCKERSMITH HH AA1 K ER0 S M IH2 TH -HOCKETT HH AA1 K IH0 T -HOCKEY HH AA1 K IY0 -HOCKEY'S HH AA1 K IY0 Z -HOCKIN HH AA1 K IH0 N -HOCKING HH AA1 K IH0 NG -HOCKLEY HH AA1 K L IY0 -HOCKMAN HH AA1 K M AH0 N -HOCKNEY HH AA1 K N IY0 -HOCKNEY'S HH AA1 K N IY0 Z -HOCTOR HH AA1 K T ER0 -HOCUS HH OW1 K AH0 S -HOCUTT HH AA1 K AH0 T -HODAK HH OW1 D AH0 K -HODAPP HH AA1 D AH0 P -HODDE HH AA1 D -HODDER HH AA1 D ER0 -HODDING HH AA1 D IH0 NG -HODDUR HH AA1 D ER0 -HODEL HH OW1 D AH0 L -HODES HH OW1 D Z -HODGDON HH AA1 JH D AH0 N -HODGE HH AA1 JH -HODGE'S HH AA1 JH IH0 Z -HODGEN HH AA1 JH AH0 N -HODGENS HH AA1 JH AH0 N Z -HODGEPODGE HH AA1 JH P AA2 JH -HODGES HH AA1 JH IH0 Z -HODGIN HH AA1 JH IH0 N -HODGINS HH AA1 JH IH0 N Z -HODGKIN HH AA1 JH K IH0 N -HODGKIN'S HH AA1 JH K IH0 N Z -HODGKINS HH AA1 JH K IH0 N Z -HODGKINSON HH AA1 JH K IH0 N S AH0 N -HODGKISS HH AA1 JH K IH0 S -HODGMAN HH AA1 JH M AH0 N -HODGMAN'S HH AA1 JH M AH0 N Z -HODGSON HH AA1 JH S AH0 N -HODKINSON HH AA1 D K IH0 N S AH0 N -HODNETT HH AA1 D N IH0 T -HODO HH OW1 D OW0 -HODSDON HH AA1 D Z D AH0 N -HODSON HH AA1 D S AH0 N -HOE HH OW1 -HOECHST HH OW1 K S T -HOECHST(2) HH OW1 SH T -HOECHST'S HH OW1 K S T S -HOECHST'S(2) HH OW1 SH T S -HOECK HH OW1 K -HOECKER HH OW1 K ER0 -HOEDOWN HH OW1 D AW2 N -HOEFER HH OW1 F ER0 -HOEFFNER HH OW1 F N ER0 -HOEFLE HH OW1 F AH0 L -HOEFLER HH OW1 F AH0 L ER0 -HOEFLER(2) HH OW1 F L ER0 -HOEFLICH HH OW1 F L IH0 K -HOEFLING HH OW1 F AH0 L IH0 NG -HOEFLING(2) HH OW1 F L IH0 NG -HOEFS HH OW1 F S -HOEFT HH OW1 F T -HOEG HH OW1 G -HOEGER HH OW1 G ER0 -HOEHN HH OW1 N -HOEHNE HH OW1 N -HOEING HH OW1 IH0 NG -HOEK HH OW1 K -HOEKSEMA HH OW1 K S IH0 M AH0 -HOEKSTRA HH OW1 K S T R AH0 -HOEL HH OW1 L -HOELL HH OW1 L -HOELLE HH OW1 L -HOELSCHER HH OW1 L SH ER0 -HOELTER HH OW1 L T ER0 -HOELTERHOFF HH OW1 L T ER0 HH AO2 F -HOELTERHOFF'S HH OW1 L T ER0 HH AO2 F S -HOELTING HH OW1 L T IH0 NG -HOELZEL HH OW1 L Z AH0 L -HOELZER HH OW1 L Z ER0 -HOEN HH OW1 N -HOENE HH AA1 IY0 N -HOENER HH OW1 N ER0 -HOENIG HH OW1 N IH0 G -HOEPER HH OW1 P ER0 -HOEPFNER HH OW1 P F N ER0 -HOEPFNER(2) HH OW1 P N ER0 -HOEPNER HH OW1 P N ER0 -HOEPPNER HH OW1 P N ER0 -HOERIG HH AO1 R IH0 G -HOERNER HH AO1 R N ER0 -HOERR HH AO1 R -HOES HH OW1 Z -HOESCH HH OW1 SH -HOESCHEN HH OW1 SH AH0 N -HOESLY HH OW1 S L IY0 -HOEVELER HH OW1 V L ER0 -HOEVET HH OW1 V EH2 T -HOEVET'S HH OW1 V EH2 T S -HOEY HH OW1 IY0 -HOF HH AA1 F -HOFACKER HH AA1 F AH0 K ER0 -HOFBAUER HH AA1 F B AW0 ER0 -HOFER HH OW1 F ER0 -HOFF HH AO1 F -HOFFA HH AO1 F AH0 -HOFFA'S HH AO1 F AH0 Z -HOFFACKER HH AO1 F AH0 K ER0 -HOFFART HH AO1 F AA0 R T -HOFFARTH HH AO1 F AA0 R TH -HOFFECKER HH AO1 F IH0 K ER0 -HOFFENBERG HH AO1 F AH0 N B ER0 G -HOFFENBERG'S HH AA1 F AH0 N B ER0 G Z -HOFFER HH AA1 F ER0 -HOFFERBER HH AO1 F ER0 B ER0 -HOFFERT HH AO1 F ER0 T -HOFFITZ HH AA1 F IH0 T S -HOFFLER HH AO1 F AH0 L ER0 -HOFFLER(2) HH AO1 F L ER0 -HOFFMAN HH AO1 F M AH0 N -HOFFMAN'S HH AO1 F M AH0 N Z -HOFFMANN HH AO1 F M AH0 N -HOFFMASTER HH AO1 F M AE2 S T ER0 -HOFFMEIER HH AO1 F M AY0 ER0 -HOFFMEISTER HH AO1 F M AY2 S T ER0 -HOFFMEYER HH AO1 F M AY0 ER0 -HOFFNER HH AO1 F N ER0 -HOFFPAUIR HH AO1 F P AW0 ER0 -HOFI HH OW1 F IY0 -HOFLAND HH AA1 F L AH0 N D -HOFLER HH AA1 F L ER0 -HOFMAN HH AA1 F M AH0 N -HOFMANN HH AA1 F M AH0 N -HOFMEISTER HH AA1 F M AY0 S T ER0 -HOFRICHTER HH AA1 F R IH0 K T ER0 -HOFSTAD HH AA1 F S T AE0 D -HOFSTETTER HH AA1 F S T EH0 T ER0 -HOFSTRA HH AA1 F S T R AH0 -HOG HH AA1 G -HOGAN HH OW1 G AA2 N -HOGAN(2) HH OW1 G AH0 N -HOGAN'S HH OW1 G AA2 N Z -HOGANS HH OW1 G AA2 N Z -HOGANS(2) HH OW1 G AH0 N Z -HOGANSON HH AA1 G AH0 N S AH0 N -HOGARTH HH OW1 G AA2 R TH -HOGARTY HH AA1 G AA2 R T IY0 -HOGBERG HH AA1 G B ER0 G -HOGE HH OW1 JH -HOGELAND HH AA1 G IH0 L AH0 N D -HOGELAND(2) HH OW1 G L AH0 N D -HOGEN HH AA1 G AH0 N -HOGENSON HH AA1 JH IH0 N S AH0 N -HOGER HH OW1 G ER0 -HOGG HH AA1 G -HOGGAN HH AA1 G AH0 N -HOGGARD HH AA1 G ER0 D -HOGGART HH AA1 G ER0 T -HOGGATT HH AA1 G AH0 T -HOGGE HH AA1 G -HOGGING HH AO1 G IH0 NG -HOGLAND HH AA1 G L AH0 N D -HOGLE HH OW1 G AH0 L -HOGLUND HH AO1 G L AH0 N D -HOGLUND'S HH AO1 G L AH0 N D Z -HOGLUNDS HH AO1 G L AH0 N D Z -HOGNOSE HH AA1 G N OW2 Z -HOGREFE HH AA1 G R IH0 F -HOGS HH AA1 G Z -HOGSED HH OW1 G S T -HOGSETT HH AA1 G S IH0 T -HOGSTON HH AA1 G S T AH0 N -HOGUE HH OW1 G -HOGWARTS HH AA1 G W AO2 R T S -HOGWASH HH AA1 G W AA2 SH -HOGWOOD HH AO1 G W UH2 D -HOH HH OW1 -HOHEISEL HH OW1 AY0 S AH0 L -HOHENBERGER HH OW1 AH0 N B ER0 G ER0 -HOHENSEE HH AA0 HH IH0 N S IY1 -HOHENSTEIN HH OW1 AH0 N S T AY0 N -HOHENSTEIN(2) HH OW1 AH0 N S T IY0 N -HOHL HH OW1 L -HOHLER HH OW1 L ER0 -HOHLT HH OW1 L T -HOHMAN HH OW1 M AH0 N -HOHMANN HH OW1 M AH0 N -HOHN HH AA1 N -HOHNER HH OW1 N ER0 -HOHNSTEIN HH OW1 N S T AY0 N -HOHNSTEIN(2) HH OW1 N S T IY0 N -HOHO HH OW1 HH OW1 -HOHORST HH OW1 HH AO2 R S T -HOHOS HH OW1 HH OW1 Z -HOHUM HH OW1 HH AH1 M -HOI HH OY1 -HOILAND HH OY0 L AE1 N D -HOILMAN HH OY1 L M AH0 N -HOISINGTON HH OY1 Z IH0 NG T AH0 N -HOIST HH OY1 S T -HOISTED HH OY1 S T AH0 D -HOISTED(2) HH OY1 S T IH0 D -HOISTING HH OY1 S T IH0 NG -HOISTS HH OY1 S T S -HOIT HH OY1 T -HOITY HH OY1 T IY0 -HOIUM HH AA1 IY0 AH0 M -HOJNACKI HH AH0 Y N AA1 T S K IY0 -HOK HH AO1 K -HOKANSON HH AA1 K AH0 N S AH0 N -HOKE HH OW1 K -HOKENSON HH AA1 K IH0 N S AH0 N -HOKEY HH OW1 K IY0 -HOKKAIDO HH OW0 K AY1 D OW0 -HOKUM HH OW1 K AH0 M -HOKURIKU HH AA2 K ER0 IY1 K UW2 -HOLA HH OW1 L AH0 -HOLADAY HH OW1 L AH0 D EY0 -HOLAHAN HH AA1 L AH0 HH AE0 N -HOLAN HH OW1 L AH0 N -HOLAWAY HH OW1 L AH0 W EY0 -HOLBEIN HH OW1 L B AY0 N -HOLBEN HH OW1 L B AH0 N -HOLBERG HH OW1 L B ER0 G -HOLBERT HH OW1 L B ER0 T -HOLBROOK HH OW1 L B R UH2 K -HOLBROOKE HH OW1 L B R UH0 K -HOLBROOKE'S HH OW1 L B R UH0 K S -HOLBROOKS HH OW1 L B R UH0 K S -HOLCK HH OW1 L K -HOLCOMB HH OW1 L K AH0 M -HOLCOMBE HH OW1 L K AH0 M -HOLD HH OW1 L D -HOLDA HH OW1 L D AH0 -HOLDAWAY HH OW1 L D AH0 W EY2 -HOLDE HH OW1 L D -HOLDEMAN HH OW1 L D M AH0 N -HOLDEN HH OW1 L D AH0 N -HOLDEN'S HH OW1 L D AH0 N Z -HOLDER HH OW1 L D ER0 -HOLDER'S HH OW1 L D ER0 Z -HOLDERBANK HH OW1 L D ER0 B AE2 NG K -HOLDERBAUM HH OW1 L D ER0 B AW0 M -HOLDERBY HH OW1 L D ER0 B IY0 -HOLDERFIELD HH OW1 L D ER0 F IY2 L D -HOLDERMAN HH OW1 L D ER0 M AH0 N -HOLDERNESS HH OW1 L D ER0 N AH0 S -HOLDERS HH OW1 L D ER0 Z -HOLDERS' HH OW1 L D ER0 Z -HOLDFAST HH OW1 L F AE2 S T -HOLDING HH OW1 L D IH0 NG -HOLDING'S HH OW1 L D IH0 NG Z -HOLDINGS HH OW1 L D IH0 NG Z -HOLDINGS' HH OW1 L D IH0 NG Z -HOLDMAN HH OW1 L D M AH0 N -HOLDORF HH OW1 L D AO0 R F -HOLDOUT HH OW1 L D AW2 T -HOLDOUTS HH OW1 L D AW2 T S -HOLDOVER HH OW1 L D OW2 V ER0 -HOLDOVERS HH OW1 L D OW2 V ER0 Z -HOLDREN HH OW1 L D ER0 AH0 N -HOLDRIDGE HH OW1 L D R IH0 JH -HOLDS HH OW1 L D Z -HOLDSWORTH HH OW1 L D Z W ER2 TH -HOLDUP HH OW1 L D AH2 P -HOLDUPS HH OW1 L D AH2 P S -HOLE HH OW1 L -HOLECEK HH AA1 L IH0 CH EH0 K -HOLED HH OW1 L D -HOLEMAN HH OW1 L M AH0 N -HOLEN HH OW1 L AH0 N -HOLES HH OW1 L Z -HOLEWINSKI HH AH0 L UW0 IH1 N S K IY0 -HOLFORD HH OW1 L F ER0 D -HOLGATE HH OW1 L G EY2 T -HOLGERSON HH OW1 L G ER0 S AH0 N -HOLGUIN HH OW1 L G IH0 N -HOLIAN HH OW1 L IY0 AH0 N -HOLICK HH AA1 L IH0 K -HOLIDAY HH AA1 L AH0 D EY2 -HOLIDAY(2) HH AA1 L IH0 D EY2 -HOLIDAY'S HH AA1 L AH0 D EY2 Z -HOLIDAYS HH AA1 L AH0 D EY2 Z -HOLIDAYSBURG HH AA1 L AH0 D EY2 Z B ER0 G -HOLIEN HH OW1 L IY0 AH0 N 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L ER0 AH0 N -HOLLERBACH HH AA1 L ER0 B AA2 K -HOLLERED HH AA1 L ER0 D -HOLLERING HH AA1 L ER0 IH0 NG -HOLLERN HH AA1 L ER0 N -HOLLERS HH AA1 L ER0 Z -HOLLETT HH AA1 L IH0 T -HOLLEY HH AA1 L IY0 -HOLLIBAUGH HH AA1 L IH0 B AO2 -HOLLICK HH AA1 L IH0 K -HOLLIDAY HH AA1 L IH0 D EY2 -HOLLIDAY'S HH AA1 L IH0 D EY2 Z -HOLLIE HH AA1 L IY0 -HOLLIER HH AO1 L IY0 ER0 -HOLLIES HH AA1 L IY0 Z -HOLLIFIELD HH AA1 L IH0 F IY2 L D -HOLLIMAN HH AA1 L IH0 M AH0 N -HOLLIMAN'S HH AA1 L IH0 M AH0 N Z -HOLLIMON HH AA1 L IH0 M AH0 N -HOLLIN HH AA1 L IH0 N -HOLLING HH AA1 L IH0 NG -HOLLINGER HH AA1 L IH0 NG ER0 -HOLLINGS HH AA1 L IH0 NG Z -HOLLINGS(2) HH AA1 L IH0 NG G Z -HOLLINGSHEAD HH AA1 L IH0 NG Z HH EH2 D -HOLLINGSWORTH HH AA1 L IH0 NG Z W ER2 TH -HOLLINGWORTH HH AA1 L IH0 NG G W ER2 TH -HOLLINS HH AA1 L IH0 N Z -HOLLINSHEAD HH AA1 L IH0 N S HH EH2 D -HOLLINSHEAD(2) HH AA1 L IH0 N Z HH EH2 D -HOLLINSWORTH HH AA1 L IH0 N S W ER2 TH -HOLLINSWORTH(2) HH AA1 L IH0 N Z W ER2 TH -HOLLIS HH AA1 L IH0 S 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AH0 D -HOLSTE HH OW1 L S T -HOLSTEAD HH OW1 L S T EH2 D -HOLSTEIN HH OW1 L S T IY2 N -HOLSTEN HH OW1 L S AH0 N -HOLSTER HH OW1 L S T ER0 -HOLSTINE HH OW1 L S T AY2 N -HOLSTON HH OW1 L S T AH0 N -HOLSTROM HH OW1 L S T R AH0 M -HOLSWORTH HH OW1 L S W ER0 TH -HOLT HH OW1 L T -HOLT'S HH OW1 L T S -HOLTAN HH OW1 L T AH0 N -HOLTE HH OW1 L T -HOLTEN HH OW1 L T AH0 N -HOLTER HH OW1 L T ER0 -HOLTERMAN HH OW1 L T ER0 M AH0 N -HOLTHAUS HH OW1 L T HH AW2 S -HOLTHUS HH OW1 L TH AH0 S -HOLTKAMP HH OW1 L T K AE2 M P -HOLTMAN HH OW1 L T M AH0 N -HOLTMANN HH OW1 L T M AH0 N -HOLTON HH OW1 L T AH0 N -HOLTORF HH OW1 L T ER0 F -HOLTROP HH OW1 L T R AH0 P -HOLTRY HH OW1 L T R IY0 -HOLTS HH OW1 L T S -HOLTSCLAW HH OW1 L T S K L AO2 -HOLTZ HH OW1 L T S -HOLTZ'S HH OW1 L T S IH0 Z -HOLTZAPPLE HH OW1 L T Z AH0 P AH0 L -HOLTZCLAW HH OW1 L T S K L AO0 -HOLTZER HH OW1 L T Z ER0 -HOLTZINGER HH OW1 L T Z IH0 NG ER0 -HOLTZMAN HH OW1 L T S M AH0 N -HOLUB HH OW1 L AH0 B -HOLUM HH OW1 L AH0 M -HOLVEN HH AO1 L V EH0 N 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K ER0 -HOMEMAKERS HH OW1 M M EY2 K ER0 Z -HOMEMAKING HH OW1 M M EY2 K IH0 NG -HOMEN HH OW1 M AH0 N -HOMEOPATHIC HH OW2 M IY0 OW0 P AE1 TH AH0 K -HOMEOPATHY HH OW2 M IY0 OW0 P AE1 TH IY0 -HOMEOSTASIS HH OW2 M IY0 OW0 S T EY1 S AH0 S -HOMEOSTATIC HH OW2 M IY0 OW0 S T AE1 T IH0 K -HOMEOWNER HH OW1 M OW2 N ER0 -HOMEOWNER'S HH OW1 M OW2 N ER0 Z -HOMEOWNERS HH OW1 M OW2 N ER0 Z -HOMEOWNERS' HH OW1 M OW2 N ER0 Z -HOMEOWNERSHIP HH OW1 M OW2 N ER0 SH IH2 P -HOMEPAGE HH OW1 M P EY2 JH -HOMEPORTING HH OW1 M P AO1 R T IH0 NG -HOMER HH OW1 M ER0 -HOMER'S HH OW1 M ER0 Z -HOMERIC HH OW0 M EH1 R IH0 K -HOMERS HH OW1 M ER0 Z -HOMERUN HH OW0 M R AH1 N -HOMERUNS HH OW0 M R AH1 N Z -HOMES HH OW1 M Z -HOMES' HH OW1 M Z -HOMESICK HH OW1 M S IH2 K -HOMESICKNESS HH OW1 M S IH2 K N AH0 S -HOMESLEY HH OW1 M Z L IY0 -HOMESPUN HH OW1 M S P AH2 N -HOMESTAKE HH OW1 M S T EY2 K -HOMESTAKE'S HH OW1 M S T EY2 K S -HOMESTATE HH OW1 M S T EY2 T -HOMESTEAD HH OW1 M S T EH2 D -HOMESTEAD'S HH OW1 M S T EH2 D Z -HOMESTEADED HH OW1 M S T EH2 D IH0 D -HOMESTEADER HH OW1 M S T EH0 D ER0 -HOMESTEADERS HH OW1 M S T EH0 D ER0 Z -HOMESTEADS HH OW1 M S T EH2 D Z -HOMESTRETCH HH OW1 M S T R EH2 CH -HOMETOWN HH OW1 M T AW2 N -HOMETOWNS HH OW1 M T AW2 N Z -HOMEWARD HH OW1 M W ER0 D -HOMEWOOD HH OW1 M W UH2 D -HOMEWORK HH OW1 M W ER2 K -HOMEWORKER HH OW1 M W ER2 K ER0 -HOMEWORKERS HH OW1 M W ER2 K ER0 Z -HOMEWORLD HH OW1 M W ER2 L D -HOMEY HH OW1 M IY0 -HOMEYER HH OW1 M IY0 ER0 -HOMICIDAL HH AA2 M AH0 S AY1 D AH0 L -HOMICIDE HH AA1 M AH0 S AY2 D -HOMICIDES HH AA1 M AH0 S AY2 D Z -HOMILETIC HH AA2 M AH0 L EH1 T IH0 K -HOMILIES HH OW1 M AH0 L IY0 Z -HOMILY HH AA1 M AH0 L IY0 -HOMINEM HH AA1 M IH0 N EH0 M -HOMING HH OW1 M IH0 NG -HOMINID HH AA1 M AH0 N IH0 D -HOMINY HH AA1 M IH0 N IY0 -HOMME HH AA1 M -HOMMEL HH AA1 M AH0 L -HOMMES HH AA1 M Z -HOMO HH OW1 M OW0 -HOMO-ERECTUS HH OW1 M OW0 IH0 R EH1 K T AH0 S -HOMOGENEITY HH AA2 M AH0 JH AH0 N IY1 AH0 T IY0 -HOMOGENEOUS HH OW2 M AH0 JH IY1 N IY0 AH0 S -HOMOGENIZATION HH 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AA1 N AH0 K ER0 -HONAN HH OW1 N AH0 N -HONASAN HH AA1 N AH0 Z AH0 N -HONCHO HH AO1 N CH OW0 -HONDA HH AO1 N D AH0 -HONDA'S HH AA1 N D AH0 Z -HONDAS HH AA1 N D AH0 S -HONDERICH HH AA1 N D ER0 IH0 K -HONDO HH AA1 N D OW0 -HONDURAN HH AA0 N D UH1 R AH0 N -HONDURANS HH AA0 N D UH1 R AH0 N Z -HONDURAS HH AA0 N D UH1 R AH0 S -HONE HH OW1 N -HONEA HH AA1 N IY0 AH0 -HONECK HH OW1 N EH0 K -HONECKER HH OW1 N EH0 K ER0 -HONECKER'S HH OW1 N EH0 K ER0 Z -HONED HH OW1 N D -HONER HH OW1 N ER0 -HONES HH OW1 N Z -HONEST AA1 N AH0 S T -HONESTLY AA1 N AH0 S T L IY0 -HONESTLY(2) AA1 N AH0 S L IY0 -HONESTOK HH OW1 N AH0 S T AO2 K -HONESTY AA1 N AH0 S T IY0 -HONEY HH AH1 N IY0 -HONEYBEE HH AH1 N IY0 B IY2 -HONEYBEES HH AH1 N IY0 B IY2 Z -HONEYCOMB HH AH1 N IY0 K OW2 M -HONEYCUTT HH AH1 N IY0 K AH0 T -HONEYDEW HH AH1 N IY0 D UW2 -HONEYMAN HH AA1 N IY0 M AH0 N -HONEYMOON HH AH1 N IY0 M UW2 N -HONEYMOONED HH AH1 N IY0 M UW2 N D -HONEYMOONER HH AH1 N IY0 M UW2 N ER0 -HONEYMOONERS HH AH1 N IY0 M UW2 N ER0 Z 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AH0 K -HORNBACK HH AO1 R N B AE2 K -HORNBAKER HH AO1 R N B AH0 K ER0 -HORNBEAK HH AO1 R N B AH0 K -HORNBECK HH AO1 R N B EH2 K -HORNBERGER HH AO1 R N B ER0 G ER0 -HORNBILL HH AO1 R N B IH2 L -HORNBILLS HH AO1 R N B IH2 L Z -HORNBLENDE HH AO1 R N B L EH2 N D -HORNBLOWER HH AO1 R N B L OW2 ER0 -HORNBOOK HH AO1 R N B UH2 K -HORNBOSTEL HH AO1 R N B AH0 S T AH0 L -HORNBOSTEL(2) HH AO1 R N B AH0 S AH0 L -HORNBROOK HH AO1 R N B R UH2 K -HORNBUCKLE HH AO1 R N B AH0 K AH0 L -HORNBURG HH AO1 R N B ER0 G -HORNBY HH AO1 R N B IY0 -HORNE HH AO1 R N -HORNED HH AO1 R N D -HORNELL HH AO2 R N EH1 L -HORNER HH AO1 R N ER0 -HORNERE HH AO1 R N ER0 -HORNET HH AO1 R N IH0 T -HORNET'S HH AO1 R N AH0 T S -HORNETS HH AO1 R N AH0 T S -HORNEY HH AO1 R N IY0 -HORNICK HH AO1 R N IH0 K -HORNIG HH AO1 R N IH0 G -HORNIK HH AO1 R N IH0 K -HORNING HH AO1 R N IH0 NG -HORNLESS HH AO1 R N L AH0 S -HORNLIKE HH AO1 R N L AY2 K -HORNOR HH AO1 R N ER0 -HORNPIPE HH AO1 R N P AY0 P -HORNS HH AO1 R N Z -HORNSBY HH AO1 R N Z B 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T S -HURWITZ HH ER1 W IH0 T S -HUSAIN HH AH1 S AY0 N -HUSAK HH UW1 S AH0 K -HUSAR HH UW1 S ER0 -HUSBAND HH AH1 Z B AH0 N D -HUSBAND'S HH AH1 Z B AH0 N D Z -HUSBANDRY HH AH1 Z B AH0 N D R IY0 -HUSBANDS HH AH1 Z B AH0 N D Z -HUSBANDS' HH AH1 S B AH0 N D Z -HUSBY HH AH1 S B IY0 -HUSCHKA HH AH1 SH K AH0 -HUSE HH Y UW1 Z -HUSEBY HH AH1 S IH0 B IY0 -HUSEMAN HH UW1 S M AH0 N -HUSEN HH UW1 S AH0 N -HUSER HH Y UW1 Z ER0 -HUSH HH AH1 SH -HUSHED HH AH1 SH T -HUSIC HH Y UW1 Z IH0 K -HUSK HH AH1 S K -HUSKA HH AH1 S K AH0 -HUSKEY HH AH1 S K IY0 -HUSKINS HH AH1 S K IH0 N Z -HUSKS HH AH1 S K S -HUSKY HH AH1 S K IY0 -HUSKY'S HH AH1 S K IY0 Z -HUSMAN HH AH1 S M AH0 N -HUSMANN HH AH1 S M AH0 N -HUSON HH UW1 S AH0 N -HUSS HH AH1 S -HUSSAIN HH UW0 S EY1 N -HUSSAR HH AH1 S ER0 -HUSSEIN HH UW0 S EY1 N -HUSSEIN'S HH UW0 S EY1 N Z -HUSSEINI HH Y UW0 S EY1 N IY0 -HUSSEINI(2) HH UW0 S EY1 N IY0 -HUSSER HH AH1 S ER0 -HUSSEY HH AH1 S IY0 -HUSSITE HH AH1 S AY2 T -HUSSMAN HH AH1 S M AH0 N -HUSSON HH AH1 S AH0 N 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HH AY1 AH0 S IH0 N TH S -HYADES HH AY1 AH0 D IY2 Z -HYAKUTAKE HH AY1 AH0 K UW0 T AA2 K IY0 -HYAKUTAKE(2) HH AY1 AH0 K Y UW0 T AA2 K IY0 -HYALURONIC HH AY2 AH0 L ER0 AA1 N IH0 K -HYAMS HH AY1 AH0 M Z -HYANNIS HH AY0 AE1 N IH0 S -HYANNISPORT HH AY0 AE1 N IH0 S P AO0 R T -HYATT HH AY1 AH0 T -HYATT'S HH AY1 AH0 T S -HYBL HH IH1 B AH0 L -HYBL'S HH IH1 B AH0 L Z -HYBRID HH AY1 B R AH0 D -HYBRID(2) HH AY1 B R IH0 D -HYBRIDIZATION HH AY2 B R AH0 D AH0 Z EY1 SH AH0 N -HYBRIDIZE HH AY1 B R AH0 D AY2 Z -HYBRIDS HH AY1 B R AH0 D Z -HYBRIENKO HH AY1 B R IY0 EH2 N K OW0 -HYBRITECH HH AY1 B R IH0 T EH2 K -HYCHE HH AY1 CH -HYCOR HH AY1 K AO2 R -HYCROFT HH AY1 K R AO2 F T -HYDE HH AY1 D -HYDE'S HH AY1 D Z -HYDEA HH AY2 D IY1 AH0 -HYDEA(2) HH AY2 D EY1 AH0 -HYDEIA HH AY1 D EY1 AH0 -HYDEN HH AY1 D AH0 N -HYDER HH AY1 D ER0 -HYDERABAD HH AY0 D EH1 R AH0 B AE2 D -HYDERABAD(2) HH AY1 D ER0 AH0 B AE2 D -HYDOCK HH AY1 D AH0 K -HYDRA HH AY1 D R AH0 -HYDRANT HH AY1 D R AH0 N T -HYDRANTS HH AY1 D R AH0 N T S 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AH0 N EY1 SH AH0 N -HYDROGENS HH AY1 D R AH0 JH AH0 N Z -HYDROGRAPHIC HH AY2 D R AH0 G R AE1 F IH0 K -HYDROLYSIS HH AY0 D R AA1 L AH0 S AH0 S -HYDROLYZE HH AY1 D R AH0 L AY2 Z -HYDROLYZED HH AY1 D R AH0 L AY2 Z D -HYDROLYZING HH AY1 D R AH0 L AY2 Z IH0 NG -HYDROMETER HH AY0 D R AA1 M AH0 T ER0 -HYDRON HH AY1 D R AH0 N -HYDROPHILIC HH AY2 D R AH0 F IH1 L IH0 K -HYDROPIC HH AY2 D R AO1 P IH0 K -HYDROPONIC HH AY2 D R AH0 P AA1 N IH0 K -HYDROPOWER HH AY1 D R OW0 P AW2 R -HYDROSOL HH AY1 D R AH0 S AA2 L -HYDROSULFIDE HH AY2 D R OW0 S AH1 L F AY2 D -HYDROTHERAPY HH AY2 D R OW0 TH EH1 R AH0 P IY0 -HYDROTHERMAL HH AY2 D R OW0 TH ER1 M AH0 L -HYDROUS HH AY1 D R AH0 S -HYDROX HH AY1 D R AO2 K S -HYDROXIDE HH AY0 D R AA1 K S AY0 D -HYDROXIDES HH AY0 D R AA1 K S AY0 D Z -HYDROXY HH AY2 D R AO1 K S IY0 -HYDSTRA HH AY1 D S T R AH0 -HYE HH AY0 -HYENA HH AY0 IY1 N AH0 -HYENAS HH AY0 IY1 N AH0 Z -HYER HH AY1 ER0 -HYERS HH AY1 ER0 Z -HYGEIA HH AY2 JH EY1 AH0 -HYGIENE HH AY1 JH IY2 N -HYGIENIST HH AY2 G IY1 N IH0 S T -HYGIENIST(2) HH AY2 G EH1 N IH0 S T -HYGIENISTS HH AY2 G IY1 N IH0 S T S -HYGIENISTS(2) HH AY2 G IY1 N IH0 S -HYGIENISTS(3) HH AY2 G EH1 N IH0 S T S -HYGIENISTS(4) HH AY2 G EH1 N IH0 S -HYGROMETER HH AY0 G R AA1 M AH0 T ER0 -HYKES HH AY1 K S -HYLAND HH AY1 L AH0 N D -HYLE HH AY1 L -HYLER HH AY1 L ER0 -HYLSA HH AY1 L S AH0 -HYLTON HH IH1 L T AH0 N -HYMAN HH AY1 M AH0 N -HYMANS HH AY1 M AH0 N Z -HYMAS HH AY1 M AH0 Z -HYMEL HH AY1 M AH0 L -HYMEN HH AY1 M AH0 N -HYMENEAL HH IH2 M AH0 N IY1 AH0 L -HYMER HH AY1 M ER0 -HYMES HH AY1 M Z -HYMIE HH AY1 M IY0 -HYMIES HH AY1 M IY0 Z -HYMN HH IH1 M -HYMNAL HH IH1 M N AH0 L -HYMNALS HH IH1 M N AH0 L Z -HYMNOLOGY HH IH0 M N AA1 L AH0 JH IY0 -HYMNS HH IH1 M Z -HYMOWITZ HH IH1 M AH0 W IH0 T S -HYMOWITZ(2) HH AY1 M AH0 W IH0 T S -HYND HH IH1 N D -HYNDAI HH IH1 N D AY1 -HYNDMAN HH IH1 N D M AH0 N -HYNDS HH IH1 N D Z -HYNEK HH AY1 N IH0 K -HYNES HH AY1 N Z -HYNES'S HH AY1 N Z IH0 Z -HYNSON HH IH1 N S AH0 N -HYOGO HH Y OW1 G OW0 -HYOTAN HH Y OW1 T AE2 N -HYPE HH AY1 P -HYPED HH AY1 P T -HYPER HH AY1 P ER0 -HYPERACTIVE HH AY2 P ER0 AE1 K T IH0 V -HYPERACTIVITY HH AY2 P ER0 AE0 K T IH1 V IH0 T IY0 -HYPERBARIC HH AY0 P ER0 B AA1 R IH0 K -HYPERBARIC(2) HH AY0 P ER1 B AE1 R IH0 K -HYPERBOLA HH AY0 P ER1 B AH0 L AH0 -HYPERBOLE HH AY0 P ER1 B AH0 L IY2 -HYPERBOLIC HH AY2 P ER0 B AA1 L IH0 K -HYPERBOREAN HH AY2 P ER0 B AO1 R IY0 AH0 N -HYPERCARD HH AY2 P ER0 K AA1 R D -HYPERCHROMATIC HH AY2 P ER0 K R OW0 M AE1 T IH0 K -HYPERCRITICAL HH AY2 P ER0 K R IH1 T IH0 K AH0 L -HYPERDRIVE HH AY1 P ER0 D R AY2 V -HYPERINFLATION HH AY2 P ER0 IH0 N F L EY1 SH AH0 N -HYPERION HH AY0 P IH1 R IY0 AH0 N -HYPERKINETIC HH AY2 P ER0 K IH0 N EH1 T IH0 K -HYPERLINK HH AY1 P ER0 L IH0 NG K -HYPERLINKS HH AY1 P ER0 L IH0 NG K S -HYPERMARKET HH AY1 P ER0 M AA2 R K IH0 T -HYPERMARKETS HH AY1 P ER0 M AA2 R K IH0 T S -HYPEROPIA HH AY2 P ER0 OW1 P IY2 AA0 -HYPERPLASIA HH AY2 P ER0 P L EY1 ZH AA0 -HYPERSENSITIVE HH AY2 P ER0 S EH1 N S IH0 T IH0 V 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EH1 R IH0 JH -INTERMARRIED IH2 N T ER0 M AE1 R IY0 D -INTERMARRY IH2 N T ER0 M AE1 R IY0 -INTERMEC IH1 N T ER0 M AH0 K -INTERMEDIA IH2 N T ER0 M IY1 D IY0 AH0 -INTERMEDIARIES IH2 N T ER0 M IY1 D IY0 EH2 R IY0 Z -INTERMEDIARIES(2) IH2 N ER0 M IY1 D IY0 EH2 R IY0 Z -INTERMEDIARY IH2 N ER0 M IY1 D IY0 EH0 R IY0 -INTERMEDIARY(2) IH2 N T ER0 M IY1 D IY0 EH0 R IY0 -INTERMEDIATE IH2 N T ER0 M IY1 D IY0 IH0 T -INTERMEDIATE(2) IH2 N ER0 M IY1 D IY0 IH0 T -INTERMEDIATES IH2 N T ER0 M IY1 D IY0 AH0 T S -INTERMEDIATES(2) IH2 N T ER0 M IY1 D IY0 EY0 T S -INTERMEDIATES(3) IH2 N ER0 M IY1 D IY0 AH0 T S -INTERMEDIATES(4) IH2 N ER0 M IY1 D IY0 EY0 T S -INTERMEDIC IH1 N T ER0 M EH2 D IH0 K -INTERMEDICS IH1 N T ER0 M EH2 D IH0 K S -INTERMET IH2 N T ER0 M EH1 T -INTERMINABLE IH2 N T ER1 M AH0 N AH0 B AH0 L -INTERMINABLY IH1 N T ER0 M IH0 N AE2 B L IY0 -INTERMINABLY(2) IH2 N T ER1 M IH0 N AH0 B L IY0 -INTERMINGLE IH2 N T ER0 M IH1 NG G AH0 L -INTERMINGLED IH2 N T ER0 M IH1 NG G AH0 L D -INTERMINGLING IH2 N 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K EY1 -K.'S K EY1 Z -KA K AA1 -KAAS K AA1 S -KAATZ K AA1 T S -KABART K AH0 B AA1 R T -KABAT K AE1 B AH0 T -KABBAH K AH0 B AH1 -KABBALAH K AH0 B AA1 L AH0 -KABBANI K AH0 B AA1 N IY0 -KABEL K AE1 B AH0 L -KABI K AE1 B IY0 -KABI(2) K AA1 B IY0 -KABILA K AH0 B IH1 L AH0 -KABILA'S K AH0 B IH1 L AH0 Z -KABIVITRUM K AH0 B IH1 V IH0 T R AH0 M -KABLE K EY1 B AH0 L -KABLER K EY1 B AH0 L ER0 -KABLER(2) K EY1 B L ER0 -KABOOM K AH0 B UW1 M -KABRAL K AH0 B R AA1 L -KABUKI K AH0 B UW1 K IY2 -KABUL K AA1 B UH0 L -KACER K EY1 S ER0 -KACH K AE1 CH -KACHEL K AE1 K AH0 L -KACHIGIAN K AH0 SH IY1 G IY2 AH0 N -KACHIGIAN(2) K AH0 SH IH1 G IY0 AH0 N -KACHMAR K AE1 K M ER0 -KACHUCK K AH0 CH AH1 K -KACHUR K AE1 CH ER0 -KACKLEY K AE1 K L IY0 -KACZMARCZYK K AA1 CH M ER0 CH IH0 K -KACZMAREK K AH0 CH M AA1 R EH0 K -KACZMARSKI K AH0 CH M AA1 R S K IY0 -KACZOR K AA1 CH ER0 -KACZOROWSKI K AH0 CH ER0 AO1 F S K IY0 -KACZYNSKI K AH0 CH IH1 N S K IY0 -KACZYNSKI(2) K AH0 Z IH1 N S K IY0 -KACZYNSKI'S K AH0 CH IH1 N S K IY0 Z -KACZYNSKI'S(2) K AH0 Z IH1 N S K IY0 Z -KADAR K AE1 D ER0 -KADAR(2) K AH0 D AA1 R -KADE K EY1 D -KADEL K AE1 D AH0 L -KADEN K EY1 D AH0 N -KADER K EY1 D ER0 -KADING K EY1 D IH0 NG -KADISH K EY1 D IH0 SH -KADLEC K AA1 D L IH0 K -KADOW K AA1 D OW0 -KADRESCU K AH0 D R EH1 S K Y UW0 -KADRMAS K AE1 D ER0 M AA2 Z -KADUMI K AH0 D UW1 M IY0 -KADY K EY1 D IY0 -KAEDING K EH1 D IH0 NG -KAEHLER K EH1 L ER0 -KAEL K EY1 L -KAELIN K EH1 L IH0 N -KAELIN'S K EH1 L IH0 N Z -KAERCHER K EH1 R K ER0 -KAESER K EY1 Z ER0 -KAESTNER K EH1 S T N ER0 -KAETZEL K EH1 T Z AH0 L -KAFELNIKOV K AH0 F EH1 L N IH0 K AO2 F -KAFELNIKOV(2) K AH0 F EH1 L N IH0 K AO2 V -KAFER K EY1 F ER0 -KAFFENBERGER K AE1 F AH0 N B ER0 G ER0 -KAFKA K AA1 F K AH0 -KAFKA'S K AA1 F K AH0 Z -KAFKAESQUE K AA1 F K AH0 EH1 S K -KAGAMI K AE1 G AH0 M IY0 -KAGAN K EY1 G AH0 N -KAGARLITSKY K AE2 G ER0 L IH1 T S K IY1 -KAGAWA K AH0 G AA1 W AH0 -KAGE K EY1 JH -KAGEL K EY1 G AH0 L -KAGEY K EY1 JH IY0 -KAGEYAMA K AA2 G IY0 AA1 M AH0 -KAGIN K EY1 G IH0 N 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K IH0 M OW1 T OW0 -KAKOS K EY1 K OW0 Z -KAKTOVIK K AE2 K T OW1 V IH0 K -KAKUEI K AE1 K Y UW0 IY0 -KAKUMARU K AA2 K UW0 M AA1 R UW0 -KAL K AE1 L -KAL(2) K EY1 EY1 EH1 L -KALAFUT K AE1 L AH0 F AH0 T -KALAL K EY1 L AH0 L -KALAMAZOO K AE2 L AH0 M AH0 Z UW1 -KALAN K EY1 L AH0 N -KALAS K AA1 L AH0 Z -KALASHNIKOV K AH0 L AE1 SH N IH0 K AA2 V -KALATA K AH0 L AA1 T AH0 -KALB K AE1 L B -KALBACH K AE1 L B AA2 K -KALBERER K AE1 L B ER0 ER0 -KALBFLEISCH K AE1 L B F L AY2 SH -KALE K EY1 L -KALEEL K AE1 L IY0 L -KALEIDA K AH0 L AY1 D AH0 -KALEIDOSCOPE K AH0 L AY1 D AH0 S K OW2 P -KALEN K EY1 L AH0 N -KALER K EY1 L ER0 -KALETA K AE1 L IH0 T AH0 -KALEY K EY1 L IY0 -KALGOORLIE K AE2 L G UW1 R L IY0 -KALI K AA1 L IY2 -KALIKOW K AE1 L IH0 K OW0 -KALIL K AE1 L AH0 L -KALIN K AE1 L IH0 N -KALINA K AH0 L AY1 N AH0 -KALININGRAD K AH0 L IH1 N IH0 NG G R AE2 D -KALINOSKI K AH0 L IH0 N AW1 S K IY0 -KALINOWSKI K AH0 L IH0 N AO1 F S K IY0 -KALINSKE K AH0 L IH1 N S K IY0 -KALINSKI K AH0 L IH1 N S K IY0 -KALIS K AE1 L IH0 S -KALISH K AE1 L IH0 SH -KALISPELL K AE1 L IH0 S P EH2 L -KALISZ K AA1 L IH0 SH -KALISZEWSKI K AH0 L IH0 SH EH1 F S K IY0 -KALIVODA K AH0 L IH0 V OW1 D AH0 -KALK K AO1 K -KALKA K AE1 L K AH0 -KALKASKA K AE0 L K AA1 S K AH0 -KALKBRENNER K AE1 L K B R IH0 N ER0 -KALL K AO1 L -KALLA K AE1 L AH0 -KALLAL K AE1 L AH0 L -KALLAM K AE1 L AH0 M -KALLAS K AE1 L AH0 Z -KALLAY K AE1 L EY0 -KALLEN K AO1 L AH0 N -KALLENBACH K AE1 L IH0 N B AA0 K -KALLENBERGER K AO1 L AH0 N B ER0 G ER0 -KALLHOFF K AE1 L HH AO0 F -KALLIEL K AE1 L IY0 AH0 L -KALLINS K AE1 L IH0 N Z -KALLIO K AE1 L IY0 OW0 -KALLIS K AE1 L IH0 S -KALLMAN K AO1 L M AH0 N -KALLMEYER K AE1 L M AY0 ER0 -KALLSTROM K AE1 L S T R AH0 M -KALLUS K AE1 L AH0 S -KALMAN K AE1 L M AH0 N -KALMANOVITZ K AE2 L M AE1 N AH0 V IH0 T S -KALMAR K AE1 L M ER0 -KALMBACH K AE1 L M B AA0 K -KALMUS K AE1 L M IH0 S -KALNINS K AE1 L N IH0 N Z -KALO K EY1 L OW0 -KALOK K AE1 L AA2 K -KALOUS K AE1 L AH0 S -KALP K AE1 L P -KALT K AO1 L T -KALTENBACH K AE1 L T IH0 N B AA0 K -KALTENBACHER K AA1 L T AH0 N B AA2 K ER0 -KALTER K AO1 L T ER0 -KALTHOFF K AE1 L TH -KALUGIN K AH0 L UW1 G AH0 N -KALUZA K AH0 L UW1 Z AH0 -KALUZNY K AH0 L AH1 Z N IY0 -KALVAR K AE1 L V AA0 R -KAM K AE1 M -KAMA K AA1 M AH0 -KAMAKAU K AA2 M AH0 K AA1 UW0 -KAMAKAU'S K AA2 M AH0 K AA1 UW0 Z -KAMAL K EY1 M AH0 L -KAMALI K AH0 M AA1 L IY0 -KAMAN K EY1 M AH0 N -KAMBER K AE1 M B ER0 -KAMCHATKA K AE2 M CH AE1 T K AH0 -KAMCHATKA(2) K AA2 M CH AA1 T K AH0 -KAMEHAMEHA K AH0 M EY1 AH0 M EY1 AH0 -KAMEHAMEHA'S K AH0 M EY1 AH0 M EY1 AH0 Z -KAMEI K AE1 M IY0 -KAMEI(2) K AA2 M EY1 -KAMEL K AA1 M AH0 L -KAMEN K AA1 M EH0 N -KAMEN(2) K EY1 M EH0 N -KAMENAR K AE1 M AH0 N ER0 -KAMENS K AA1 M EH0 N Z -KAMENS(2) K EY1 M EH0 N Z -KAMENSKY K AH0 M EH1 N S K IY0 -KAMENTSEV K AH0 M EH1 N T S AA2 V -KAMER K EY1 M ER0 -KAMERER K AE1 M ER0 ER0 -KAMIKAZE K AA2 M AH0 K AA1 Z IY0 -KAMIN K AA1 M IY0 N -KAMIN(2) K EY1 M IH2 N -KAMIN(3) K AE1 M IH2 N -KAMINER K AE1 M IH0 N ER0 -KAMINS K AE1 M IH0 N Z -KAMINS(2) K EY1 M IH2 N Z -KAMINSKI K AH0 M IH1 N S K IY0 -KAMINSKY K AH0 M IH1 N S K IY0 -KAMIR K AH0 M IH1 R -KAMKE K AE1 M K IY0 -KAMLER K AE1 M AH0 L ER0 -KAMLER(2) K AE1 M L ER0 -KAMM K AE1 M -KAMMAN K AE1 M AH0 N -KAMMER K AE1 M ER0 -KAMMERER K AE1 M ER0 ER0 -KAMMERZELL K AE1 M ER0 Z AH0 L -KAMMEYER K AE1 M AY0 ER0 -KAMNEVA K AE2 M N EY1 V AH0 -KAMP K AE1 M P -KAMP'S K AE1 M P S -KAMPA K AE1 M P AH0 -KAMPALA K AH0 M P AA1 L AH0 -KAMPE K AE1 M P -KAMPELMAN K AE1 M P AH0 L M AH0 N -KAMPEN K AE1 M P AH0 N -KAMPER K AE1 M P ER0 -KAMPF K AE1 M P F -KAMPFER K AE1 M P F ER0 -KAMPHAUS K AE1 M P HH AW2 S -KAMPMAN K AE1 M P M AH0 N -KAMPMANN K AE1 M P M AH0 N -KAMPS K AE1 M P S -KAMPSCHULTE K AE1 M P SH UH2 L T IY0 -KAMRA K AE1 M R AH0 -KAMRADT K AE1 M R AH0 T -KAMRAN K AE1 M R AH0 N -KAMRATH K AE1 M R AH0 TH -KAMSTRA K AE1 M S T R AH0 -KAN K AE1 N -KANA K AE1 N AH0 -KANADE K AH0 N AA1 D EY2 -KANADY K AE1 N AH0 D IY0 -KANAGY K AE1 N AH0 JH IY0 -KANAI K AH0 N AY1 -KANAK K AE1 N AH0 K 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NG K AH0 -KANKAKEE K AE1 NG K IH0 K IY0 -KANKAKU K AA2 N K AA1 K UW0 -KANN K AE1 N -KANNAN K AA1 N AH0 N -KANNE K AE1 N -KANNENBERG K AE1 N AH0 N B ER0 G -KANNER K AE1 N ER0 -KANNO K AE1 N OW0 -KANO K AA1 N OW0 -KANODE K AH0 N OW1 D -KANON K EY1 N AH0 N -KANOUSE K AA0 N OW0 UW1 S EY0 -KANSAI K AE0 N S AY1 -KANSALLIS K AE2 N S AE1 L IH0 S -KANSAN K AE1 N Z AH0 N -KANSAN'S K AE1 N Z AH0 N Z -KANSANS K AE1 N Z AH0 N Z -KANSANS' K AE1 N Z AH0 N Z -KANSAS K AE1 N Z AH0 S -KANSAS' K AE1 N Z AH0 S -KANSAS'S K AE1 N Z AH0 S IH0 Z -KANSAS-CITY K AE1 N Z AH0 S IH1 T IY0 -KANSAS-CITY(2) K AE1 N Z AH0 S S IH1 T IY0 -KANSIAN K AE1 N Z IY0 AH0 N -KANT K AE1 N T -KANTER K AE1 N T ER0 -KANTER'S K AE1 N T ER0 Z -KANTIAN K AE1 N T IY0 AH0 N -KANTLE K AE1 N T AH0 L -KANTNER K AE1 N T N ER0 -KANTOLA K AH0 N T OW1 L AH0 -KANTOR K AE1 N T ER0 -KANTOR'S K AE1 N T ER0 Z -KANTOR'S(2) K AE1 N T AO0 R Z -KANTROWITZ K AE1 N T R AH0 W IH0 T S -KANTZ K AE1 N T S -KANZ K AE1 N Z -KANZI K AA1 N Z IY0 -KANZLER K AE1 N Z L ER0 -KAO K AW1 -KAO(2) K EY1 OW2 -KAOHSIUNG K EY2 OW1 S IY0 AH0 NG -KAOLIN K AW1 L IH0 N -KAOLIN(2) K EY1 OW0 L IH0 N -KAORI K AO1 R IY0 -KAPAUN K AE1 P AW0 N -KAPFER K AE1 P F ER0 -KAPINOS K AE1 P IH0 N OW0 Z -KAPINOS(2) K AE1 P IY0 N OW0 Z -KAPITAN K AE1 P IH0 T AH0 N -KAPLAIN K AE1 P L AH0 N -KAPLAIN'S K AE1 P L AH0 N Z -KAPLAN K AE1 P L AH0 N -KAPLAN'S K AE1 P L AH0 N Z -KAPLER K EY1 P AH0 L ER0 -KAPLER(2) K EY1 P L ER0 -KAPLIN K AE1 P L IH0 N -KAPLOW K AE1 P L OW0 -KAPLOWITZ K AA1 P L AH0 W IH0 T S -KAPNER K AE1 P N ER0 -KAPNICK K AE1 P N IH0 K -KAPOOR K AH0 P UW1 R -KAPOR K EY1 P ER0 -KAPOSI K AH0 P OW1 S IY0 -KAPOSI'S K AH0 P OW1 S IY0 Z -KAPP K AE1 P -KAPPA K AE1 P AH0 -KAPPEL K AE1 P AH0 L -KAPPELER K AE1 P AH0 L ER0 -KAPPELMAN K AE1 P AH0 L M AH0 N -KAPPER K AE1 P ER0 -KAPPES K AE1 P S -KAPPLER K AE1 P L ER0 -KAPPUS K AE1 P AH0 S -KAPRAL K AE1 P R AH0 L -KAPRAYOON K AE1 P R AH0 Y UW0 N -KAPS K AE1 P S -KAPTON K AE1 P T AH0 N -KAPTUR K AE1 P T ER0 -KAPUR K AE1 P ER0 -KAPUSCINSKI K AH0 P AH0 S CH IH1 N S K IY0 -KAPUSTA K AH0 P AH1 S T AH0 -KARA K EH1 R AH0 -KARABAKH K EH1 R AH0 B AE0 K -KARABIN K AA0 R AA1 B IY0 N -KARACH K AE1 R AH0 CH -KARACHI K AA0 R AA1 CH IY0 -KARADZIC K AA0 R AA1 JH IH0 K -KARADZIC'S K AA0 R AA1 JH IH0 K S -KARAFFA K AE1 R AH0 F AH0 -KARAJAN K EH1 R AH0 JH AH0 N -KARAKOS K AA1 R AH0 K AO2 S -KARAM K AE1 R AH0 M -KARAMI K ER0 AA1 M IY0 -KARAN K EH1 R AH0 N -KARAN'S K EH1 R AH0 N Z -KARANICKI K EH2 R AH0 N IH1 K IY0 -KARANITSKI K EH2 R AH0 N IH1 T S K IY0 -KARAOKE K EH2 R IY0 OW1 K IY0 -KARAS K AA1 R AH0 Z -KARASAWA K AA2 R AH0 S AA1 W AH0 -KARASEK K ER0 AA1 S EH0 K -KARASIK K ER0 AA1 S IH0 K -KARAT K EH1 R AH0 T -KARATE K ER0 AA1 T IY0 -KARATIRM K ER1 AH0 T ER0 M -KARATSU K ER0 AE1 T S UW1 -KARATZ K EH1 R AH0 T S -KARBAN K AA1 R B AH0 N -KARBASSIOUN K AA2 R B AE1 S IY0 UW2 N -KARBER K AA1 R B ER0 -KARBOWSKI K ER0 B AO1 F S K IY0 -KARCH K AA1 R K -KARCHER K AA1 R CH ER0 -KARCZ K AA1 R CH -KARCZEWSKI K ER0 CH EH1 F S K IY0 -KARDASHIAN K AA1 D AH0 SH EY2 N -KARDASHIAN'S K AA1 D AH0 SH EY2 N Z -KARDELL K AA1 R D AH0 L -KARDOS K AA1 R D OW0 Z -KAREEM K ER0 IY1 M -KAREEM'S K ER0 IY1 M Z -KAREL K EH1 R AH0 L -KARELIAN K ER0 IY1 L IY0 AH0 N -KARELS K EH1 R AH0 L Z -KAREMA K AH0 R EH1 M AH0 -KAREMA'S K AH0 R EH1 M AH0 Z -KAREN K EH1 R AH0 N -KAREN'S K EH1 R AH0 N Z -KARENINA K EH2 R EH0 N IY1 N AH0 -KARENINA(2) K AH0 R EH1 N IH0 N AH0 -KARET K EH1 R AH0 T -KARG K AA1 R G -KARGER K AA1 R G ER0 -KARGES K AA1 R JH IH0 Z -KARGONAOV K AA1 R G AH0 N AO2 V -KARI K EH1 R IY0 -KARIBU K EH2 R IY1 B UW0 -KARIM K ER0 IY1 M -KARIMI K AA0 R IY1 M IY0 -KARIN K EH1 R IH0 N -KARINA K AA0 R IY1 N AH0 -KARINO K EH2 R IY1 N OW0 -KARIOTIS K AA2 R IY0 OW1 T IH0 S -KARIS K EH1 R IY0 Z -KARL K AA1 R L -KARL'S K AA1 R L Z -KARLA K AA1 R L AH0 -KARLE K AA1 R AH0 L -KARLEN K AA1 R L AH0 N -KARLHEINZ K AA1 R L HH AY1 N Z -KARLIK K AA1 R L IH0 K -KARLIN K AA1 R L IH0 N -KARLINE K AA1 R L AY2 N -KARLOW K AA1 R L OW0 -KARLS K AA1 R L Z -KARLSEN K AA1 R L S AH0 N -KARLSON K AA1 R L S AH0 N -KARLSRUHE K AA1 R L Z R UW2 -KARLSSON K AA1 R L S AH0 N -KARMA K AA1 R M AH0 -KARMAN K AA1 R M AH0 N -KARN K AA1 R N -KARNAL K AA1 R N AH0 L -KARNER K AA1 R N ER0 -KARNES K AA1 R N Z -KARNEY K AA1 R N IY0 -KARNICKI K AA0 R N IH1 K IY0 -KARNOW K AA1 R N OW0 -KARNOW(2) K AA1 R N AW0 -KARNS K AA1 R N Z -KARNSUND K AA1 R N S AH0 N D -KAROL K EH1 R AO0 L -KAROLINA K EH2 R AH0 L AY1 N AH0 -KAROLINE K EH1 R AH0 L AY2 N -KAROLINSKA K EH2 R OW0 L IH1 N S K AH0 -KAROLY K ER0 OW1 L IY0 -KAROLYI K ER0 OW1 L Y IY0 -KAROLYN K AE1 R AH0 L IH0 N -KARON K EH1 R AH0 N -KAROS K AA1 R OW0 S -KAROUN K ER0 UW1 N -KAROW K AA1 R OW0 -KARP K AA1 R P -KARPATKIN K AA2 R P AE1 T K IH0 N -KARPEL K AA1 R P AH0 L -KARPEN K AA1 R P AH0 N -KARPF K AA1 R P F -KARPINSKI K ER0 P IH1 N S K IY0 -KARPOV K AA1 R P AA2 V -KARPOWICZ K AA1 R P AH0 V IH0 CH -KARR K AA1 R -KARRAKER K AA1 R AH0 K ER0 -KARRAS K AE1 R AH0 Z -KARREN K AE1 R AH0 N -KARRER K AA1 R ER0 -KARRICK K AE1 R IH0 K -KARRIKER K AE1 R IH0 K ER0 -KARRY K EH1 R IY0 -KARSH K AA1 R SH -KARSHNER K AA1 R SH N ER0 -KARSON K AA1 R S AH0 N -KARST K AA1 R S T -KARSTADT K AA1 R S T AE2 T -KARSTEN K AA1 R S T AH0 N -KARSTENS K AA1 R S T AH0 N Z -KARSTETTER K AA1 R S T IH0 T ER0 -KARTASASMITA K AA0 R T AE2 S AH0 S M IY1 T AH0 -KARTCHNER K AA1 R CH N ER0 -KARTER K AA1 R T ER0 -KARTES K AA1 R T S -KARUN K ER0 UW1 N -KARVONEN K AA1 R V AH0 N AH0 N -KARWOSKI K ER0 V AW1 S K IY0 -KARWOWSKI K ER0 V AO1 F S K IY0 -KARY K EH1 R IY0 -KARYDAKIS K EH2 R IY0 D AE1 K IH0 S -KARYN K AE1 R IH0 N -KARYRRHEXIS K EH2 R IY0 R EH1 K S IH0 S -KASABIAN K AH0 S EY1 B IY0 AH0 N -KASAGIC K AH0 S AA1 G IH0 K -KASAL K EY1 S AH0 L -KASBROL K AE1 Z B R AO0 L -KASBROLS K AE1 Z B R AO0 L Z -KASCH K AE1 SH -KASCHAK K AE1 SH AH0 K -KASDORF K AE1 S D AO0 R F -KASE K EY1 Z -KASEL K AE1 S AH0 L -KASELL K AE1 S AH0 L -KASEMAN K EY1 S M AH0 N -KASER K EY1 Z ER0 -KASESE K AH0 S IY1 Z IY0 -KASEY K AE1 S IY0 -KASH K AE1 SH -KASHA K AA1 SH 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EH1 L M AH0 N -KELLNER K EH1 L N ER0 -KELLOGG K EH1 L AO0 G -KELLOGG'S K EH1 L AO0 G Z -KELLOGGS K EH1 L AO0 G Z -KELLOUGH K EH1 L AW0 -KELLOW K EH1 L OW0 -KELLS K EH1 L Z -KELLUM K EH1 L AH0 M -KELLWOOD K EH1 L W UH2 D -KELLY K EH1 L IY0 -KELLY'S K EH1 L IY0 Z -KELLYANNE K EH1 L IY0 AE1 N -KELLYS K EH1 L IY0 Z -KELM K EH1 L M -KELMAN K EH1 L M AH0 N -KELNER K EH1 L N ER0 -KELNHOFER K EH1 L N HH AH0 F ER0 -KELP K EH1 L P -KELPS K EH1 L P S -KELSALL K EH1 L S AH0 L -KELSAY K EH1 L S EY0 -KELSCH K EH1 L SH -KELSEY K EH1 L S IY0 -KELSO K EH1 L S OW0 -KELSO'S K EH1 L S OW0 Z -KELSOE K EH1 L S OW0 -KELSON K EH1 L S AH0 N -KELTER K EH1 L T ER0 -KELTING K EH1 L T IH0 NG -KELTNER K EH1 L T N ER0 -KELTON K EH1 L T AH0 N -KELTY K EH1 L T IY0 -KELTZ K EH1 L T S -KELVAN K EH1 L V AH0 N -KELVEN K EH1 L V AH0 N -KELVIN K EH1 L V AH0 N -KELVIN(2) K EH1 L V IH0 N -KELVIN'S K EH1 L V IH0 N Z -KEM K EH1 M -KEMAL K AH0 M AA1 L -KEMBEL K EH1 M B AH0 L -KEMBLE K EH1 M B AH0 L -KEMENY K EH1 M IH0 N IY0 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K IH1 N S ER0 -KINSEY K IH1 N Z IY0 -KINSHASA K IH0 N SH AA1 S AH0 -KINSHASA(2) K IH0 N SH AE1 S AH0 -KINSHIP K IH1 N SH IH2 P -KINSINGER K IH1 N S IH0 N JH ER0 -KINSLER K IH1 N S AH0 L ER0 -KINSLER(2) K IH1 N S L ER0 -KINSLEY K IH1 N Z L IY0 -KINSLEY'S K IH1 N Z L IY0 Z -KINSLOW K IH1 N S L OW2 -KINSMAN K IH1 N Z M AE2 N -KINST K IH1 N S T -KINSTLER K IH1 N S T L ER0 -KINSTON K IH1 N S T AH0 N -KINT K IH1 N T -KINTER K IH1 N T ER0 -KINTIGH K IH1 N T AY0 -KINTNER K IH1 N T N ER0 -KINTON K IH1 N T AH0 N -KINTZ K IH1 N T S -KINTZEL K IH1 N T Z AH0 L -KINYON K IH1 N Y AH0 N -KINZEL K IH1 N Z AH0 L -KINZER K IH1 N Z ER0 -KINZEY K IH1 N Z IY0 -KINZIE K IH1 N Z IY0 -KINZLER K IH1 N Z L ER0 -KINZLMAIER K IH1 N Z AH0 L M AY2 ER0 -KIOSK K IY1 AO2 S K -KIOSKS K IY1 AO2 S K S -KIOUS K AY1 AH0 S -KIP K IH1 P -KIPER K AY1 P ER0 -KIPFER K IH1 P F ER0 -KIPLING K IH1 P L IH0 NG -KIPLINGER K IH1 P AH0 L IH0 NG ER0 -KIPLINGER(2) K IH1 P L IH0 NG ER0 -KIPLINGER'S K IH1 P L IH2 NG ER0 Z -KIPNIS K IH1 P N IH0 S -KIPP K IH1 P -KIPPER K IH1 P ER0 -KIPPERMAN K IH1 P ER0 M AH0 N -KIPPERS K IH1 P ER0 Z -KIPPES K IH1 P S -KIPPUR K IH1 P ER0 -KIR K IH1 R -KIRA K IH1 R AA0 -KIRACOFE K IH1 R AH0 K OW2 F -KIRALY K IH1 R AH0 L IY0 -KIRBY K ER1 B IY0 -KIRBY'S K ER1 B IY0 Z -KIRCH K ER1 K -KIRCHBERG K ER1 K B ER0 G -KIRCHBERG(2) K ER1 CH B ER0 G -KIRCHBERGER K ER1 CH B ER0 G ER0 -KIRCHEN K ER1 K AH0 N -KIRCHER K ER1 K ER0 -KIRCHGESSNER K ER1 K G IH0 S N ER0 -KIRCHHOFER K ER1 K HH AH0 F ER0 -KIRCHHOFF K ER1 K HH AO0 F -KIRCHMAN K ER1 K M AH0 N -KIRCHNER K ER1 K N ER0 -KIRCHNERS K ER1 K N ER0 Z -KIRCHOFF K ER1 K HH AO0 F -KIRGAN K ER1 G AH0 N -KIRGIZ K IH1 R G IH0 Z -KIRI K IH1 R IY0 -KIRIBATI K IH2 R IH1 B AA1 T IY0 -KIRIN K IH1 R IH0 N -KIRK K ER1 K -KIRK'S K ER1 K S -KIRKBRIDE K ER1 K B R AY0 D -KIRKBY K ER1 K B IY0 -KIRKEBY K ER1 K IH0 B IY0 -KIRKENDALL K ER0 K EH1 N D AH0 L -KIRKENDOLL K ER0 K EH1 N D OW0 L -KIRKER K ER1 K ER0 -KIRKEY K ER1 K IY0 -KIRKHAM K ER1 K HH AH0 M -KIRKHART K ER1 K HH AA0 R T -KIRKLAND K ER1 K L AH0 N D -KIRKLAND'S K ER1 K L AH0 N D Z -KIRKLEY K ER1 K L IY0 -KIRKLIN K ER1 K L IH0 N -KIRKMAN K ER1 K M AH0 N -KIRKNER K ER1 K N ER0 -KIRKPATRICK K ER0 K P AE1 T R IH0 K -KIRKPATRICK(2) K ER0 P AE1 T R IH0 K -KIRKS K ER1 K S -KIRKSEY K ER1 K S IY0 -KIRKUK K IH2 R K UH1 K -KIRKUM K IH1 R K AH0 M -KIRKWOOD K ER1 K W UH0 D -KIRLEY K ER1 L IY0 -KIRLIN K ER1 L IH0 N -KIRMSE K ER1 M S IY0 -KIRN K ER1 N -KIRNAN K ER1 N AH0 N -KIRNER K ER1 N ER0 -KIROUAC K AY1 R AW0 AE0 K -KIROV K IH1 R AA0 V -KIRSCH K ER1 SH -KIRSCHBAUM K ER1 SH B AW0 M -KIRSCHENBAUM K ER1 SH AH0 N B AW0 M -KIRSCHENMANN K ER1 SH AH0 N M AH0 N -KIRSCHMAN K ER1 SH M AH0 N -KIRSCHNER K ER1 SH N ER0 -KIRSH K ER1 SH -KIRSHBAUM K ER1 SH B AW2 M -KIRSHENBAUM K ER1 SH AH0 N B AW0 M -KIRSHFINK K ER1 SH F IH0 NG K -KIRSHNER K ER1 SH N ER0 -KIRST K ER1 S T -KIRSTEIN K ER1 S T AY0 N -KIRSTEIN(2) K ER1 S T IY0 N -KIRSTEN K ER1 S T AH0 N -KIRSTIE K ER1 S T IY0 -KIRSTIN K ER1 S T IH0 N -KIRT K ER1 T -KIRTLAND K ER1 T L AH0 N D -KIRTLEY K ER1 T L IY0 -KIRTON K ER1 T AH0 N -KIRTS K ER1 T S -KIRVEN K ER1 V AH0 N -KIRWAN K ER1 W AO0 N -KIRWIN K ER1 W IH0 N -KIRYAS K IH1 R Y AH0 S -KIRYAS(2) K IH2 R Y AA1 S -KIRYAT K IH1 R Y AH0 T -KIRYAT(2) K IH2 R Y AA1 T -KIS K IH1 S -KISAMORE K IY0 S AA1 M AO0 R -KISAN K IH1 Z AH0 N -KISCH K IH1 SH -KISCHELL K IH1 SH AH0 L -KISCO K IH1 S K OW0 -KISE K AY1 Z -KISER K AY1 Z ER0 -KISH K IH1 SH -KISHA K IH1 SH AH0 -KISHBAUGH K IH1 SH B AW0 -KISHI K IY1 SH IY0 -KISHORE K IY1 SH AO2 R -KISIEL K IH1 S IY0 L -KISKA K IH1 S K AH0 -KISKA'S K IH1 S K AH0 Z -KISLER K IH1 S AH0 L ER0 -KISLER(2) K IH1 S L ER0 -KISLING K IH1 S AH0 L IH0 NG -KISLING(2) K IH1 S L IH0 NG -KISMAYU K IH0 S M AA1 Y UW0 -KISMAYU(2) K IH0 Z M AA1 Y UW0 -KISMET K IH1 Z M IH0 T -KISMETS K IH1 Z M IH0 T S -KISNER K IH1 S N ER0 -KISOR K AY1 Z ER0 -KISS K IH1 S -KISSACK K IH1 S AH0 K -KISSAM K IH1 S AH0 M -KISSANE K IH1 S AH0 N -KISSED K IH1 S T -KISSEE K IH1 S IY1 -KISSEL K IH1 S AH0 L -KISSELL K IH1 S AH0 L -KISSER K IH1 S ER0 -KISSES K IH1 S AH0 Z -KISSES(2) K IH1 S IH0 Z -KISSICK K IH1 S IH0 K -KISSIMMEE K IH0 S IH1 M IY0 -KISSING K IH1 S IH0 NG -KISSINGER K IH1 S IH0 N JH ER0 -KISSINGER'S K IH1 S IH0 N JH ER0 Z -KISSLER K IH1 S L ER0 -KISSLING K IH1 S L IH0 NG -KISSNER K IH1 S N ER0 -KIST K IH1 S T -KISTER K IH1 S T ER0 -KISTLER K IH1 S T L ER0 -KISTNER K IH1 S T N ER0 -KISZCZAK K IH1 Z AE0 K -KISZCZAK(2) K IH1 SH AE0 K -KIT K IH1 T -KITA K IY1 T AH0 -KITAGAWA K IY0 T AA0 G AA1 W AH0 -KITAJIMA K IY2 T AH0 JH IY1 M AH0 -KITAMURA K IY0 T AA0 M UH1 R AH0 -KITCAT K IH1 T K AE2 T -KITCH K IH1 CH -KITCHEL K IH1 CH AH0 L -KITCHELL K IH1 CH AH0 L -KITCHEN K IH1 CH AH0 N -KITCHEN'S K IH1 CH AH0 N Z -KITCHENAID K IH1 CH AH0 N EY2 D -KITCHENER K IH1 CH AH0 N ER0 -KITCHENER(2) K IH1 CH N ER0 -KITCHENETTE K IH2 CH AH0 N EH1 T -KITCHENS K IH1 CH AH0 N Z -KITCHENWARE K IH1 CH AH0 N W EH2 R -KITCHIN K IH1 CH IH0 N -KITCHING K IH1 CH IH0 NG -KITCHINGS K IH1 CH IH0 NG Z -KITE K AY1 T -KITES K AY1 T S -KITHCART K IH1 TH K AA2 R T -KITIMAT K IH1 T IH0 M AE0 T -KITING K AY1 T IH0 NG -KITMAN K IH1 T M AH0 N -KITNER K IH1 T N ER0 -KITOWSKI K IH0 T AO1 F S K IY0 -KITS K IH1 T S -KITSCH K IH1 CH -KITSON K IH1 T S AH0 N -KITT K IH1 T -KITTANEH K IH0 T AA1 N EH2 -KITTEL K IH1 T AH0 L -KITTELL K IH1 T AH0 L -KITTELSON K IH1 T IH0 L S AH0 N -KITTEN K IH1 T AH0 N -KITTENS K IH1 T AH0 N Z -KITTERMAN K IH1 T ER0 M AH0 N -KITTINGER K IH1 T IH0 NG ER0 -KITTIWAKE K IH1 T IH0 W EY2 K -KITTLE K IH1 T AH0 L -KITTLER K IH1 T AH0 L ER0 -KITTLES K IH1 T AH0 L Z -KITTLESON K IH1 T AH0 L S AH0 N -KITTMAN K IH1 T M AH0 N -KITTNER K IH1 T N ER0 -KITTREDGE K IH1 T R IH0 JH -KITTRELL K IH1 T R AH0 L -KITTS K IH1 T S -KITTY K IH1 T IY0 -KITZ K IH1 T S -KITZHABER K IH1 T S HH EY2 B ER0 -KITZHABER(2) K IH1 T S HH AA2 B ER0 -KITZMAN K IH1 T S M AH0 N -KITZMILLER K IH1 T S M IH2 L ER0 -KIVELA K IH1 V IH0 L AH0 -KIVETT K IH1 V IH0 T -KIVI K IH1 V IY0 -KIWANI K IH0 W AA1 N IH0 -KIWANIS K IH0 W AA1 N IH0 S -KIWI K IY1 W IY0 -KIWI'S K IY1 W IY0 Z -KIWIS K IY1 W IY0 Z -KIYOHIDA K IY2 OW0 HH IY1 D AH0 -KIYOSHI K IY0 OW1 SH IY0 -KIYOTAKA K IY2 OW0 T AA1 K AH0 -KIZER K AY1 Z ER0 -KIZZIAH K IH1 Z IY0 AH0 -KJAR K Y AA1 R -KJAR(2) K EY1 JH EY1 EY1 AA1 R -KJELL K Y EH1 L -KJELLBERG K Y EH1 L B ER0 G -KJELLBERG(2) JH EH1 L B ER0 G -KJENSTAD K Y EH1 N S T AE2 D -KJOS K Y AO1 S -KKK K EY1 K EY1 K EY1 -KLAAS K L AA1 S -KLAASSEN K L AA1 S AH0 N -KLABUNDE K L AE1 B AH0 N D -KLADSTRUP K L AE1 D S T R AH0 P -KLADSTRUP'S K L AE1 D S T R AH0 P S -KLAGES K L EY1 JH IH0 Z -KLAHN K L AE1 N -KLAHR K L AE1 R -KLAIBER K L EY1 B ER0 -KLAMATH-FALLS K L AE1 M AH0 TH F AO1 L Z -KLAMER K L EY1 M ER0 -KLAMM K L AE1 M -KLAMMER K L AE1 M ER0 -KLAMON K L EY1 M AH0 N -KLAN K L AE1 N -KLAN'S K L AE1 N Z -KLANG K L AE1 NG -KLANN K L AE1 N -KLANS K L AE1 N Z -KLANSMAN K L AE1 N Z M AH0 N -KLANSMEN K L AE1 N Z M AH0 N -KLANWATCH K L AE1 N W AA2 CH -KLAPP K L AE1 P -KLAPPER K L AE1 P ER0 -KLAPPERICH K L AE1 P ER0 IH0 K -KLAR K L AA1 R -KLARE K L EH1 R -KLARICH K L AE1 R IH0 K -KLARMAN K L AA1 R M AH0 N -KLAS K L AE1 S -KLASE K L EY1 Z -KLASEN K L EY1 S AH0 N -KLASS K L AE1 S -KLASSEN K L AE1 S AH0 N -KLATSKY K L AE1 T S K IY0 -KLATT K L AE1 T -KLAUBER K L AW1 B ER0 -KLAUER K L AW1 ER0 -KLAUS K L AW1 S -KLAUSER K L AW1 S ER0 -KLAUSING K L AW1 S IH0 NG -KLAUSNER K L AW1 S N ER0 -KLAVER K L EY1 V ER0 -KLAWITTER K L AO1 IH0 T ER0 -KLAY K L EY1 -KLAYMAN K L EY1 M AH0 N -KLEBAN K L EH1 B AH0 N -KLEBBA K L EH1 B AH0 -KLEBE K L IY1 B -KLEBER K L IY1 B ER0 -KLECK K L EH1 K -KLECKA K L EH1 K AH0 -KLECKER K L EH1 K ER0 -KLECKNER K L EH1 K N ER0 -KLECZKA K L EH1 K S K AH0 -KLEE K L IY1 -KLEE'S K L IY1 Z -KLEEMAN K L IY1 M AH0 N -KLEEN K L IY1 N -KLEENEX K L IY1 N AH0 K S -KLEER K L IH1 R -KLEES K L IY1 Z -KLEFFNER K L EH1 F N ER0 -KLEIBER K L AY1 B ER0 -KLEIER K L AY1 ER0 -KLEIMAN K L AY1 M AH0 N -KLEIN K L AY1 N -KLEIN'S K L AY1 N Z -KLEINBERG K L AY1 N B ER0 G -KLEINDIENST K L AY1 N D IY2 N S T -KLEINE K L AY1 N -KLEINER K L AY1 N ER0 -KLEINERT K L AY1 N ER0 T -KLEINFELD K L AY1 N F EH2 L D -KLEINFELTER K L AY1 N F IH0 L T ER0 -KLEINHANS K L AY1 N HH AH0 N Z -KLEINHENZ K L AY1 N HH IH0 N S -KLEINKNECHT K L AY1 NG K N IH0 K T -KLEINMAN K L AY1 N M AH0 N -KLEINPASTE K L AY1 N P EY2 S T -KLEINPETER K L AY1 N P IY0 T ER0 -KLEINSASSER K L AY1 N S AH0 S ER0 -KLEINSCHMIDT K L AY1 N SH M IH2 T -KLEINSMITH K L AY1 N S M IH0 TH -KLEINWORT K L AY1 N W ER0 T -KLEIS K L AY1 Z -KLEISS K L AY1 S -KLEIST K L AY1 S T -KLEJNA K L EY1 N ER0 -KLEM K L EH1 M -KLEMA K L EH1 M AH0 -KLEMAN K L EH1 M AH0 N -KLEMANN K L EH1 M AH0 N -KLEMENS K L EH1 M AH0 N S -KLEMENT K L EH1 M AH0 N T -KLEMM K L EH1 M -KLEMME K L EH1 M -KLEMMER K L EH1 M ER0 -KLEMP K L EH1 M P -KLEMPNER K L EH1 M P N ER0 -KLEMZ K L EH1 M Z -KLENGE K L EH1 N JH -KLENK K L EH1 NG K -KLENKE K L EH1 NG K -KLENSCH K L EH1 N SH -KLEPAC K L EH1 P AH0 K -KLEPFER K L EH1 P F ER0 -KLEPPE K L EH1 P -KLEPPER K L EH1 P ER0 -KLEPPINGER K L EH1 P IH0 NG G ER0 -KLEPTOCRAT K L EH1 P T AH0 K R AE0 T -KLEPTOMANIAC K L EH2 P T AH0 M EY1 N IY0 AE0 K -KLERK K L ER1 K -KLERK'S K L ER1 K S -KLESCH K L EH1 SH -KLESKEN K L EH1 S K AH0 N -KLESS K L EH1 S -KLETT K L EH1 T -KLEUBECK K L UW1 B EH0 K -KLEVE K L IY1 V -KLEVEN K L IY1 V AH0 N -KLEVER K L EH1 V ER0 -KLEY K L EY1 -KLEZMATIC K L EH0 Z M AE1 T IH0 K -KLEZMATICS K L EH0 Z M AE1 T IH0 K S -KLEZMER K L EH1 Z M ER0 -KLICH K L IH1 CH -KLICK K L IH1 K -KLICKOVIC K L IH1 K AH0 V IH2 K -KLIEBERT K L IY1 B ER0 T -KLIEG K L IY1 G -KLIEMAN K L AY1 M AH0 N -KLIER K L AY1 ER0 -KLIETHERMES K L IY1 TH ER0 M Z -KLIEWER K L IY1 W ER0 -KLIGMAN K L IH1 G M AH0 N -KLIM K L IH1 M -KLIMA K L AY1 M AH0 -KLIMAS K L AY1 M AH0 Z -KLIMASZEWSKI K L IH0 M AH0 SH EH1 F S K IY0 -KLIMCZAK K L IH1 M CH AE0 K -KLIMEK K L IH1 M IH0 K -KLIMENT K L AY1 M AH0 N T -KLIMER K L IH1 M ER0 -KLIMER(2) K L AY1 M ER0 -KLIMOWICZ K L IH1 M AH0 V IH0 CH -KLINCK K L IH1 NG K -KLINDT K L IH1 N T -KLINDWORTH K L IH1 N D W ER0 TH -KLINE K L AY1 N -KLINEDINST K L IH1 N IH0 D IH0 N S T -KLINEDINST(2) K L AY1 N D IH0 N S T -KLINEFELTER K L IH1 N IH0 F IH0 L T ER0 -KLINEFELTER(2) K L AY1 N F IH0 L T ER0 -KLING K L IH1 NG -KLINGAMAN K L IH1 NG G AH0 M AH0 N -KLINGBEIL K L IH1 NG B AY0 L -KLINGBERG K L IH1 NG B ER0 G -KLINGE K L IH1 N JH -KLINGEL K L IH1 NG G AH0 L -KLINGENBERG K L IH1 NG AH0 N B ER0 G -KLINGENSMITH K L IH1 NG G IH0 N S M IH0 TH -KLINGER K L IH1 NG ER0 -KLINGER'S K L IH1 NG ER0 Z -KLINGERMAN K L IH1 NG ER0 M AH0 N -KLINGHOFFER K L IH1 NG HH AO2 F ER0 -KLINGLER K L IH1 NG G AH0 L ER0 -KLINGLER(2) K L IH1 NG G L ER0 -KLINGMAN K L IH1 NG M AH0 N -KLINGON K L IH1 NG G AH0 N -KLINGONS K L IH1 NG G AH0 N Z -KLINK K L IH1 NG K -KLINKE K L IH1 NG K -KLINKER K L IH1 NG K ER0 -KLINKHAMMER K L IH1 NG K HH AE0 M ER0 -KLINKHAMMER'S K L IH1 NG K HH AE0 M ER0 Z -KLINKNER K L IH1 NG K N ER0 -KLINT K L IH1 N T -KLINTWORTH K L IH1 N T W ER0 TH -KLINZING K L IH1 N Z IH0 NG -KLIPFEL K L IH1 P F AH0 L -KLIPP K L IH1 P -KLIPPEL K L IH1 P AH0 L -KLISH K L IH1 SH -KLITZ K L IH1 T S -KLITZKE K L IH1 T S K IY0 -KLIXSHAVICH K L IH0 K S AE1 V IH0 CH -KLOBERDANZ K L AA1 B ER0 D AH0 N S -KLOC K L AA1 K -KLOCEK K L OW1 S IH0 K -KLOCK K L AA1 K -KLOCKE K L AA1 K -KLOECKNER K L OW1 K N ER0 -KLOEHN K L OW1 N -KLOEPFER K L OW1 P F ER0 -KLOEPPEL K L OW1 P AH0 L -KLOEPPER K L OW1 P ER0 -KLOIBER K L OY1 B ER0 -KLOM K L AO1 M -KLOMP K L AA1 M P -KLONDIKE K L AA1 N D AY2 K -KLONOWSKI K L AH0 N AO1 F S K IY0 -KLONTZ K L AA1 N T S -KLOOS K L UW1 Z -KLOOSTER K L UW1 S T ER0 -KLOOSTERMAN K L UW1 S T ER0 M AH0 N -KLOPF K L AA1 P F -KLOPFENSTEIN K L AA1 P F AH0 N S T AY0 N -KLOPFENSTEIN(2) K L AA1 P F AH0 N S T IY0 N -KLOPFER K L AA1 P F ER0 -KLOPP K L AA1 P -KLOPPENBURG K L AA1 P AH0 N B ER0 G -KLOS K L AO1 S -KLOSE K L OW1 Z -KLOSINSKI K L AH0 S IH1 N S K IY0 -KLOSOWSKI K L AH0 S AO1 F S K IY0 -KLOSS K L AO1 S -KLOSSNER K L AA1 S N ER0 -KLOSTER K L AO1 S T ER0 -KLOSTERMAN K L AA1 S T ER0 M AH0 N -KLOSTERMANN K L AA1 S T ER0 M AH0 N -KLOTH K L AA1 TH -KLOTZ K L AA1 T S -KLOTZBACH K L AA1 T S B AA0 K -KLU K L UW1 -KLU(2) K EY2 EH2 L Y UW1 -KLUCEVSEK K L UW0 S EH1 V S EH0 K -KLUCK K L AH1 K -KLUDT K L AH1 D T -KLUENDER K L UH1 N D ER0 -KLUESNER K L UH1 S N ER0 -KLUEVER K L UH1 V ER0 -KLUG K L AH1 G -KLUGE K L UW1 JH -KLUGE'S K L UW1 JH IH0 Z -KLUGER K L UW1 G ER0 -KLUGH K L AH1 -KLUGMAN K L AH1 G M AH0 N -KLUGT K L AH1 T -KLUKAS K L UW1 K AH0 Z -KLUMB K L AH1 M -KLUMP K L AH1 M P -KLUMPP K L AH1 M P -KLUNDER K L AH1 N D ER0 -KLUNDT K L AH1 N T -KLUNK K L AH1 NG K -KLUS K L AH1 S -KLUSMAN K L AH1 S M AH0 N -KLUTE K L UW1 T -KLUTH K L UW1 TH -KLUTTS K L AH1 T S -KLUTTZ K L AH1 T S -KLUTZ K L AH1 T S -KLUTZNICK K L AH1 T S N IH0 K -KLUVER K L UW1 V ER0 -KLUWER K L UW1 W ER0 -KLUX K L AH1 K S -KLYM K L IH1 M -KLYNVELD K L IH1 N V EH2 L D -KMART K EY1 M AA2 R T -KMART'S K EY1 M AA2 R T S -KMETZ K AH0 M EH1 T S -KMIEC K AH0 M IY1 K -KMIECIK K AH0 M IY1 CH IH0 K -KNAACK N AA1 K -KNAAK N AA1 K -KNAB N AE1 B -KNABB N AE1 B -KNABE N EY1 B -KNABLE N EY1 B AH0 L -KNACK N AE1 K -KNACKS N AE1 K S -KNACKSTEDT N AE1 K S T IH0 T -KNAGGS N AE1 G Z -KNAPE N EY1 P -KNAPIK N AE1 P IH0 K -KNAPKE N EY1 P K -KNAPP N AE1 P -KNAPP'S N AE1 P S -KNAPPENBERGER N AE1 P AH0 N B ER0 G ER0 -KNAPPER N AE1 P ER0 -KNAPSACK N AE1 P S AE2 K -KNAPTON N AE1 P T AH0 N -KNARR N AE1 R -KNAUB N AO1 B -KNAUER N AW1 ER0 -KNAUF N AO1 F -KNAUFF N AO1 F -KNAUP N AO1 P -KNAUS N AO1 Z -KNAUSS N AO1 S -KNAVE N EY1 V -KNAVES N EY1 V Z -KNBC K EY1 EH1 N B IY1 S IY1 -KNEAD N IY1 D -KNEADING N IY1 D IH0 NG -KNEAFSEY N IY1 F S IY0 -KNEALE N IY1 L -KNEBEL N EH1 B AH0 L -KNECHT N EH1 K T -KNECHTEL N EH1 K T AH0 L -KNEE N IY1 -KNEEBONE N IY1 B OW2 N -KNEECAP N IY1 K AE2 P -KNEECAPS N IY1 K AE2 P S -KNEECE N IY1 S -KNEED N IY1 D -KNEEING N IY1 IH0 NG -KNEEL N IY1 L -KNEELAND N IY1 L AH0 N D -KNEELING N IY1 L IH0 NG -KNEER N IH1 R -KNEES N IY1 Z -KNEIP N IY1 P -KNEISEL N AY1 S AH0 L -KNEISLEY N IY1 S L IY0 -KNELL N EH1 L -KNELLER N EH1 L ER0 -KNELT N EH1 L T -KNEPP N EH1 P -KNEPPER N 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-KNIGHTHOOD N AY1 T HH UH2 D -KNIGHTLY N AY1 T L IY0 -KNIGHTON N AY1 T AH0 N -KNIGHTS N AY1 T S -KNILL N IH1 L -KNIN K EY1 EH1 N AY1 EH1 N -KNIN(2) K N IH1 N -KNIN(3) N IH1 N -KNIPE N AY1 P -KNIPFER N IH1 P F ER0 -KNIPL N IH1 P AH0 L -KNIPP N IH1 P -KNIPPA N IH1 P AH0 -KNIPPEL N IH1 P AH0 L -KNIPPENBERG N IH1 P AH0 N B ER0 G -KNIPPER N IH1 P ER0 -KNIPPLE N IH1 P AH0 L -KNISELY N AY1 Z L IY0 -KNISKERN N IH1 S K ER0 N -KNISLEY N IH1 S L IY0 -KNISPEL N IH1 S P AH0 L -KNISS N IH1 S -KNIT N IH1 T -KNITS N IH1 T S -KNITTED N IH1 T AH0 D -KNITTED(2) N IH1 T IH0 D -KNITTEL N IH1 T AH0 L -KNITTER N IH1 T ER0 -KNITTING N IH1 T IH0 NG -KNITTLE N IH1 T AH0 L -KNITWEAR N IH1 T W EH2 R -KNIVES N AY1 V Z -KNIVETON N AY1 V T AH0 N -KNOB N AA1 B -KNOBBE N AA1 B -KNOBBY N AA1 B IY0 -KNOBEL N OW1 B AH0 L -KNOBLAUCH N AA1 B L AW0 K -KNOBLE N OW1 B AH0 L -KNOBLOCH N AA1 B L AH0 K -KNOBLOCK N AA1 B L AA0 K -KNOBS N AA1 B Z -KNOCH N AA1 K -KNOCHE N AA1 CH -KNOCHEL N AA1 K AH0 L -KNOCK N AA1 K -KNOCKDOWN N AA1 K D AW2 N -KNOCKED N AA1 K T -KNOCKING N AA1 K IH0 NG -KNOCKOFF N AA1 K AO2 F -KNOCKOFFS N AA1 K AO2 F S -KNOCKOUT N AA1 K AW2 T -KNOCKOUTS N AA1 K AW2 T S -KNOCKS N AA1 K S -KNODE N OW1 D -KNODEL N OW1 D AH0 L -KNODLE N OW1 D AH0 L -KNOEBEL N OW1 B AH0 L -KNOEDLER N OW1 D AH0 L ER0 -KNOEDLER(2) N OW1 D L ER0 -KNOELL N OW1 L -KNOFF N AO1 F -KNOGO N OW1 G OW0 -KNOKE N OW1 K -KNOLES N OW1 L Z -KNOLL N OW1 L -KNOLL'S N OW1 L Z -KNOOP N UW1 P -KNOP N AA1 P -KNOPE N OW1 P -KNOPF N AA1 P F -KNOPF(2) N AA1 F -KNOPP N AA1 P -KNORR N AO1 R -KNOST N AA1 S T -KNOT N AA1 T -KNOTEK N OW1 T IH0 K -KNOTH N AA1 TH -KNOTS N AA1 T S -KNOTT N AA1 T -KNOTT'S N AA1 T S -KNOTTED N AA1 T IH0 D -KNOTTS N AA1 T S -KNOTTY N AA1 T IY0 -KNOUFF N OW1 F -KNOUS N AO1 S -KNOUSE N AW1 S -KNOW N OW1 -KNOW-HOW N OW1 HH AW2 -KNOWED N OW1 D -KNOWER N OW1 ER0 -KNOWING N OW1 IH0 NG -KNOWINGLY N OW1 IH0 NG L IY0 -KNOWLEDGE N AA1 L AH0 JH -KNOWLEDGE(2) N AA1 L IH0 JH -KNOWLEDGEABLE N AA1 L AH0 JH AH0 B AH0 L -KNOWLEDGEABLY N AA1 L IH0 JH AH0 B L IY0 -KNOWLEDGEWARE N AA1 L IH0 JH W EH2 R -KNOWLES N OW1 L Z -KNOWLTON N OW1 L T AH0 N -KNOWN N OW1 N -KNOWNS N OW1 N Z -KNOWS N OW1 Z -KNOX N AA1 K S -KNOX'S N AA1 K S IH0 Z -KNOXVILLE N AA1 K S V IH0 L -KNOY N OY1 -KNUCKLE N AH1 K AH0 L -KNUCKLED N AH1 K AH0 L D -KNUCKLES N AH1 K AH0 L Z -KNUDSEN K N UW1 D S AH0 N -KNUDSEN'S K N UW1 D S AH0 N Z -KNUDSON K N UW1 D S AH0 N -KNUDTSON K N UW1 T S AH0 N -KNUEPPEL K N UW1 P AH0 L -KNUPP K N AH1 P -KNUST K N AH1 S T -KNUT K N AH1 T -KNUTE K N UW1 T -KNUTH K N UW1 TH -KNUTS K N AH1 T S -KNUTSEN N AH1 T S AH0 N -KNUTSON N UW1 T S AH0 N -KNUTZEN N AH1 T Z AH0 N -KO K OW1 -KOALA K OW0 AA1 L AH0 -KOALAS K OW0 AA1 L AH0 Z -KOBA K OW1 B AA0 -KOBACKER K OW1 B AE2 K ER0 -KOBAK K OW1 B AH0 K -KOBAYASHI K OW2 B AA0 Y AA1 SH IY0 -KOBE K OW1 B EY0 -KOBE'S K OW1 B EY0 Z -KOBEL K OW1 B AH0 L -KOBER K OW1 B ER0 -KOBERSTEIN K OW1 B ER0 S T AY0 N -KOBERSTEIN(2) K OW1 B ER0 S T IY0 N -KOBES K OW1 B Z -KOBLE K OW1 B AH0 L -KOBLER K OW1 B AH0 L ER0 -KOBLER(2) K OW1 B L ER0 -KOBREN K AA1 B R AH0 N -KOBRIN K AA1 B R IH0 N -KOBRIN'S K AA1 B R IH0 N Z -KOBS K AA1 B Z -KOBUS K OW1 B AH0 S -KOBY K OW1 B IY0 -KOBYLARZ K AH0 B IH1 L ER0 Z -KOBYLINSKI K AH0 B IH0 L IH1 N S K IY0 -KOBZA K AA1 B Z AH0 -KOCAK K OW1 K AH0 K -KOCH K AO1 CH -KOCH(2) K OW1 K -KOCHAN K AA1 K AH0 N -KOCHANEK K AA1 K AH0 N IH0 K -KOCHANOWSKI K AH0 HH AH0 N AO1 F S K IY0 -KOCHANSKI K AH0 HH AE1 N S K IY0 -KOCHEL K AA1 K AH0 L -KOCHENDORFER K AA1 K IH0 N D AO0 R F ER0 -KOCHER K AO1 CH ER0 -KOCHEVAR K AH0 HH EH1 V ER0 -KOCHIS K AA1 K IH0 S -KOCHMAN K AA1 K M AH0 N -KOCI K OW1 S IY0 -KOCIAN K OW1 SH AH0 N -KOCIEMBA K OW2 S IY0 EH1 M B AH0 -KOCINSKI K AH0 CH IH1 N S K IY0 -KOCIOLEK K OW2 S IY0 OW1 L EH0 K -KOCIS K OW1 S IH0 S -KOCK K AA1 K -KOCOUREK K AH0 K UH1 R EH0 K -KOCSIS K AA1 K S IH0 S -KOCUR K OW1 K ER0 -KOCUR'S K OW1 K ER0 Z -KOCUREK K AH0 K Y UW1 R EH0 K -KODACOLOR K OW1 D AH0 K AH2 L ER0 -KODAK K OW1 D AE2 K -KODAK'S K OW1 D AE2 K S -KODAMA K OW0 D AA1 M AH0 -KODIAK K OW1 D IY0 AE2 K -KODO K OW1 D OW0 -KOEBEL K OW1 B AH0 L -KOEDINGER K OW1 D IH0 NG ER0 -KOEGEL K OW1 G AH0 L -KOEGLER K OW1 G AH0 L ER0 -KOEGLER(2) K OW1 G L ER0 -KOEHL K OW1 L -KOEHLER K OW1 L ER0 -KOEHN K OW1 N -KOEHNE K OW1 N -KOEHNEN K OW1 N AH0 N -KOEKI K OW1 K IY0 -KOELLE K OW1 L -KOELLER K OW1 L ER0 -KOELLING K OW1 L IH0 NG -KOELSCH K OW1 L SH -KOELZER K OW1 L Z ER0 -KOEN K OW1 N -KOENEMAN K AA1 IY0 N M AH0 N -KOENEN K OW1 N AH0 N -KOENIG K ER1 N IH0 G -KOENIGS K ER1 N IH0 G Z -KOENIGSBERG K OW1 N IH0 G Z B ER0 G -KOENIGSFELD K OW1 N IH0 G Z F EH2 L D -KOEP K OW1 P -KOEPKE K OW1 P K -KOEPP K OW1 P -KOEPPE K OW1 P -KOEPPEL K OW1 P AH0 L -KOEPPEN K OW1 P AH0 N -KOEPSEL K OW1 P S EH0 L -KOEPSELL K OW1 P S EH0 L -KOERBER K AO1 R B ER0 -KOERNER K AO1 R N ER0 -KOERNKE K AO1 R N AH0 K IY0 -KOESTER K AO1 S T ER0 -KOESTERS K AO1 S T ER0 Z -KOESTLER K OW1 S T L ER0 -KOESTNER K OW1 S T N ER0 -KOETHER K OW1 DH ER0 -KOETJE K OW1 JH AH0 -KOETTER K OW1 T ER0 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OW1 N ER0 -KOHNKE K AA1 NG K -KOHOUT K OW1 AW0 T -KOHR K AO1 R -KOHRING K AO1 R IH0 NG -KOHRS K AO1 R Z -KOHTARO K OW0 T AA1 R OW0 -KOHTORO K OW0 T AO1 R OW0 -KOHUT K OW1 AH0 T -KOICHI K OW0 IY1 CH IY0 -KOIDO K OY1 D OW0 -KOIKE K OY1 K -KOITO K OY1 T OW0 -KOITO(2) K OY1 T OW2 -KOITO(3) K OW2 IY1 T OW2 -KOIVISTO K OY2 V IH1 S T OW0 -KOJAK K OW1 JH AE2 K -KOJI K OW1 JH IY0 -KOJIMA K AH0 Y AY1 M AH0 -KOK K AA1 K -KOKAN K OW1 K AH0 N -KOKAN'S K OW1 K AH0 N Z -KOKATE K OW2 K AA1 T EY2 -KOKATE(2) K OW2 K AA1 T EY0 -KOKATE'S K OW2 K AA1 T EY2 Z -KOKATE'S(2) K OW2 K AA1 T EY0 Z -KOKE K OW1 K -KOKEN K OW1 K AH0 N -KOKER K OW1 K ER0 -KOKES K OW1 K S -KOKESH K AA1 K IH0 SH -KOKI K OW1 K IY0 -KOKINDA K AH0 K IH1 N D AH0 -KOKO K OW1 K OW0 -KOKO'S K OW1 K OW0 Z -KOKOMO K OW1 K AH0 M OW2 -KOKOSCHKA K AH0 K AO1 SH K AH0 -KOKOSZKA K AH0 K AA1 SH K AH0 -KOKUSAI K AA1 K Y UW0 S AY2 -KOL K OW1 L -KOLAKOWSKI K AH0 L AH0 K AO1 F S K IY0 -KOLANDER K AA1 L AH0 N D ER0 -KOLAR K OW1 L ER0 -KOLARIK K AH0 L AA1 R IH0 K -KOLASA K AH0 L AA1 S AH0 -KOLASINSKI K AH0 L AH0 S IH1 N S K IY0 -KOLB K OW1 L B -KOLBE K OW1 L B -KOLBECK K AA1 L B EH0 K -KOLBER K OW1 L B ER0 -KOLBERG K OW1 L B ER0 G -KOLBERT K OW1 L B ER0 T -KOLBO K OW1 L B OW0 -KOLDEN K OW1 L D AH0 N -KOLE K OW1 L -KOLEK K OW1 L EH0 K -KOLENDA K AH0 L EH1 N D AH0 -KOLESAR K AH0 L EH1 S ER0 -KOLICH K AA1 L IH0 HH -KOLIN K OW1 L IH0 N -KOLINSKI K AH0 L IH1 N S K IY0 -KOLK K OW1 K -KOLKA K OW1 L K AH0 -KOLKER K OW1 K ER0 -KOLL K AA1 L -KOLLAR K AA1 L ER0 -KOLLASCH K AA1 L AH0 SH -KOLLATH K AA1 L AH0 TH -KOLLE K AA1 L -KOLLEK K AO1 L EH0 K -KOLLEK(2) K OW1 L EH0 K -KOLLER K AA1 L ER0 -KOLLI K AA1 L IY0 -KOLLING K AA1 L IH0 NG -KOLLMAN K AA1 L M AH0 N -KOLLMANN K AA1 L M AH0 N -KOLLMEYER K AA1 L M AY0 ER0 -KOLLMORGEN K OW0 L M AO1 R G AH0 N -KOLLMORGEN(2) K AO0 L M AO1 R G AH0 N -KOLM K OW1 M -KOLMAN K AA1 L M AH0 N -KOLODNY K AH0 L AA1 D N IY0 -KOLODZIEJ K AH0 L AA1 D Z IY0 JH -KOLODZIEJSKI K AH0 L AA2 JH IY0 EY1 S K IY0 -KOLOJEJCHICK K OW2 L OW0 JH EY1 CH IH0 K -KOLOKOFF K AA1 L AH0 K AO2 F -KOLOSKI K AH0 L AW1 S K IY0 -KOLOWICH K AA1 L AH0 W IH0 CH -KOLP K OW1 L P -KOLSKI K OW1 L S K IY0 -KOLSKY K OW1 L S K IY0 -KOLSTAD K OW1 L S T AH0 D -KOLTER K OW1 L T ER0 -KOLTERMAN K OW1 L T ER0 M AH0 N -KOLTON K OW1 L T AH0 N -KOLTS K OW1 L T S -KOLTZ K OW1 L T S -KOMABA K OW0 M AA1 B AH0 -KOMAG K OW1 M AE0 G -KOMAN K OW1 M AH0 N -KOMANSKY K OW0 M AE1 N S K IY0 -KOMAR K OW1 M ER0 -KOMARA K OW0 M AA1 R AH0 -KOMAREK K OW0 M AA1 R EH0 K -KOMARIK K OW0 M AA1 R IH0 K -KOMARIK'S K OW0 M AA1 R IH0 K S -KOMATANI K AO1 M AH0 T AA1 N IY2 -KOMATSU K OW0 M AA1 T S UW1 -KOMBAT K AA1 M B AE0 T -KOMER K OW1 M ER0 -KOMERCNI K OW2 M ER1 CH N IY0 -KOMI K OW1 M IY0 -KOMINE K OW1 M AY2 N -KOMINEFT K AA1 M IH0 N EH0 F T -KOMINEK K AO1 M AH0 N EH2 K -KOMISAR K AH0 M IH1 S ER0 -KOMISAR(2) K AA1 M IH0 S AA0 R -KOMMER K AA1 M ER0 -KOMODO K AH0 M OW1 D OW0 -KOMODO(2) K OW0 M OW1 D OW0 -KOMORI K OW0 M AO1 R IY0 -KOMORNY K OW0 M AO1 R N IY0 -KOMOROWSKI K AH0 M ER0 AO1 F S K IY0 -KOMOTO K OW0 M OW1 T OW0 -KOMP K AA1 M P -KOMPANEK K AA2 M P AA1 N EH2 K -KOMSOMOL K AA1 M S OW0 M AH0 L -KOMSOMOL'S K AA1 M S OW0 M AH0 L Z -KOMURA K OW2 M UH1 R AH0 -KON K AA1 N -KONA K OW1 N AH0 -KONAGA K AH0 N AA1 G AH0 -KONARSKI K AH0 N AA1 R S K IY0 -KONCZAL K AA1 N CH AH0 L -KONDAS K AA1 N D AH0 Z -KONDO K AA1 N D OW0 -KONDRACKI K AH0 N D R AA1 T S K IY0 -KONDRAT K AA1 N D R AH0 T -KONECNY K AH0 N EH1 K N IY0 -KONEN K AA1 N AH0 N -KONG K AO1 NG -KONG(2) K AO1 NG G -KONG'S K AO1 NG Z -KONG'S(2) K AO1 NG G Z -KONGER K AA1 NG G ER0 -KONGERS K AA1 NG G ER0 Z -KONGSBERG K AO1 NG Z B ER0 G -KONGSBERG(2) K AO1 NG G Z B ER0 G -KONGSBERG'S K AO1 NG Z B ER0 G Z -KONGSBERG'S(2) K AO1 NG G Z B ER0 G Z -KONICA K AA1 N IH0 K AH0 -KONICEK K AA1 N IH0 CH EH2 K -KONICKI K AH0 N IH1 T S K IY0 -KONIECZKA K AH0 N IY1 CH K AH0 -KONIECZNY K AH0 N IY1 CH N IY0 -KONIG K AA1 N IH0 G -KONING K OW1 N IH0 NG -KONINKLIJKE K AA2 N IH0 NG K L IY1 K IY0 -KONISHI K OW0 N IY1 SH IY0 -KONISHIROKU K AA2 N IH2 SH IH0 R OW1 K UW2 -KONITZER K AA1 N IH0 T Z ER0 -KONKEL K AA1 NG K AH0 L -KONKLE K AA1 NG K AH0 L -KONKOL K AA1 NG K AO0 L -KONNER K AA1 N ER0 -KONO K OW1 N OW0 -KONOLD K AA1 N OW0 L D -KONOP K OW1 N AH0 P -KONOPKA K AH0 N OW1 P K AH0 -KONRAD K AA1 N R AH0 D -KONRATH K AA1 N R AH0 TH -KONSTANTIN K AA1 N S T IH0 N T IY2 N -KONSULTAT K AA2 N S AH0 L T AA1 T -KONTOS K AA1 N T OW0 Z -KONTRA K AA1 N T R AH0 -KONTRAS K AA1 N T R AH0 S -KONTROLLBANK K AA1 N T R AH0 L B AE2 NG K -KONWINSKI K AH0 N V IH1 N S K IY0 -KONZ K AA1 N Z -KONZEN K AA1 N Z AH0 N -KONZI K AA1 N Z IY0 -KONZI'S K AA1 N Z IY0 Z -KOO K UW1 -KOOB K UW1 B -KOOGLER K UW1 G L ER0 -KOOI K UW1 IY0 -KOOIKER K UW1 IH0 K ER0 -KOOIMAN K UW1 IH0 M AH0 N -KOOISTRA K UW0 IH1 Z T R AH0 -KOOK K UW1 K -KOOKEN K UW1 K AH0 N -KOOKER K UH1 K ER0 -KOOKS K UW1 K S -KOOKY K UW1 K IY0 -KOOL K UW1 L -KOOLHAAS K UW1 L HH AA0 S -KOON K UW1 N -KOON'S K UW1 N Z -KOONCE K UW1 N S -KOONE K UW1 N -KOONIG K UW1 N IH0 G -KOONING K UW1 N IH0 NG -KOONS K UW1 N Z -KOONTS K UW1 N T S -KOONTZ K UW1 N T S -KOOP K UW1 P -KOOP'S K UW1 P S -KOOPMAN K UW1 P M AH0 N -KOOPMANN K UW1 P M AH0 N -KOOPS K UW1 P S -KOOR K UW1 R -KOORS K UH1 R Z -KOOS K UW1 Z -KOOSER K UW1 Z ER0 -KOOTENAY K UW1 T AH0 N EY2 -KOOY K UW1 IY0 -KOOYMAN K AA1 OY0 M AH0 N -KOPACZ K AA1 P AH0 CH -KOPAS K OW1 P AH0 Z -KOPCHEV K AA1 P CH AH0 V -KOPCZYNSKI K AH0 P CH IH1 N S K IY0 -KOPE K OW1 P -KOPEC K OW1 P IH0 K -KOPECKY K AH0 P EH1 T S K IY0 -KOPEK K OW1 P AH0 K -KOPEKS K OW1 P AH0 K S -KOPEL K OW1 P AH0 L -KOPELMAN K OW1 P AH0 L M AH0 N -KOPER K OW1 P ER0 -KOPERA K AH0 P IH1 R AH0 -KOPERSKI K AH0 P ER1 S K IY0 -KOPETSKI K AH0 P EH1 T S K IY0 -KOPF K AO1 P F -KOPINSKI K AH0 P IH1 N S K IY0 -KOPISCHKE K AH0 P IH1 SH K IY0 -KOPIT K AA1 P IH0 T -KOPKA K OW1 P K AH0 -KOPKE K OW1 P K -KOPKO K OW1 P K OW0 -KOPLAN K AA1 P L AH0 N -KOPLIN K AA1 P L IH0 N -KOPLOVITZ K AA1 P L AH0 V IH0 T S -KOPP K AA1 P -KOPPE K AA1 P -KOPPEL K AA1 P AH0 L -KOPPEL'S K AA1 P AH0 L Z -KOPPELL K AA1 P AH0 L -KOPPELMAN K AA1 P AH0 L M AH0 N -KOPPEN K AA1 P AH0 N -KOPPENHAVER K AA1 P IH0 N HH AH0 V ER0 -KOPPER K AA1 P ER0 -KOPPERS K AA1 P ER0 Z -KOPPERS' K AA1 P ER0 Z -KOPPES K AA1 P S -KOPPLE K AA1 P AH0 L -KOPPLIN K AA1 P L IH0 N -KOPRIVA K AA1 P R IH0 V AH0 -KOPROWSKI K AH0 P R AO1 F S K IY0 -KOPS K AA1 P S -KOPUSOVIC K AH0 P UW1 S AH0 V IH0 K -KOPY K AA1 P IY0 -KORA K AO1 R AH0 -KORAL K AO1 R AH0 L -KORAN K AO0 R AA1 N -KORANDA K ER0 AE1 N D AH0 -KORANIC K AO0 R AE1 N IH0 K -KORB K AO1 R B -KORBA K AO1 R B AH0 -KORBEL K AO1 R B AH0 L -KORBER K AO1 R B ER0 -KORBREN K AO1 R B R EH0 N -KORBY K AO1 R B IY0 -KORCZAK K AO1 R CH AE0 K -KORDA K AO1 R D AH0 -KORDELL K AO0 R D EH1 L -KOREA K AO2 R IY1 AH0 -KOREA(2) K R IY1 AH0 -KOREA(3) K ER0 R IY1 AH0 -KOREA'S K AO2 R IY1 AH0 Z -KOREA'S(2) K R IY1 AH0 Z -KOREA'S(3) K ER0 R IY1 AH0 Z -KOREAGATE K AO2 R IY1 AH0 G EY2 T -KOREAGATE(2) K ER0 R IY1 AH0 G EY2 T -KOREAN K AO2 R IY1 AH0 N -KOREAN(2) K R IY1 AH0 N -KOREAN(3) K ER0 R IY1 AH0 N -KOREAN'S K R IY1 AH0 N Z -KOREAN'S(2) K ER0 IY1 AH0 N Z -KOREANS K AO2 R IY1 AH0 N Z -KOREANS(2) K R IY1 AH0 N Z -KOREANS(3) K ER0 R IY1 AH0 N Z -KOREANS' K AO2 R IY1 AH0 N Z -KOREANS'(2) K R IY1 AH0 N Z -KOREANS'(3) K ER0 R IY1 AH0 N Z -KOREAS K AO1 R IY0 AH0 Z -KOREAS(2) K R IY0 AH0 Z -KOREAS(3) K ER0 R IY0 AH0 Z -KOREATOWN K ER0 IY1 AH0 T AW2 N -KORELL K AO1 R EH0 L -KOREN K AO1 R AH0 N -KORENEK K AO1 R IH0 N IH0 K -KORESH K AO2 R EH1 SH -KORESH'S K AO2 R EH1 SH AH0 Z -KORET K AO1 R AH0 T -KOREY K AO1 R IY0 -KORF K AO1 R F -KORFF K AO1 R F -KORFHAGE K AO1 R F HH IH0 JH -KORHONEN K AO1 R HH AH0 N AH0 N -KORINEK K AO1 R IH0 N IH0 K -KORMAN K AO1 R M AH0 N -KORMOS K AO1 R M OW0 Z -KORN K AO1 R N -KORNACKI K ER0 N AA1 T S K IY0 -KORNBERG K AO1 R N B ER0 G -KORNBLUM K AO1 R N B L AH0 M -KORNBLUTH K AO1 R N B L UW0 TH -KORNEGAY K AO1 R N IH0 G EY0 -KORNER K AO1 R N ER0 -KORNFELD K AO1 R N F EH0 L D -KORNHAUSER K AO1 R N HH AW0 Z ER0 -KORNREICH K AO1 R N R AY0 K -KORNS K AO1 N Z -KOROL K AO1 R AO0 L -KOROLOGOS K AO0 R AA2 L OW1 G OW0 S -KORONA K ER0 OW1 N AH0 -KOROTICH K AO1 R AH0 T IH0 CH -KORPELA K ER0 P IY1 L AH0 -KORPI K AO1 R P IY0 -KORRY K AO1 R IY0 -KORRY'S K AO1 R IY0 Z -KORSON K AO1 R S AH0 N -KORT K AO1 R T -KORTE K AO1 R T -KORTEN K AO1 R T AH0 N -KORTH K AO1 R TH -KORTHALS K AO1 R TH AH0 L Z -KORTMAN K AO1 R T M AH0 N -KORTUM K AO1 R T AH0 M -KORTZ K AO1 R T S -KORUNA K AO0 R UW1 N AA2 -KORVA K AO1 R V AA2 -KORVER K AO1 R V ER2 -KORY K AO1 R IY2 -KORYAGIN K AO2 R Y AA1 G IH0 N -KORZENIEWSKI K ER0 Z IH2 N IY0 EH1 F S K IY0 -KORZENIEWSKI(2) K AO2 R Z AH0 N UW1 F S K IY0 -KOS K AA1 S -KOSA K OW1 S AA2 -KOSAK K OW1 S AH0 K -KOSAKOWSKI K AH0 S AH0 K AO1 F S K IY2 -KOSAN K OW1 Z AH0 N -KOSANKE K AA1 S AH0 NG K -KOSANOVICH K AH0 S AE1 N AH0 V IH0 CH -KOSAR K OW1 S ER0 -KOSBERG K AO1 Z B ER0 G -KOSBIE K AA1 Z B IY0 -KOSCH K AO1 SH -KOSCHECK K AO1 S CH EH0 K -KOSCHECK'S K AO1 S CH EH0 K S -KOSCIELNIAK K AH0 S CH IY1 L N IY0 AE0 K -KOSCINSKI K AH0 S CH IH1 N S K IY0 -KOSCO K OW1 S K OW0 -KOSECOFF K OW1 S AH0 K AO0 F -KOSEK K OW1 S EH0 K -KOSEL K OW1 S AH0 L -KOSER K OW1 Z ER0 -KOSH K AA1 SH -KOSHER K OW1 SH ER0 -KOSHERED K OW1 SH ER0 D -KOSHERING K OW1 SH ER0 IH0 NG -KOSHERS K OW1 SH ER0 S -KOSIBA K OW0 S IY1 B AH0 -KOSIER K OW1 S IY0 ER0 -KOSIK K OW1 S IH0 K -KOSIN K OW1 S IH0 N -KOSINSKI K AH0 S IH1 N S K IY0 -KOSKA K OW1 S K AH0 -KOSKELA K AH0 S K IY1 L AH0 -KOSKEY K AA1 S K IY0 -KOSKI K AW1 S K IY0 -KOSKINEN K AA1 S K IH0 N AH0 N -KOSKO K OW1 S K OW0 -KOSKOTAS K AO2 S K OW1 T AH0 S -KOSKY K AA1 S K IY0 -KOSLOSKI K AH0 S L AW1 S K IY0 -KOSLOSKY K AH0 S L OW1 S K IY0 -KOSLOW K AA1 S L OW0 -KOSLOW'S K AA1 Z L OW2 Z -KOSLOWSKI K AH0 S L AO1 F S K IY0 -KOSMAN K AA1 S M AH0 N -KOSMATKA K AH0 S M AA1 T K AH0 -KOSMETSKY K AA2 Z M EH1 T S K IY0 -KOSMETSKY'S K AA2 Z M EH1 T S K IY0 Z -KOSMICKI K AH0 S M IH1 T S K IY0 -KOSNOVSKY K AA2 Z N AA1 F S K IY0 -KOSNOVSKY'S K AA2 Z N AA1 F S K IY0 Z -KOSOVO K OW1 S OW0 V OW2 -KOSOWSKI K AH0 S AO1 F S K IY0 -KOSOWSKY K AH0 S AW1 S K IY0 -KOSS K AO1 S -KOSSMAN K AO1 S M AH0 N -KOSSOW K AA1 S OW0 -KOSSUTH K AA1 S AH0 TH -KOST K AA1 S T -KOSTA K OW1 S T AH0 -KOSTAL K AA1 S T AH0 L -KOSTAS K OW1 S T AH0 Z -KOSTECKI K AH0 S T EH1 T S K IY0 -KOSTEK K AA1 S T EH0 K -KOSTELECKY K AH0 S T EH0 L EH1 T S K IY0 -KOSTELNIK K AH0 S T EH1 L N IH0 K -KOSTER K AA1 S T ER0 -KOSTIC K AA1 S T IH0 K -KOSTICK K OW1 S T IH0 K -KOSTKA K AA1 S T K AH0 -KOSTMAYER K AO1 S T M EY2 ER0 -KOSTOFF K AA1 S T AO0 F -KOSTRZEWA K AH0 S T R AH0 Z EH1 W AH0 -KOSTRZEWSKI K AO2 S T ER0 Z EH1 W S K IY0 -KOSUB K OW1 S AH0 B -KOSY K OW1 S IY0 -KOSYAKOV K OW1 S Y AA0 K AO0 V -KOSYGIN K OW1 S IH0 G IH0 N -KOSYGIN(2) K OW1 S IY0 G IH0 N -KOT K AA1 T -KOTARA K OW0 T AA1 R AH0 -KOTARSKI K AH0 T AA1 R S K IY0 -KOTAS K OW1 T AH0 Z -KOTCH K AA1 CH -KOTE K OW1 T -KOTECKI K AH0 T EH1 T S K IY0 -KOTEK K OW1 T EH2 K -KOTELES K AA1 T EH0 L EH0 Z -KOTH K AA1 TH -KOTHARI K AA1 TH ER0 IY0 -KOTHE K OW1 DH -KOTILA K AH0 T AY1 L AH0 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OW1 Z AH0 -KOZAK K OW1 Z AH0 K -KOZAKIEWICZ K AH0 Z AA1 K AH0 V IH0 CH -KOZAR K OW1 Z ER0 -KOZBERG K AA1 Z B ER0 G -KOZEL K OW1 Z AH0 L -KOZEMCHAK K OW2 Z EH1 M CH AE2 K -KOZICKI K AH0 Z IH1 T S K IY0 -KOZIEL K AA1 Z IY0 L -KOZIK K OW1 Z IH0 K -KOZIKOWSKI K AH0 Z IH0 K AO1 F S K IY0 -KOZINSKI K AH0 Z IH1 N S K IY0 -KOZIOL K AA1 Z IY0 AO0 L -KOZLIK K AA1 Z L IH0 K -KOZLOFF K AA1 Z L AO0 F -KOZLOSKI K AH0 Z L AW1 S K IY0 -KOZLOW K AA1 Z L OW0 -KOZLOWSKI K AH0 Z L AO1 F S K IY0 -KOZMA K OW1 Z M AH0 -KOZMINSKI K AH0 Z M IH1 N S K IY0 -KOZNOVSKY K AA2 Z N AA1 F S K IY0 -KOZNOVSKY'S K AA2 Z N AA1 F S K IY0 Z -KOZO K OW1 Z OW0 -KOZOL K OW1 Z AH0 L -KOZUB K OW1 Z AH0 B -KOZUCH K AA1 Z AH0 HH -KOZYREV K AA1 Z ER0 EH2 V -KOZYREV'S K AA1 Z ER0 EH2 V Z -KPMG K EY1 P IY1 EH1 M JH IH1 -KRAAI K R AA1 IY0 -KRAATZ K R AA1 T S -KRABBE K R AE1 B -KRABBENHOFT K R AE1 B IH0 N HH AH0 F T -KRABILL K R AE1 B AH0 L -KRACH K R AE1 CH -KRACHT K R AE1 K T -KRACK K R AE1 K -KRACKE K R AE1 K -KRAEGER K R EH1 G ER0 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AH0 L -KRANE K R EY1 N -KRANER K R EY1 N ER0 -KRANICH K R AE1 N IH0 CH -KRANS K R AE1 N Z -KRANTZ K R AE1 N T S -KRANZ K R AE1 N Z -KRANZLER K R AE1 N Z L ER0 -KRAPELS K R AE1 P AH0 L Z -KRAPF K R AE1 P F -KRAPP K R AE1 P -KRAPRAYOON K R AE1 P R AA0 Y UW0 N -KRAS K R AE1 S -KRASINSKI K R AH0 S IH1 N S K IY0 -KRASKA K R AA1 S K AH0 -KRASNER K R AE1 S N ER0 -KRASNOFF K R AE1 S N AO0 F -KRASNOW K R AA1 S N OW0 -KRASNOYARSK K R AE0 S N OY1 AA0 R S K -KRASNY K R AE1 Z N IY0 -KRASOWSKI K R AH0 S AO1 F S K IY0 -KRASS K R AE1 S -KRASZEWSKI K R AH0 SH EH1 F S K IY0 -KRAT K R AE1 T -KRATKY K R AE1 T K IY0 -KRATOCHVIL K R AE1 T AH0 K V AH0 L -KRATOS K R AE1 T AO2 S -KRATT K R AE1 T -KRATZ K R AE1 T S -KRATZER K R EY1 T Z ER0 -KRATZKE K R AE1 T S K IY0 -KRAUER K R AW1 R -KRAUS K R AW1 S -KRAUSE K R AO1 S -KRAUSER K R AW1 S ER0 -KRAUSHAAR K R AW1 SH AA2 R -KRAUSKOPF K R AW1 S K AO0 F -KRAUSS K R AW1 S -KRAUSSE K R AO1 S -KRAUSZ K R AW1 SH -KRAUT K R AW1 T -KRAUTER K R AW1 T ER0 -KRAUTH K R AO1 TH -KRAUTHAMMER K R AW1 T HH AE2 M ER0 -KRAUZE K R AW1 Z -KRAVCHUK K R AA1 V CH UH2 K -KRAVCHUK'S K R AA1 V CH UH2 K Z -KRAVETZ K R AE1 V IH0 T S -KRAVIS K R AE1 V IH0 S -KRAVITZ K R AE1 V IH0 T S -KRAWCCYKIEWI K R AW2 CH IH0 K UW1 IY0 -KRAWCHUK K R AO1 CH AH0 K -KRAWCZAK K R AA1 V CH AE0 K -KRAWCZYK K R AA1 V CH IH0 K -KRAWIEC K R AA1 V IY0 K -KRAWITZ K R AA1 W IH0 T S -KRAY K R EY1 -KRAYNAK K R EY1 N AH0 K -KREAGER K R IY1 G ER0 -KREAMER K R IY1 M ER0 -KREBBS K R EH1 B Z -KREBS K R EH1 B Z -KREBS'S K R EH1 B Z IH0 Z -KREBSBACH K R EH1 B Z B AA0 K -KRECH K R EH1 K -KRECKO K R EH1 K OW0 -KREDIETBANK K R EH0 D IY0 T B AE1 NG K -KREDIT K R EH1 T IH0 T -KREDITANSTALT K R EH0 D IH1 T AH2 N S T AO2 L T -KREEGER K R IY1 G ER0 -KREFT K R EH1 F T -KREG K R EH1 G -KREGEL K R EH1 G AH0 L -KREGER K R IY1 G ER0 -KREH K R EH1 -KREHBIEL K R EH1 B IY0 L -KREHER K R EH1 R -KREICHER K R AY1 K ER0 -KREIDER K R AY1 D ER0 -KREIDLER K R AY1 D AH0 L ER0 -KREIDLER(2) K R AY1 D L ER0 -KREIFELS K R AY1 F AH0 L Z 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IH0 K -KRENKE K R EH1 NG K -KRENN K R EH1 N -KRENTZ K R EH1 N T S -KRENWINKLE K R EH1 N W IH2 NG K AH0 L -KRENWINKLE'S K R EH1 N W IH2 NG K AH0 L Z -KRENZ K R EH1 N Z -KRENZER K R EH1 N Z ER0 -KREPPS K R EH1 P S -KREPS K R EH1 P S -KRESA K R IY1 S ER0 -KRESGE K R EH1 S G IY0 -KRESLOVSKY K R EH0 S L AO1 V S K IY0 -KRESS K R EH1 S -KRESSE K R EH1 S -KRESSER K R EH1 S ER0 -KRESSIN K R EH1 S IH0 N -KRESSLER K R EH1 S L ER0 -KRETCHMAN K R EH1 CH M AH0 N -KRETCHMER K R EH1 CH M ER0 -KRETSCH K R EH1 CH -KRETSCHMAR K R EH1 CH M ER0 -KRETSCHMER K R EH1 CH M ER0 -KRETZ K R EH1 T S -KRETZER K R EH1 T Z ER0 -KRETZSCHMAR K R EH1 CH M ER0 -KREUGER K R OY1 G ER0 -KREUL K R UW1 L -KREUSER K R OY1 S ER0 -KREUTER K R OY1 T ER0 -KREUTZ K R UW1 T S -KREUZER K R UW1 Z ER0 -KREWSON K R UW1 S AH0 N -KREY K R EY1 -KRIBS K R IH1 B Z -KRICHBAUM K R IH1 K B AW0 M -KRICK K R IH1 K -KRIDER K R AY1 D ER0 -KRIDLER K R IH1 D L ER0 -KRIEBEL K R IY1 B AH0 L -KRIEG K R IY1 G -KRIEGEL K R IY1 G AH0 L -KRIEGER K R IY1 G 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M EH1 S ER0 Z -MESSRS.(2) M IH0 S UW1 R Z -MESSY M EH1 S IY0 -MEST M EH1 S T -MESTA M EH1 S T AH0 -MESTAS M EH1 S T AH0 Z -MESTEK M EH1 S T EH2 K -MESTER M EH1 S T ER0 -MESTIZO M EH0 S T IY1 Z OW0 -MESTON M EH1 S T AH0 N -MESTRALLET M EH1 S T R AH0 L EH2 T -MESTRE M EH1 S T ER0 -MESTROVIC M EH1 S T R OW0 V IH0 K -MESZAROS M IH0 SH AA1 R OW0 Z -MET M EH1 T -MET'S M EH1 T S -META M IY1 T AH0 -METABOLIC M EH2 T AH0 B AA1 L IH0 K -METABOLISM M AH0 T AE1 B AH0 L IH2 Z AH0 M -METABOLISMS M AH0 T AE1 B AH0 L IH2 Z AH0 M Z -METABOLITE M AH0 T AE1 B AH0 L AY2 T -METABOLITES M AH0 T AE1 B AH0 L AY2 T S -METABOLIZE M AH0 T AE1 B AH0 L AY2 Z -METACARPAL M EH2 T AH0 K AA1 R P AH0 L -METACARPALS M EH2 T AH0 K AA1 R P AH0 L Z -METAGOGUE M EH1 T AH0 G AO2 G -METAGOGUED M EH1 T AH0 G AO2 G D -METAIRIE M AH0 T EH1 R IY0 -METAL M EH1 T AH0 L -METAL'S M EH1 T AH0 L Z -METALL M EH1 T AO1 L -METALLATZ M EH1 T AE1 L AH0 T S -METALLGESELLSCHAFT M EH2 T AH0 L G EH1 S AH0 L SH AE2 F T -METALLGESELLSCHAFT'S M 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AH0 T IH0 NG -MISINTERPRETS M IH0 S IH0 N T ER1 P R AH0 T S -MISJUDGE M IH0 S JH AH1 JH -MISJUDGED M IH0 S JH AH1 JH D -MISJUDGES M IH0 S JH AH1 JH IH0 Z -MISJUDGING M IH0 S JH AH1 JH IH0 NG -MISJUDGMENT M IH0 S JH AH1 JH M AH0 N T -MISJUDGMENTS M IH0 S JH AH1 JH M AH0 N T S -MISKA M IH1 S K AH0 -MISKE M IH1 S K -MISKELL M IH1 S K AH0 L -MISKITO M IH0 S K IY1 T OW0 -MISKITOS M IH0 S K IY1 T OW0 S -MISKO M IH1 S K OW0 -MISLABEL M IH0 S L EY1 B AH0 L -MISLABELED M IH0 S L EY1 B AH0 L D -MISLABELING M IH0 S L EY1 B AH0 L IH0 NG -MISLABELING(2) M IH0 S L EY1 B L IH0 NG -MISLABELS M IH0 S L EY1 B AH0 L Z -MISLAID M IH0 S L EY1 D -MISLEAD M IH0 S L IY1 D -MISLEADING M IH0 S L IY1 D IH0 NG -MISLEADINGLY M IH0 S L IY1 D IH0 NG L IY0 -MISLEADS M IH0 S L IY1 D Z -MISLED M IH0 S L EH1 D -MISMANAGE M IH0 S M AE1 N IH0 JH -MISMANAGED M IH0 S M AE1 N IH0 JH D -MISMANAGEMENT M IH0 S M AE1 N IH0 JH M AH0 N T -MISMANAGES M IH0 S M AE1 N IH0 JH AH0 Z -MISMANAGING M IH0 S M AE1 N IH0 JH IH0 NG -MISMATCH 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AA0 N Z -NEGENT N EH1 G AH0 N T -NEGENT(2) N EH1 JH AH0 N T -NEGEV N EH1 G EH2 V -NEGLECT N AH0 G L EH1 K T -NEGLECT(2) N IH0 G L EH1 K T -NEGLECTED N AH0 G L EH1 K T AH0 D -NEGLECTED(2) N IH0 G L EH1 K T IH0 D -NEGLECTFUL N IH0 G L EH1 K T F AH0 L -NEGLECTING N IH0 G L EH1 K T IH0 NG -NEGLECTS N IH0 G L EH1 K T S -NEGLEY N EH1 G L IY0 -NEGLIA N EH1 G L IY0 AH0 -NEGLIGENCE N EH1 G L AH0 JH AH0 N S -NEGLIGENCE(2) N EH1 G L IH0 JH AH0 N S -NEGLIGENT N EH1 G L AH0 JH AH0 N T -NEGLIGENT(2) N EH1 G L IH0 JH AH0 N T -NEGLIGENTLY N EH1 G L IH0 JH AH0 N T L IY0 -NEGLIGIBLE N EH1 G L AH0 JH AH0 B AH0 L -NEGLIGIBLE(2) N EH1 G L IH0 JH AH0 B AH0 L -NEGOTIABLE N AH0 G OW1 SH AH0 B AH0 L -NEGOTIATE N AH0 G OW1 SH IY0 EY2 T -NEGOTIATE(2) N IH0 G OW1 SH IY0 EY2 T -NEGOTIATED N AH0 G OW1 SH IY0 EY2 T AH0 D -NEGOTIATED(2) N IH0 G OW1 SH IY0 EY2 T IH0 D -NEGOTIATES N IH0 G OW1 SH IY0 EY2 T S -NEGOTIATING N IH0 G OW1 SH IY0 EY2 T IH0 NG -NEGOTIATION N IH0 G OW2 SH IY0 EY1 SH AH0 N -NEGOTIATIONS N AH0 G 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IH0 NG -NON-SPECIFIC N AA2 N S P AH0 S IH1 F IH0 K -NON-SPORTING N AA0 N S P AO1 R T IH0 NG -NON-STANDARD N AA0 N S T AE1 N D ER0 D -NON-STARTER N AA0 N S T AA1 R T ER0 -NON-STARTERS N AA0 N S T AA1 R T ER0 Z -NON-STICK N AA0 N S T IH1 K -NON-STOP N AA2 N S T AA1 P -NON-STOPS N AA2 N S T AA1 P S -NON-STRATEGIC N AA0 N S T R AH0 T IY1 JH IH0 K -NON-SUPERVISORY N AA0 N S UW2 P ER0 V AY1 Z ER0 IY0 -NON-SURGICAL N AA0 N S ER1 JH IH0 K AH0 L -NON-TAXABLE N AA0 N T AE1 K S AH0 B AH0 L -NON-TECHNICAL N AA0 N T EH1 K N IH0 K AH0 L -NON-TOXIC N AA0 N T AA1 K S IH0 K -NON-TRADITIONAL N AA2 N T R AH0 D IH1 SH AH0 N AH0 L -NON-TRANSFERABLE N AA2 N T R AE2 N S F ER1 AH0 B AH0 L -NON-TURBO N AO0 N T ER1 B OW0 -NON-TURBOS N AO0 N T ER1 B OW0 Z -NON-UNION N AA0 N Y UW1 N Y AH0 N -NON-UNIONIZED N AA0 N Y UW1 N Y AH0 N AY2 Z D -NON-UTILITY N AA2 N Y UW0 T IH1 L IH0 T IY0 -NON-VERBAL N AA0 N V ER1 B AH0 L -NON-VIOLENCE N AA0 N V AY1 AH0 L AH0 N S -NON-VIOLENT N AA0 N V AY1 AH0 L AH0 N T -NON-VOLATILE N AA0 N V AA1 L AH0 T AH0 L -NON-VOTING N AA0 N V OW1 T IH0 NG -NON-WHITE N AA1 N W AY1 T -NON-WHITE(2) N AA1 N HH W AY1 T -NON-WHITES N AA1 N W AY1 T S -NON-WHITES(2) N AA1 N HH W AY1 T S -NON-WOVEN N AA1 N W OW1 V IH0 N -NON-WOVENS N AA1 N W OW1 V AH0 N Z -NON-ZERO N AA0 N Z IY1 R OW0 -NON-ZERO-SUM N AA0 N Z IY1 R OW0 S AH2 M -NONA N AA1 N AA0 -NONACADEMIC N AA0 N AE2 K AH0 D EH1 M IH0 K -NONACCRUAL N AA2 N AH0 K R UW1 AH0 L -NONACCRUING N AA2 N AH0 K R UW1 IH0 NG -NONAGGRESSION N AA2 N AH0 G R EH1 SH AH0 N -NONAGRICULTURAL N AA2 N AE2 G R IH0 K AH1 L CH ER0 AH0 L -NONALCOHOLIC N AA2 N AE2 L K AH0 HH AA1 L IH0 K -NONALIGN N AA1 N AH0 L AY2 N -NONALIGNED N AA1 N AH0 L AY2 N D -NONAUTOMOTIVE N AA2 N AO2 T OW0 M OW1 T IH0 V -NONBANK N AA1 N B AE1 NG K -NONBANKING N AA1 N B AE1 NG K IH0 NG -NONBELIEVER N AA2 N B AH0 L IY1 V ER0 -NONBELIEVERS N AA2 N B AH0 L IY1 V ER0 Z -NONBINDING N AA1 N B AY1 N D IH0 NG -NONBITING N AA0 N B AY1 T IH0 NG -NONBUILDING N AA1 N B IH1 L D IH0 NG -NONBUSINESS N AA1 N B IH1 Z N AH0 S -NONCALLABLE N AA0 N K AO1 L AH0 B AH0 L -NONCASH N AA1 N K AE1 SH -NONCE N AA1 N S -NONCHALANCE N AA1 N SH AH0 L AA1 N S -NONCHALANT N AA2 N SH AH0 L AA1 N T -NONCHALANTLY N AA1 N SH AH0 L AA1 N T L IY0 -NONCOLOR N AA0 N K AH1 L ER0 -NONCOMBATANT N AA2 N K AH0 M B AE1 T AH0 N T -NONCOMBATANTS N AA2 N K AH0 M B AE1 T AH0 N T S -NONCOMMERCIAL N AA1 N K AH0 M ER1 SH AH0 L -NONCOMMITTAL N AA1 N K AH0 M IH1 T AH0 L -NONCOMMUNIST N AA1 N K AA1 M Y UW0 N IH0 S T -NONCOMMUNISTS N AA1 N K AA1 M Y UW0 N IH0 S T S -NONCOMPETE N AA0 N K AH0 M P IY1 T -NONCOMPETITIVE N AA2 N K AH0 M P EH1 T AH0 T IH0 V -NONCOMPLIANCE N AA2 N K AH0 M P L AY1 AH0 N S -NONCONFORMIST N AA2 N K AH0 N F AO1 R M IH0 S T -NONCONFORMISTS N AA2 N K AH0 N F AO1 R M AH0 S T S -NONCONFORMITY N AA2 N K AH0 N F AO1 R M AH0 T IY0 -NONCONTRACT N AA0 N K AA1 N T R AE2 K T -NONCONTROVERSIAL N AA0 N K AA2 N T R AH0 V ER1 SH AH0 L -NONCONVERTIBLE N AA0 N K AH0 N V ER1 T AH0 B AH0 L -NONCORE N AA1 N K AO1 R -NONCORPORATE N AA0 N K AO1 R P R AH0 T -NONCRIMINAL N AA0 N K R IH1 M IH0 N AH0 L -NONCUMULATIVE N AA0 N K Y UW1 M Y AH0 L AH0 T IH0 V -NONDEDUCTIBLE N AA0 N D IH0 D AH1 K T IH0 B AH0 L -NONDEFENSE N AA0 N D IH0 F EH1 N S -NONDESCRIPT N AA1 N D IH0 S K R IH1 P T -NONDIRECT N AA1 N D ER0 EH1 K T -NONDISCRIMINATE N AA0 N D IH2 S K R IH1 M AH0 N AH0 T -NONDISCRIMINATION N AA0 N D IH2 S K R IH0 M IH0 N EY1 SH AH0 N -NONDISCRIMINATORY N AA1 N D IH0 S K R IH1 M AH0 N AH0 T AO2 R IY0 -NONDOLLAR N AA1 N D AA1 L ER0 -NONDURABLE N AA0 N D UH1 R AH0 B AH0 L -NONDURABLES N AA0 N D UH1 R AH0 B AH0 L Z -NONE N AH1 N -NONECONOMIC N AA2 N EH2 K AH0 N AA1 M IH0 K -NONELECTRICAL N AA0 N IH0 L EH1 K T R IH0 K AH0 L -NONEMERGENCY N AA2 N IH0 M ER1 JH AH0 N S IY0 -NONENTITY N AA0 N EH1 N T AH0 T IY0 -NONESSENTIAL N AA2 N IH0 S EH1 N SH AH0 L -NONESUCH N AH1 N S AH1 CH -NONETHELESS N AH2 N DH AH0 L EH1 S -NONEVENT N AA1 N IH0 V EH1 N T -NONEXCLUSIVE N AA2 N IH0 K S K L UW1 S IH0 V -NONEXECUTIVE N AA1 N IH0 G Z EH1 K Y AH0 T IH0 V -NONEXISTENT N AA2 N AH0 G Z IH1 S T AH0 N T -NONFARM N AA1 N F AA1 R M -NONFAT N AA1 N F AE1 T -NONFATAL N AA1 N F EY1 T AH0 L -NONFERROUS N AA0 N F EH1 R AH0 S -NONFICTION N AA0 N F IH1 K SH AH0 N -NONFINANCIAL N AA0 N F AH0 N AE1 N SH AH0 L -NONFINANCIAL(2) N AA0 N F AY0 N AE1 N SH AH0 L -NONFOOD N AA1 N F UW1 D -NONGOVERNMENT N AA0 N G AH1 V ER0 N M AH0 N T -NONGOVERNMENTAL N AA0 N G AH2 V ER0 N M EH1 N T AH0 L -NONGREEK N AA2 N G R IY1 K -NONHUMAN N AA0 N HH Y UW1 M AH0 N -NONIE N AA1 N IY0 -NONINFLATIONARY N AA2 N IH0 N F L EY1 SH AH0 N EH2 R IY0 -NONINTEREST N AA0 N IH1 N T R AH0 S T -NONINTERFERENCE N AA2 N IH2 N T ER0 F IH1 R AH0 N S -NONINTERVENTION N AA2 N IH2 N T ER0 V EH1 N SH AH0 N -NONJET N AA1 N JH EH1 T -NONJETS N AA1 N JH EH1 T S -NONLETHAL N AA0 N L IY1 TH AH0 L -NONLINEAR N AA0 N L IH1 N IY2 ER0 -NONLITURGICAL N AA0 N L AH0 T ER1 JH IH0 K AH0 L -NONMANAGEMENT N AA0 N M AE1 N IH0 JH M AH0 N T -NONMANUFACTURING N AA2 N M AE2 N Y AH0 F AE1 K CH ER0 IH0 NG -NONMEMBER N AA0 N M EH1 M B ER0 -NONMEMBERS N AA0 N M EH1 M B ER0 Z -NONMILITARY N AA0 N M IH1 L AH0 T EH2 R IY0 -NONNATIVE N AA1 N EY1 T IH0 V -NONNATIVES N AA1 N EY1 T IH0 V Z -NONNEGOTIABLE N AA2 N AH0 G OW1 SH AH0 B AH0 L -NONNEMACHER N AA1 N IH0 M AH0 K ER0 -NONNUCLEAR N AA1 N UW1 K L IY2 ER0 -NONOPERATING N AA1 N AO1 P ER0 EY2 T IH0 NG -NONPACIFIST N AA2 N P AE1 S IH0 F IH0 S T -NONPACIFISTS N AA2 N P AE1 S IH0 F IH0 S T S -NONPARTISAN N AA0 N P AA1 R T AH0 Z AH0 N -NONPAYING N AA0 N P EY1 IH0 NG -NONPAYMENT N AA0 N P EY1 M AH0 N T -NONPERFORMING N AA0 N P ER0 F AO1 R M IH0 NG -NONPLANAR N AA0 N P L EY1 N ER0 -NONPLUSS N AA0 N P L AH1 S -NONPLUSSED N AA0 N P L AH1 S T -NONPOISONOUS N AA0 N P OY1 Z AH0 N AH0 S -NONPOLITICAL N AA2 N P AH0 L IH1 T IH0 K AH0 L -NONPRESCRIPTION N AA2 N P R AH0 S K R IH1 P SH AH0 N -NONPRODUCTIVE N AA2 N P R AH0 D AH1 K T IH0 V -NONPROFESSIONAL N AA2 N P R AH0 F EH1 SH AH0 N AH0 L -NONPROFESSIONALS N AA0 N P R AH0 F EH1 SH AH0 N AH0 L Z -NONPROFIT N AA0 N P R AA1 F AH0 T -NONPROFITS N AA1 N P R AA1 F IH0 T S -NONPROLIFERATION N AA0 N P R AH0 L IH2 F ER0 EY1 SH AH0 N -NONPUBLIC N AA0 N P AH1 B L IH0 K -NONQUALIFIED N AA0 N K W AA1 L AH0 F AY2 D -NONRACIAL N AA0 N R EY1 SH AH0 L -NONRECURRING N AA0 N R IH0 K ER1 IH0 NG -NONREFUNDABLE N AA0 N R IH0 F AH1 N D AH0 B AH0 L -NONREGULATED N AA0 N R EH1 G Y AH0 L EY2 T IH0 D -NONRELIGIOUS N AA2 N R IH0 L IH1 JH AH0 S -NONRENEWABLE N AA0 N R IY0 N UW1 AH0 B AH0 L -NONRESIDENT N AA0 N R EH1 Z AH0 D AH0 N T -NONRESIDENTIAL N AA2 N R EH2 Z AH0 D EH1 N SH AH0 L -NONRESIDENTS N AA0 N R EH1 Z AH0 D AH0 N T S -NONRESPONSIVE N AA0 N R IH0 S P AA1 N S IH0 V -NONRESTRICTED N AA0 N R IH0 S T R IH1 K T IH0 D -NONRESTRICTED(2) N AA0 N R IY0 S T R IH1 K T IH0 D -NONROMAN N AA0 N R OW1 M AH0 N -NONRULING N AA0 N R UW1 L IH0 NG -NONSECTARIAN N AA2 N S EH0 K T EH1 R IY0 AH0 N -NONSENSE N AA1 N S EH0 N S -NONSENSICAL N AA0 N S EH1 N S IH0 K AH0 L -NONSMOKER N AA0 N S M OW1 K ER0 -NONSMOKERS N AA0 N S M OW1 K ER0 Z -NONSMOKERS' N AA1 N S M OW1 K ER0 Z -NONSMOKING N AA1 N S M OW1 K IH0 NG -NONSPECIFIC N AA2 N S P AH0 S IH1 F IH0 K -NONSPORTING N AA0 N S P AO1 R T IH0 NG -NONSTANDARD N AA0 N S T AE1 N D ER0 D -NONSTARTER N AA0 N S T AA1 R T ER0 -NONSTARTERS N AA0 N S T AA1 R T ER0 Z -NONSTICK N AA0 N S T IH1 K -NONSTOP N AA2 N S T AA1 P -NONSTOPS N AA2 N S T AA1 P S -NONSTRATEGIC N AA0 N S T R AH0 T IY1 JH IH0 K -NONSURGICAL N AA0 N S ER1 JH IH0 K AH0 L -NONTAXABLE N AA0 N T AE1 K S AH0 B AH0 L -NONTECHNICAL N AA0 N T EH1 K N IH0 K AH0 L -NONTOXIC N AA0 N T AA1 K S IH0 K -NONTRADITIONAL N AA2 N T R AH0 D IH1 SH AH0 N AH0 L -NONTRANSFERABLE N AA2 N T R AE2 N S F ER1 AH0 B AH0 L -NONTURBO N AO0 N T ER1 B OW0 -NONTURBOS N AO0 N T ER1 B OW0 Z -NONUNION N AA0 N Y UW1 N Y AH0 N -NONUNIONIZED N AA0 N Y UW1 N Y AH0 N AY2 Z D -NONUTILITY N AA2 N Y UW0 T IH1 L IH0 T IY0 -NONVERBAL N AA0 N V ER1 B AH0 L -NONVIOLENCE N AA0 N V AY1 AH0 L AH0 N S -NONVIOLENT N AA0 N V AY1 AH0 L AH0 N T -NONVOLATILE N AA0 N V AA1 L AH0 T AH0 L -NONVOTING N AA0 N V OW1 T IH0 NG -NONWHITE N AA1 N W AY1 T -NONWHITE(2) N AA1 N HH W AY1 T -NONWHITES N AA1 N W AY1 T S -NONWHITES(2) N AA1 N HH W AY1 T S -NONWOVEN N AA1 N W OW1 V IH0 N -NONWOVENS N AA1 N W OW1 V AH0 N Z -NOODLE N UW1 D AH0 L -NOODLES N UW1 D AH0 L Z -NOOK N UH1 K -NOOKS N UH1 K S -NOON N UW1 N -NOONAN N UW1 N AH0 N -NOONE N UW1 N -NOONEY N UW1 N IY0 -NOONS N UW1 N Z -NOONTIME N UW1 N T AY2 M -NOORDA N UH1 R D AH0 -NOOSE N UW1 S -NOPE N OW1 P -NOR N AO1 R -NORA N AO1 R AH0 -NORA'S N AO1 R AH0 Z -NORAH N AO1 R AH0 -NORAMCO N AO0 R AE1 M K OW0 -NORANDA N AO0 R AE1 N D AH0 -NORANDA'S N AO0 R AE1 N D AH0 Z -NORBA N AO1 R B AH0 -NORBECK N AO1 R B EH0 K -NORBERG N AO1 R B ER0 G -NORBERT N AO1 R B ER0 T -NORBERTA N AO0 R B EH1 R T AH0 -NORBERTO N AO0 R B ER1 T OW2 -NORBURY N AO1 R B EH0 R IY0 -NORBY N AO1 R B IY0 -NORCEN N AO1 R S AH0 N -NORCEN'S N AO1 R S AH0 N Z -NORCIA N AO1 R CH AH0 -NORCO N AO1 R K OW0 -NORCROSS N AO1 R K R AO0 S -NORD N AO1 R D -NORDAHL N AO1 R D AA0 L -NORDAN N AO1 R D AH0 N -NORDBANKEN N AO1 R D B AE2 NG K AH0 N -NORDBERG N AO1 R D B ER0 G -NORDBY N AO1 R D B IY0 -NORDEEN N ER0 D IY1 N -NORDELL N AO1 R D AH0 L -NORDEN N AO1 R D AH0 N -NORDER N AO1 R D ER0 -NORDGREN N AO1 R D G R EH0 N -NORDIC N AO1 R D IH0 K -NORDICA N AO1 R D IH0 K AH0 -NORDICTRACK N AO1 R D IH0 K T R AE2 K -NORDIN N AO1 R D IH0 N -NORDINE N AO0 R D IY1 N IY0 -NORDIO N AO1 R D IY0 OW0 -NORDISK N AO1 R D IH2 S K -NORDLING N AO1 R D AH0 L IH0 NG -NORDLING(2) N AO1 R D L IH0 NG -NORDLUND N AO1 R D L AH0 N D -NORDMAN N AO1 R D M AH0 N -NORDMANN N AO1 R D M AH0 N -NORDMEYER N AO1 R D M AY0 ER0 -NORDQUIST N AO1 R D K W IH0 S T -NORDSON N AO1 R D S AH0 N -NORDSTROM N AO1 R D S T R AH0 M -NORDSTROM'S N AO1 R D S T R AH0 M Z -NORDSTROMS N AO1 R D S T R AH0 M Z -NORDYKE N AO1 R D AY2 K -NORECO N AO0 R EH1 K OW0 -NORED N AO1 R D -NOREEN N ER0 IY1 N -NOREIGA N AO0 R EY1 G AH0 -NORELCO N ER0 EH1 L K OW0 -NORELL N AO1 R AH0 L -NOREM N AO1 R IH0 M -NOREN N AO1 R AH0 N -NORENBERG N AO1 R AH0 N B ER0 G -NORENCO N AO1 R AH0 N K OW0 -NORENKO N AH0 R EH1 N K OW0 -NOREX N AO1 R AH0 K S -NORFLEET N AO1 R F L IY2 T -NORFOLK N AO1 R F AH0 K -NORFOLK'S N AO1 R F AH0 K S -NORGAARD N AO1 R G AA0 R D -NORGARD N AO1 R G ER0 D -NORGE N AO1 R JH -NORGREN N AO1 R G R AH0 N -NORICK N AO1 R IH0 K -NORIEGA N AO2 R IY0 EY1 G AH0 -NORIEGA'S N AO2 R IY0 EY1 G AH0 Z -NORIEGAS N AO2 R IY0 EY1 G AH0 S -NORIKO N AO2 R IY1 K OW2 -NORILSK N AO2 R IH1 L S K -NORIMATSU N AO2 R IY0 M AA1 T S UW0 -NORINCHUKIN N AO2 R IH0 N CH UW1 K IH0 N -NORINKO N AO2 R IH1 NG K OW0 -NORITA N AH0 R IY1 T AH0 -NORK N AO1 R K -NORKUS N AO1 R K IH0 S -NORLAND N AO1 R L AH0 N D -NORLANDER N AO1 R L AH0 N D ER0 -NORLIN N AO1 R L IH0 N -NORLING N AO1 R L IH0 NG -NORM N AO1 R M -NORMA N AO1 R M AH0 -NORMA-JEAN N AO2 R M AH0 JH IY1 N -NORMAL N AO1 R M AH0 L -NORMALCY N AO1 R M AH0 L S IY0 -NORMALITY N AO2 R M AE1 L AH0 T IY0 -NORMALIZATION N AO2 R M AH0 L IH0 Z EY1 SH AH0 N -NORMALIZE N AO1 R M AH0 L AY2 Z -NORMALIZED N AO1 R M AH0 L AY2 Z D -NORMALIZES N AO1 R M AH0 L AY2 Z IH0 Z -NORMALIZING N AO1 R M AH0 L AY2 Z IH0 NG -NORMALLY N AO1 R M AH0 L IY0 -NORMALLY(2) N AO1 R M L IY0 -NORMAN N AO1 R M AH0 N -NORMAN'S N AO1 R M AH0 N Z -NORMAND N AO1 R M AH0 N D -NORMANDIE N AO1 R M AH0 N D IY0 -NORMANDIN N AO1 R M AH0 N D IH0 N -NORMANDY N AO1 R M AH0 N D IY0 -NORMATIVE N AO1 R M AH0 T IH0 V -NORMCORE N AO1 R M K AO2 R -NORMENT N AO1 R M AH0 N T -NORMICK N AO1 R M IH0 K -NORMIE N AO1 R M IY0 -NORMILE N AO1 R M AY0 L -NORMING N AO1 R M IH0 NG -NORMINGTON N AO1 R M IH0 NG T AH0 N -NORMOYLE N AO1 R M OY0 L -NORMS N AO1 R M Z -NORMY N AO1 R M IY0 -NORNA N AO1 R N AH0 -NORODOM N AO1 R OW0 D AH2 M -NORODOM(2) N AO1 R AH0 D AH2 M -NORPLANT N AO1 R P L AE2 N T -NORPLANT'S N AO1 R P L AE2 N T S -NORQUIST N AO1 R K W IH0 S T -NORRED N AO1 R D -NORRELL N AO1 R AH0 L -NORRIE N AO1 R IY0 -NORRINGTON N AO1 R IH0 NG T AH0 N -NORRIS N AO1 R IH0 S -NORRIS'S N AO1 R AH0 S 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K AH0 L -QUIZZING K W IH1 Z IH0 NG -QUO K W OW1 -QUOD K W AO1 D -QUOIN K W OY1 N -QUON K W AA1 N -QUORA K W AO1 R AH0 -QUORUM K W AO1 R AH0 M -QUORUMS K W AO1 R AH0 M Z -QUOTA K W OW1 T AH0 -QUOTABLE K W OW1 T AH0 B AH0 L -QUOTAS K W OW1 T AH0 Z -QUOTATION K W OW0 T EY1 SH AH0 N -QUOTATIONS K W OW0 T EY1 SH AH0 N Z -QUOTE K W OW1 T -QUOTED K W OW1 T IH0 D -QUOTES K W OW1 T S -QUOTH K W OW1 TH -QUOTIENT K W OW1 SH AH0 N T -QUOTING K W OW1 T IH0 NG -QUOTRON K W OW1 T R AA0 N -QUOTRON'S K W OW1 T R AA0 N Z -QURESHEY K UH0 R EY1 SH EY0 -QURESHI K UH0 R EY1 SH IY0 -R AA1 R -R'S AA1 R Z -R. 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M OW1 N AH0 -RAMONDA R AH0 M AA1 N D AH0 -RAMONE R AH0 M OW1 N -RAMOS R AA1 M OW0 S -RAMOS(2) R EY1 M OW0 S -RAMP R AE1 M P -RAMPAGE R AE1 M P EY2 JH -RAMPAGED R AE0 M P EY1 JH D -RAMPAGED(2) R AE1 M P EY2 JH D -RAMPAGER R AE1 M P EY2 JH ER0 -RAMPAGES R AE1 M P EY2 JH IH0 Z -RAMPAGING R AE1 M P EY2 JH IH0 NG -RAMPAGING(2) R AE1 M P AH0 JH IH0 NG -RAMPANT R AE1 M P AH0 N T -RAMPART R AE1 M P AA2 R T -RAMPARTS R AE1 M P AA2 R T S -RAMPELL R AE0 M P EH1 L -RAMPEY R AE1 M P IY0 -RAMPING R AE1 M P IH0 NG -RAMPLEY R AE1 M P L IY0 -RAMPS R AE1 M P S -RAMPY R AE1 M P IY0 -RAMQVIST R AE1 M K V IH2 S T -RAMQVIST(2) R AE1 M K W IH2 S T -RAMROD R AE1 M R AA2 D -RAMS R AE1 M Z -RAMSAY R AE1 M Z IY0 -RAMSAY(2) R AE1 M S EY2 -RAMSBURG R AE1 M S B ER0 G -RAMSDELL R AE1 M S D AH0 L -RAMSDEN R AE1 M S D AH0 N -RAMSER R AE1 M Z ER0 -RAMSES R AE1 M S IY2 Z -RAMSEUR R AH0 M S ER1 -RAMSEY R AE1 M Z IY0 -RAMSEY'S R AE1 M Z IY0 Z -RAMSEYER R AE1 M Z IY0 ER0 -RAMSEYS R AE1 M Z IY0 Z -RAMSEYS(2) R AE1 M S IY0 Z 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R AE1 N S AH0 M -RANSOM'S R AE1 N S AH0 M Z -RANSOME R AE1 N S AH0 M -RANSOMS R AE1 N S AH0 M Z -RANSON R AE1 N S AH0 N -RANSONE R AE1 N S AH0 N -RANT R AE1 N T -RANTA R AE1 N T AH0 -RANTALA R AA0 N T AA1 L AH0 -RANTED R AE1 N T IH0 D -RANTING R AE1 N T IH0 NG -RANTOUL R AE2 N T UW1 L -RANTZ R AE1 N T S -RANUM R AE1 N AH0 M -RANZ R AE1 N Z -RAO R AW1 -RAO'S R AW1 Z -RAOUL R AA0 UW1 L -RAOUX R AE0 UW1 -RAP R AE1 P -RAPACIOUS R AH0 P AE1 SH IH0 S -RAPACIOUS(2) R AH0 P EY1 SH IH0 S -RAPACZ R AA1 P AH0 CH -RAPANELLI R AE2 P AH0 N EH1 L IY0 -RAPAPORT R AE1 P AH0 P AO2 R T -RAPE R EY1 P -RAPED R EY1 P T -RAPER R EY1 P ER0 -RAPERS R EY1 P ER0 Z -RAPES R EY1 P S -RAPESEED R EY1 P S IY2 D -RAPHAEL R AA2 F AY0 EH1 L -RAPHAEL(2) R AA2 F Y EH1 L -RAPHAEL(3) R EY1 F IY0 AH0 L -RAPHAELA R AE1 F AY0 EH1 L AH0 -RAPHAELITE R AE0 F Y EH1 L AY0 T -RAPHALIAN R AH0 F EY1 L IY0 AH0 N -RAPHEL R AE1 F AH0 L -RAPID R AE1 P AH0 D -RAPID(2) R AE1 P IH0 D -RAPIDITY R AH0 P IH1 D AH0 T IY0 -RAPIDLY R AE1 P AH0 D L 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D IH0 V IH2 Z AH0 M -RECIDIVIST R AH0 S IH1 D IH0 V IH2 S T -RECIDIVISTS R AH0 S IH1 D IH0 V IH2 S T S -RECINE R EH0 CH IY1 N IY0 -RECIO R EH1 CH IY0 OW0 -RECIPE R EH1 S AH0 P IY0 -RECIPE'S R EH1 S AH0 P IY0 Z -RECIPES R EH1 S AH0 P IY0 Z -RECIPIENT R AH0 S IH1 P IY0 AH0 N T -RECIPIENT(2) R IH0 S IH1 P IY0 AH0 N T -RECIPIENT'S R IH0 S IH1 P IY0 AH0 N T S -RECIPIENTS R IH0 S IH1 P IY0 AH0 N T S -RECIPIENTS' R IH0 S IH1 P IY0 AH0 N T S -RECIPROCAL R IH0 S IH1 P R AH0 K AH0 L -RECIPROCANT R IY0 S IH1 P R AH0 K AH0 N T -RECIPROCANTS R IY0 S IH1 P R AH0 K AH0 N T S -RECIPROCATE R IH0 S IH1 P R AH0 K EY2 T -RECIPROCATED R IH0 S IH1 P R AH0 K EY2 T IH0 D -RECIPROCATES R IH0 S IH1 P R AH0 K EY2 T S -RECIPROCATING R IH0 S IH1 P R AH0 K EY2 T IH0 NG -RECIPROCITY R EH2 S IH0 P R AA1 S IH0 T IY0 -RECISION R IH0 S IH1 ZH AH0 N -RECISIONS R IH0 S IH1 ZH AH0 N Z -RECISSION R AH0 S IH1 SH AH0 N -RECITAL R AH0 S AY1 T AH0 L -RECITALS R IH0 S AY1 T AH0 L Z -RECITATION R EH2 S AH0 T EY1 SH AH0 N 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D -RECLINER R IH0 K L AY1 N ER0 -RECLINES R IH0 K L AY1 N Z -RECLINING R IH0 K L AY1 N IH0 NG -RECLINING(2) R IY0 K L AY1 N IH0 NG -RECLUSE R IH0 K L UW1 S -RECLUSIVE R IH0 K L UW1 S IH0 V -RECLUSIVE(2) R IY0 K L UW1 S IH0 V -RECO R EH1 K OW0 -RECOGNISE R EH1 K AH0 G N AY2 Z -RECOGNISED R EH1 K AH0 G N AY2 Z D -RECOGNISES R EH1 K AH0 G N AY2 Z IH0 Z -RECOGNISING R EH1 K AH0 G N AY2 Z IH0 NG -RECOGNITION R EH2 K AH0 G N IH1 SH AH0 N -RECOGNITION(2) R EH2 K IH0 G N IH1 SH AH0 N -RECOGNITION'S R EH2 K IH0 G N IH1 SH AH0 N Z -RECOGNIZABLE R EH2 K AH0 G N AY1 Z AH0 B AH0 L -RECOGNIZABLY R EH1 K AH0 G N AY2 Z AH0 B L IY0 -RECOGNIZANCE R IH0 K AA1 N AH0 Z AH0 N S -RECOGNIZE R EH1 K AH0 G N AY2 Z -RECOGNIZED R EH1 K AH0 G N AY2 Z D -RECOGNIZES R EH1 K AH0 G N AY2 Z AH0 Z -RECOGNIZES(2) R EH1 K AH0 G N AY2 Z IH0 Z -RECOGNIZING R EH1 K AH0 G N AY2 Z IH0 NG -RECOIL R IY0 K OY1 L -RECOILED R IY0 K OY1 L D -RECOILS R IY0 K OY1 L Z -RECOLLECT R EH2 K AH0 L EH1 K T -RECOLLECT(2) R IY2 K AH0 L EH1 K T 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K AH0 N OY1 T ER0 -RECONQUER R IY0 K AO1 NG K ER0 -RECONQUERED R IY0 K AO1 NG K ER0 D -RECONSIDER R IY2 K AH0 N S IH1 D ER0 -RECONSIDERATION R IY0 K AH0 N S IH2 D ER0 EY1 SH AH0 N -RECONSIDERED R IY2 K AH0 N S IH1 D ER0 D -RECONSIDERING R IY2 K AH0 N S IH1 D ER0 IH0 NG -RECONSTITUTE R IY0 K AA1 N S T AH0 T UW2 T -RECONSTITUTED R IY0 K AA1 N S T AH0 T UW2 T IH0 D -RECONSTITUTING R IY0 K AA1 N S T IH0 T UW2 T IH0 NG -RECONSTRUCT R IY2 K AH0 N S T R AH1 K T -RECONSTRUCTED R IY2 K AH0 N S T R AH1 K T IH0 D -RECONSTRUCTING R IY2 K AH0 N S T R AH1 K T IH0 NG -RECONSTRUCTION R IY2 K AH0 N S T R AH1 K SH AH0 N -RECONSTRUCTIONS R IY2 K AH0 N S T R AH1 K SH AH0 N Z -RECONSTRUCTIVE R IY2 K AH0 N S T R AH1 K T IH0 V -RECONVENE R IY0 K AH0 N V IY1 N -RECONVENED R IY0 K AH0 N V IY1 N D -RECONVENES R IY0 K AH0 N V IY1 N Z -RECORD R AH0 K AO1 R D -RECORD(2) R EH1 K ER0 D -RECORD(3) R IH0 K AO1 R D -RECORD'S R EH1 K ER0 D Z -RECORDABLE R IH0 K AO1 R D AH0 B AH0 L -RECORDED R AH0 K AO1 R D IH0 D 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ER0 D -RECOVERIES R IH0 K AH1 V ER0 IY0 Z -RECOVERING R AH0 K AH1 V ER0 IH0 NG -RECOVERING(2) R IH0 K AH1 V ER0 IH0 NG -RECOVERS R IH0 K AH1 V ER0 Z -RECOVERY R IH0 K AH1 V R IY0 -RECOVERY(2) R IH0 K AH1 V ER0 IY0 -RECOVERY'S R IH0 K AH1 V ER0 IY0 Z -RECOVERY'S(2) R IH0 K AH1 V R IY0 Z -RECREATE R EH1 K R IY0 EY2 T -RECREATE(2) R IY0 K R IY0 EY1 T -RECREATED R EH1 K R IY0 EY2 T IH0 D -RECREATED(2) R IY0 K R IY0 EY1 T IH0 D -RECREATES R EH1 K R IY0 EY2 T S -RECREATES(2) R IY0 K R IY0 EY1 T S -RECREATING R EH1 K R IY0 EY2 T IH0 NG -RECREATING(2) R IY0 K R IY0 EY1 T IH0 NG -RECREATION R EH2 K R IY0 EY1 SH AH0 N -RECREATIONAL R EH2 K R IY0 EY1 SH AH0 N AH0 L -RECRIMINATE R IH0 K R IH2 M IH0 N EY1 T -RECRIMINATION R IH0 K R IH2 M IH0 N EY1 SH AH0 N -RECRIMINATIONS R IH0 K R IH2 M IH0 N EY1 SH AH0 N Z -RECROSS R IY0 K R AO1 S -RECRUIT R AH0 K R UW1 T -RECRUIT(2) R IH0 K R UW1 T -RECRUIT(3) R IY0 K R UW1 T -RECRUIT'S R IY0 K R UW1 T S -RECRUITED R IH0 K R UW1 T IH0 D -RECRUITED(2) R IY0 K R 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SH AH0 N -REFLECTIONS R IH0 F L EH1 K SH AH0 N Z -REFLECTIVE R IH0 F L EH1 K T IH0 V -REFLECTONE R IY0 F L EH1 K T OW2 N -REFLECTONE'S R IY0 F L EH1 K T OW2 N Z -REFLECTOR R IH0 F L EH1 K T ER0 -REFLECTORS R IH0 F L EH1 K T ER0 Z -REFLECTS R IH0 F L EH1 K T S -REFLEX R IY1 F L EH0 K S -REFLEXES R IY1 F L EH0 K S AH0 Z -REFLEXIVE R AH0 F L EH1 K S IH0 V -REFLEXIVELY R IY0 F L EH1 K S IH0 V L IY0 -REFLEXIVITY R IY2 F L EH2 K S IH1 V IH0 T IY0 -REFLUX R IY1 F L AH2 K S -REFOCUS R IY0 F OW1 K AH0 S -REFOCUSED R IY0 F OW1 K AH0 S T -REFOCUSES R IY0 F OW1 K AH0 S IH0 Z -REFOCUSING R IY0 F OW1 K AH0 S IH0 NG -REFOREST R IY0 F AO1 R AH0 S T -REFORESTATION R IY2 F AO0 R AH0 S T EY1 SH AH0 N -REFORM R AH0 F AO1 R M -REFORM(2) R IH0 F AO1 R M -REFORMA R IH0 F AO1 R M AH0 -REFORMATION R EH2 F ER0 M EY1 SH AH0 N -REFORMATORIES R IH0 F AO1 R M AH0 T AO2 R IY0 Z -REFORMATORY R IH0 F AO1 R M AH0 T AO2 R IY0 -REFORMED R IH0 F AO1 R M D -REFORMER R IH0 F AO1 R M ER0 -REFORMERS R IH0 F AO1 R M ER0 Z 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UW1 L AH0 -REGULAR R EH1 G Y AH0 L ER0 -REGULAR(2) R EY1 G Y AH0 L ER0 -REGULARITY R EH2 G Y AH0 L EH1 R AH0 T IY0 -REGULARLY R EH1 G Y AH0 L ER0 L IY0 -REGULARS R EH1 G Y AH0 L ER0 Z -REGULATE R EH1 G Y AH0 L EY2 T -REGULATED R EH1 G Y AH0 L EY2 T IH0 D -REGULATES R EH1 G Y AH0 L EY2 T S -REGULATING R EH1 G Y AH0 L EY2 T IH0 NG -REGULATINGLY R EH1 G Y AH0 L EY2 T IH0 NG L IY0 -REGULATION R EH2 G Y AH0 L EY1 SH AH0 N -REGULATIONS R EH2 G Y AH0 L EY1 SH AH0 N Z -REGULATIVE R EH1 G Y AH0 L EY2 T IH0 V -REGULATOR R EH1 G Y AH0 L EY2 T ER0 -REGULATOR'S R EH1 G Y AH0 L EY2 T ER0 Z -REGULATORS R EH1 G Y AH0 L EY2 T ER0 Z -REGULATORS' R EH1 G Y AH0 L EY2 T ER0 Z -REGULATORY R EH1 G Y AH0 L AH0 T AO2 R IY0 -REGURGITATE R IH2 G ER1 JH AH0 T EY2 T -REGWAN R EH1 G W AA0 N -REH R EH1 -REHA R IY1 HH AH0 -REHAB R IY0 HH AE0 B -REHABILITATE R IY2 HH AH0 B IH1 L AH0 T EY2 T -REHABILITATE(2) R IY2 AH0 B IH1 L AH0 T EY2 T -REHABILITATED R IY2 HH AH0 B IH1 L AH0 T EY2 T IH0 D -REHABILITATED(2) R IY2 AH0 B IH1 L AH0 T EY2 T IH0 D -REHABILITATING R IY2 HH AH0 B IH1 L AH0 T EY2 T IH0 NG -REHABILITATING(2) R IY2 AH0 B IH1 L AH0 T EY2 T IH0 NG -REHABILITATION R IY2 HH AH0 B IH2 L AH0 T EY1 SH AH0 N -REHABILITATION(2) R IY2 AH0 B IH2 L AH0 T EY1 SH AH0 N -REHABILITATIONS R IY2 HH AH0 B IH2 L AH0 T EY1 SH AH0 N Z -REHABILITATIONS(2) R IY2 AH0 B IH2 L AH0 T EY1 SH AH0 N Z -REHABILITATIVE R IY2 HH AH0 B IH1 L AH0 T EY2 T IH0 V -REHABILITATIVE(2) R IY2 AH0 B IH1 L AH0 T EY2 T IH0 V -REHABILITATOR R IY2 HH AH0 B IH1 L AH0 T EY0 T ER0 -REHABILITATORS R IY2 HH AH0 B IH1 L AH0 T EY0 T ER0 Z -REHABILITY R IY2 HH AH0 B IH1 L AH0 T IY0 -REHABILITY(2) R IY2 AH0 B IH1 L AH0 T IY0 -REHAK R EH1 HH AH0 K -REHASH R IY0 HH AE1 SH -REHASHING R IY0 HH AE1 SH IH0 NG -REHBEIN R EH1 B AY0 N -REHBEIN(2) R EH1 B IY0 N -REHBERG R EH1 B ER0 G -REHBERGER R EH1 B ER0 G ER0 -REHDER R EH1 D ER0 -REHEAR R IY0 HH IY1 R -REHEARING R IY0 HH IY1 R IH0 NG -REHEARINGS R IY0 HH IY1 R IH0 NG Z -REHEARSAL R IH0 HH ER1 S AH0 L 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OW1 Z IH0 NG -REIMPOSITION R IY2 IH0 M P AH0 Z IH1 SH AH0 N -REIMS R IY1 M Z -REIN R EY1 N -REINA R EY0 IY1 N AH0 -REINALDO R EY2 N AA1 L D OW0 -REINARD R AY1 N ER0 D -REINBOLD R AY1 N B OW2 L D -REINBOLT R AY1 N B OW2 L T -REINCARNATE R IY2 IH0 N K AA1 R N EY2 T -REINCARNATED R IY2 IH0 N K AA1 R N EY2 T IH0 D -REINCARNATION R IY2 IH0 N K AA0 R N EY1 SH AH0 N -REINCKE R AY1 NG K IY0 -REINCORPORATE R IY2 IH0 N K AO1 R P ER0 EY2 T -REINCORPORATING R IY2 IH0 N K AO1 R P ER0 EY2 T IH0 NG -REINCORPORATION R IY2 IH0 N K AO2 R P ER0 EY1 SH AH0 N -REINDEER R EY1 N D IH2 R -REINDEL R AY1 N D AH0 L -REINDERS R AY1 N D ER0 Z -REINDL R AY1 N D AH0 L -REINDUSTRIALIZE R IY0 IH0 N D AH1 S T R IY0 AH0 L AY0 Z -REINE R EY1 N -REINECK R AY1 N EH0 K -REINECKE R AY1 N IH0 K IY0 -REINED R EY1 N D -REINEKE R AY1 N IH0 K -REINER R AY1 N ER0 -REINER'S R AY1 N ER0 Z -REINERS R AY1 N ER0 Z -REINERT R AY1 N ER0 T -REINERTSEN R AY1 N ER0 T S AH0 N -REINERTSON R AY1 N ER0 T S AH0 N -REINES R EY1 N Z -REINEY R EY1 N IY0 -REINFORCE R IY2 IH0 N F AO1 R S -REINFORCED R IY2 IH0 N F AO1 R S T -REINFORCEMENT R IY2 IH0 N F AO1 R S M AH0 N T -REINFORCEMENTS R IY2 IH0 N F AO1 R S M AH0 N T S -REINFORCES R IY2 IH0 N F AO1 R S IH0 Z -REINFORCING R IY2 IH0 N F AO1 R S IH0 NG -REINGOLD R AY1 NG G OW0 L D -REINHARD R AY1 N HH AA2 R D -REINHARDT R AY1 N HH AA2 R T -REINHART R AY1 N HH AA2 R T -REINHART'S R AY1 N HH AA0 R T S -REINHEIMER R AY1 N HH AY2 M ER0 -REINHOLD R AY1 N HH OW0 L T -REINHOLZ R AY1 N HH OW0 L Z -REINIG R AY1 N IH0 G -REINING R EY1 N IH0 NG -REININGER R AY1 N IH0 NG ER0 -REINITZ R AY1 N IH0 T S -REINKE R EY1 NG K -REINKING R AY1 NG K IH0 NG -REINOEHL R AY1 N OW0 L -REINS R EY1 N Z -REINSCH R AY1 N SH -REINSDORF R AY1 N Z D AO2 R F -REINSEL R AY1 N S AH0 L -REINSPECT R IY2 IH0 N S P EH1 K T -REINSPECTION R IY2 IH0 N S P EH1 K SH AH0 N -REINSPECTIONS R IY2 IH0 N S P EH1 K SH AH0 N S -REINSTALL R IY2 IH0 N S T AA1 L -REINSTALLED R IY2 IH0 N S T AA1 L D -REINSTALLS R IY2 IH0 N S T AA1 L Z 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IY2 IH0 N T R AH0 D UW1 S IH0 NG -REINTRODUCTION R IY2 IH0 N T R AH0 D AH1 K SH AH0 N -REINTS R AY1 N T S -REINVENT R IY2 IH0 N V EH1 N T -REINVENTED R IY2 IH0 N V EH1 N T IH0 D -REINVENTING R IY2 IH0 N V EH1 N T IH0 NG -REINVENTION R IY2 IH0 N V EH1 N SH AH0 N -REINVEST R IY2 IH0 N V EH1 S T -REINVESTED R IY2 IH0 N V EH1 S T IH0 D -REINVESTING R IY2 IH0 N V EH1 S T IH0 NG -REINVESTMENT R IY2 IH0 N V EH1 S T M AH0 N T -REINVESTMENT(2) R IY2 IH0 N V EH1 S M AH0 N T -REINVESTS R IY2 IH0 N V EH1 S T S -REINVESTS(2) R IY2 IH0 N V EH1 S S -REINVESTS(3) R IY2 IH0 N V EH1 S -REINVIGORATE R IY2 IH0 N V IH1 G ER0 EY2 T -REINVIGORATED R IY2 IH0 N V IH1 G ER0 EY2 T IH0 D -REINVIGORATING R IY2 IH0 N V IH1 G ER0 EY2 T IH0 NG -REINVIGORATION R IY2 IH0 N V IH2 G ER0 EY1 SH AH0 N -REINVITE R IY2 IH0 N V AY1 T -REINVITED R IY2 IH0 N V AY1 T IH0 D -REINWALD R AY1 N W AO2 L D -REIS R IY1 Z -REISCH R AY1 SH -REISCHAUER R AY1 SH AW0 R -REISCHMAN R AY1 SH M AH0 N -REISDORF R AY1 S D AO0 R F -REISE R IY1 S 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-RELUCTANTLY R IH0 L AH1 K T AH0 N T L IY0 -RELY R IH0 L AY1 -RELY(2) R IY0 L AY1 -RELYEA R EH1 L Y EY2 -RELYING R IY0 L AY1 IH0 NG -REM R EH1 M -REMADE R IY0 M EY1 D -REMAIN R IH0 M EY1 N -REMAIN(2) R IY0 M EY1 N -REMAINDER R IH0 M EY1 N D ER0 -REMAINDER(2) R IY0 M EY1 N D ER0 -REMAINED R IH0 M EY1 N D -REMAINED(2) R IY0 M EY1 N D -REMAINING R IH0 M EY1 N IH0 NG -REMAINING(2) R IY0 M EY1 N IH0 NG -REMAINS R IH0 M EY1 N Z -REMAINS(2) R IY0 M EY1 N Z -REMAKE R IY1 M EY1 K -REMAKES R IY1 M EY1 K S -REMAKING R IY1 M EY1 K IH0 NG -REMALEY R EH1 M AH0 L IY0 -REMALY R IY1 M AH0 L IY0 -REMAND R IH0 M AE1 N D -REMANDED R IH0 M AE1 N D IH0 D -REMANUFACTURE R IY2 M AE2 N Y UW0 F AE1 K CH ER0 -REMANUFACTURE(2) R IY2 M AE2 N Y AH0 F AE1 K CH ER0 -REMANUFACTURED R IY2 M AE2 N Y UW0 F AE1 K CH ER0 D -REMANUFACTURED(2) R IY2 M AE2 N Y AH0 F AE1 K CH ER0 D -REMARK R IH0 M AA1 R K -REMARK(2) R IY0 M AA1 R K -REMARKABLE R IH0 M AA1 R K AH0 B AH0 L -REMARKABLE(2) R IY0 M AA1 R K AH0 B AH0 L -REMARKABLY R IH0 M AA1 R K AH0 B L IY0 -REMARKABLY(2) R IY0 M AA1 R K AH0 B L IY0 -REMARKED R IH0 M AA1 R K T -REMARKED(2) R IY0 M AA1 R K T -REMARKET R IY0 M AA1 R K AH0 T -REMARKETED R IY0 M AA1 R K AH0 T IH0 D -REMARKETING R IY0 M AA1 R K AH0 T IH0 NG -REMARKING R IH0 M AA1 R K IH0 NG -REMARKS R IH0 M AA1 R K S -REMARKS(2) R IY0 M AA1 R K S -REMARRIAGE R IY0 M EH1 R IH0 JH -REMARRIED R IY0 M EH1 R IY0 D -REMARRY R IY0 M EH1 R IY0 -REMARRYING R IY0 M EH1 R IY0 IH0 NG -REMATCH R IY1 M AE1 CH -REMBERT R EH1 M B ER0 T -REMBOLD R EH1 M B OW2 L D -REMBRANDT R EH1 M B R AE2 N T -REMBRANDT'S R EH1 M B R AE2 N T S -REMBRANDTS R EH1 M B R AE2 N T S -REMCO R EH1 M K OW0 -REMEDIAL R IH0 M IY1 D IY0 AH0 L -REMEDIATE R IY0 M IY1 D IY0 AH0 T -REMEDIATE(2) R IY0 M IY1 D IY0 EY2 T -REMEDIATION R IH0 M IY2 D IY0 EY1 SH AH0 N -REMEDIED R EH1 M AH0 D IY0 D -REMEDIES R EH1 M AH0 D IY0 Z -REMEDY R EH1 M AH0 D IY0 -REMEDYING R EH1 M AH0 D IY0 IH0 NG -REMEMBER R IH0 M EH1 M B ER0 -REMEMBER(2) R IY0 M EH1 M B ER0 -REMEMBERED R IH0 M EH1 M B ER0 D -REMEMBERED(2) R IY0 M EH1 M B ER0 D -REMEMBERING R IH0 M EH1 M B ER0 IH0 NG -REMEMBERING(2) R IY0 M EH1 M B ER0 IH0 NG -REMEMBERING(3) R IH0 M EH1 M B R IH0 NG -REMEMBERING(4) R IY0 M EH1 M B R IH0 NG -REMEMBERS R IH0 M EH1 M B ER0 Z -REMEMBERS(2) R IY0 M EH1 M B ER0 Z -REMEMBRANCE R IY0 M EH1 M B R AH0 N S -REMEMBRANCES R IH0 M EH1 M B R AH0 N S IH0 Z -REMER R IY1 M ER0 -REMERCHANDISE R IY0 M ER1 CH AH0 N D AY2 Z -REMERCHANDISED R IY0 M ER1 CH AH0 N D AY2 Z D -REMERCI R IH0 M ER1 S IY0 -REMI R EH1 M IY0 -REMIC R EH1 M IH0 K -REMICK R EH1 M IH0 K -REMICS R EH1 M IH0 K S -REMIGIO R IH0 M IH1 JH IY0 OW0 -REMILLARD R EH1 M IH0 L ER0 D -REMIND R IY0 M AY1 N D -REMINDED R IY0 M AY1 N D IH0 D -REMINDER R IY0 M AY1 N D ER0 -REMINDERS R IY0 M AY1 N D ER0 Z -REMINDING R IY0 M AY1 N D IH0 NG -REMINDS R IY0 M AY1 N D Z -REMINGTON R EH1 M IH0 NG T AH0 N -REMINGTONS R EH1 M IH0 NG T AH0 N Z -REMINISCE R EH2 M AH0 N IH1 S -REMINISCED R EH2 M AH0 N IH1 S T -REMINISCENCE R EH2 M AH0 N IH1 S AH0 N S -REMINISCENCES R EH2 M AH0 N IH1 S AH0 N S IH0 Z -REMINISCENT R EH2 M AH0 N IH1 S AH0 N T -REMINISCES R EH2 M AH0 N IH1 S IH0 Z -REMINISCING R EH2 M AH0 N IH1 S IH0 NG -REMIS R EY1 M IY0 Z -REMISS R IY0 M IH1 S -REMISSES R IY0 M IH1 S IH0 Z -REMISSION R IY0 M IH1 SH AH0 N -REMISSIONS R IY0 M IH1 SH AH0 N Z -REMIT R IY0 M IH1 T -REMITTANCE R IY0 M IH1 T AH0 N S -REMITTANCE(2) R AH0 M IH1 T AH0 N S -REMITTANCES R IY0 M IH1 T AH0 N S IH0 Z -REMITTANCES(2) R AH0 M IH1 T AH0 N S IH0 Z -REMITTED R IY0 M IH1 T IH0 D -REMLEY R EH1 M L IY0 -REMLINGER R EH1 M AH0 L IH0 NG ER0 -REMLINGER(2) R EH1 M L IH0 NG ER0 -REMME R EH1 M -REMMEL R EH1 M AH0 L -REMMERS R EH1 M ER0 Z -REMMERT R EH1 M ER0 T -REMMINGTON R EH1 M IH0 NG T IH0 N -REMMY R EH1 M IY0 -REMNANT R EH1 M N AH0 N T -REMNANTS R EH1 M N AH0 N T S -REMNICK R EH1 M N IH0 K -REMO R EH1 M OW0 -REMODEL R IY0 M AA1 D AH0 L -REMODELED R IY0 M AA1 D AH0 L D -REMODELING R IH0 M AA1 D AH0 L IH0 NG 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EH1 N F R AW0 -RENGEL R EH1 NG G AH0 L -RENGO R EH1 NG G OW0 -RENGO'S R EH1 NG G OW0 Z -RENICK R EH1 N IH0 K -RENIER R IY1 N IY0 ER0 -RENIN R IY1 N AH0 N -RENISON R EH1 N IH0 S AH0 N -RENITA R EH0 N IY1 T AH0 -RENK R EH1 NG K -RENKEN R EH1 NG K AH0 N -RENKO R EH1 NG K OW0 -RENMINBI R EH0 N M IH1 N B IY0 -RENN R EH1 N -RENNA R EH1 N AH0 -RENNARD R IH0 N AA1 R D -RENNE R EH1 N -RENNELS R EH1 N AH0 L Z -RENNER R EH1 N ER0 -RENNERT R EH1 N ER0 T -RENNET R EH1 N AH0 T -RENNICK R EH1 N IH0 K -RENNIE R EH1 N IY0 -RENNINGER R EH1 N IH0 NG ER0 -RENNO R EH1 N OW0 -RENNY R EH1 N IY0 -RENO R IY1 N OW0 -RENO'S R IY1 N OW0 Z -RENOIR R EH0 N W AA1 R -RENOIRS R EH0 N W AA1 R Z -RENOMINATE R IY0 N AO1 M IH0 N EY2 T -RENOMINATED R IY0 N AA1 M AH0 N EY2 T IH0 D -RENOMINATION R IY1 N AA2 M AH0 N EY1 SH AH0 N -RENOUF R AH0 N UW1 F -RENOUNCE R IH0 N AW1 N S -RENOUNCED R IH0 N AW1 N S T -RENOUNCES R IH0 N AW1 N S IH0 Z -RENOUNCING R IH0 N AW1 N S IH0 NG -RENOVATABLE R EH1 N AH0 V EY2 T AH0 B AH0 L -RENOVATE R EH1 N AH0 V EY2 T -RENOVATED R EH1 N AH0 V EY2 T IH0 D -RENOVATING R EH1 N AH0 V EY2 T IH0 NG -RENOVATION R EH2 N AH0 V EY1 SH AH0 N -RENOVATIONS R EH1 N AH0 V EY2 SH AH0 N Z -RENOVATOR R EH1 N AH0 V EY2 T ER0 -RENOVATORS R EH1 N AH0 V EY2 T ER0 Z -RENOWN R IH0 N AW1 N -RENOWNED R IH0 N AW1 N D -RENQUIST R EH1 N K W IH0 S T -RENS R EH1 N Z -RENSBERGER R EH1 N S B ER0 G ER0 -RENSCH R EH1 N SH -RENSCHLER R EH1 N SH AH0 L ER0 -RENSCHLER(2) R EH1 N SH L ER0 -RENSHAW R EH1 N SH AO2 -RENSING R EH1 N S IH0 NG -RENSSELAER R EH2 N S AH0 L IH1 R -RENSTROM R EH1 N S T R AH0 M -RENT R EH1 N T -RENTA R EH1 N T AH0 -RENTABLE R EH1 N T AH0 B AH0 L -RENTAL R EH1 N T AH0 L -RENTALS R EH1 N T AH0 L Z -RENTAS R EH1 N T AH0 Z -RENTED R EH1 N T IH0 D -RENTER R EH1 N T ER0 -RENTERIA R EH0 N T EH1 R IY0 AH0 -RENTERS R EH1 N T ER0 Z -RENTFRO R EH1 N T F R OW0 -RENTFROW R EH1 N T F R AW0 -RENTIER R EH1 T IY2 ER0 -RENTING R EH1 N T IH0 NG -RENTMEESTER R EH1 N T M IY2 S T ER0 -RENTON R EH1 N T AH0 N -RENTS R EH1 N T S -RENTSCH R EH1 N CH -RENTSCHLER R EH1 N CH L ER0 -RENTZ R EH1 N T S -RENUNCIATE R IH0 N AH1 N S IY0 EY2 T -RENUNCIATION R IH0 N AH2 N S IY0 EY1 SH AH0 N -RENUZIT R IY0 N UW1 Z IH0 T -RENWICK R EH1 N W IH0 K -RENY R IY1 N IY0 -RENZ R EH1 N Z -RENZI R EH1 N Z IY0 -RENZULLI R EH0 N Z UW1 L IY0 -REO R IY1 OW0 -REOCCUPY R IY0 AO1 K Y UW2 P AY0 -REOCCUR R IY0 AH0 K ER1 -REOFFEND R IY0 AO0 F EH1 N D -REOFFENDED R IY0 AO0 F EH1 N D IH0 D -REOFFER R IY0 AO1 F ER0 -REOFFERED R IY0 AO1 F ER0 D -REOFFERING R IY0 AO1 F ER0 IH0 NG -REOPEN R IY0 OW1 P AH0 N -REOPENED R IY0 OW1 P AH0 N D -REOPENING R IY0 OW1 P AH0 N IH0 NG -REOPENS R IY0 OW1 P AH0 N Z -REORDER R IY0 AO1 R D ER0 -REORDERING R IY0 AO1 R D ER0 IH0 NG -REORGANIZATION R IY2 AO0 R G AH0 N AH0 Z EY1 SH AH0 N -REORGANIZATIONS R IY0 AO2 R G AH0 N IH0 Z EY1 SH AH0 N Z -REORGANIZE R IY0 AO1 R G AH0 N AY2 Z -REORGANIZED R IY0 AO1 R G AH0 N AY2 Z D -REORGANIZES R IY0 AO1 R G AH0 N AY2 Z IH0 Z -REORGANIZING R IY0 AO1 R G AH0 N AY2 Z IH0 NG -REORIENT R IY0 AO1 R IY0 EH0 N T -REORIENTATE R IY0 AO1 R IY0 EH0 N T EY0 T -REP R EH1 P -REP(2) R EH0 P R IY0 Z EH1 T AH0 T IH0 V -REP'S R EH1 P S -REP. R EH0 P R IY0 Z EH1 T AH0 T IH0 V -REPACK R IY0 P AE1 K -REPACKAGE R IY2 P AE1 K IH0 JH -REPACKAGED R IY1 P AE0 K IH0 JH D -REPACKAGES R IY2 P AE1 K IH0 JH IH0 Z -REPACKAGING R IY1 P AE0 K IH0 JH IH0 NG -REPACKED R IY0 P AE1 K T -REPAID R IY0 P EY1 D -REPAINT R IY0 P EY1 N T -REPAINTED R IY0 P EY1 N T IH0 D -REPAINTING R IY0 P EY1 N T IH0 NG -REPAIR R IH0 P EH1 R -REPAIRABLE R IH0 P EH1 R AH0 B AH0 L -REPAIRED R IH0 P EH1 R D -REPAIRED(2) R IY0 P EH1 R D -REPAIRING R IH0 P EH1 R IH0 NG -REPAIRING(2) R IY0 P EH1 R IH0 NG -REPAIRMAN R IH0 P EH1 R M AE2 N -REPAIRMAN(2) R IY0 P EH1 R M AE2 N -REPAIRMEN R IH0 P EH1 R M EH2 N -REPAIRS R IH0 P EH1 R Z -REPAIRS(2) R IY0 P EH1 R Z -REPAP R IY1 P AE0 P -REPARATION R EH2 P ER0 EY1 SH AH0 N -REPARATIONS R EH2 P ER0 EY1 SH AH0 N Z -REPARTEE R EH2 P ER0 T IY1 -REPASKY R IH0 P AA1 S K IY0 -REPASS R IY0 P AE1 S -REPATRIATE R IY0 P EY1 T R IY0 EY2 T -REPATRIATED R IY0 P EY1 T R IY0 EY2 T IH0 D -REPATRIATING R IY0 P EY1 T R IY0 EY2 T IH0 NG -REPATRIATION R IY0 P EY2 T R IY0 EY1 SH AH0 N -REPAY R IY0 P EY1 -REPAYABLE R IY0 P EY1 AH0 B AH0 L -REPAYING R IY0 P EY1 IH0 NG -REPAYMENT R IY0 P EY1 M AH0 N T -REPAYMENTS R IY0 P EY1 M AH0 N T S -REPAYS R IY2 P EY1 Z -REPEAL R IH0 P IY1 L -REPEAL(2) R IY0 P IY1 L -REPEALED R IH0 P IY1 L D -REPEALED(2) R IY0 P IY1 L D -REPEALING R IY0 P IY1 L IH0 NG -REPEALS R IY0 P IY1 L Z -REPEAT R IH0 P IY1 T -REPEAT(2) R IY0 P IY1 T -REPEATABLE R IH0 P IY1 T AH0 B AH0 L -REPEATED R IH0 P IY1 T IH0 D -REPEATED(2) R IY0 P IY1 T IH0 D -REPEATEDLY R IH0 P IY1 T IH0 D L IY0 -REPEATER R IH0 P IY1 T ER0 -REPEATERS R IH0 P IY1 T ER0 Z -REPEATING R IH0 P IY1 T IH0 NG -REPEATING(2) R IY0 P IY1 T IH0 NG -REPEATS R IH0 P IY1 T S -REPEATS(2) R IY0 P IY1 T S -REPEL R IH0 P EH1 L -REPELLED R AH0 P EH1 L D -REPELLED(2) R IH0 P EH1 L D -REPELLENT R IH0 P EH1 L AH0 N T -REPELLENTS R IH0 P EH1 L AH0 N T S -REPELLING R AH0 P EH1 L IH0 NG -REPELS R IH0 P EH1 L Z -REPENT R IH0 P EH1 N T -REPENTANCE R IH0 P EH1 N T AH0 N S -REPENTANT R IH0 P EH1 N T AH0 N T -REPENTED R IH0 P EH1 N T IH0 D -REPENTING R IH0 P EH1 N T IH0 NG -REPENTS R IH0 P EH1 N T S -REPERCUSSION R IY2 P ER0 K AH1 SH AH0 N -REPERCUSSION(2) R IY2 P R AH0 K AH1 SH AH0 N -REPERCUSSIONS R IY2 P ER0 K AH1 SH AH0 N Z -REPERCUSSIONS(2) R IY2 P R AH0 K AH1 SH AH0 N Z -REPERFUSION R EH2 P ER0 F Y UW1 ZH AH0 N -REPERTOIRE R EH1 P ER0 T W AA2 R -REPERTORY R EH1 P ER0 T AO2 R IY0 -REPETITION R EH2 P AH0 T IH1 SH AH0 N -REPETITIONS R EH2 P AH0 T IH1 SH AH0 N Z -REPETITIOUS R EH2 P AH0 T IH1 SH AH0 S -REPETITIVE R IH0 P EH1 T IH0 T IH0 V -REPETTI R EH0 P EH1 T IY0 -REPETTO R EH0 P EH1 T OW0 -REPH R EH1 F -REPHRASE R IY0 F R EY1 Z -REPINSKI R IH0 P IH1 N S K IY0 -REPKA R EH1 P K AH0 -REPKO R EH1 P K OW0 -REPLACE R IY2 P L EY1 S -REPLACE(2) ER0 P L EY1 S -REPLACEABLE R IY2 P L EY1 S AH0 B AH0 L -REPLACED R IY2 P L EY1 S T -REPLACEMENT R IH0 P L EY1 S M AH0 N T -REPLACEMENTS R IY0 P L EY1 S M AH0 N T S -REPLACES R IH0 P L EY1 S IH0 Z -REPLACING R IH0 P L EY1 S IH0 NG -REPLANT R IY0 P L AE1 N T -REPLANTED R IY0 P L AE1 N T IH0 D -REPLANTED(2) R IY0 P L AE1 N IH0 D -REPLANTING R IY0 P L AE1 N T IH0 NG -REPLAY R IY0 P L EY1 -REPLAYED R IY0 P L EY1 D -REPLAYING R IY0 P L EY1 IH0 NG -REPLAYS R IY0 P L EY1 Z -REPLENISH R IY0 P L EH1 N IH0 SH -REPLENISHED R IY0 P L EH1 N IH0 SH T -REPLENISHING R IY0 P L EH1 N IH0 SH IH0 NG -REPLENISHMENT R IH0 P L EH1 N IH0 SH M AH0 N T -REPLETE R IY0 P L IY1 T -REPLICA R EH1 P L IH0 K AH0 -REPLICAS R EH1 P L AH0 K AH0 Z -REPLICASE R EH1 P L IH0 K EY2 Z -REPLICATE R EH1 P L AH0 K EY2 T -REPLICATED R EH1 P L IH0 K EY2 T IH0 D -REPLICATES R EH1 P L IH0 K EY2 T S -REPLICATING R EH1 P L IH0 K EY2 T IH0 NG -REPLICATION R EH2 P L AH0 K EY1 SH AH0 N -REPLICATOR R EH1 P L IH0 K EY2 T ER0 -REPLICATORS R EH1 P L IH0 K EY2 T ER0 S -REPLIED R IH0 P L AY1 D -REPLIED(2) R IY0 P L AY1 D -REPLIES R IH0 P L AY1 Z -REPLIES(2) R IY0 P L AY1 Z -REPLIGEN R IH2 P L AY1 JH IH0 N -REPLIGEN'S R IH2 P L AY1 JH IH0 N Z 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EH1 N T AH0 T IH0 V Z -REPRESENTATIVE'S(2) R EH2 P R IH0 Z EH1 N AH0 T IH0 V Z -REPRESENTATIVES R EH2 P R AH0 Z EH1 N T AH0 T IH0 V Z -REPRESENTATIVES(2) R EH2 P R IH0 Z EH1 N T AH0 T IH0 V Z -REPRESENTATIVES(3) R EH2 P R AH0 Z EH1 N AH0 T IH0 V Z -REPRESENTATIVES(4) R EH2 P R IH0 Z EH1 N AH0 T IH0 V Z -REPRESENTATIVES' R EH2 P R AH0 S EH1 N T AH0 T IH0 V Z -REPRESENTATIVES'(2) R EH2 P R AH0 S EH1 N AH0 T IH0 V Z -REPRESENTED R EH2 P R IH0 Z EH1 N T IH0 D -REPRESENTING R EH2 P R IH0 Z EH1 N T IH0 NG -REPRESENTS R EH2 P R IH0 Z EH1 N T S -REPRESS R IY0 P R EH1 S -REPRESSED R IY0 P R EH1 S T -REPRESSING R IY0 P R EH1 S IH0 NG -REPRESSION R IY0 P R EH1 SH AH0 N -REPRESSIONS R IY0 P R EH1 SH AH0 N Z -REPRESSIVE R IY0 P R EH1 S IH0 V -REPRICE R IY0 P R AY1 S -REPRICED R IY0 P R AY1 S T -REPRICING R IY0 P R AY1 S IH0 NG -REPRIEVE R IY0 P R IY1 V -REPRIMAND R EH1 P R AH0 M AE2 N D -REPRIMANDED R EH1 P R AH0 M AE2 N D IH0 D -REPRIMANDS R EH1 P R AH0 M AE2 N D Z -REPRINT R IY0 P R IH1 N T 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ER1 V Z -RESERVED R IH0 Z ER1 V D -RESERVED(2) R IY0 Z ER1 V D -RESERVEESE R EH2 Z ER0 V IY1 S -RESERVES R IH0 Z ER1 V Z -RESERVES(2) R IY0 Z ER1 V Z -RESERVING R IH0 Z ER1 V IH0 NG -RESERVING(2) R IY0 Z ER1 V IH0 NG -RESERVIST R IH0 Z ER1 V IH0 S T -RESERVISTS R IH0 Z ER1 V IH0 S T S -RESERVOIR R EH1 Z AH0 V W AA2 R -RESERVOIR(2) R EH1 Z ER0 V W AA2 R -RESERVOIRS R EH1 Z ER0 V W AA2 R Z -RESET R IY0 S EH1 T -RESET(2) R IY1 S EH0 T -RESETAR R EH1 S IH0 T ER0 -RESETING R IY0 S EH1 T IH0 NG -RESETING(2) R IY1 S EH0 T IH0 NG -RESETS R IY0 S EH1 T S -RESETS(2) R IY1 S EH0 T S -RESETTABLE R IY0 S EH1 T AH0 B AH0 L -RESETTLE R IY0 S EH1 T AH0 L -RESETTLED R IY0 S EH1 T AH0 L D -RESETTLEMENT R IY0 S EH1 T AH0 L M AH0 N T -RESH R EH1 SH -RESHAPE R IY0 SH EY1 P -RESHAPED R IY0 SH EY1 P T -RESHAPING R IY0 SH EY1 P IH0 NG -RESHOT R IY0 SH AO1 T -RESHUFFLE R IY0 SH AH1 F AH0 L -RESHUFFLED R IY0 SH AH1 F AH0 L D -RESHUFFLING R IY0 SH AH1 F AH0 L IH0 NG -RESHUFFLING(2) R IY0 SH AH1 F L IH0 NG 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IY1 Z -RESIGNING R IH0 Z AY1 N IH0 NG -RESIGNING(2) R IY0 Z AY1 N IH0 NG -RESIGNING(3) R IY0 S AY1 N IH0 NG -RESIGNS R IH0 Z AY1 N Z -RESIGNS(2) R IY0 Z AY1 N Z -RESIGNS(3) R IY0 S AY1 N Z -RESILIENCE R IH0 Z IH1 L IY0 AH0 N S -RESILIENCE(2) R IH0 Z IH1 L Y AH0 N S -RESILIENCY R IH0 Z IH1 L Y AH0 N S IY0 -RESILIENT R IH0 Z IH1 L Y AH0 N T -RESIN R EH1 Z AH0 N -RESIN(2) R EH1 Z IH0 N -RESINS R EH1 Z AH0 N Z -RESINS(2) R EH1 Z IH0 N Z -RESIST R IH0 Z IH1 S T -RESIST(2) R IY0 Z IH1 S T -RESISTANCE R IH0 Z IH1 S T AH0 N S -RESISTANCE(2) R IY0 Z IH1 S T AH0 N S -RESISTANT R IH0 Z IH1 S T AH0 N T -RESISTANT(2) R IY0 Z IH1 S T AH0 N T -RESISTED R IH0 Z IH1 S T IH0 D -RESISTED(2) R IY0 Z IH1 S T IH0 D -RESISTENCE R IH0 Z IH1 S T AH0 N S -RESISTING R IH0 Z IH1 S T IH0 NG -RESISTING(2) R IY0 Z IH1 S T IH0 NG -RESISTIVENESS R IH2 Z IH1 S T IH2 V N AH0 S -RESISTOR R IH0 Z IH1 S T ER0 -RESISTORS R IH0 Z IH1 S T ER0 Z -RESISTS R IH0 Z IH1 S T S -RESISTS(2) R IY0 Z IH1 S T S -RESISTS(3) R IH0 Z IH1 S -RESISTS(4) R IY0 Z IH1 S -RESKE R EH1 S K -RESLER R EH1 Z L ER0 -RESNAIS R EH0 S N EY1 -RESNER R EH1 S N ER0 -RESNICK R EH1 Z N IH0 K -RESNICK'S R EH1 Z N IH0 K S -RESNIK R EH1 S N IH0 K -RESO R IH1 Z OW0 -RESO(2) R IY1 Z OW0 -RESO(3) R IH1 S OW0 -RESO(4) R IY1 S OW0 -RESO'S R IY1 S OW0 Z -RESO'S(2) R IY1 Z OW0 Z -RESO'S(3) R IH1 S OW0 Z -RESO'S(4) R IH1 Z OW0 Z -RESOLD R IY0 S OW1 L D -RESOLUTE R EH1 Z AH0 L UW2 T -RESOLUTELY R EH1 S AH0 L UW2 T L IY0 -RESOLUTELY(2) R EH1 Z AH0 L UW2 T L IY0 -RESOLUTION R EH2 Z AH0 L UW1 SH AH0 N -RESOLUTION'S R EH2 Z AH0 L UW1 SH AH0 N Z -RESOLUTIONS R EH2 Z AH0 L UW1 SH AH0 N Z -RESOLVE R IY0 Z AA1 L V -RESOLVED R IY0 Z AA1 L V D -RESOLVES R IY0 Z AA1 L V Z -RESOLVING R IY0 Z AA1 L V IH0 NG -RESONANCE R EH1 Z AH0 N AH0 N S -RESONANT R EH1 Z AH0 N AH0 N T -RESONATE R EH1 Z AH0 N EY2 T -RESONATED R EH1 Z AH0 N EY2 T IH0 D -RESONATES R EH1 Z AH0 N EY2 T S -RESONATING R EH1 Z AH0 N EY2 T IH0 NG -RESORT R IH0 Z AO1 R T -RESORT(2) R IY0 Z AO1 R T -RESORT(3) R IY0 S AO1 R T -RESORT'S R IH0 Z AO1 R T S -RESORTED R IH0 Z AO1 R T IH0 D -RESORTED(2) R IY0 Z AO1 R T IH0 D -RESORTED(3) R IY0 S AO1 R T IH0 D -RESORTING R IH0 Z AO1 R T IH0 NG -RESORTING(2) R IY0 Z AO1 R T IH0 NG -RESORTING(3) R IY0 S AO1 R T IH0 NG -RESORTS R IH0 Z AO1 R T S -RESORTS(2) R IY0 Z AO1 R T S -RESORTS(3) R IY0 S AO1 R T S -RESORTS' R IH0 Z AO1 R T S -RESOUND R IY2 S AW1 N D -RESOUND(2) R IY2 Z AW1 N D -RESOUNDING R IY0 S AW1 N D IH0 NG -RESOUNDINGLY R IH0 Z AW1 N D IH0 NG L IY0 -RESOURCE R IY1 S AO0 R S -RESOURCEFUL R IY0 S AO1 R S F AH0 L -RESOURCEFULNESS R IY0 S AO1 R S F AH0 L N AH0 S -RESOURCES R IY1 S AO0 R S IH0 Z -RESOURCES' R IY1 S AO0 R S IH0 Z -RESPAWN R IY2 S P AA1 N -RESPEAK R IY2 S P IY1 K -RESPECT R IH0 S P EH1 K T -RESPECT(2) R IY0 S P EH1 K T -RESPECTABILITY R IY0 S P EH2 K T AH0 B IH1 L IH0 T IY0 -RESPECTABLE R IH0 S P EH1 K T AH0 B AH0 L -RESPECTABLE(2) R IY0 S P EH1 K T AH0 B AH0 L -RESPECTABLY R IY0 S P EH1 K T AH0 B L IY0 -RESPECTED R IH0 S P EH1 K T IH0 D -RESPECTED(2) R IY0 S P EH1 K T IH0 D -RESPECTFUL R IH0 S P EH1 K T F AH0 L -RESPECTFULLY R IH0 S P EH1 K T F AH0 L IY0 -RESPECTING R IY0 S P EH1 K T IH0 NG -RESPECTIVE R IH0 S P EH1 K T IH0 V -RESPECTIVE(2) R IY0 S P EH1 K T IH0 V -RESPECTIVELY R IH0 S P EH1 K T IH0 V L IY0 -RESPECTS R IH0 S P EH1 K T S -RESPECTS(2) R IY0 S P EH1 K T S -RESPECTS(3) R AH0 S P EH1 K S -RESPECTS(4) R IY0 S P EH1 K S -RESPESS R EY1 S P IH0 S -RESPIRATION R EH2 S P ER0 EY1 SH AH0 N -RESPIRATOR R EH1 S P ER0 EY2 T ER0 -RESPIRATORS R EH1 S P ER0 EY2 T ER0 Z -RESPIRATORY R EH1 S P ER0 AH0 T AO2 R IY0 -RESPIRONIC R EH2 S P ER0 AA1 N IH0 K -RESPIRONICS R EH2 S ER0 AA1 N IH0 K S -RESPITE R EH1 S P IH0 T -RESPLENDENT R IY0 S P L EH1 N D AH0 N T -RESPOND R IH0 S P AA1 N D -RESPOND(2) R IY0 S P AA1 N D -RESPONDED R IH0 S P AA1 N D IH0 D -RESPONDED(2) R IY0 S P AA1 N D AH0 D -RESPONDED(3) R IY0 S P AA1 N D IH0 D -RESPONDENT R IH0 S P AA1 N D AH0 N T -RESPONDENTS R IH0 S P AA1 N D AH0 N T S -RESPONDENTS' R IH0 S P AA1 N D AH0 N T S -RESPONDER R IH0 S P AA1 N D ER0 -RESPONDERS R IH0 S P AA1 N D ER0 Z -RESPONDING R IH0 S P AA1 N D IH0 NG -RESPONDING(2) R IY0 S P AA1 N D IH0 NG -RESPONDS R IH0 S P AA1 N D Z -RESPONDS(2) R IY0 S P AA1 N D Z -RESPONSE R IH0 S P AA1 N S -RESPONSE(2) R IY0 S P AA1 N S -RESPONSES R IH0 S P AA1 N S IH0 Z -RESPONSES(2) R IY0 S P AA1 N S AH0 Z -RESPONSES(3) R IY0 S P AA1 N S IH0 Z -RESPONSIBILITIES R IY0 S P AA2 N S AH0 B IH1 L AH0 T IY0 Z -RESPONSIBILITY R IY0 S P AA2 N S AH0 B IH1 L AH0 T IY0 -RESPONSIBLE R IY0 S P AA1 N S AH0 B AH0 L -RESPONSIBLY R IH0 S P AA1 N S AH0 B L IY0 -RESPONSIVE R IH0 S P AA1 N S IH0 V -RESPONSIVENESS R IH0 S P AA1 N S IH0 V N AH0 S -RESPRESS R EH1 S P R IH0 S -RESS R EH1 S -RESSA R EH1 S AH0 -RESSA'S R EH1 S AH0 Z -RESSEGUIE R EH1 S IH0 G W IY0 -RESSEL R EH1 S AH0 L -RESSLER R EH1 S L ER0 -REST R EH1 S T -RESTAGE R IY0 S T EY1 JH -RESTAGED R IY0 S T EY1 JH D -RESTAINO R EH0 S T AA0 IY1 N OW0 -RESTART R IY0 S T AA1 R T -RESTARTED R IY0 S T AA1 R T IH0 D -RESTARTING R IY0 S T AA1 R T IH0 NG -RESTATE R IY0 S T EY1 T -RESTATED R IY0 S T EY1 T IH0 D -RESTATEMENT R IY0 S T EY1 T M AH0 N T -RESTATEMENTS R IY0 S T EY1 T M AH0 N T S -RESTATES R IY0 S T EY1 T S -RESTATING R IY0 S T EY1 T IH0 NG -RESTAURANT R EH1 S T ER0 AA2 N T -RESTAURANT(2) R EH1 S T R AA2 N T -RESTAURANT'S R EH1 S T ER0 AA2 N T S -RESTAURANT'S(2) R EH1 S T R AA2 N T S -RESTAURANTEUR R EH0 S T R AA0 N T ER1 -RESTAURANTEURS R EH0 S T R AA0 N T ER1 Z -RESTAURANTS R EH1 S T ER0 AA2 N T S -RESTAURANTS(2) R EH1 S T R AA2 N T S -RESTAURANTS' R EH1 S T ER0 AA2 N T S -RESTAURANTS'(2) R EH1 S T R AA2 N T S -RESTAURATEUR R EH2 S T ER0 AH0 T ER1 -RESTAURATEUR(2) R EH2 S T R AH0 T ER1 -RESTAURATEURS R EH2 S T ER0 AH0 T ER1 Z -RESTAURATEURS(2) R EH2 S T R AH0 T ER1 Z -RESTED R EH1 S T AH0 D -RESTED(2) R EH1 S T IH0 D -RESTER R EH1 S T ER0 -RESTFUL R EH1 S T F AH0 L -RESTING R EH1 S T IH0 NG -RESTITUTE R EH1 S T IH0 T UW2 T -RESTITUTION R EH2 S T IH0 T UW1 SH AH0 N -RESTIVE R EH1 S T IH0 V -RESTIVENESS R EH1 S T IH0 V N AH0 S -RESTIVO R EH0 S T IY1 V OW0 -RESTLESS R EH1 S T L AH0 S -RESTLESSLY R EH1 S T L AH0 S L IY0 -RESTLESSNESS R EH1 S T L AH0 S N AH0 S -RESTO R EH1 S T OW0 -RESTOCK R IY0 S T AA1 K -RESTOCKED R IY0 S T AA1 K T -RESTOCKING R IY0 S T AA1 K IH0 NG -RESTON R EH1 S T AH0 N -RESTORATION R EH2 S T ER0 EY1 SH AH0 N -RESTORATIONS R EH2 S T ER0 EY1 SH AH0 N Z -RESTORATIVE R AH0 S T AO1 R AH0 T IH0 V -RESTORE R IH0 S T AO1 R -RESTORED R IH0 S T AO1 R D -RESTORER R IH0 S T AO1 R ER0 -RESTORES R IH0 S T AO1 R Z -RESTORING R IH0 S T AO1 R IH0 NG -RESTRAIN R IY0 S T R EY1 N -RESTRAINED R IY0 S T R EY1 N D -RESTRAINING R IY0 S T R EY1 N IH0 NG -RESTRAINS R IY0 S T R EY1 N Z -RESTRAINT R IH0 S T R EY1 N T -RESTRAINT(2) R IY0 S T R EY1 N T -RESTRAINTS R IH0 S T R EY1 N T S -RESTRAINTS(2) R IY0 S T R EY1 N T S -RESTREPO R EH0 S T R EH1 P OW0 -RESTRICT R IY0 S T R IH1 K T -RESTRICTED R IY0 S T R IH1 K T AH0 D -RESTRICTED(2) R IY0 S T R IH1 K T IH0 D -RESTRICTING R IY0 S T R IH1 K T IH0 NG -RESTRICTION R IY0 S T R IH1 K SH AH0 N -RESTRICTIONS R IY0 S T R IH1 K SH AH0 N Z -RESTRICTIVE R IY0 S T R IH1 K T IH0 V -RESTRICTIVENESS R AH0 S T R IH1 K T IH0 V N AH0 S -RESTRICTS R IY0 S T R IH1 K T S -RESTROOM R EH1 S T R UW2 M -RESTROOMS R EH1 S T R UW2 M Z -RESTRUCTURE R IY0 S T R AH1 K CH ER0 -RESTRUCTURED R IY0 S T R AH1 K CH ER0 D -RESTRUCTURES R IY0 S T R AH1 K CH ER0 Z -RESTRUCTURING R IY0 S T R AH1 K CH ER0 IH0 NG -RESTRUCTURINGS R IY0 S T R AH1 K CH ER0 IH0 NG Z -RESTS R EH1 S T S -RESTYLE R IY0 S T AY1 L -RESTYLED R IY0 S T AY1 L D -RESUBMIT R IY2 S AH0 B M IH1 T -RESUBMITTED R IY2 S AH0 B M IH1 T IH0 D -RESUBMITTING R IY2 S AH0 B M IH1 T IH0 NG -RESULT R IH0 Z AH1 L T -RESULT(2) R IY0 Z AH1 L T -RESULTANT R IY0 Z AH1 L T AH0 N T -RESULTED R IH0 Z AH1 L T IH0 D -RESULTED(2) R IY0 Z AH1 L T AH0 D -RESULTED(3) R IY0 Z AH1 L T IH0 D -RESULTING R IH0 Z AH1 L T IH0 NG -RESULTING(2) R IY0 Z AH1 L T IH0 NG -RESULTS R IH0 Z AH1 L T S -RESULTS(2) R IY0 Z AH1 L T S -RESUME R IH0 Z UW1 M -RESUME(2) R IY0 Z UW1 M -RESUME(3) R EH1 Z AH0 M EY2 -RESUMED R IH0 Z UW1 M D -RESUMED(2) R IY0 Z UW1 M D -RESUMES R IH0 Z UW1 M Z -RESUMES(2) R IY0 Z UW1 M Z -RESUMES(3) R EH1 Z AH0 M EY2 Z -RESUMING R IH0 Z UW1 M IH0 NG -RESUMING(2) R IY0 Z UW1 M IH0 NG -RESUMPTION R IH0 Z AH1 M P SH AH0 N -RESUMPTION(2) R IY0 Z AH1 M P SH AH0 N -RESUMPTION(3) R IH0 Z AH1 M SH AH0 N -RESUMPTION(4) R IY0 Z AH1 M SH AH0 N -RESUPPLY R IY0 S AH0 P L AY1 -RESURFACE R IY0 S ER1 F AH0 S -RESURFACED R IY0 S ER1 F AH0 S T -RESURFACING R IY0 S ER1 F AH0 S IH0 NG -RESURGENCE R IY0 S ER1 JH AH0 N S -RESURGENCY R IH0 S ER1 JH AH0 N S IY0 -RESURGENCY(2) R IY0 S ER1 JH AH0 N S IY0 -RESURGENT R IH0 S ER1 JH AH0 N T -RESURGENT(2) R IY0 S ER1 JH AH0 N T -RESURGING R IY0 S ER1 JH IH0 NG -RESURRECT R EH2 Z ER0 EH1 K T -RESURRECTED R EH2 Z ER0 EH1 K T IH0 D -RESURRECTING R EH2 Z ER0 EH1 K T IH0 NG -RESURRECTION R EH2 Z ER0 EH1 K SH AH0 N -RESUSCITATE R IH0 S AH1 S IH0 T EY2 T -RESUSCITATE(2) R IY0 S AH1 S IH0 T EY2 T -RESUSCITATED R IH0 S AH1 S IH0 T EY2 T IH0 D -RESUSCITATING R IH0 S AH1 S IH0 T EY2 T IH0 NG -RESUSCITATION R IH0 S AH2 S IH0 T EY1 SH AH0 N -RESVERATROL R EH0 S V ER1 AH0 T R AO0 L -RET R EH1 T -RETA R EH1 T AH0 -RETABLOS R IY0 T AE1 B L OW0 S -RETAIL R IY1 T EY2 L -RETAILED R IY1 T EY2 L D -RETAILER R IY1 T EY2 L ER0 -RETAILER'S R IY1 T EY2 L ER0 Z -RETAILERS R IY1 T EY2 L ER0 Z -RETAILERS' R IY1 T EY2 L ER0 Z -RETAILING R IY1 T EY2 L IH0 NG -RETAILING'S R IY1 T EY2 L IH0 NG Z -RETAILS R IY1 T EY2 L Z -RETAIN R IH0 T EY1 N -RETAIN(2) R IY0 T EY1 N -RETAINED R IH0 T EY1 N D -RETAINED(2) R IY0 T EY1 N D -RETAINER R IH0 T EY1 N ER0 -RETAINER(2) R IY0 T EY1 N ER0 -RETAINERS R IH0 T EY1 N ER0 Z -RETAINING R IH0 T EY1 N IH0 NG -RETAINING(2) R IY0 T EY1 N IH0 NG -RETAINS R IH0 T EY1 N Z -RETAINS(2) R IY0 T EY1 N Z -RETAKE R IY1 T EY1 K -RETAKE(2) R IY0 T EY1 K -RETAKEN R IY0 T EY1 K AH0 N -RETAKING R IY0 T EY1 K IH0 NG -RETALIATE R IH0 T AE1 L IY0 EY2 T -RETALIATE(2) R IY0 T AE1 L IY0 EY2 T -RETALIATED R IH0 T AE1 L IY0 EY2 T IH0 D -RETALIATED(2) R IY0 T AE1 L IY0 EY2 T IH0 D -RETALIATING R IH0 T AE1 L IY0 EY2 T IH0 NG -RETALIATION R IY0 T AE2 L IY0 EY1 SH AH0 N -RETALIATORY R IY0 T AE1 L Y AH0 T AO2 R IY0 -RETANA R EH0 T AE1 N AH0 -RETARD R IH0 T AA1 R D -RETARD(2) R IY0 T AA1 R D -RETARDANT R IY0 T AA1 R D AH0 N T -RETARDATION R IY0 T AA0 R D EY1 SH AH0 N -RETARDED R IH0 T AA1 R D IH0 D -RETARDED(2) R IY0 T AA1 R D AH0 D -RETARDED(3) R IY0 T AA1 R D IH0 D -RETARDING R IH0 T AA1 R D IH0 NG -RETARDING(2) R IY0 T AA1 R D IH0 NG -RETARDS R IH0 T AA1 R D Z -RETARDS(2) R IY0 T AA1 R D Z -RETARGET R IY0 T AA1 R G AH0 T -RETARGETER R IY0 T AA1 R G AH0 T ER0 -RETARGETERS R IY0 T AA1 R G AH0 T ER0 Z -RETARGETING R IY0 T AA1 R G AH0 T IH0 NG -RETELL R IY0 T EH1 L -RETELLING R IY0 T EH1 L IH0 NG -RETEMEYER R EH1 T AH0 M AY2 R -RETENTION R IY0 T EH1 N SH AH0 N -RETEST R IY1 T EH1 S T -RETESTED R IY0 T EH1 S T IH0 D -RETESTING R IY0 T EH1 S T IH0 NG -RETESTS R IY1 T EH1 S T S -RETHERFORD R IH0 TH ER1 F ER0 D -RETHINK R IY0 TH IH1 NG K -RETHINKING R IY0 TH IH1 NG K IH0 NG -RETHOUGHT R IY0 TH AO1 T -RETI R EH1 T IY0 -RETICENCE R EH1 T IH0 S AH0 N S -RETICENT R EH1 T IH0 S AH0 N T -RETIN R EH1 T IH0 N -RETINA R EH1 T AH0 N AH0 -RETINAL R EH1 T AH0 N AH0 L -RETINOBLASTOMA R EH2 T IH0 N OW2 B L AE2 S T OW1 M AH0 -RETINOID R EH1 T IH0 N OY0 D -RETINOIDS R EH1 T IH0 N OY0 D Z -RETINOL R EH1 T IH0 N AO2 L -RETINUE R EH1 T AH0 N UW2 -RETINYL R EH1 T IH0 N AH0 L -RETIRE R IH0 T AY1 R -RETIRE(2) R IY0 T AY1 R -RETIRE(3) R IY2 T AY1 ER0 -RETIRED R IH0 T AY1 R D -RETIRED(2) R IY0 T AY1 ER0 D -RETIRED(3) R IY0 T AY1 R D -RETIREE R IY0 T AY1 R IY1 -RETIREE'S R IH0 T AY2 R IY1 Z -RETIREES R IY0 T AY1 R IY1 Z -RETIREES' R IH0 T AY2 R IY1 Z -RETIREMENT R IY0 T AY1 ER0 M AH0 N T -RETIREMENT(2) R IH0 T AY1 ER0 M AH0 N T -RETIREMENT'S R IH0 T AY1 R M AH0 N T S -RETIREMENT'S(2) R IY0 T AY1 R M AH0 N T S -RETIREMENTS R IH0 T AY1 R M AH0 N T S -RETIREMENTS(2) R IY0 T AY1 R M AH0 N T S -RETIRES R IH0 T AY1 R Z -RETIRES(2) R IY0 T AY1 ER0 Z -RETIRES(3) R IY0 T AY1 R Z -RETIRING R IH0 T AY1 R IH0 NG -RETIRING(2) R IY0 T AY1 ER0 IH0 NG -RETIRING(3) R IY0 T AY1 R IH0 NG -RETLIN R EH1 T L IH0 N -RETO R IY1 T UW1 -RETOOK R IY0 T UH1 K -RETOOL R IY0 T UW1 L -RETOOLED R IY0 T UW1 L D -RETOOLING R IY0 T UW1 L IH0 NG -RETORT R IY1 T AO2 R T -RETORTED R IY0 T AO1 R T IH0 D -RETORTS R IH0 T AO1 R T S -RETORTS(2) R IY0 T AO1 R T S -RETOUCHING R IY0 T AH1 CH IH0 NG -RETRACE R IY0 T R EY1 S -RETRACED R IY0 T R EY1 S T -RETRACEMENT R IY0 T R EY1 S M AH0 N T -RETRACING R IY0 T R EY1 S IH0 NG -RETRACT R IY0 T R AE1 K T -RETRACTABLE R IY0 T R AE1 K T AH0 B AH0 L -RETRACTED R IY0 T R AE1 K T AH0 D -RETRACTING R IY0 T R AE1 K T IH0 NG -RETRACTION R IY0 T R AE1 K SH AH0 N -RETRACTS R IY0 T R AE1 K T S -RETRAIN R IY0 T R EY1 N -RETRAINED R IY0 T R EY1 N D -RETRAINING R IY0 T R EY1 N IH0 NG -RETRANSMISSION R IY2 T R AE0 N Z M IH1 SH AH0 N -RETREAD R IY0 T R EH1 D -RETREADING R IY0 T R EH1 D IH0 NG -RETREADS R IY0 T R EH1 D Z -RETREAT R IY0 T R IY1 T -RETREATED R IY0 T R IY1 T AH0 D -RETREATED(2) R IY0 T R IY1 T IH0 D -RETREATING R IY0 T R IY1 T IH0 NG -RETREATS R IY0 T R IY1 T S -RETRENCH R IY0 T R EH1 N CH -RETRENCHED R IY0 T R EH1 N CH T -RETRENCHING R IY0 T R EH1 N CH IH0 NG -RETRENCHMENT R IY0 T R EH1 N CH M AH0 N T -RETRENCHMENTS R IY0 T R EH1 N CH M AH0 N T S -RETRIAL R IY0 T R AY1 AH0 L -RETRIBUTION R EH2 T R AH0 B Y UW1 SH AH0 N -RETRIED R IY0 T R AY1 D -RETRIEVAL R IH0 T R IY1 V AH0 L -RETRIEVAL(2) R IY0 T R IY1 V AH0 L -RETRIEVE R IH0 T R IY1 V -RETRIEVE(2) R IY0 T R IY1 V -RETRIEVED R IY0 T R IY1 V D -RETRIEVER R IY0 T R IY1 V ER0 -RETRIEVERS R IY0 T R IY1 V ER0 Z -RETRIEVES R IH0 T R IY1 V Z -RETRIEVES(2) R IY0 T R IY1 V Z -RETRIEVING R IY0 T R IY1 V IH0 NG -RETRO R EH1 T R OW0 -RETROACTIVE R EH2 T R OW0 AE1 K T IH0 V -RETROACTIVELY R EH2 T R OW0 AE1 K T IH0 V L IY0 -RETROACTIVITY R EH2 T R OW0 AE0 K T IH1 V IH0 T IY0 -RETROCESSION R EH2 T R OW0 S EH1 SH AH0 N -RETROCESSIONARIES R EH2 T R OW0 S EH1 SH AH0 N EH0 R IY0 Z -RETROCESSIONARY R EH2 T R OW0 S EH1 SH AH0 N EH0 R IY0 -RETROFIT R EH1 T R OW0 F IH2 T -RETROFITS R EH1 T R OW0 F IH2 T S -RETROFITTED R EH1 T R OW2 F IH2 T IH0 D -RETROFITTER R EH1 T R OW2 F IH2 T ER0 -RETROFITTING R EH1 T R OW0 F IH2 T IH0 NG -RETROGRADE R EH1 T R AH0 G R EY2 D -RETROSPECT R EH1 T R AH0 S P EH2 K T -RETROSPECTIVE R EH2 T R AH0 S P EH1 K T IH0 V -RETROSPECTIVELY R EH2 T R OW0 S P EH1 K T IH0 V L IY0 -RETROVIR R EH1 T R OW0 V IH2 R -RETROVIRUS R EH2 T R OW0 V AY1 R AH0 S -RETROVIRUSES R EH2 T R OW0 V AY1 R AH0 S IH0 Z -RETRY R IY0 T R AY1 -RETRYING R IY0 T R AY1 IH0 NG -RETTBERG R EH1 T B ER0 G -RETTER R EH1 T ER0 -RETTEW R EH1 CH UW0 -RETTIG R EH1 T IH0 G -RETTINGER R EH1 T IH0 N JH ER0 -RETTINGER(2) R EH1 T IH0 NG ER0 -RETTKE R EH1 T K IY0 -RETTON R EH1 T AH0 N -RETURN R IH0 T ER1 N -RETURN(2) R IY0 T ER1 N -RETURNABLE R IY0 T ER1 N AH0 B AH0 L -RETURNED R IH0 T ER1 N D -RETURNED(2) R IY0 T ER1 N D -RETURNEE R IH0 T ER0 N IY1 -RETURNEES R IH0 T ER0 N IY1 Z -RETURNING R IH0 T ER1 N IH0 NG -RETURNING(2) R IY0 T ER1 N IH0 NG -RETURNS R IH0 T ER1 N Z -RETURNS(2) R IY0 T ER1 N Z -RETURNS' R AH0 T ER1 N Z -RETURNS'(2) R IY0 T ER1 N Z -RETZ R EH1 T S -RETZER R EH1 T Z ER0 -RETZLAFF R EH1 T Z L AH0 F -RETZLOFF R EH1 T Z L AO0 F -REUBEN R UW1 B AH0 N -REUBER R OY1 B ER0 -REUL R UW1 L -REULAND R OY1 L AH0 N D -REULE R UW1 L -REUM R IY1 AH0 M -REUNIFICATION R IY0 UW2 N AH0 F AH0 K EY1 SH AH0 N -REUNIFIED R IY0 UW1 N AH0 F AY2 D -REUNIFY R IY0 UW1 N AH0 F AY2 -REUNION R IY0 UW1 N Y AH0 N -REUNIONS R IY0 UW1 N Y AH0 N Z -REUNITE R IY2 UW0 N AY1 T -REUNITED R IY2 UW0 N AY1 T IH0 D -REUNITES R IY2 UW0 N AY1 T S -REUNITING R IY2 UW0 N AY1 T IH0 NG -REUSABLE R IY0 UW1 Z AH0 B AH0 L -REUSCH R OY1 SH -REUSE R IY0 Y UW1 S -REUSE(2) R IY0 Y UW1 Z -REUSED R IY0 UW1 Z D -REUSING R IY0 Y UW1 Z IH0 NG -REUSS R 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IH0 N T EH1 L -RINTEL'S R IH0 N T EH1 L Z -RINTELS R IH0 N T EH1 L Z -RINTELS' R IH0 N T EH1 L Z -RIO R IY1 OW0 -RIO'S R IY1 OW0 Z -RIO-DE-JANEIRO R IY1 OW0 D IY1 JH AH0 N EH1 R OW2 -RIOJAS R IY0 OW1 Y AA0 Z -RIOLO R IY0 OW1 L OW0 -RION R AY1 AH0 N -RIOPEL R IY0 AH0 P EH1 L -RIOPELLE R IY0 AH0 P EH1 L IY0 -RIORDAN R IH1 R D AH0 N -RIORDAN'S R IH1 R D AH0 N Z -RIOS R IY1 OW0 S -RIOT R AY1 AH0 T -RIOTED R AY1 AH0 T IH0 D -RIOTER R AY1 AH0 T ER0 -RIOTERS R AY1 AH0 T ER0 Z -RIOTING R AY1 AH0 T IH0 NG -RIOTOUS R AY1 AH0 T AH0 S -RIOTS R AY1 AH0 T S -RIOTS' R AY1 AH0 T S -RIOUX R UW1 -RIP R IH1 P -RIPA R IY1 P AH0 -RIPE R AY1 P -RIPEN R AY1 P AH0 N -RIPENED R AY1 P AH0 N D -RIPENING R AY1 P AH0 N IH0 NG -RIPENING(2) R AY1 P N IH0 NG -RIPHAGEN R IH1 P AA2 G AH0 N -RIPKA R IY1 P K AH0 -RIPKE R IH1 P K -RIPKEN R IH1 P K AH0 N -RIPKEN'S R IH1 P K AH0 N Z -RIPKIN R IH1 P K IH0 N -RIPLEY R IH1 P L IY0 -RIPOFF R IH1 P AO2 F -RIPOFFS R IH1 P AO2 F S -RIPOSTE R IH0 P OW1 S T -RIPOSTES R IH0 P AO1 S T S -RIPP R IH1 P -RIPPE R IH1 P -RIPPED R IH1 P T -RIPPEE R IH1 P IY1 -RIPPEL R IH1 P AH0 L -RIPPEON R IH1 P IY0 AH0 N -RIPPER R IH1 P ER0 -RIPPERGER R IH1 P ER0 G ER0 -RIPPETOE R IH1 P IH0 T OW0 -RIPPEY R IH1 P IY0 -RIPPING R IH1 P IH0 NG -RIPPLE R IH1 P AH0 L -RIPPLED R IH1 P AH0 L D -RIPPLES R IH1 P AH0 L Z -RIPPLING R IH1 P AH0 L IH0 NG -RIPPLING(2) R IH1 P L IH0 NG -RIPPLINGER R IH1 P AH0 L IH0 NG ER0 -RIPPLINGER(2) R IH1 P L IH0 NG ER0 -RIPPON R IH1 P AH0 N -RIPPY R IH1 P IY0 -RIPS R IH1 P S -RISA R IY1 S AH0 -RISBERG R IH1 S B ER0 G -RISC R IH1 S K -RISCH R IH1 SH -RISDEN R IH1 S D AH0 N -RISDON R IH1 S D AH0 N -RISE R AY1 Z -RISEN R IH1 Z AH0 N -RISENHOOVER R IH1 S IH0 N HH UW0 V ER0 -RISENHOOVER(2) R AY1 Z IH0 N HH UW0 V ER0 -RISER R AY1 Z ER0 -RISES R AY1 Z AH0 Z -RISES(2) R AY1 Z IH0 Z -RISH R IH1 SH -RISHEL R IH1 SH AH0 L -RISHELL R IH1 SH AH0 L -RISHER R IH1 SH ER0 -RISI R IY1 S IY0 -RISIN R AY1 Z IH0 N -RISING R AY1 Z IH0 NG -RISINGER R AY1 Z IH0 NG ER0 -RISK R IH1 S K 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-RITTHALER R IH1 TH AH0 L ER0 -RITTLE R IH1 T AH0 L -RITTMAN R IH1 T M AH0 N -RITTNER R IH1 T N ER0 -RITTS R IH1 T S -RITUAL R IH1 CH UW0 AH0 L -RITUALISTIC R IH2 CH UW0 AH0 L IH1 S T IH0 K -RITUALISTICALLY R IH2 CH UW0 AH0 L IH1 S T IH0 K AH0 L IY0 -RITUALISTICALLY(2) R IH2 CH UW0 AH0 L IH1 S T IH0 K L IY0 -RITUALS R IH1 CH UW0 AH0 L Z -RITZ R IH1 T S -RITZEL R IH1 T Z AH0 L -RITZENTHALER R IH1 T Z IH0 N TH AH0 L ER0 -RITZENTHALER(2) R IH1 T S IH0 N TH AA2 L ER0 -RITZER R IH1 T Z ER0 -RITZERT R IH1 T Z ER0 T -RITZIEST R IH1 T S IY0 AH0 S T -RITZMAN R IH1 T S M AH0 N -RITZY R IH1 T S IY0 -RIUNITE R IY0 UW0 N AY1 T -RIUNITE(2) R IY0 UW0 N AY1 T IY0 -RIVA R IY1 V AH0 -RIVAL R AY1 V AH0 L -RIVAL'S R AY1 V AH0 L Z -RIVALED R AY1 V AH0 L D -RIVALING R AY1 V AH0 L IH0 NG -RIVALING(2) R AY1 V L IH0 NG -RIVALRIES R AY1 V AH0 L R IY0 Z -RIVALRY R AY1 V AH0 L R IY0 -RIVALS R AY1 V AH0 L Z -RIVALS' R AY1 V AH0 L Z -RIVARD R IH1 V ER0 D -RIVAS R IY1 V AA0 Z -RIVEN R IH1 V AH0 N -RIVENBARK R IH1 V 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IY0 Z -ROBBIN R AA1 B IH0 N -ROBBING R AA1 B IH0 NG -ROBBINS R AA1 B IH0 N Z -ROBBS R AA1 B Z -ROBBY R AA1 B IY0 -ROBBY'S R AA1 B IY0 Z -ROBE R OW1 B -ROBECK R OW1 B EH2 K -ROBED R OW1 B D -ROBEL R OW1 B AH0 L -ROBELLO R OW0 B EH1 L OW0 -ROBELO R AA2 B EH1 L OW0 -ROBER R OW1 B ER0 -ROBERDS R AA1 B ER0 D Z -ROBERG R OW1 B ER0 G -ROBERGE R AA1 B ER0 JH -ROBERS R OW1 B ER0 Z -ROBERSON R AA1 B ER0 S AH0 N -ROBERT R AA1 B ER0 T -ROBERT'S R AA1 B ER0 T S -ROBERTA R AH0 B ER1 T AH0 -ROBERTI R AH0 B ER1 T IY0 -ROBERTO R AH0 B EH1 R T OW0 -ROBERTO(2) R OW0 B EH1 R T OW0 -ROBERTS R AA1 B ER0 T S -ROBERTS' R AA1 B ER0 T S -ROBERTS'S R AA1 B ER0 T S IH0 Z -ROBERTSHAW R AA1 B ER1 T SH AO1 -ROBERTSON R AA1 B ER0 T S AH0 N -ROBERTSON'S R AA1 B ER0 T S AH0 N Z -ROBES R OW1 B Z -ROBESON R OW1 B IH0 S AH0 N -ROBEY R OW1 B IY0 -ROBIA R OW1 B IY0 AH0 -ROBICHAUD R AA1 B IH0 SH OW2 -ROBICHAUX R AA1 B IH0 SH OW2 -ROBICHEAUX R AA1 B IH0 SH OW2 -ROBIDEAU R AA1 B IH0 D OW2 -ROBIDOUX R AA1 B IH0 D UW0 -ROBIE R OW1 B IY0 -ROBILLARD R AA1 B IH0 L ER0 D -ROBIN R AA1 B AH0 N -ROBIN(2) R AA1 B IH0 N -ROBIN'S R AA1 B AH0 N Z -ROBIN'S(2) R AA1 B IH0 N Z -ROBINA R AH0 B IY1 N AH0 -ROBINETT R AA1 B IH0 N EH0 T -ROBINETTE R AA1 B IH0 N EH2 T -ROBINHOOD R AA1 B IH0 N HH UH2 D -ROBINIA R OW0 B IY1 N IY0 AH0 -ROBINS R AA1 B AH0 N Z -ROBINS' R AA1 B AH0 N Z -ROBINS'S R AA1 B AH0 N Z IH0 Z -ROBINSON R AA1 B AH0 N S AH0 N -ROBINSON'S R AA1 B AH0 N S AH0 N Z -ROBINSONS R AA1 B AH0 N S AH0 N Z -ROBINSONS' R AA1 B AH0 N S AH0 N Z -ROBISHAW R AA1 B IH0 SH AO2 -ROBISON R AA1 B IH0 S AH0 N -ROBITAILLE R AA1 B IH0 T EY2 L -ROBITUSSIN R AA2 B IH0 T AH1 S IH0 N -ROBITUSSIN(2) R OW2 B AH0 T AH1 S IH0 N -ROBL R AA1 B AH0 L -ROBLE R OW1 B AH0 L -ROBLEDO R OW0 B L EY1 D OW0 -ROBLEE R AA1 B L IY2 -ROBLES R OW1 B AH0 L Z -ROBLEY R AA1 B L IY0 -ROBLIN R AA1 B L IH0 N -ROBLING R OW1 B AH0 L IH0 NG -ROBLING(2) R OW1 B L IH0 NG -ROBNETT R AA1 B N IH0 T -ROBO R OW1 B OW0 -ROBOCOP R OW1 B OW0 K AA2 P -ROBOT R OW1 B AA2 T 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AA1 L AY0 Z -SCALISI S K AA0 L IY1 S IY0 -SCALLAN S K AE1 L AH0 N -SCALLION S K AE1 L Y AH0 N -SCALLIONS S K AE1 L Y AH0 N Z -SCALLON S K AE1 L AH0 N -SCALLOP S K AE1 L AH0 P -SCALLOPED S K AA1 L AH0 P T -SCALLOPS S K AE1 L AH0 P S -SCALLY S K AE1 L IY0 -SCALP S K AE1 L P -SCALPED S K AE1 L P T -SCALPEL S K AE1 L P AH0 L -SCALPELS S K AE1 L P AH0 L Z -SCALPER S K AE1 L P ER0 -SCALPERS S K AE1 L P ER0 Z -SCALPING S K AE1 L P IH0 NG -SCALPS S K AE1 L P S -SCALZI S K AA1 L Z IY0 -SCALZITTI S K AA0 L Z IY1 T IY0 -SCALZO S K AA1 L Z OW0 -SCAM S K AE1 M -SCAMMED S K AE1 M D -SCAMMELL S K AE1 M AH0 L -SCAMMER S K AE1 M ER0 -SCAMMERS S K AE1 M ER0 Z -SCAMMON S K AE1 M AH0 N -SCAMPER S K AE1 M P ER0 -SCAMPERED S K AE1 M P ER0 D -SCAMPERING S K AE1 M P ER0 IH0 NG -SCAMS S K AE1 M Z -SCAN S K AE1 N -SCANDAL S K AE1 N D AH0 L -SCANDAL'S S K AE1 N D AH0 L Z -SCANDALIZE S K AE1 N D AH0 L AY2 Z -SCANDALIZED S K AE1 N D AH0 L AY2 Z D -SCANDALOUS S K AE1 N D AH0 L AH0 S -SCANDALS S K AE1 N D AH0 L Z 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L -SCHENECTADY S K AH0 N EH1 K T AH0 D IY0 -SCHENECTADY'S S K AH0 N EH1 K T AH0 D IY0 Z -SCHENK SH EH1 NG K -SCHENKEL SH EH1 NG K AH0 L -SCHENKEN SH EH1 NG K AH0 N -SCHENKER SH EH1 NG K ER0 -SCHENLEY SH EH1 N L IY0 -SCHEPER SH IY1 P ER0 -SCHEPERS SH IY1 P ER0 Z -SCHEPIS SH EH1 P IH0 S -SCHEPP SH EH1 P -SCHER SH ER1 -SCHERB SH ER1 B -SCHERBARTH SH ER1 B AA0 R TH -SCHERER SH IH1 R ER0 -SCHERER'S SH EH1 R ER0 Z -SCHERF SH ER1 F -SCHERFF SH ER1 F -SCHERGER SH ER1 G ER0 -SCHERING SH ER1 IH0 NG -SCHERING(2) SH EH1 R IH0 NG -SCHERING'S SH EH1 R IH0 NG Z -SCHERLIS SH ER1 L IH0 S -SCHERMAN SH ER1 M AH0 N -SCHERMER SH ER1 M ER0 -SCHERMERHORN SH ER1 M ER0 HH ER0 N -SCHERR SH EH1 R -SCHERRER SH EH1 R ER0 -SCHERTZ SH ER1 T S -SCHERTZER SH ER1 T S ER0 -SCHERZ SH ER1 Z -SCHERZER SH ER1 Z ER0 -SCHERZINGER SH ER1 Z IH0 NG ER0 -SCHETTER SH EH1 T ER0 -SCHETTINO SH EH1 T IY0 N OW0 -SCHETTLER SH EH1 T AH0 L ER0 -SCHETTLER(2) SH EH1 T L ER0 -SCHEU SH OY1 -SCHEUER SH OY1 ER0 -SCHEUERMAN SH OY1 ER0 M AH0 N 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-SCHIMMELBUSCH SH IH1 M AH0 L B UH2 SH -SCHIMMING SH IH1 M IH0 NG -SCHIMPF SH IH1 M P F -SCHINDEL SH IH1 N D AH0 L -SCHINDLER SH IH1 N D L ER0 -SCHINDLER'S SH IH1 N D L ER0 Z -SCHINKE S K IH1 NG K -SCHINKEL SH IH1 NG K AH0 L -SCHIPANI S K IY0 P AA1 N IY0 -SCHIPHOL S K IH1 P AH0 L -SCHIPKE SH IH1 P K IY0 -SCHIPPER SH IH1 P ER0 -SCHIPPERS SH IH1 P ER0 Z -SCHIRALDI S K IH0 R AA1 L D IY0 -SCHIRM SH ER1 M -SCHIRMER SH ER1 M ER0 -SCHIRO S K IH1 R OW0 -SCHIRTZINGER SH ER1 T Z IH0 NG ER0 -SCHISLER SH IH1 S AH0 L ER0 -SCHISLER(2) SH IH1 S L ER0 -SCHISM S K IH1 Z AH0 M -SCHISMS S K IH1 Z AH0 M Z -SCHISSEL SH IH1 S AH0 L -SCHISSLER SH IH1 S AH0 L ER0 -SCHISSLER(2) SH IH1 S L ER0 -SCHIST SH IH1 S T -SCHISTS SH IH1 S T S -SCHIZOPHRENIA S K IH2 T S AH0 F R IY1 N IY0 AH0 -SCHIZOPHRENIC SH IH2 Z AH0 F R EH1 N IH0 K -SCHLABACH SH L AE1 B AA0 K -SCHLACHTER SH L AE1 K T ER0 -SCHLACK SH L AE1 K -SCHLAFER SH L EY1 F ER0 -SCHLAFLY SH L AE1 F L IY0 -SCHLAG SH L AE1 G -SCHLAGEL SH L AE1 G AH0 L -SCHLAGER SH L EY1 G ER0 -SCHLAGETER SH L AE1 G IY0 T ER0 -SCHLAKE SH L EY1 K -SCHLANG SH L AE1 NG -SCHLANGEN SH L AE1 NG AH0 N -SCHLANGER SH L AE1 NG ER0 -SCHLARB SH L AA1 R B -SCHLATER SH L EY1 T ER0 -SCHLATTER SH L AE1 T ER0 -SCHLAUCH SH L AW1 K -SCHLECHT SH L EH1 K T -SCHLECHTER SH L EH1 K T ER0 -SCHLEE SH L IY1 -SCHLEETER SH L IY1 T ER0 -SCHLEGEL SH L EY1 G AH0 L -SCHLEGELMILCH SH L EH1 G IH0 L M IH0 L K -SCHLEICH SH L AY1 K -SCHLEICHER SH L AY1 K ER0 -SCHLEIF SH L AY1 F -SCHLEIFER SH L AY1 F ER0 -SCHLEIGER SH L AY1 G ER0 -SCHLEIMER SH L AY1 M ER0 -SCHLEIN SH L AY1 N -SCHLEIS SH L AY1 Z -SCHLEMMER SH L EH1 M ER0 -SCHLENDER SH L EH1 N D ER0 -SCHLENKER SH L EH1 NG K ER0 -SCHLEPP SH L EH1 P -SCHLERETH SH L EH1 R IH0 TH -SCHLESINGER SH L EH1 S IH0 N JH ER0 -SCHLESSER SH L EH1 S ER0 -SCHLESSINGER SH L EH1 S IH0 N JH ER0 -SCHLESWIG SH L EH1 S W IH0 G -SCHLEY SH L EY1 -SCHLEYER SH L EY1 ER0 -SCHLICHER SH L IH1 K ER0 -SCHLICHT SH L IH1 K T -SCHLICHTER SH L IH1 K T ER0 -SCHLICHTING SH L IH1 K T IH0 NG 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-SHANKMAN SH AE1 NG K M AH0 N -SHANKS SH AE1 NG K S -SHANLEY SH AE1 N L IY0 -SHANNA SH AE1 N AH0 -SHANNAHAN SH AE1 N AH0 HH AE0 N -SHANNON SH AE1 N AH0 N -SHANNON'S SH AE1 N AH0 N Z -SHANSEV SH AE1 N S EH0 V -SHANSEV(2) SH AE1 N S EH0 F -SHANT SH AA1 N T -SHANTI SH AE1 N T IY0 -SHANTIES SH AE1 N T IY0 Z -SHANTY SH AE1 N T IY0 -SHANTYTOWN SH AE1 N T IY0 T AW2 N -SHANTYTOWNS SH AE1 N T IY0 T AW2 N Z -SHANTZ SH AE1 N T S -SHAO SH AW1 -SHAOLIN SH AA1 OW0 L IH2 N -SHAP SH AE1 P -SHAPE SH EY1 P -SHAPED SH EY1 P T -SHAPELESS SH EY1 P L AH0 S -SHAPELY SH EY1 P L IY0 -SHAPERO SH AH0 P IH1 R OW0 -SHAPES SH EY1 P S -SHAPING SH EY1 P IH0 NG -SHAPIRA SH AH0 P IH1 R AH0 -SHAPIRO SH AH0 P IH1 R OW0 -SHAPIRO'S SH AH0 P IH1 R OW0 Z -SHAPIROS SH AH0 P IH1 R OW0 Z -SHAPLEY SH AE1 P L IY0 -SHAPP SH AE1 P -SHAPPELL SH AE1 P AH0 L -SHAQ SH AE1 K -SHAQUILLE SH AH0 K IY1 L -SHARA SH AA1 R AH0 -SHARAA SH AA1 R AH0 -SHARANSKY SH ER0 AE1 N S K IY0 -SHARAR SH ER0 AA1 R -SHARBER SH AA1 R B ER0 -SHARBONO SH AA0 R B 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N AE1 N IH0 G AH0 N -SHENANIGANS SH AH0 N AE1 N IH0 G AH0 N Z -SHENBERGER SH EH1 N B ER0 G ER0 -SHENEFIELD SH EH1 N IH0 F IY2 L D -SHENEMAN SH IY1 N M AH0 N -SHENG SH EH1 NG -SHENG-FEN SH EH1 NG F EH1 N -SHENICE SH AH0 N IY1 S -SHENK SH EH1 NG K -SHENKER SH EH1 NG K ER0 -SHENKMAN SH EH1 NG K M AH0 N -SHENTON SH EH1 N T AH0 N -SHENYANG SH EH0 N Y AE1 NG -SHENZHEN SH EH1 N ZH EH2 N -SHEP SH EH1 P -SHEPARD SH EH1 P ER0 D -SHEPARD'S SH EH1 P ER0 D Z -SHEPARDSON SH EH1 P AA0 R D S AH0 N -SHEPERD SH EH1 P ER0 D -SHEPHARD SH EH1 P ER0 D -SHEPHEARD SH EH1 P ER0 D -SHEPHERD SH EH1 P ER0 D -SHEPHERD'S SH EH1 P ER0 D Z -SHEPHERDED SH EH1 P ER0 D IH0 D -SHEPHERDING SH EH1 P ER0 D IH0 NG -SHEPHERDS SH EH1 P ER0 D Z -SHEPLER SH EH1 P L ER0 -SHEPLEY SH EH1 P L IY0 -SHEPP SH EH1 P -SHEPPARD SH EH1 P ER0 D -SHEPPARD'S SH EH1 P ER0 D Z -SHEPPARDS SH EH1 P ER0 D Z -SHEPPER SH EH1 P ER0 -SHEPPERD SH EH1 P ER0 D -SHEPPERSON SH EH1 P ER0 S AH0 N -SHEPPY SH EH1 P IY0 -SHER SH ER1 -SHERAK SH EH1 R AE0 K 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N -SITTS S IH1 T S -SITUATE S IH1 CH UW0 EY2 T -SITUATED S IH1 CH UW0 EY2 T IH0 D -SITUATION S IH2 CH UW0 EY1 SH AH0 N -SITUATION'S S IH2 CH UW0 EY1 SH AH0 N Z -SITUATIONAL S IH2 CH UW0 EY1 SH AH0 N AH0 L -SITUATIONS S IH2 CH UW0 EY1 SH AH0 N Z -SITUS S AY1 T AH0 S -SITZ S IH1 T S -SITZE S IH1 T Z -SITZER S IH1 T Z ER0 -SITZES S IH1 T S IH0 Z -SITZMAN S IH1 T S M AH0 N -SITZMANN S IH1 T S M AH0 N -SIU S IY1 UW0 -SIUDA S IY0 UW1 D AH0 -SIVAK S IH1 V AH0 K -SIVER S AY1 V ER0 -SIVERLING S IH1 V ER0 L IH0 NG -SIVERSON S IH1 V ER0 S AH0 N -SIVERTSEN S IH1 V ER0 T S AH0 N -SIVERTSON S IH1 V ER0 T S AH0 N -SIVILS S IH1 V AH0 L Z -SIVLEY S IH1 V L IY0 -SIVY S IH1 V IY0 -SIWEK S IH1 W IH0 K -SIX S IH1 K S -SIX'S S IH1 K S IH0 Z -SIX-PACK S IH1 K S P AE2 K -SIXED S IH1 K S T -SIXER S IH1 K S ER0 -SIXERS S IH1 K S ER0 Z -SIXES S IH1 K S IH0 Z -SIXFOLD S IH1 K S F OW2 L D -SIXTEEN S IH0 K S T IY1 N -SIXTEEN(2) S IH1 K S T IY1 N -SIXTEEN'S S IH2 K S T IY1 N Z -SIXTEENS S IH1 K S T IY1 N Z 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AA1 L AH0 -SKALICKY S K AH0 L IH1 K IY0 -SKALLA S K AE1 L AH0 -SKALSKI S K AA1 L S K IY0 -SKALSKY S K AA1 L S K IY0 -SKANDIA S K AE1 N D IY0 ER0 -SKANDIA(2) S K AE1 N D IY0 AH0 -SKANDIA(3) S K AE1 N D Y AH0 -SKANDINAVISKA S K AE2 N D IH0 N AH0 V IH1 S K AH0 -SKANSKA S K AE1 N S K AH0 -SKARDA S K AA1 R D AH0 -SKARE S K EH1 R -SKASE S K EY1 Z -SKATE S K EY1 T -SKATEBOARD S K EY1 T B AO2 R D -SKATEBOARDING S K EY1 T B AO2 R D IH0 NG -SKATED S K EY1 T IH0 D -SKATER S K EY1 T ER0 -SKATERS S K EY1 T ER0 Z -SKATES S K EY1 T S -SKATING S K EY1 T IH0 NG -SKEAT S K IY1 T -SKEEL S K IY1 L -SKEELS S K IY1 L Z -SKEEN S K IY1 N -SKEENS S K IY1 N Z -SKEES S K IY1 Z -SKEET S K IY1 T -SKEETE S K IY1 T -SKEETER S K IY1 T ER0 -SKEETERS S K IY1 T ER0 Z -SKEETS S K IY1 T S -SKEFFINGTON S K EH1 F IH0 NG T AH0 N -SKEHAN S K EY1 AH0 N -SKEIN S K EY1 N -SKEINS S K EY1 N Z -SKELETAL S K EH1 L AH0 T AH0 L -SKELETON S K EH1 L AH0 T AH0 N -SKELETONS S K EH1 L AH0 T AH0 N Z -SKELLENGER S K EH1 L IH0 N JH ER0 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IH1 K IY0 -SKIBINSKI S K IH0 B IH1 N S K IY0 -SKIBO S K AY1 B OW0 -SKIBO(2) S K IY1 B OW0 -SKID S K IH1 D -SKIDDED S K IH1 D IH0 D -SKIDDING S K IH1 D IH0 NG -SKIDGEL S K IH1 JH AH0 L -SKIDMORE S K IH1 D M AO0 R -SKIDS S K IH1 D Z -SKIED S K IY1 D -SKIER S K AY1 R -SKIER(2) S K IY1 ER0 -SKIERS S K IY1 ER0 Z -SKIES S K AY1 Z -SKIFF S K IH1 F -SKIFFINGTON S K IH1 F IH0 NG T AH0 N -SKIFFS S K IH1 F S -SKIING S K IY1 IH0 NG -SKILES S K AY1 L Z -SKILL S K IH1 L -SKILLED S K IH1 L D -SKILLEN S K IH1 L AH0 N -SKILLERN S K IH1 L ER0 N -SKILLET S K IH1 L AH0 T -SKILLFUL S K IH1 L F AH0 L -SKILLFULLY S K IH1 L F AH0 L IY0 -SKILLIN S K IH1 L IH0 N -SKILLING S K IH1 L IH0 NG -SKILLINGS S K IH1 L IH0 NG Z -SKILLMAN S K IH1 L M AH0 N -SKILLS S K IH1 L Z -SKILTON S K IH1 L T AH0 N -SKIM S K IH1 M -SKIMMED S K IH1 M D -SKIMMER S K IH1 M ER0 -SKIMMERS S K IH1 M ER0 Z -SKIMMING S K IH1 M IH0 NG -SKIMP S K IH1 M P -SKIMPIER S K IH1 M P IY0 ER0 -SKIMPIEST S K IH1 M P IY0 AH0 S T -SKIMPING S K IH1 M P IH0 NG -SKIMPY S K IH1 M P IY0 -SKIN S K IH1 N -SKIN'S S K IH1 N Z -SKINFLINT S K IH1 N F L IH0 N T -SKINHEAD S K IH1 N HH EH2 D -SKINHEADS S K IH1 N HH EH2 D Z -SKINKS S K IH1 NG K S -SKINLESS S K IH1 N L AH0 S -SKINNED S K IH1 N D -SKINNER S K IH1 N ER0 -SKINNER'S S K IH1 N ER0 Z -SKINNIER S K IH1 N IY0 ER0 -SKINNIEST S K IH1 N IY0 AH0 S T -SKINNY S K IH1 N IY0 -SKINS S K IH1 N Z -SKINS' S K IH1 N Z -SKIP S K IH1 P -SKIPJACK S K IH1 P JH AE2 K -SKIPJACK'S S K IH1 P JH AE2 K S -SKIPJACKS S K IH1 P JH AE2 K S -SKIPP S K IH1 P -SKIPPA S K IH1 P AH0 -SKIPPA'S S K IH1 P AH0 Z -SKIPPED S K IH1 P T -SKIPPER S K IH1 P ER0 -SKIPPER'S S K IH1 P ER0 Z -SKIPPERS S K IH1 P ER0 Z -SKIPPING S K IH1 P IH0 NG -SKIPPY S K IH1 P IY0 -SKIPS S K IH1 P S -SKIPTON S K IH1 P T AH0 N -SKIPWITH S K IH1 P W IH2 TH -SKIPWORTH S K IH1 P W ER2 TH -SKIRDALL S K ER1 D AA2 L -SKIRMISH S K ER1 M IH0 SH -SKIRMISHES S K ER1 M IH0 SH IH0 Z -SKIRMISHING S K ER1 M IH0 SH IH0 NG -SKIRT S K ER1 T -SKIRTED S K ER1 T IH0 D 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-SKYLINE S K AY1 L AY2 N -SKYLINES S K AY1 L AY2 N Z -SKYLITE S K AY1 L AY2 T -SKYLITES S K AY1 L AY2 T S -SKYNET S K AY1 N EH2 T -SKYPE S K AY1 P -SKYROCKET S K AY1 R AA2 K AH0 T -SKYROCKETED S K AY1 R AA2 K AH0 T IH0 D -SKYROCKETING S K AY1 R AA2 K AH0 T IH0 NG -SKYROCKETS S K AY1 R AA2 K AH0 T S -SKYSCRAPER S K AY1 S K R EY2 P ER0 -SKYSCRAPERS S K AY1 S K R EY2 P ER0 Z -SKYTEL S K AY1 T EH2 L -SKYWALKER S K AY1 W AO2 K ER0 -SKYWARD S K AY1 W ER0 D -SKYWAVE S K AY1 W EY2 V -SKYWAY S K AY1 W EY2 -SKYWEST S K AY1 W EH2 S T -SKYWRITER S K AY1 R AY2 T ER0 -SKYWRITERS S K AY1 R AY2 T ER0 Z -SKYWRITING S K AY1 R AY2 T IH0 NG -SLAB S L AE1 B -SLABAUGH S L AE1 B AO0 -SLABS S L AE1 B Z -SLABY S L EY1 B IY0 -SLACK S L AE1 K -SLACKED S L AE1 K T -SLACKEN S L AE1 K AH0 N -SLACKENED S L AE1 K AH0 N D -SLACKENING S L AE1 K AH0 N IH0 NG -SLACKENS S L AE1 K AH0 N Z -SLACKER S L AE1 K ER0 -SLACKERS S L AE1 K ER0 Z -SLACKNESS S L AE1 K N AH0 S -SLACKS S L AE1 K S -SLADE S L EY1 D -SLADEK S L AE1 D IH0 K -SLADKY S L AE1 D K IY0 -SLAG S L AE1 G -SLAGEL S L AE1 G AH0 L -SLAGER S L EY1 G ER0 -SLAGGING S L AE1 G IH0 NG -SLAGHT S L AE1 T -SLAGLE S L EY1 G AH0 L -SLAGTER S L AE1 G T ER0 -SLAIN S L EY1 N -SLAINE S L EY1 N -SLALOM S L AA1 L AH0 M -SLAM S L AE1 M -SLAMA S L AA1 M AH0 -SLAMMED S L AE1 M D -SLAMMER S L AE1 M ER0 -SLAMMING S L AE1 M IH0 NG -SLAMS S L AE1 M Z -SLANDER S L AE1 N D ER0 -SLANDERED S L AE1 N D ER0 D -SLANDERER S L AE1 N D ER0 ER0 -SLANDERING S L AE1 N D ER0 IH0 NG -SLANDEROUS S L AE1 N D ER0 AH0 S -SLANDERS S L AE1 N D ER0 Z -SLANE S L EY1 N -SLANEY S L EY1 N IY0 -SLANG S L AE1 NG -SLANINA S L AA0 N IY1 N AH0 -SLANKARD S L AE1 NG K ER0 D -SLANT S L AE1 N T -SLANTED S L AE1 N T IH0 D -SLANTED(2) S L AE1 N AH0 D -SLANTED(3) S L AE1 N IH0 D -SLANTING S L AE1 N T IH0 NG -SLAP S L AE1 P -SLAPDASH S L AE1 P D AE2 SH -SLAPE S L EY1 P -SLAPHAPPY S L AE1 P HH AE2 P IY0 -SLAPPED S L AE1 P T -SLAPPEY S L AE1 P IY0 -SLAPPING S L AE1 P IH0 NG -SLAPS S L AE1 P S -SLAPSTICK S L AE1 P S T IH2 K -SLASH S L AE1 SH -SLASHED S L AE1 SH T -SLASHER S L AE1 SH ER0 -SLASHES S L AE1 SH IH0 Z -SLASHING S L AE1 SH IH0 NG -SLAT S L AE1 T -SLATE S L EY1 T -SLATED S L EY1 T IH0 D -SLATEN S L EY1 T AH0 N -SLATER S L EY1 T ER0 -SLATER'S S L EY1 T ER0 Z -SLATERS S L EY1 T ER0 Z -SLATES S L EY1 T S -SLATHER S L AE1 DH ER0 -SLATHERED S L AE1 DH ER0 D -SLATHERING S L AE1 DH ER0 IH0 NG -SLATHERS S L AE1 DH ER0 Z -SLATING S L EY1 T IH0 NG -SLATKIN S L AE1 T K IH0 N -SLATON S L AE1 T AH0 N -SLATS S L AE1 T S -SLATTEN S L AE1 T AH0 N -SLATTER S L AE1 T ER0 -SLATTERY S L AE1 T ER0 IY0 -SLATTON S L AE1 T AH0 N -SLAUGH S L AO1 -SLAUGHTER S L AO1 T ER0 -SLAUGHTERED S L AO1 T ER0 D -SLAUGHTERHOUSE S L AO1 T ER0 HH AW2 S -SLAUGHTERHOUSES S L AO1 T ER0 HH AW2 S IH0 Z -SLAUGHTERING S L AO1 T ER0 IH0 NG -SLAUGHTERINGS S L AO1 T ER0 IH0 NG Z -SLAUGHTERS S L AO1 T ER0 Z -SLAUSON S L AW1 Z AH0 N -SLAV S L AA1 V -SLAVA S L AA1 V AH0 -SLAVE S L EY1 V -SLAVEN S L EY1 V AH0 N -SLAVENS S L EY1 V AH0 N Z -SLAVERY S L EY1 V ER0 IY0 -SLAVES S L EY1 V Z -SLAVIC S L AA1 V IH0 K -SLAVICK S L AE1 V IH0 K -SLAVIK S L AA1 V IH0 K -SLAVIN S L AE1 V IH0 N -SLAVINSKI S L AH0 V IH1 N S K IY0 -SLAVISH S L EY1 V IH0 SH -SLAVISHLY S L AE1 V IH0 SH L IY0 -SLAVONIA S L AH0 V OW1 N IY0 AH0 -SLAVONIA(2) S L AH0 V OW1 N Y AH0 -SLAVS S L AA1 V Z -SLAW S L AO1 -SLAWINSKI S L AA0 V IH1 N S K IY0 -SLAWSON S L AO1 S AH0 N -SLAY S L EY1 -SLAYBACK S L EY1 B AE2 K -SLAYBAUGH S L EY1 B AO2 -SLAYDEN S L EY1 D AH0 N -SLAYDON S L EY1 D AH0 N -SLAYER S L EY1 ER0 -SLAYING S L EY1 IH0 NG -SLAYINGS S L EY1 IH0 NG Z -SLAYMAKER S L EY1 M EY2 K ER0 -SLAYTER S L EY1 T ER0 -SLAYTON S L EY1 T AH0 N -SLEASMAN S L IY1 Z M AH0 N -SLEATOR S L EY1 T ER0 -SLEAZE S L IY1 Z -SLEAZY S L IY1 Z IY0 -SLECHTA S L EH1 CH T AH0 -SLED S L EH1 D -SLEDD S L EH1 D -SLEDDING S L EH1 D IH0 NG -SLEDGE S L EH1 JH -SLEDGEHAMMER S L EH1 JH HH AE2 M ER0 -SLEDS S L EH1 D Z -SLEDZ S L EH1 D Z -SLEE S L IY1 -SLEEK S L IY1 K -SLEEKER S L IY1 K ER0 -SLEEKEST S L IY1 K AH0 S T -SLEEKLY S L IY1 K L IY0 -SLEEMAN S L IY1 M AH0 N -SLEEP S L IY1 P -SLEEPER S L IY1 P ER0 -SLEEPERS S L IY1 P ER0 Z -SLEEPILY S L IY1 P AH0 L IY0 -SLEEPINESS S L IY1 P IY0 N AH0 S -SLEEPING S L IY1 P IH0 NG -SLEEPLESS S L IY1 P L AH0 S -SLEEPOVER S L IY1 P OW2 V ER0 -SLEEPOVERS S L IY1 P OW2 V ER0 Z -SLEEPS S L IY1 P S -SLEEPWALK S L IY1 P W AO2 K -SLEEPWALKER S L IY1 P W AO2 K ER0 -SLEEPWALKERS S L IY1 P W AO2 K ER0 Z -SLEEPWALKING S L IY1 P W AO2 K IH0 NG -SLEEPWEAR S L IY1 P W EH2 R -SLEEPY S L IY1 P IY0 -SLEET S L IY1 T -SLEETER S L IY1 T ER0 -SLEETH S L IY1 TH -SLEEVE S L IY1 V -SLEEVED S L IY1 V D -SLEEVELESS S L IY1 V L IH0 S -SLEEVES S L IY1 V Z -SLEIGH S L EY1 -SLEIGHT S L AY1 T -SLEIPNER S L AY1 P N ER0 -SLEMMER S L EH1 M ER0 -SLEMP S L EH1 M P -SLENDER S L EH1 N D ER0 -SLENTZ S L EH1 N T S -SLEPIAN S L IY1 P IY0 AH0 N -SLEPIAN(2) S L IY1 P Y AH0 N -SLEPT S L EH1 P T -SLESSENGER S L EH1 S EH0 N G ER0 -SLETTEN S L EH1 T AH0 N -SLEUTH S L UW1 TH -SLEUTHING S L UW1 TH IH0 NG -SLEUTHS S L UW1 TH S -SLEVEN S L IY1 V AH0 N -SLEVIN S L EH1 V IH0 N -SLEW S L UW1 -SLEZAK S L EH1 Z AH0 K -SLICE S L AY1 S -SLICED S L AY1 S T -SLICER S L AY1 S ER0 -SLICES S L AY1 S AH0 Z -SLICES(2) S L AY1 S IH0 Z -SLICING S L AY1 S IH0 NG -SLICK S L IH1 K -SLICKED S L IH1 K T -SLICKER S L IH1 K ER0 -SLICKERS S L IH1 K ER0 Z -SLICKEST S L IH1 K AH0 S T -SLICKLY S L IH1 K L IY0 -SLICKS S L IH1 K S -SLID S L IH1 D -SLIDE S L AY1 D -SLIDER S L AY1 D ER0 -SLIDES S L AY1 D Z -SLIDING S L AY1 D IH0 NG -SLIFE S L AY1 F -SLIFER S L AY1 F ER0 -SLIFKA S L IH1 F K AH0 -SLIFKO S L IH1 F K OW0 -SLIGAR S L IH1 G ER0 -SLIGER S L AY1 G ER0 -SLIGH S L AY1 -SLIGHT S L AY1 T -SLIGHTED S L AY1 T IH0 D -SLIGHTEST S L AY1 T AH0 S T -SLIGHTING S L AY1 T IH0 NG -SLIGHTLY S L AY1 T L IY0 -SLIGHTS S L AY1 T S -SLIKER S L AY1 K ER0 -SLIM S L IH1 M -SLIMAK S L IH1 M AH0 K -SLIME S L AY1 M -SLIMMED S L IH1 M D -SLIMMER S L IH1 M ER0 -SLIMMEST S L IH1 M AH0 S T -SLIMMING S L IH1 M IH0 NG -SLIMP S L IH1 M P -SLIMS S L IH1 M Z -SLIMY S L AY1 M IY0 -SLINEY S L IH1 N IY0 -SLING S L IH1 NG -SLINGER S L IH1 NG ER0 -SLINGERLAND S L IH1 NG G ER0 L AH0 N D -SLINGING S L IH1 NG IH0 NG -SLINGS S L IH1 NG Z -SLINGSHOT S L IH1 NG SH AA2 T -SLINGSHOTS S L IH1 NG SH AA2 T S -SLINKARD S L IH1 NG K ER0 D -SLINKER S L IH1 NG K ER0 -SLINKY S L IH1 NG K IY0 -SLIP S L IH1 P -SLIP-ON S L IH1 P AA2 N -SLIP-ONS S L IH1 P AA2 N Z -SLIPPAGE S L IH1 P IH0 JH -SLIPPED S L IH1 P T -SLIPPER S L IH1 P ER0 -SLIPPERS S L IH1 P ER0 Z -SLIPPERY S L IH1 P ER0 IY0 -SLIPPERY(2) S L IH1 P R IY0 -SLIPPING S L IH1 P IH0 NG -SLIPS S L IH1 P S -SLIPSHOD S L IH1 P SH AA2 D -SLIT S L IH1 T -SLITER S L IY1 T ER0 -SLITHER S L IH1 DH ER0 -SLITHERING S L IH1 DH ER0 IH0 NG -SLITS S L IH1 T S -SLITTING S L IH1 T IH0 NG -SLIVA S L IY1 V AH0 -SLIVER S L IH1 V ER0 -SLIVERS S L IH1 V ER0 Z -SLIVINSKI S L IH0 V IH1 N S K IY0 -SLIVKA S L IH1 V K AH0 -SLIVOVITZ S L IH1 V AH0 V IH0 T S -SLIWA S L AY1 V AH0 -SLIWINSKI S L IH0 V IH1 N S K IY0 -SLOAN S L OW1 N -SLOAN'S S L OW1 N Z -SLOANE S L OW1 N -SLOAT S L OW1 T -SLOATE S L OW1 T -SLOB S L AA1 B -SLOBBER S L AA1 B ER0 -SLOBBERING S L AA1 B ER0 IH0 NG -SLOBODA S L AH0 B OW1 D AH0 -SLOBODAN S L OW1 B OW0 D AA2 N -SLOBOGIN S L AH0 B OW1 G AH0 N -SLOBOGIN'S S L AH0 B OW1 G AH0 N Z -SLOBS S L AA1 B Z -SLOCAN S L OW1 K AH0 N -SLOCAN'S S L OW1 K AH0 N Z -SLOCOMB S L OW1 K AH0 M -SLOCUM S L OW1 K AH0 M -SLOCUMB S L OW1 K AH0 M -SLOE S L OW1 -SLOG S L AA1 G -SLOGAN S L OW1 G AH0 N -SLOGANS S L OW1 G AH0 N Z -SLOGGED S L AA1 G D -SLOGGING S L AA1 G IH0 NG -SLOKUM S L OW1 K AH0 M -SLOMA S L OW1 M AH0 -SLOMAN S L OW1 M AH0 N -SLOMINSKI S L AH0 M IH1 N S K IY0 -SLOMSKI S L AA1 M S K IY0 -SLONAKER S L AA1 N AH0 K ER0 -SLONE S L OW1 N -SLONIKER S L AA1 N IH0 K ER0 -SLOOP S L UW1 P -SLOP S L AA1 P -SLOPE S L OW1 P -SLOPER S L OW1 P ER0 -SLOPES S L OW1 P S -SLOPING S L OW1 P IH0 NG -SLOPPIER S L AA1 P IY0 ER0 -SLOPPIEST S L AA1 P IY0 AH0 S T -SLOPPILY S L AA1 P AH0 L IY0 -SLOPPINESS S L AA1 P IY0 N AH0 S -SLOPPY S L AA1 P IY0 -SLORC S L AO1 R K -SLOSH S L AA1 SH -SLOSHING S L AA1 SH IH0 NG -SLOSS S L AO1 S -SLOT S L AA1 T -SLOTA S L OW1 T AH0 -SLOTH S L OW1 TH -SLOTHOWER S L AA1 TH OW0 ER0 -SLOTNICK S L AA1 T N IH0 K -SLOTS S L AA1 T S -SLOTT S L AA1 T -SLOTTED S L AA1 T IH0 D -SLOTTING S L AA1 T IH0 NG -SLOUCH S L AW1 CH -SLOUCHES S L AW1 CH IH0 Z -SLOUCHING S L AW1 CH IH0 NG -SLOUGH S L AH1 F -SLOUGHS S L AH1 F S -SLOVACEK S L AA1 V AH0 CH EH0 K -SLOVAK S L OW1 V AE0 K -SLOVAK(2) S L OW1 V AA0 K -SLOVAKIA S L OW2 V AA1 K IY2 AH0 -SLOVAKIA'S S L OW0 V AA1 K IY2 AH0 Z -SLOVAKIA'S(2) S L OW0 V AE1 K IY0 AH0 Z -SLOVAKIAN S L OW1 V AA0 K IY2 AH0 N -SLOVAKS S L OW1 V AE0 K S -SLOVAKS(2) S L OW1 V AA0 K S -SLOVENE S L OW2 V IY1 N -SLOVENES S L OW2 V IY1 N Z -SLOVENIA S L OW2 V IY1 N IY0 AH0 -SLOVENIAN S L OW2 V IY1 N IY0 AH0 N -SLOVENLINESS S L AH1 V AH0 N L IY0 N AH0 S -SLOVENLY S L AH1 V AH0 N L IY0 -SLOVER S L OW1 V ER0 -SLOVES S L OW1 V Z -SLOVO S L OW1 V OW0 -SLOVONIA S L AH0 V OW1 N IY0 AH0 -SLOVONIA(2) S L AH0 V OW1 N Y AH0 -SLOW S L OW1 -SLOWDOWN S L OW1 D AW2 N -SLOWDOWNS S L OW1 D AW2 N Z -SLOWED S L OW1 D -SLOWER S L OW1 ER0 -SLOWEST S L OW1 AH0 S T -SLOWEY S L OW1 IY0 -SLOWIK S L OW1 IH0 K -SLOWING S L OW1 IH0 NG -SLOWINSKI S L OW0 IH1 N S K IY0 -SLOWLY S L OW1 L IY0 -SLOWNESS S L OW1 N AH0 S -SLOWPOKE S L OW1 P OW2 K -SLOWS S L OW1 Z -SLUDER S L UW1 D ER0 -SLUDGE S L AH1 JH -SLUDGY S L AH1 JH IY0 -SLUG S L AH1 G -SLUGA S L UW1 G AH0 -SLUGFEST S L AH1 G F EH2 S T -SLUGGED S L AH1 G D -SLUGGER S L AH1 G ER0 -SLUGGERS S L AH1 G ER0 Z -SLUGGING S L AH1 G IH0 NG -SLUGGISH S L AH1 G IH0 SH -SLUGGISHLY S L AH1 G IH0 SH L IY0 -SLUGGISHNESS S L AH1 G IH0 SH N AH0 S -SLUGS S L AH1 G Z -SLUICE S L UW1 S -SLUICING S L UW1 S IH0 NG -SLUITER S L UW1 T ER0 -SLUKA S L UW1 K AH0 -SLUM S L AH1 M -SLUMBER S L AH1 M B ER0 -SLUMBERING S L AH1 M B ER0 IH0 NG -SLUMP S L AH1 M P -SLUMPED S L AH1 M P T -SLUMPING S L AH1 M P IH0 NG -SLUMPS S L AH1 M P S -SLUMS S L AH1 M Z -SLUNG S L AH1 NG -SLUPIC S L UW1 P IH0 K -SLUR S L ER1 -SLURP S L ER1 P -SLURRED S L ER1 D -SLURRING S L ER1 IH0 NG -SLURRY S L ER1 IY0 -SLURS S L ER1 Z -SLUSH S L AH1 SH -SLUSHAYETE S L UH0 SH EY0 EH1 T -SLUSHER S L AH1 SH ER0 -SLUSS S L AH1 S -SLUSSER S L AH1 S ER0 -SLUT S L AH1 T -SLUTS S L AH1 T S -SLUTSKY S L AH1 T S K IY0 -SLUTTY S L AH1 T IY2 -SLUTZ S L AH1 T S -SLUTZKY S L AH1 T S K IY0 -SLUYTER S L AY1 T ER0 -SLY S L AY1 -SLYE S L AY1 -SLYLY S L AY1 L IY0 -SLYNESS S L AY1 N AH0 S -SLYTER S L AY1 T ER0 -SMABY S M EY1 B IY0 -SMACK S M AE1 K -SMACKED S M AE1 K T -SMACKING S M AE1 K IH0 NG -SMACKS S M AE1 K S -SMAIL S M EY1 L -SMALDONE S M AE1 L D AH0 N -SMALE S M EY1 L -SMALL S M AO1 L -SMALLCAP S M AO1 L K AE2 P -SMALLEN S M AO1 L AH0 N -SMALLER S M AO1 L ER0 -SMALLEST S M AO1 L AH0 S T -SMALLEY S M AA1 L IY0 -SMALLING S M AO1 L IH0 NG -SMALLISH S M AO1 L IH0 SH -SMALLMAN S M AO1 L M AH0 N -SMALLNESS S M AO1 L N AH0 S -SMALLPOX S M AO1 L P AA2 K S -SMALLRIDGE S M AO1 L R IH2 JH -SMALLS S M AO1 L Z -SMALLTALK S M AO1 L T AO2 K -SMALLTOWN S M AO1 L T AW2 N -SMALLWOOD S M AO1 L W UH2 D -SMALTZ S M AE1 L T S -SMARMY S M AA1 R M IY0 -SMARR S M AE1 R -SMART S M AA1 R T -SMART-ASS S M AA1 R T AE2 S -SMARTALECK S M AA1 R T EH0 L AH0 K -SMARTASS S M AA1 R T AE1 S -SMARTCARD S M AA1 R T K AA2 R D -SMARTED S M AA1 R T IH0 D -SMARTEN S M AA1 R T AH0 N -SMARTENED S M AA1 R T AH0 N D -SMARTENING S M AA1 R T AH0 N IH0 NG -SMARTER S M AA1 R T ER0 -SMARTEST S M AA1 R T AH0 S T -SMARTIES S M AA1 R T IY0 Z -SMARTING S M AA1 R T IH0 NG -SMARTISH S M AA1 R T IH0 SH -SMARTLY S M AA1 R T L IY0 -SMARTMONEY S M AA1 R T M AH2 N IY0 -SMARTNESS S M AA1 R T N IH0 S -SMARTPHONE S M AA1 R T F OW2 N -SMARTPHONES S M AA1 R T F OW2 N Z -SMARTS S M AA1 R T S -SMARTT S M AA1 R T -SMARTY S M AA1 R T IY0 -SMASH S M AE1 SH -SMASHED S M AE1 SH T -SMASHER S M AE1 SH ER0 -SMASHES S M AE1 SH IH0 Z -SMASHING S M AE1 SH IH0 NG -SMATHER S M AE1 DH ER0 -SMATHERS S M AE1 DH ER0 Z -SMATTER S M AE1 T ER0 -SMATTERING S M AE1 T ER0 IH0 NG -SMAY S M EY1 -SMEAD S M IY1 D -SMEAL S M IY1 L -SMEAR S M IH1 R -SMEARED S M IH1 R D -SMEARING S M IH1 R IH0 NG -SMEARS S M IH1 R Z -SMEBY S M IY1 B IY0 -SMEDBERG S M EH1 D B ER0 G -SMEDLEY S M EH1 D L IY0 -SMEE S M IY1 -SMEETING S M IY1 T IH0 NG -SMEJKAL S M EH1 JH K AH0 L -SMELCER S M EH1 L S ER0 -SMELL S M EH1 L -SMELLED S M EH1 L D -SMELLEY S M EH1 L IY0 -SMELLING S M EH1 L IH0 NG -SMELLS S M EH1 L Z -SMELLY S M EH1 L IY0 -SMELSER S M EH1 L S ER0 -SMELT S M EH1 L T -SMELTER S M EH1 L T ER0 -SMELTER'S S M EH1 L T ER0 Z -SMELTERS S M EH1 L T ER0 Z -SMELTING S M EH1 L T IH0 NG -SMELTZ S M EH1 L T S -SMELTZER S M EH1 L T Z ER0 -SMESTAD S M EH1 S T AH0 D -SMET S M EH1 T -SMETANA S M EH1 T AH0 N AH0 -SMETHERS S M EH1 DH ER0 Z -SMETHURST S M EH1 TH ER0 S T -SMETZER S M EH1 T Z ER0 -SMICK S M IH1 K -SMID S M IH1 D -SMIDDY S M IH1 D IY0 -SMIDGEN S M IH1 JH AH0 N -SMIDT S M IH1 D T -SMIGEL S M IH1 JH AH0 L -SMIGELSKI S M IH0 G EH1 L S K IY0 -SMIGIEL S M IH1 JH IY0 L -SMIGIELSKI S M IH0 G IY1 L S K IY0 -SMILE S M AY1 L -SMILED S M AY1 L D -SMILES S M AY1 L Z -SMILEY S M AY1 L IY0 -SMILGIS S M IH1 L G IH0 Z -SMILGIS(2) S M IH1 L JH IH0 Z -SMILIE S M IH1 L IY0 -SMILING S M AY1 L IH0 NG -SMILINGLY S M AY1 L IH0 NG L IY0 -SMILLIE S M IH1 L IY0 -SMILOW S M IH1 L OW0 -SMINK S M IH1 NG K -SMIRK S M ER1 K -SMIRKING S M ER1 K IH0 NG -SMIRKS S M ER1 K S -SMIRNOFF S M ER1 N AO0 F -SMIRNOV S M ER1 N AA0 F -SMIRNOVA S M ER2 N OW1 V AH0 -SMISEK S M IH1 S EH0 K -SMIT S M IH1 T -SMITH S M IH1 TH -SMITH'S S M IH1 TH S -SMITHBURG S M IH1 TH B ER0 G -SMITHEE S M IH1 TH IY1 -SMITHER S M IH1 DH ER0 -SMITHEREEN S M IH2 DH ER0 IY1 N -SMITHEREENS S M IH2 DH ER0 IY1 N Z -SMITHERMAN S M IH1 DH ER0 M AH0 N -SMITHERS S M IH1 DH ER0 Z -SMITHEY S M IH1 TH IY0 -SMITHFIELD S M IH1 TH F IY0 L D -SMITHHART S M IH1 TH HH AA2 R T -SMITHKLINE S M IH1 TH K L AY2 N -SMITHKLINE'S S M IH1 TH K L AY2 N Z -SMITHS S M IH1 TH S -SMITHSON S M IH1 TH S AH0 N -SMITHSON'S S M IH1 TH S AH0 N Z -SMITHSONIAN S M IH0 TH S OW1 N IY0 AH0 N -SMITHSONIAN(2) S M IH0 S OW1 N IY0 AH0 N -SMITHSONIAN'S S M IH2 TH S OW1 N IY0 AH0 N Z -SMITHSONIAN'S(2) S M IH2 S OW1 N IY0 AH0 N Z -SMITHTOWN S M IH1 TH T AW2 N -SMITHWICK S M IH1 TH W IH2 K -SMITLEY S M IH1 T L IY0 -SMITS S M IH1 T S -SMITTEN S M IH1 T AH0 N -SMITTLE S M IH1 T AH0 L -SMITTY S M IH1 T IY0 -SMOAK S M OW1 K -SMOCK S M AA1 K -SMOG S M AA1 G -SMOGGIEST S M AA1 G IY0 AH0 S T -SMOGGY S M AO1 G IY0 -SMOKE S M OW1 K -SMOKED S M OW1 K T -SMOKEJUMPER S M OW1 K JH AH2 M P ER0 -SMOKEJUMPERS S M OW1 K JH AH2 M P ER0 Z -SMOKELESS S M OW1 K L AH0 S -SMOKER S M OW1 K ER0 -SMOKER'S S M OW1 K ER0 Z -SMOKERS S M OW1 K ER0 Z -SMOKERS' S M OW1 K ER0 Z -SMOKES S M OW1 K S -SMOKESCREEN S M OW1 K S K R IY2 N -SMOKESTACK S M OW1 K S T AE2 K -SMOKESTACKS S M OW1 K S T AE2 K S -SMOKEY S M OW1 K IY0 -SMOKING S M OW1 K IH0 NG -SMOKING'S S M OW1 K IH0 NG Z -SMOKY S M OW1 K IY0 -SMOLA S M OW1 L AA0 -SMOLAK S M OW1 L AH0 K -SMOLDER S M OW1 L D ER0 -SMOLDERING S M OW1 L D ER0 IH0 NG -SMOLEN S M AA1 L AH0 N -SMOLENSK S M OW0 L EH1 N S K -SMOLENSKI S M AH0 L EH1 N S K IY0 -SMOLEY S M OW1 L IY0 -SMOLIK S M OW1 L IH0 K -SMOLIN S M OW1 L IH0 N -SMOLINSKI S M AH0 L IH1 N S K IY0 -SMOLINSKY S M AH0 L IH1 N S K IY0 -SMOLKA S M OW1 L K AH0 -SMOOCH S M UW1 CH -SMOOCHES S M UW1 CH IH0 Z -SMOOT S M UW1 T -SMOOTH S M UW1 DH -SMOOTH-SHAVEN S M UW2 TH SH EY1 V AH0 N -SMOOTHED S M UW1 DH D -SMOOTHEN S M UW1 DH IH0 N -SMOOTHENS S M UW1 DH IH0 N Z -SMOOTHER S M UW1 DH ER0 -SMOOTHEST S M UW1 DH AH0 S T -SMOOTHING S M UW1 DH IH0 NG -SMOOTHLY S M UW1 DH L IY0 -SMOOTHNESS S M UW1 DH N AH0 S -SMOOTHS S M UW1 DH Z -SMOOTS S M UW1 T S -SMORGASBORD S M AO1 R G AH0 S B AO2 R D -SMOSH S M UH1 SH -SMOSHED S M UH1 SH T -SMOSHES S M UH1 SH AH0 Z -SMOSHING S M UH1 SH IH0 NG -SMOTHER S M AH1 DH ER0 -SMOTHERED S M AH1 DH ER0 D -SMOTHERING S M AH1 DH ER0 IH0 NG -SMOTHERMAN S M AH1 DH ER0 M AH0 N -SMOTHERMON S M AA1 TH ER0 M OW0 N -SMOTHERS S M AH1 DH ER0 Z -SMOTRICH S M AA1 T R IH0 K -SMOUSE S M AW1 S -SMOYER S M OY1 ER0 -SMREKAR S M R EH1 K ER0 -SMREKAR(2) S M ER1 IH0 K ER0 -SMS EH2 S EH2 M EH1 S -SMUCK S M AH1 K -SMUCKER S M AH1 K ER0 -SMUDGE S M AH1 JH -SMUDGED S M AH1 JH D -SMUG S M AH1 G -SMUGGLE S M AH1 G AH0 L -SMUGGLED S M AH1 G AH0 L D -SMUGGLER S M AH1 G L ER0 -SMUGGLER(2) S M AH1 G AH0 L ER0 -SMUGGLER'S S M AH1 G L ER0 Z -SMUGGLERS S M AH1 G L ER0 Z -SMUGGLERS(2) S M AH1 G AH0 L ER0 Z -SMUGGLERS' S M AH1 G L ER0 Z -SMUGGLERS'(2) S M AH1 G AH0 L ER0 Z -SMUGGLING S M AH1 G L IH0 NG -SMUGGLING(2) S M AH1 G AH0 L IH0 NG -SMUGLY S M AH1 G L IY0 -SMUGNESS S M AH1 G N AH0 S -SMULL S M AH1 L -SMULLEN S M AH1 L AH0 N -SMURF S M ER1 F -SMURFIT S M ER1 F IH0 T -SMURFS S M ER1 F S -SMUT S M AH1 T -SMUTTY S M AH1 T IY2 -SMYERS S M AY1 ER0 Z -SMYLIE S M IH1 L IY0 -SMYLY S M IH1 L IY0 -SMYRE S M AY1 R -SMYRNA S M ER1 N AH0 -SMYSER S M AY1 S ER0 -SMYTH S M AY1 TH -SMYTH(2) S M IH1 TH -SMYTHE S M AY1 DH -SNABEL S N AA1 B AH0 L -SNACK S N AE1 K -SNACKBAR S N AE1 K B AA0 R -SNACKED S N AE1 K T -SNACKING S N AE1 K IH0 NG -SNACKS S N AE1 K S -SNACKWELL S N AE1 K W EH2 L -SNACKWELL'S S N AE1 K W EH2 L Z -SNACKWELLS S N AE1 K W EH2 L Z -SNADER S N EY1 D ER0 -SNAFU S N AE1 F UW0 -SNAFUS S N AE1 F AH0 S -SNAG S N AE1 G -SNAGGED S N AE1 G D -SNAGGER S N AE1 G ER0 -SNAGGERS S N AE1 G ER0 Z -SNAGGING S N AE1 G IH0 NG -SNAGS S N AE1 G Z -SNAIL S N EY1 L -SNAIL'S S N EY1 L Z -SNAILS S N EY1 L Z -SNAKE S N EY1 K -SNAKE'S S N EY1 K S -SNAKEBITE S N EY1 K B AY2 T -SNAKEBITES S N EY1 K B AY2 T S -SNAKELIKE S N EY1 K L AY2 K -SNAKES S N EY1 K S -SNAKING S N EY1 K IH0 NG -SNAP S N AE1 P -SNAPCHAT S N AE1 P CH AE2 T -SNAPDRAGON S N AE1 P D R AE2 G AH0 N -SNAPDRAGONS S N AE1 P D R AE2 G AH0 N Z -SNAPE S N EY1 P -SNAPP S N AE1 P -SNAPPED S N AE1 P T -SNAPPER S N AE1 P ER0 -SNAPPIEST S N AE1 P IH0 EH2 S T -SNAPPING S N AE1 P IH0 NG -SNAPPLE S N AE1 P AH0 L -SNAPPLE'S S N AE1 P AH0 L Z -SNAPPLES S N AE1 P AH0 L Z 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EY1 S P AO2 R T -SPACEPORTS S P EY1 S P AO2 R T S -SPACER S P EY1 S ER0 -SPACERS S P EY1 S ER0 Z -SPACES S P EY1 S AH0 Z -SPACES(2) S P EY1 S IH0 Z -SPACESHIP S P EY1 S SH IH2 P -SPACESHIPS S P EY1 S SH IH2 P S -SPACESUIT S P EY1 S UW2 T -SPACESUITS S P EY1 S UW2 T S -SPACEWALK S P EY1 S W AA2 K -SPACEWALKING S P EY1 S W AA2 K IH0 NG -SPACEWALKS S P EY1 S W AA2 K S -SPACEX S P EY1 S AH0 K S -SPACEY S P EY1 S IY0 -SPACIAL S P EY1 SH AH0 L -SPACING S P EY1 S IH0 NG -SPACIOUS S P EY1 SH AH0 S -SPACK S P AE1 K -SPACKLE S P AE1 K AH0 L -SPACKMAN S P AE1 K M AH0 N -SPADA S P AA1 D AH0 -SPADACCINI S P AA0 D AA0 CH IY1 N IY0 -SPADAFORA S P AA0 D AA0 F AO1 R AH0 -SPADAFORE S P AA0 D AO1 F AO0 R -SPADARO S P AA0 D AA1 R OW0 -SPADE S P EY1 D -SPADER S P EY1 D ER0 -SPADES S P EY1 D Z -SPADEWORK S P EY1 D W ER2 K -SPADONI S P AA0 D OW1 N IY0 -SPADY S P EY1 D IY0 -SPAETH S P IY1 TH -SPAFFORD S P AE1 F ER0 D -SPAGHETTI S P AH0 G EH1 T IY0 -SPAGNA S P AE1 G N AH0 -SPAGNOLA S P AA0 G N OW1 L AH0 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D AH0 L -SPEIER S P AY1 ER0 -SPEIGHT S P EY1 T -SPEIGHTS S P EY1 T S -SPEIGNER S P AY1 G N ER0 -SPEIR S P IH1 R -SPEIRS S P IH1 R Z -SPEISER S P AY1 Z ER0 -SPEKTR S P EH1 K T ER0 -SPEKTR'S S P EH1 K T ER0 Z -SPELL S P EH1 L -SPELLACY S P EH1 L AH0 S IY0 -SPELLBINDING S P EH1 L B AY2 N D IH0 NG -SPELLBOUND S P EH1 L B AW2 N D -SPELLED S P EH1 L D -SPELLER S P EH1 L ER0 -SPELLERS S P EH1 L ER0 Z -SPELLING S P EH1 L IH0 NG -SPELLING'S S P EH1 L IH0 NG Z -SPELLINGS S P EH1 L IH0 NG Z -SPELLMAN S P EH1 L M AH0 N -SPELLMAN'S S P EH1 L M AH0 N Z -SPELLMEYER S P EH1 L M AY0 ER0 -SPELLMEYER'S S P EH1 L M AY0 ER0 Z -SPELLS S P EH1 L Z -SPELMAN S P EH1 L M AH0 N -SPELTZ S P EH1 L T S -SPELUNK S P AH0 L AH1 NG K -SPELUNKED S P AH0 L AH1 NG K T -SPELUNKER S P AH0 L AH1 NG K ER0 -SPELUNKERS S P AH0 L AH1 NG K ER0 Z -SPELUNKING S P AH0 L AH1 NG K IH0 NG -SPELUNKS S P AH0 L AH1 NG K S -SPENCE S P EH1 N S -SPENCER S P EH1 N S ER0 -SPENCER'S S P EH1 N S ER0 Z -SPENCERS S P EH1 N S ER0 Z -SPEND S P EH1 N D -SPENDABLE S P EH1 N D AH0 B AH0 L -SPENDER S P EH1 N D ER0 -SPENDERS S P EH1 N D ER0 Z -SPENDING S P EH1 N D IH0 NG -SPENDLEY S P EH1 N D L IY0 -SPENDLOVE S P EH1 N D L AH2 V -SPENDS S P EH1 N D Z -SPENDS(2) S P EH1 N Z -SPENDTHRIFT S P EH1 N D TH R IH2 F T -SPENGLER S P IH1 NG AH0 L ER0 -SPENGLER(2) S P IH1 NG L ER0 -SPENNER S P EH1 N ER0 -SPENO S P EH1 N OW0 -SPENS S P EH1 N S -SPENSER S P EH1 N S ER0 -SPENSER'S S P EH1 N S ER0 Z -SPENSERS S P EH1 N S ER0 Z -SPENT S P EH1 N T -SPERA S P EH1 R AH0 -SPERANZA S P ER0 AA1 N Z AH0 -SPERBECK S P ER1 B EH0 K -SPERBER S P ER1 B ER0 -SPERDUTO S P ER0 D UW1 T OW0 -SPERL S P ER1 L -SPERLE S P ER1 L -SPERLICH S P ER1 L IH0 K -SPERLING S P ER1 L IH0 NG -SPERM S P ER1 M -SPERMS S P ER1 M Z -SPERO S P EH1 R OW0 -SPEROS S P EH1 R OW0 Z -SPERRAZZA S P ER0 AA1 T S AH0 -SPERRFRIST S P EH1 R F R IH0 S T -SPERRY S P EH1 R IY0 -SPESSARD S P EH1 S ER0 D -SPETH S P EH1 TH -SPETHMANN S P EH1 TH M AH0 N -SPETSNAZ S P EH1 T S N AE0 Z -SPEVAK S P EH1 V AH0 K -SPEW S P Y UW1 -SPEWED S P Y UW1 D -SPEWING S P Y UW1 IH0 NG -SPEWS S P Y UW1 Z -SPEY S P EY1 -SPEYER S P EY1 ER0 -SPEZIALE S P EH0 Z IY0 AA1 L IY0 -SPEZZANO S P EH0 T S AA1 N OW0 -SPHAR S F AA1 R -SPHERE S F IH1 R -SPHERES S F IH1 R Z -SPHERICAL S F EH1 R IH0 K AH0 L -SPHEROID S F IH1 R OY2 D -SPHINCTER S F IH1 NG K T ER0 -SPHINCTERS S F IH1 NG K T ER0 Z -SPHINX S F IH1 NG K S -SPIC S P IH1 K -SPICE S P AY1 S -SPICED S P AY1 S T -SPICELAND S P AY1 S L AE2 N D -SPICER S P AY1 S ER0 -SPICES S P AY1 S AH0 Z -SPICES(2) S P AY1 S IH0 Z -SPICEY S P AY1 S IY0 -SPICHER S P IH1 CH ER0 -SPICING S P AY1 S IH0 NG -SPICKARD S P IH1 K ER0 D -SPICKLER S P IH1 K L ER0 -SPICUZZA S P IY0 K UW1 T S AH0 -SPICY S P AY1 S IY0 -SPIDEL S P IH1 D AH0 L -SPIDELL S P IH1 D AH0 L -SPIDER S P AY1 D ER0 -SPIDER-MAN S P AY1 D ER0 M AE0 N -SPIDERMAN S P AY1 D ER0 M AE0 N -SPIDERS S P AY1 D ER0 Z -SPIDLE S P AY1 D AH0 L -SPIE S P IY1 -SPIED S P AY1 D -SPIEGEL S P IY1 G AH0 L -SPIEGELBERG S P IY1 G AH0 L B ER0 G -SPIEGELMAN S P IY1 G AH0 L M AH0 N -SPIEGLER S P IY1 G L ER0 -SPIEKER S P IY1 K ER0 -SPIEL S P IY1 L -SPIELBERG S P IY1 L B ER0 G -SPIELBERG'S S P IY1 L B ER0 G Z -SPIELBERGER S P IY1 L B ER0 G ER0 -SPIELER S P IY1 L ER0 -SPIELMAN S P IY1 L M AH0 N -SPIELMANN S P IY1 L M AH0 N -SPIELVOGEL S P IY1 L V OW2 G AH0 L -SPIER S P AY1 ER0 -SPIERING S P AY1 ER0 IH0 NG -SPIERS S P AY1 ER0 Z -SPIES S P AY1 Z -SPIESS S P IY1 Z -SPIETH S P IY1 TH # NAME -SPIETH(2) S P AY1 AH0 TH # OLD -SPIETH'S S P IY1 TH S -SPIEWAK S P IY1 W AE2 K -SPIFFING S P IH1 F IH0 NG -SPIFFY S P IH1 F IY0 -SPIGHT S P AY1 T -SPIGNER S P AY1 G N ER0 -SPIGOT S P IH1 G AH0 T -SPIGOTS S P IH1 G AH0 T S -SPIKE S P AY1 K -SPIKED S P AY1 K T -SPIKER S P AY1 K ER0 -SPIKES S P AY1 K S -SPIKING S P AY1 K IH0 NG -SPIKY S P AY1 K IY0 -SPILDE S P IH1 L D -SPILKER S P IH1 L K ER0 -SPILL S P IH1 L -SPILL'S S P IH1 L Z -SPILLAGE S P IH1 L IH0 JH -SPILLANE S P IH1 L AH0 N -SPILLED S P IH1 L D -SPILLER S P IH1 L ER0 -SPILLERS S P IH1 L ER0 Z -SPILLING S P IH1 L IH0 NG -SPILLMAN S P IH1 L M AH0 N -SPILLOVER S P IH1 L OW2 V ER0 -SPILLS S P IH1 L Z -SPILLWAY S P IH1 L W EY2 -SPILMAN S P IH1 L M AH0 N -SPILOTRO S P IH0 L AA1 T R OW0 -SPILT S P IH1 L T -SPIN S P IH1 N -SPINA S P IY1 N AH0 -SPINACH S P IH1 N AH0 CH -SPINAL S P AY1 N AH0 L -SPINALE S P IY0 N AA1 L IY0 -SPINDEL S P IH1 N D AH0 L -SPINDLE S P IH1 N D AH0 L -SPINDLER S P IH1 N D AH0 L ER0 -SPINDLER(2) S P IH1 N D L ER0 -SPINE S P AY1 N -SPINELESS S P AY1 N L AH0 S -SPINELLA S P IH0 N EH1 L AH0 -SPINELLI S P IH0 N EH1 L IY0 -SPINELLO S P IH0 N EH1 L OW0 -SPINES S P AY1 N Z -SPINFIZZ S P IH1 N F IH0 Z -SPINIFEX S P IH1 N IH0 F EH0 K S -SPINK S P IH1 NG K -SPINKS S P IH1 NG K S -SPINKS'S S P IH1 NG K S IH0 Z -SPINNER S P IH1 N ER0 -SPINNER'S S P IH1 N ER0 Z -SPINNERS S P IH1 N ER0 Z -SPINNEY S P IH1 N IY0 -SPINNING S P IH1 N IH0 NG -SPINO S P IY1 N OW0 -SPINOFF S P IH1 N AO2 F -SPINOFFS S P IH1 N AO2 F S -SPINOLA S P IY0 N OW1 L AH0 -SPINOSA S P IY0 N OW1 S AH0 -SPINOZA S P IH0 N OW1 Z AH0 -SPINS S P IH1 N Z -SPINSTER S P IH1 N S T ER0 -SPINY S P AY1 N IY0 -SPIRA S P IH1 R AH0 -SPIRAL S P AY1 R AH0 L -SPIRALED S P AY1 R AH0 L D -SPIRALING S P AY1 R AH0 L IH0 NG -SPIRALLING S P AY1 R AH0 L IH0 NG -SPIRALS S P AY1 R AH0 L Z -SPIRE S P AY1 R -SPIRES S P AY1 R Z -SPIRIT S P IH1 R AH0 T -SPIRIT(2) S P IH1 R IH0 T -SPIRIT'S S P IH1 R IH0 T S -SPIRITED S P IH1 R IH0 T IH0 D -SPIRITEDNESS S P IH1 R IH0 T IH0 D N AH0 S -SPIRITO S P IH0 R IY1 T OW2 -SPIRITS S P IH1 R IH0 T S -SPIRITUAL S P IH1 R IH0 CH UW2 AH0 L -SPIRITUALISM S P IH1 R IH0 CH UW2 AH0 L IH2 Z M -SPIRITUALIST S P IH1 R IH0 CH UW2 AH0 L IH2 S T -SPIRITUALISTS S P IH1 R IH0 CH UW2 AH0 L IH2 S T S -SPIRITUALITY S P IH2 R IH0 CH UW2 AE1 L AH0 T IY2 -SPIRITUALLY S P IH1 R IH0 CH UW2 AH0 L IY2 -SPIRITUALS S P IH1 R IH0 CH UW2 AH0 L Z -SPIRO S P IH1 R OW2 -SPIRO'S S P IH1 R OW2 Z -SPIROS S P IH1 R OW2 Z -SPISAK S P IH1 S AH0 K -SPIT S P IH1 T -SPITALE S P IY0 T AA1 L IY0 -SPITBALL S P IH1 T B AO2 L -SPITBALL'S S P IH1 T B AO2 L Z -SPITE S P AY1 T -SPITEFUL S P AY1 T F AH0 L -SPITERI S P IY0 T EH1 R IY0 -SPITLER S P IH1 T L ER0 -SPITS S P IH1 T S -SPITTING S P IH1 T IH0 NG -SPITTLE S P IH1 T AH0 L -SPITTLER S P IH1 T L ER0 -SPITZ S P IH1 T S -SPITZER S P IH1 T Z ER0 -SPITZLEY S P IH1 T S L IY0 -SPITZNAGEL S P IH1 T S N EY2 G AH0 L -SPIVA S P IY1 V AH0 -SPIVACK S P IH1 V AH0 K -SPIVAK S P IH1 V AH0 K -SPIVEY S P IH1 V IY0 -SPIWAK S P IH1 V AH0 K -SPIZZIRRI S P IY0 T S IH1 R IY0 -SPLAIN S P L EY1 N -SPLAINE S P L EY1 N -SPLASH S P L AE1 SH -SPLASHED S P L AE1 SH T -SPLASHES S P L AE1 SH IH0 Z -SPLASHING S P L AE1 SH IH0 NG -SPLASHY S P L AE1 SH IY0 -SPLAT S P L AE1 T -SPLATTER S P L AE1 T ER0 -SPLATTERED S P L AE1 T ER0 D -SPLATTERING S P L AE1 T AH0 R IH2 NG -SPLATTERS S P L AE1 T ER0 Z -SPLAWN S P L AO1 N -SPLEEN S P L IY1 N -SPLEISSON S P EY1 S AH0 N -SPLENDID S P L EH1 N D IH0 D -SPLENDIDLY S P L EH1 N D AH0 D L IY0 -SPLENDOR S P L EH1 N D ER0 -SPLENIC S P L EH1 N IH0 K -SPLENIC(2) S P L IY1 N IH0 K -SPLICE S P L AY1 S -SPLICED S P L AY1 S T -SPLICES S P L AY1 S IH0 Z -SPLICHAL S P L IH1 CH AH0 L -SPLICING S P L AY1 S IH0 NG -SPLINT S P L IH1 N T -SPLINTER S P L IH1 N T ER0 -SPLINTERED S P L IH1 N T ER0 D -SPLINTERING S P L IH1 N T ER0 IH0 NG -SPLINTERS S P L IH1 N T ER0 Z -SPLINTERY S P L IH1 N T ER0 IY0 -SPLINTS S P L IH1 N T S -SPLIT S P L IH1 T -SPLITS S P L IH1 T S -SPLITT S P L IH1 T -SPLITTER S P L IH1 T ER0 -SPLITTING S P L IH1 T IH0 NG -SPLURGE S P L ER1 JH -SPLURGED S P L ER1 JH D -SPLURGES S P L ER1 JH IH0 S -SPLURGING S P L ER1 JH IH0 NG -SPOARER S P AO1 R ER0 -SPOCK S P AA1 K -SPODEN S P OW1 D AH0 N -SPOELSTRA S P OW1 L S T R AA0 -SPOERL S P AO1 R L -SPOFFORD S P AA1 F ER0 D -SPOGLI S P AA1 G L IY0 -SPOHN S P AA1 N -SPOHR S P AA1 R -SPOIL S P OY1 L -SPOILAGE S P OY1 L AH0 JH -SPOILAGE(2) S P OY1 L IH0 JH -SPOILED S P OY1 L D -SPOILER S P OY1 L ER0 -SPOILERS S P OY1 L ER0 Z -SPOILING S P OY1 L IH0 NG -SPOILS S P OY1 L Z -SPOKANE S P OW0 K AE1 N -SPOKANE(2) S P OW0 K EY1 N -SPOKE S P OW1 K -SPOKEN S P OW1 K AH0 N -SPOKENLY S P OW1 K AH0 N L IY0 -SPOKES S P OW1 K S -SPOKESMAN S P OW1 K S M AH0 N -SPOKESMEN S P OW1 K S M IH0 N -SPOKESPEOPLE S P OW1 K S P IY2 P AH0 L -SPOKESPERSON S P OW1 K S P ER0 S AH0 N -SPOKESPERSONS S P OW1 K S P ER0 S AH0 N Z -SPOKESWOMAN S P OW1 K S W UH2 M AH0 N -SPOKESWOMEN S P OW1 K S W IH2 M AH0 N -SPOLETO S P OW0 L EY1 T OW0 -SPOLETTO S P OW0 L EY1 T OW0 -SPOLETTO'S S P OW0 L EY1 T OW0 Z -SPOMER S P OW1 M ER0 -SPONAUGLE S P AA1 N AO0 G AH0 L -SPONG S P AO1 NG -SPONGE S P AH1 N JH -SPONGEBOB S P AH1 N JH B AA2 B -SPONGED S P AH1 N JH D -SPONGEFORM S P AH1 N JH F AO0 R M -SPONGES S P AH1 N JH AH0 Z -SPONGIFORM S P AH1 N JH IH0 F AO2 R M -SPONGY S P AH1 N JH IY0 -SPONSEL S P AA1 N S AH0 L -SPONSELLER S P AA1 N S AH0 L ER0 -SPONSLER S P AA1 N S AH0 L ER0 -SPONSLER(2) S P AA1 N S L ER0 -SPONSOR S P AA1 N S ER0 -SPONSOR'S S P AA1 N S ER0 Z -SPONSORED S P AA1 N S ER0 D -SPONSORING S P AA1 N S ER0 IH0 NG -SPONSORS S P AA1 N S ER0 Z -SPONSORSHIP S P AA1 N S ER0 SH IH2 P -SPONSORSHIPS S P AA1 N S ER0 SH IH2 P S -SPONTANEITY S P AA2 N T AH0 N IY1 AH0 T IY0 -SPONTANEOUS S P AA0 N T EY1 N IY0 AH0 S -SPONTANEOUSLY S P AA0 N T EY1 N IY0 AH0 S L IY0 -SPOOF S P UW1 F -SPOOFED S P UW1 F T -SPOOFING S P UW1 F IH0 NG -SPOOFS S P UW1 F S -SPOOK S P UW1 K -SPOOKED S P UW1 K T -SPOOKS S P UW1 K S -SPOOKY S P UW1 K IY0 -SPOOL S P UW1 L -SPOON S P UW1 N -SPOONEMORE S P UW1 N M AO0 R -SPOONER S P UW1 N ER0 -SPOONFUL S P UW1 N F UH2 L -SPOONFULS S P UW1 N F UH2 L Z -SPOONS S P UW1 N Z -SPOOR S P UH1 R -SPOOR'S S P UH1 R Z -SPORADIC S P ER0 AE1 D IH0 K -SPORADICALLY S P ER0 AE1 D IH0 K L IY0 -SPORCK S P AO1 R K -SPORE S P AO1 R -SPORER S P AO1 R ER0 -SPORES S P AO1 R Z -SPORKIN S P AO1 R K IH0 N -SPORKIN'S S P AO1 R K IH0 N Z -SPORLEDER S P AO1 R L IH0 D ER0 -SPORN S P AO1 R N -SPOROPHYTE S P AO1 R F AY2 T -SPOROPHYTES S P AO1 R F AY2 T S -SPORRER S P AO1 ER0 R -SPORT S P AO1 R T -SPORT'S S P AO1 R T S -SPORTED S P AO1 R T IH0 D -SPORTIER S P AO1 R T IY0 ER0 -SPORTING S P AO1 R T IH0 NG -SPORTINGLY S P AO1 R T IH0 NG L IY0 -SPORTS S P AO1 R T S -SPORTS' S P AO1 R T S -SPORTSBAR S P AO1 R T S B AA2 R -SPORTSCASTER S P AO1 R T S K AE2 S T ER0 -SPORTSCASTERS S P AO1 R T S K AE2 S T ER0 Z -SPORTSCHANNEL S P AO1 R T S CH AE1 N AH0 L -SPORTSCLUB S P AO1 R T S K L AH2 B -SPORTSMAN S P AO1 R T S M AH0 N -SPORTSMANSHIP S P AO1 R T S M AH0 N SH IH2 P -SPORTSMEN S P AO1 R T S M IH0 N -SPORTSTER S P AO1 R T S T ER0 -SPORTSWEAR S P AO1 R T S W EH2 R -SPORTSWRITER S P AO1 R T S R AY2 T ER0 -SPORTSWRITERS S P AO1 R T S R AY2 T ER0 Z -SPORTY S P AO1 R T IY0 -SPOSATO S P OW0 S AA1 T OW0 -SPOSITO S P OW0 S IY1 T OW0 -SPOT S P AA1 T -SPOTIFY S P AO1 T AH0 F AY2 -SPOTLESS S P AA1 T L AH0 S -SPOTLIGHT S P AA1 T L AY2 T -SPOTLIGHTED S P AA1 T L AY2 T IH0 D -SPOTLIGHTING S P AA1 T L AY2 T IH0 NG -SPOTLIGHTS S P AA1 T L AY2 T S -SPOTO S P OW1 T OW0 -SPOTS S P AA1 T S -SPOTTED S P AA1 T IH0 D -SPOTTER S P AA1 T ER0 -SPOTTERS S P AA1 T ER0 Z -SPOTTING S P AA1 T IH0 NG -SPOTTS S P AA1 T S -SPOTTY S P AA1 T IY0 -SPOUSAL S P AW1 Z AH0 L -SPOUSE S P AW1 S -SPOUSE'S S P AW1 S IH0 Z -SPOUSES S P AW1 S IH0 Z -SPOUT S P AW1 T -SPOUTED S P AW1 T IH0 D -SPOUTING S P AW1 T IH0 NG -SPOUTS S P AW1 T S -SPRACKLEN S P R AE1 K AH0 L AH0 N -SPRADLEY S P R AE1 D L IY0 -SPRADLIN S P R AE1 D L IH0 N -SPRADLING S P R AE1 D L IH0 NG -SPRAGG S P R AE1 G -SPRAGGINS S P R AE1 G IH0 N Z -SPRAGUE S P R EY1 G -SPRAGUE'S S P R EY1 G Z -SPRAIN S P R EY1 N -SPRAINED S P R EY1 N D -SPRAINING S P R EY1 N IH0 NG -SPRAINS S P R EY1 N Z -SPRAKER S P R EY1 K ER0 -SPRANG S P R AE1 NG -SPRANGER S P R AE1 NG ER0 -SPRANKLE S P R AE1 NG K AH0 L -SPRATLEY S P R AE1 T L IY0 -SPRATLIN S P R AE1 T L IH0 N -SPRATLING S P R AE1 T L IH0 NG -SPRATT S P R AE1 T -SPRAWL S P R AO1 L -SPRAWLED S P R AO1 L D -SPRAWLING S P R AO1 L IH0 NG -SPRAWLS S P R AO1 L Z -SPRAY S P R EY1 -SPRAYBERRY S P R EY1 B EH2 R IY0 -SPRAYED S P R EY1 D -SPRAYER S P R EY1 ER0 -SPRAYERS S P R EY1 ER0 Z -SPRAYING S P R EY1 IH0 NG -SPRAYS S P R EY1 Z -SPREAD S P R EH1 D -SPREADER S P R EH1 D ER0 -SPREADING S P R EH1 D IH0 NG -SPREADS S P R EH1 D Z -SPREADSHEET S P R EH1 D SH IY2 T -SPREADSHEETS S P R EH1 D SH IY2 T S -SPRECHER S P R EH1 K ER0 -SPRECKELS S P R EH1 K AH0 L Z -SPREE S P R IY1 -SPREEMAN S P R IY1 M AH0 N -SPREEN S P R IY1 N -SPREES S P R IY1 Z -SPREHE S P R IY1 HH -SPREITZER S P R AY1 T Z ER0 -SPRENG S P R EH1 NG -SPRENGER S P R EH1 N JH ER0 -SPRENKLE S P R EH1 NG K AH0 L -SPRICK S P R IH1 K -SPRIGG S P R IH1 G -SPRIGGED S P R IH1 G D -SPRIGGS S P R IH1 G Z -SPRIGHTLY S P R AY1 T L IY0 -SPRING S P R IH1 NG -SPRING(2) S P ER0 IH1 NG -SPRING'S S P R IH1 NG Z -SPRINGBOARD S P R IH1 NG B AO2 R D -SPRINGBORN S P R IH1 NG G B ER0 N -SPRINGDALE S P R IH1 NG D EY2 L -SPRINGER S P R IH1 NG ER0 -SPRINGERVILLE S P R IH1 NG ER0 V IH2 L -SPRINGFIELD S P R IH1 NG F IY2 L D -SPRINGFIELD'S S P R IH1 NG F IY2 L D Z -SPRINGING S P R IH1 NG IH0 NG -SPRINGMAN S P R IH1 NG M AH0 N -SPRINGS S P R IH1 NG Z -SPRINGS(2) S P ER0 IH1 NG Z -SPRINGSTEAD S P R IH1 NG S T EH2 D -SPRINGSTEEN S P R IH1 NG S T IY2 N -SPRINGSTEEN'S S P R IH1 NG S T IY2 N Z -SPRINGSTON S P R IH1 NG S T AH0 N -SPRINGTIME S P R IH1 NG T AY2 M -SPRINKEL S P R IH1 NG K AH0 L -SPRINKLE S P R IH1 NG K AH0 L -SPRINKLED S P R IH1 NG K AH0 L D -SPRINKLER S P R IH1 NG K L ER0 -SPRINKLER(2) S P R IH1 NG K AH0 L ER0 -SPRINKLERS S P R IH1 NG K L ER0 Z -SPRINKLERS(2) S P R IH1 NG K AH0 L ER0 Z -SPRINKLES S P R IH1 NG K AH0 L Z -SPRINKLING S P R IH1 NG K L IH0 NG -SPRINKLING(2) S P R IH1 NG K AH0 L IH0 NG -SPRINT S P R IH1 N T -SPRINT'S S P R IH1 N T S -SPRINTED S P R IH1 N T IH0 D -SPRINTER S P R IH1 N T ER0 -SPRINTERS S P R IH1 N T ER0 Z -SPRINTING S P R IH1 N T IH0 NG -SPRINTS S P R IH1 N T S -SPRITE S P R AY1 T -SPRIZZO S P R IH1 Z OW0 -SPROAT S P R OW1 T -SPROCK S P R AA1 K -SPROCKET S P R AA1 K AH0 T -SPROGUS S P R OW1 G AH0 S -SPROGUS'S S P R OW1 G AH0 S IH0 Z -SPROLES S P R OW1 L Z -SPRONG S P R AO1 NG -SPROTT S P R AA1 T -SPROUL S P R AW1 L -SPROULE S P R UW1 L -SPROULL S P R UW1 L -SPROULS S P R AW1 L Z -SPROUSE S P R AW1 S -SPROUT S P R AW1 T -SPROUTAPHILE S P R AW1 T AH0 F AY0 L -SPROUTAPHILES S P R AW1 T AH0 F AY0 L Z -SPROUTED S P R AW1 T IH0 D -SPROUTING S P R AW1 T IH0 NG -SPROUTS S P R AW1 T S -SPROW S P R AW1 -SPROWL S P R AW1 L -SPROWLE S P R AW1 AH0 L -SPROWLS S P R AW1 L Z -SPRUCE S P R UW1 S -SPRUCED S P R UW1 S T -SPRUCING S P R UW1 S IH0 NG -SPRUELL S P R UW1 L -SPRUIELL S P R UW1 L -SPRUILL S P R UW1 L -SPRUNG S P R AH1 NG -SPRUNGER S P R AH1 NG ER0 -SPRY S P R AY1 -SPUD S P AH1 D -SPUD'S S P AH1 D Z -SPUDS S P AH1 D Z -SPUHLER S P UW1 L ER0 -SPUN S P AH1 N -SPUNK S P AH1 NG K -SPUNKY S P AH1 NG K IY0 -SPUR S P ER1 -SPURDLE S P ER1 D AH0 L -SPURGE S P ER1 JH -SPURGEON S P ER1 JH IH0 N -SPURGIN S P ER1 JH IH0 N -SPURIOUS S P Y UH1 R IY0 AH0 S -SPURIOUS(2) S P ER1 IY0 AH0 S -SPURLIN S P ER1 L IH0 N -SPURLING S P ER1 L IH0 NG -SPURLOCK S P ER1 L AA2 K -SPURN S P ER1 N -SPURNED S P ER1 N D -SPURNING S P ER1 N IH0 NG -SPURNS S P ER1 N Z -SPURR S P ER1 -SPURRED S P ER1 D -SPURRIER S P ER1 IY0 ER0 -SPURRING S P ER1 IH0 NG -SPURS S P ER1 Z -SPURT S P ER1 T -SPURTED S P ER1 T IH0 D -SPURTING S P ER1 T IH0 NG -SPURTS S P ER1 T S -SPUTNIK S P AH1 T N IH0 K -SPUTNIKS S P AH1 T N IH0 K S -SPUTTER S P AH1 T ER0 -SPUTTERED S P AH1 T ER0 D -SPUTTERING S P AH1 T ER0 IH0 NG -SPUTTERS S P AH1 T ER0 Z -SPUTUM S P Y UW1 T AH0 M -SPY S P AY1 -SPY'S S P AY1 Z -SPYCATCHER S P AY1 K AE2 CH ER0 -SPYCHALSKI S P IH0 HH AA1 L S K IY0 -SPYGLASS S P AY1 G L AE2 S -SPYING S P AY1 IH0 NG -SPYKER S P AY1 K ER0 -SPYWARE S P AY1 W EH2 R -SQL EH2 S K Y UW2 EH1 L -SQL(2) S IY1 K W UH0 L -SQUABBLE S K W AA1 B AH0 L -SQUABBLED S K W AA1 B AH0 L D -SQUABBLES S K W AA1 B AH0 L Z -SQUABBLING S K W AA1 B AH0 L IH0 NG -SQUABBLING(2) S K W AA1 B L IH0 NG -SQUAD S K W AA1 D -SQUAD'S S K W AA1 D Z -SQUADRON S K W AA1 D R AH0 N -SQUADRONS S K W AA1 D R AH0 N Z -SQUADS S K W AA1 D Z -SQUALID S K W AA1 L AH0 D -SQUALL S K W AO1 L -SQUALLS S K W AO1 L Z -SQUALOR S K W AA1 L ER0 -SQUAMOUS S K W EY1 M AH0 S -SQUANDER S K W AA1 N D ER0 -SQUANDERED S K W AA1 N D ER0 D -SQUANDERING S K W AA1 N D ER0 IH0 NG -SQUANDERS S K W AA1 N D ER0 Z -SQUARE S K W EH1 R -SQUARED S K W EH1 R D -SQUARELY S K W EH1 R L IY0 -SQUARES S K W EH1 R Z -SQUARING S K W EH1 R IH0 NG -SQUASH S K W AA1 SH -SQUASHED S K W AA1 SH T -SQUASHING S K W AA1 SH IH0 NG -SQUASHY S K W AA1 SH IY0 -SQUAT S K W AA1 T -SQUATS S K W AA1 T S -SQUATTER S K W AA1 T ER0 -SQUATTERS S K W AA1 T ER0 Z -SQUATTING S K W AA1 T IH0 NG -SQUATTY S K W AA1 T IY0 -SQUAWK S K W AO1 K -SQUAWKING S K W AO1 K IH0 NG -SQUAWKS S K W AO1 K S -SQUEAK S K W IY1 K -SQUEAKED S K W IY1 K T -SQUEAKER S K W IY1 K ER0 -SQUEAKERS S K W IY1 K ER0 Z -SQUEAKING S K W IY1 K IH0 NG -SQUEAKS S K W IY1 K S -SQUEAKY S K W IY1 K IY0 -SQUEAL S K W IY1 L -SQUEALING S K W IY1 L IH0 NG -SQUEALS S K W IY1 L Z -SQUEAMISH S K W IY1 M IH0 SH -SQUEAMISHNESS S K W IY1 M IH0 SH N AH0 S -SQUEEGEE S K W IY1 JH IY0 -SQUEEZE S K W IY1 Z -SQUEEZED S K W IY1 Z D -SQUEEZES S K W IY1 Z IH0 Z -SQUEEZING S K W IY1 Z IH0 NG -SQUELCH S K W EH1 L CH -SQUELCHED S K W EH1 L CH T -SQUELCHING S K W EH1 L CH IH0 NG -SQUIBB S K W IH1 B -SQUIBB'S S K W IH1 B Z -SQUID S K W IH1 D -SQUIER S K W AY1 ER0 -SQUIERS S K W AY1 ER0 Z -SQUIGGLE S K W IH1 G AH0 L -SQUIGGLES S K W IH1 G AH0 L Z -SQUILLACE S K W IH1 L AH0 S -SQUILLACOTE S K W IH1 L AH0 K OW0 T -SQUILLACOTE'S S K W IH1 L AH0 K OW0 T S -SQUILLANTE S K W IY0 L AA1 N T EY0 -SQUINT S K W IH1 N T -SQUINTING S K W IH1 N T IH0 NG -SQUIRE S K W AY1 R -SQUIRES S K W AY1 ER0 Z -SQUIRM S K W ER1 M -SQUIRMING S K W ER1 M IH0 NG -SQUIRMS S K W ER1 M Z -SQUIRREL S K W ER1 AH0 L -SQUIRRELED S K W ER1 AH0 L D -SQUIRRELS S K W ER1 AH0 L Z -SQUIRT S K W ER1 T -SQUIRTED S K W ER1 T IH0 D -SQUIRTING S K W ER1 T IH0 NG -SQUIRTS S K W ER1 T S -SQUISHY S K W IH1 SH IY0 -SQUITIERI S K W IY0 T IH1 R IY0 -SQUYRES S K W AY1 R Z -SR S IY1 N Y ER0 -SR(2) S IH1 S T ER0 -SRADER SH R EY1 D ER0 -SRAM SH R AE1 M -SRAM(2) EH1 S R AE1 M -SRAMEK SH R AE1 M IH0 K -SRDAN S ER1 D AE2 N -SREBRENICA S R EY2 B R EH0 N IY1 T S AH0 -SREBRENICA(2) SH R EY2 B R AH0 N IY1 T S AH0 -SREBRENICA'S S R EY2 B R EH0 N IY1 T S AH0 Z -SREBRENICA'S(2) SH R EY2 B R AH0 N IY1 T S AH0 Z -SREMAC S R IY1 M AE2 K -SRI SH R IY1 -SRI(2) EH1 S AA1 R AY1 -SRI(3) S R IY1 -SRI-LANKA S R IY1 L AE1 NG K AH0 -SRI-LANKA(2) SH R IY1 L AE1 NG K AH0 -SRINAGAR S R IY1 N AH0 G AA2 R -SRINAGAR(2) SH R IY1 N AH0 G AA2 R -SRINAGARB S R IY1 N AH0 G AA2 R B -SRINAGARB(2) SH R IY1 N AH0 G AA2 R B -SRINIVAS S R IY1 N IH0 V AA2 S -SRINIVASAN S R IY0 N IY2 V AA0 S AA1 N -SRINIVASAN(2) SH R IY0 N IY2 V AA0 S AA1 N -SRIRAM S R IY1 R AA2 M -SRIRAM(2) SH R IY1 R AA2 M -SRIVASTAVA S R IY0 V AA0 S T AA1 V AH0 -SRIVASTAVA(2) SH R IY0 V AA0 S T AA1 V AH0 -SROCK SH R AA1 K -SROGE SH R OW1 G -SROKA SH R OW1 K AH0 -SROUFE SH R OW1 F -SRPSKA S R AH1 P S K AH0 -SSANGYONG S AE1 NG Y AO2 NG -SSN EH1 S EH1 S EH1 N -ST S T R IY1 T -ST(2) S EY1 N T -ST-CHARLES S EY1 N T CH AA1 R AH0 L Z -ST-CLAIR S EY1 N T K L EH1 R -ST-CLAIRE S EY1 N T K L EH1 R -ST-CYR S EY1 N T K IH1 R -ST-CYR(2) S EY1 N T S IH1 R -ST-DENIS S EY1 N T D EH1 N IH0 S -ST-DENNIS S EY1 N T D EH1 N IH0 S -ST-GEORGE S EY1 N T JH AO1 R JH -ST-GERMAIN S EY1 N T JH ER2 M EY1 N -ST-GERMAINE S EY1 N T JH ER2 M EY1 N -ST-GERMAINE(2) S AA1 N ZH ER2 M EY1 N -ST-HILAIRE S EY1 N T HH IH0 L EY1 R -ST-JACQUES S EY1 N T JH AA1 K S -ST-JACQUES(2) S AA1 N ZH AA1 K S -ST-JAMES S EY1 N T JH EY1 M Z -ST-JEAN S EY1 N T JH IY1 N -ST-JOHN S EY1 N T JH AA1 N -ST-JOHNS S EY1 N T JH AA1 N Z -ST-JULIEN S EY1 N T JH UW1 L IY0 AH0 N -ST-LAURENT S EY1 N T L AO1 R AH0 N T -ST-LAURENT(2) S AA1 N L AO2 R AO1 N T -ST-LAWRENCE S EY1 N T L AO1 R AH0 N S -ST-LOUIS S EY1 N T L UW1 AH0 S -ST-LOUIS(2) S EY1 N T L UW1 IY0 -ST-LUCIA S EY1 N T L UW1 SH AH0 -ST-LUCIA(2) S EY1 N T L UW2 S IY1 AH0 -ST-MARIE S EY1 N T M ER0 IY1 -ST-MARTIN S EY1 N T M AA1 R T IH0 N -ST-MARY S EY1 N T M EH1 R IY0 -ST-PETER S EY1 N T P IY1 T ER0 -ST-PETERSBURG S EY1 N T P IY1 T ER0 Z B ER0 G -ST-PIERRE S EY1 N T P Y EH1 R -ST-THOMAS S EY1 N T AA1 M AH0 S -STA S T AH1 -STA(2) EH1 S T IY1 EY1 -STAAB S T AA1 B -STAACK S T AA1 K -STAAL S T AA1 L -STAAR S T AA1 R -STAAR'S S T AA1 R Z -STAAS S T AA1 Z -STAAT S T AA1 T -STAATS S T AA1 T S -STAB S T AE1 B -STABBED S T AE1 B D -STABBING S T AE1 B IH0 NG -STABBINGS S T AE1 B IH0 NG Z -STABENOW S T AE1 B IH0 N OW0 -STABILE S T EY1 B IH0 L -STABILITY S T AH0 B IH1 L IH0 T IY0 -STABILIZATION S T EY2 B AH0 L AH0 Z EY1 SH AH0 N -STABILIZE S T EY1 B AH0 L AY2 Z -STABILIZED S T EY1 B AH0 L AY2 Z D -STABILIZER S T EY1 B AH0 L AY2 Z ER0 -STABILIZERS S T EY1 B AH0 L AY2 Z ER0 Z -STABILIZES S T EY1 B AH0 L AY2 Z AH0 Z -STABILIZES(2) S T EY1 B AH0 L AY2 Z IH0 Z -STABILIZING S T EY1 B AH0 L AY2 Z IH0 NG -STABLE S T EY1 B AH0 L -STABLER S T EY1 B AH0 L ER0 -STABLER(2) S T EY1 B L ER0 -STABLES S T EY1 B AH0 L Z -STABLEST S T EY1 B AH0 L AH0 S T -STABLEY S T AE1 B L IY0 -STABS S T AE1 B Z -STAC S T AE1 K -STAC'S S T AE1 K S -STACCATO S T AH0 K AA1 T OW2 -STACEY S T EY1 S IY0 -STACH S T AE1 CH -STACHNIK S T AE1 K N IH0 K -STACHOWIAK S T AH0 CH AW1 IY0 AE0 K -STACHOWICZ S T AA1 CH AH0 V IH0 CH -STACHOWSKI S T AH0 CH AO1 F S K IY0 -STACHURA S T AA0 CH UH1 R AH0 -STACHURSKI S T AH0 CH ER1 S K IY0 -STACIA S T AA1 CH AH0 -STACIE S T EY1 S IY0 -STACK S T AE1 K -STACKED S T AE1 K T -STACKER S T AE1 K ER0 -STACKHOUSE S T AE1 K HH AW2 S -STACKING S T AE1 K IH0 NG -STACKPOLE S T AE1 K P OW2 L -STACKS S T AE1 K S -STACY S T EY1 S IY0 -STADE S T EY1 D -STADEL S T AE1 D AH0 L -STADELMAN S T AE1 D AH0 L M AH0 N -STADER S T EY1 D ER0 -STADIA S T EY1 D IY0 AA0 -STADIUM S T EY1 D IY0 AH0 M -STADIUM'S S T EY1 D IY0 AH0 M Z -STADIUMS S T EY1 D IY0 AH0 M Z -STADLER S T AE1 D L ER0 -STADNIK S T AE1 D N IH0 K -STADT S T AE1 D T -STADTLANDER SH T AE1 T L AE2 N D ER0 -STADTMILLER SH T AE1 T M IH1 L ER0 -STAEBELL S T EH1 B EH0 L -STAEBLER S T EY1 L ER0 -STAEHLE S T EY1 L -STAFF S T AE1 F -STAFF'S S T AE1 F S -STAFFA S T AA1 F AA2 -STAFFED S T AE1 F T -STAFFER S T AE1 F ER0 -STAFFERS S T AE1 F ER0 Z -STAFFIERI S T AA0 F IH1 R IY0 -STAFFING S T AE1 F IH0 NG -STAFFORD S T AE1 F ER0 D -STAFFORD'S S T AE1 F ER0 D Z -STAFFORDSHIRE S T AE1 F ER0 D SH ER0 -STAFFORDSHIRE(2) S T AE1 F ER0 D SH AY2 ER0 -STAFFORDSHIRE'S S T AE1 F ER0 D SH ER0 Z -STAFFORDSHIRE'S(2) S T AE1 F ER0 D SH AY2 ER0 Z -STAFFS S T AE1 F S -STAG S T AE1 G -STAG-PARTY S T AE1 G P AE2 R T IY0 -STAGE S T EY1 JH -STAGECOACH S T EY1 JH K OW2 CH -STAGECRAFT S T EY1 JH K R AE2 F T -STAGED S T EY1 JH D -STAGEHAND S T EY1 JH HH AE2 N D -STAGEHANDS S T EY1 JH HH AE2 N D Z -STAGER S T EY1 JH ER0 -STAGES S T EY1 JH AH0 Z -STAGES(2) S T EY1 JH IH0 Z -STAGFLATE S T AE2 G F L EY1 T -STAGFLATION S T AE0 G F L EY1 SH AH0 N -STAGG S T AE1 G -STAGGER S T AE1 G ER0 -STAGGERED S T AE1 G ER0 D -STAGGERING S T AE1 G ER0 IH2 NG -STAGGERINGLY S T AE1 G ER0 IH2 NG L IY2 -STAGGERS S T AE1 G ER0 Z -STAGGS S T AE1 G Z -STAGING S T EY1 JH IH0 NG -STAGLIANO S T AA0 G L IY0 AA1 N OW2 -STAGNANT S T AE1 G N AH0 N T -STAGNARO S T AA0 G N AA1 R OW2 -STAGNATE S T AE1 G N EY2 T -STAGNATED S T AE1 G N EY2 T IH0 D -STAGNATING S T AE1 G N EY2 T IH0 NG -STAGNATION S T AE0 G N EY1 SH AH0 N -STAGNER S T AE1 G N ER0 -STAHL S T AA1 L -STAHLBERG S T AA1 L B ER0 G -STAHLE S T EY1 HH AH0 L -STAHLECKER S T AA1 L IH0 K ER0 -STAHLER S T AA1 L ER0 -STAHLEY S T AE1 L IY0 -STAHLHUT S T AA1 L HH AH0 T -STAHLMAN S T AA1 L M AH0 N -STAHLY S T AE1 L IY0 -STAHMER S T AA1 M ER0 -STAHNKE S T AE1 NG K -STAHR S T AE1 R -STAI S T AA1 IY0 -STAIB S T EY1 B -STAID S T EY1 D -STAIGER S T AY1 G ER0 -STAILEY S T EY1 L IY0 -STAIN S T EY1 N -STAINBACK S T EY1 N B AE2 K -STAINBROOK S T EY1 N B R UH2 K -STAINED S T EY1 N D -STAINES S T EY1 N Z -STAINING S T EY1 N IH0 NG -STAINLESS S T EY1 N L AH0 S -STAINMASTER S T EY1 N M AE2 S T ER0 -STAINS S T EY1 N Z -STAIR S T EH1 R -STAIRCASE S T EH1 R K EY2 S -STAIRCASES S T EH1 R K EY2 S IH0 Z -STAIRS S T EH1 R Z -STAIRWAY S T EH1 R W EY2 -STAIRWAYS S T EH1 R W EY2 Z -STAIRWELL S T EH1 R W EH2 L -STAIRWELLS S T EH1 R W EH2 L Z -STAKE S T EY1 K -STAKE'S S T EY1 K S -STAKE-OUT S T EY1 K AW1 T -STAKED S T EY1 K T -STAKEHOLDER S T EY1 K HH OW2 L D ER0 -STAKEHOLDERS S T EY1 K HH OW2 L D ER0 Z -STAKEOUT S T EY1 K AW2 T -STAKEOUTS S T EY1 K AW2 T S -STAKER S T EY1 K ER0 -STAKES S T EY1 K S -STAKING S T EY1 K IH0 NG -STALAG S T AE1 L AE2 G -STALAGMITE S T AE1 L AH0 G M AY2 T -STALCUP S T AO1 L K AH2 P -STALDER S T AO1 L D ER0 -STALE S T EY1 L -STALEMATE S T EY1 L M EY2 T -STALEMATED S T EY1 L M EY2 T IH0 D -STALEY S T EY1 L IY0 -STALEY'S S T EY1 L IY0 Z -STALIN S T AA1 L AH0 N -STALIN'S S T AA1 L IH0 N Z -STALINGRAD S T AE1 L IH0 N G R AE2 D -STALINISM S T AE1 L IH0 N IH2 Z AH0 M -STALINIST S T AA1 L IH0 N IH0 S T -STALINISTIC S T AA2 L IH0 N IH1 S T IH0 K -STALINISTS S T AE1 L IH0 N IH0 S T S -STALINIZATION S T AE2 L IH0 N IH0 Z EY1 SH AH0 N -STALINIZE S T AE1 L IH0 N AY2 Z -STALK S T AO1 K -STALKED S T AO1 K T -STALKER S T AO1 K ER0 -STALKERS S T AO1 K ER0 Z -STALKING S T AO1 K IH0 NG -STALKS S T AO1 K S -STALKY S T AO1 K IY0 -STALL S T AO1 L -STALLARD S T AE1 L ER0 D -STALLCUP S T AO1 L K AH2 P -STALLED S T AO1 L D -STALLER S T AO1 L ER0 -STALLIBRASS S T AE1 L IH0 B R AE0 S -STALLING S T AO1 L IH0 NG -STALLINGS S T AO1 L IH0 NG Z -STALLION S T AE1 L Y AH0 N -STALLIONS S T AE1 L Y AH0 N Z -STALLKAMP S T AO1 L K AE2 M P -STALLMAN S T AO1 L M AH0 N -STALLONE S T AH0 L OW1 N -STALLONE'S S T AH0 L OW1 N Z -STALLS S T AO1 L Z -STALLSMITH S T AO1 L S M IH2 TH -STALLWORTH S T AO1 L W ER2 TH -STALNAKER S T AE1 L N AH0 K ER0 -STALOFF S T AE1 L AO0 F -STALON S T EY1 L AH0 N -STALOWA S T AH0 L OW1 AH0 -STALTER S T AO1 L T ER0 -STALVEY S T AE1 L V IY0 -STALWART S T AO1 L W ER0 T -STALWARTS S T AO1 L W ER0 T S -STALZER S T EY1 L Z ER0 -STAM S T AE1 M -STAMAND S T AE1 M AH0 N D -STAMANT S T AE1 M AH0 N T -STAMAS S T AA1 M AH0 Z -STAMATY S T AE1 M AH0 T IY0 -STAMBAUGH S T AE1 M B AO0 -STAMBERG S T AE1 M B ER0 G -STAMENSON S T EY1 M AH0 N S AH0 N -STAMER S T EY1 M ER0 -STAMEY S T EY1 M IY0 -STAMFORD S T AE1 M F ER0 D -STAMINA S T AE1 M AH0 N AH0 -STAMLER S T AE1 M L ER0 -STAMM S T AE1 M -STAMMEN S T AE1 M AH0 N -STAMMER S T AE1 M ER0 -STAMOS S T EY1 M OW0 Z -STAMOUR S T AH0 M UH1 R -STAMP S T AE1 M P -STAMPED S T AE1 M P T -STAMPEDE S T AE0 M P IY1 D -STAMPEDED S T AE0 M P IY1 D IH0 D -STAMPEDING S T AE0 M P IY1 D IH0 NG -STAMPER S T AE1 M P ER0 -STAMPFLI S T AE1 M P F L IY0 -STAMPING S T AE1 M P IH0 NG -STAMPINGS S T AE1 M P IH0 NG Z -STAMPLEY S T AE1 M P L IY0 -STAMPS S T AE1 M P S -STAN S T AE1 N -STAN'S S T AE1 N Z -STANADYNE S T AE1 N AH0 D AY2 N -STANALAND S T AE1 N AH0 L AH0 N D -STANARD S T AE1 N ER0 D -STANAWAY S T AE1 N AH0 W EY0 -STANBACK S T AE1 N B AE2 K -STANBERRY S T AE1 N B EH2 R IY0 -STANBERY S T AE1 N B ER0 IY0 -STANBIC S T AE1 N B IH0 K -STANBROUGH S T AE1 N B R AW0 -STANBURY S T AE1 N B EH2 R IY0 -STANCE S T AE1 N S -STANCES S T AE1 N S IH0 Z -STANCH S T AE1 N CH -STANCHED S T AE1 N CH T -STANCHFIELD S T AE1 N CH F IY0 L D -STANCHING S T AE1 N CH IH0 NG -STANCIK S T AE1 N S IH0 K -STANCIL S T AE1 N S IH0 L -STANCILL S T AE1 N S IH0 L -STANCLIFF S T AE1 N K L IH0 F -STANCO S T AE1 NG K OW0 -STANCZAK S T AE1 N CH AE0 K -STANCZYK S T AE1 N CH IH0 K -STAND S T AE1 N D -STAND-BY S T AE1 N D B AY2 -STAND-UP S T AE1 N D AH2 P -STANDA S T AE1 N D AH0 -STANDALONE S T AE1 N D AH0 L OW2 N -STANDARD S T AE1 N D ER0 D -STANDARD'S S T AE1 N D ER0 D Z -STANDARD-BEARER S T AE1 N D ER0 D B EH1 R ER0 -STANDARD-BEARERS S T AE1 N D ER0 D B EH1 R ER0 Z -STANDARDIZATION S T AE0 N D ER0 D IH0 Z EY1 SH AH0 N -STANDARDIZE S T AE1 N D ER0 D AY2 Z -STANDARDIZED S T AE1 N D ER0 D AY2 Z D -STANDARDIZING S T AE1 N D ER0 D AY2 Z IH0 NG -STANDARDS S T AE1 N D ER0 D Z -STANDBY S T AE1 N D B AY1 -STANDBYS S T AE1 N D B AY2 Z -STANDEFER S T AE1 N D IH0 F ER0 -STANDEN S T AE1 N D AH0 N -STANDER S T AE1 N D ER0 -STANDERFER S T AE1 N D ER0 F ER0 -STANDERFORD S T AE1 N D ER0 F ER0 D -STANDEX S T AE1 N D AH0 K S -STANDFIELD S T AE1 N D F IY2 L D -STANDIFER S T AE1 N D IH0 F ER0 -STANDIFORD S T AE1 N D IH0 F ER0 D -STANDIN' S T AE1 N D IH0 N -STANDING S T AE1 N D IH0 NG -STANDINGS S T AE1 N D IH0 NG Z -STANDISH S T AE1 N D IH0 SH -STANDLEE S T AE1 N D L IY2 -STANDLEY S T AE1 N D L IY0 -STANDOFF S T AE1 N D AO2 F -STANDOFFS S T AE1 N D AO2 F S -STANDOUT S T AE1 N D AW2 T -STANDOUTS S T AE1 N D AW2 T S -STANDPOINT S T AE1 N D P OY2 N T -STANDPOINTS S T AE1 N D P OY2 N T S -STANDRE S T AE1 N D ER0 -STANDRIDGE S T AE1 N D R IH0 JH -STANDS S T AE1 N D Z -STANDSTILL S T AE1 N D S T IH2 L -STANDUP S T AE1 N D AH2 P -STANEK S T AE1 N IH0 K -STANFIELD S T AE1 N F IY2 L D -STANFILL S T AE1 N F AH0 L -STANFORD S T AE1 N F ER0 D -STANFORD'S S T AE1 N F ER0 D Z -STANFORTH S T AE1 N F ER0 TH -STANG S T AE1 NG -STANGA S T AA1 NG G AH0 -STANGE S T AE1 N JH -STANGEL S T EY1 NG G AH0 L -STANGELAND S T EY1 NG G IH0 L AH0 N D -STANGELAND(2) S T EY1 NG G L AH0 N D -STANGELO S T AA0 NG G EH1 L OW0 -STANGER S T AE1 NG ER0 -STANGL S T AE1 NG G AH0 L -STANGLAND S T AE1 NG G L AH0 N D -STANGLE S T AE1 NG G AH0 L -STANGLER S T AE1 NG G AH0 L ER0 -STANGLER(2) S T AE1 NG G L ER0 -STANGO S T AA1 NG G OW0 -STANHOPE S T AE1 N AH0 P -STANIAR S T EY1 N IY0 AA0 R -STANICH S T AE1 N IH0 CH -STANIFER S T AE1 N IH0 F ER0 -STANIS S T AE1 N IH0 S -STANISH S T AE1 N IH0 SH -STANISLAS S T AH0 N IH1 S L AH0 S -STANISLAV S T AE1 N IH0 S L AA0 V -STANISLAW S T AE1 N IH0 S L AO2 -STANISLAWSKI S T AH0 N IH0 S L AA1 F S K IY0 -STANISZEWSKI S T AH0 N IH0 SH EH1 F S K IY0 -STANK S T AE1 NG K -STANKE S T AE1 NG K -STANKEVICH S T AE1 NG K AH0 V IH2 CH -STANKEY S T AE1 NG K IY0 -STANKIEWICZ S T AE1 N K AH0 V IH0 CH -STANKO S T AE1 NG K OW0 -STANKOVICH S T AE1 NG K AH0 V IH0 CH -STANKOWSKI S T AH0 NG K AO1 F S K IY0 -STANKUS S T AE1 NG K AH0 S -STANLEIGH S T AE1 N L AH0 -STANLEY S T AE1 N L IY0 -STANLEY'S S T AE1 N L IY0 Z -STANLEYTOWN S T AE1 N L IY0 T AW2 N -STANLINE S T AE1 N L AY2 N -STANLY S T AE1 N L IY0 -STANMORE S T AE1 N M AO0 R -STANN S T AE1 N -STANNARD S T AE1 N ER0 D -STANNIE S T AE1 N IY0 -STANO S T AA1 N OW0 -STANPHILL S T AE1 N P HH IH2 L -STANSBERRY S T AE1 N S B EH2 R IY0 -STANSBURY S T AE1 N S B EH0 R IY0 -STANSEL S T AE1 N S AH0 L -STANSELL S T AE1 N S AH0 L -STANSFIELD S T AE1 N S F IY0 L D -STANSKY S T AE1 N S K IY0 -STANT S T AE1 N T -STANTON S T AE1 N T AH0 N -STANWAY S T AE1 N W EY2 -STANWICK S T AE1 N W IH0 K -STANWOOD S T AE1 N W UH2 D -STANZA S T AE1 N Z AH0 -STANZAS S T AE1 N Z AH0 Z -STANZIONE S T AA0 N Z IY0 OW1 N IY0 -STAPEL S T AE1 P AH0 L -STAPF S T AE1 P F -STAPH S T AE1 F -STAPHYLOCOCCUS S T AE2 F AH0 L AH0 K AO1 K AH0 S -STAPLE S T EY1 P AH0 L -STAPLED S T EY1 P AH0 L D -STAPLER S T EY1 P AH0 L ER0 -STAPLER(2) S T EY1 P L ER0 -STAPLERS S T EY1 P AH0 L ER0 Z -STAPLERS(2) S T EY1 P L ER0 Z -STAPLES S T EY1 P AH0 L Z -STAPLETON S T EY1 P AH0 L T AH0 N -STAPLEY S T AE1 P L IY0 -STAPLING S T EY1 P AH0 L IH0 NG -STAPLING(2) S T EY1 P L IH0 NG -STAPP S T AE1 P -STAR S T AA1 R -STAR'S S T AA1 R Z -STARACE S T AA0 R AA1 CH IY0 -STARBIRD S T AA1 R B ER2 D -STARBOARD S T AA1 R B ER0 D -STARBOARDS S T AA1 R B ER0 R D Z -STARBOARDS(2) S T AA1 R B AO2 R D Z -STARBUCK S T AA1 R B AH2 K -STARBUCK'S S T AA1 R B AH2 K S -STARBUCKS S T AA1 R B AH2 K S -STARCEVICH S T AA1 R S IH0 V IH0 CH -STARCH S T AA1 R CH -STARCHED S T AA1 R CH T -STARCHER S T AA1 R CH ER0 -STARCHES S T AA1 R CH IH0 Z -STARCHLIKE S T AA1 R CH L AY2 K -STARCHY S T AA1 R CH IY0 -STARCK S T AA1 R K -STARCKMANN S T AA1 R K M AH0 N -STARCRAFT S T AA1 R K R AE2 F T -STARCROSS S T AA1 R K R AO2 S -STARDEL S T AA1 R D EH2 L -STARDENT S T AA1 R D EH2 N T -STARDOM S T AA1 R D AH0 M -STARDUST S T AA1 R D AH2 S T -STARE S T EH1 R -STARED S T EH1 R D -STARER S T EH1 R ER0 -STARES S T EH1 R Z -STARFISH S T AA1 R F IH2 SH -STARFLEET S T AA1 R F IY2 T -STARFLEET'S S T AA1 R F IY2 T S -STARGATE S T AA1 R G EY2 T -STARGATES S T AA1 R G EY2 T S -STARGAZER S T AA1 R G EY2 Z ER0 -STARGAZERS S T AA1 R G EY2 Z ER0 Z -STARIN S T AE1 R IH0 N -STARIN' S T EH1 R IH0 N -STARING S T EH1 R IH0 NG -STARK S T AA1 R K -STARK'S S T AA1 R K S -STARKE S T AA1 R K -STARKEL S T AA1 R K AH0 L -STARKER S T AA1 R K ER0 -STARKERS S T AA1 R K ER0 Z -STARKES S T AA1 R K S -STARKEY S T AA1 R K IY2 -STARKIST S T AA1 R K IH0 S T -STARKLY S T AA1 R K L IY0 -STARKMAN S T AA1 R K M AH0 N -STARKNESS S T AA1 R K N IH0 S -STARKOVICH S T AA1 R K AH0 V IH0 CH -STARKS S T AA1 R K S -STARKWEATHER S T AA1 R K W EH2 DH ER0 -STARLET S T AA1 R L AH0 T -STARLETS S T AA1 R L AH0 T S -STARLIGHT S T AA1 R L AY2 T -STARLIKE S T AA1 R L AY2 K -STARLIN S T AA1 R L IH0 N -STARLING S T AA1 R L IH0 NG -STARLIPER S T AA1 R L IH0 P ER0 -STARLIT S T AA1 R L IH0 T -STARMAN S T AA1 R M AH0 N -STARMER S T AA1 R M ER0 -STARN S T AA1 R N -STARNER S T AA1 R N ER0 -STARNES S T AA1 R N Z -STARNS S T AA1 R N Z -STARODUBSTEV S T AA2 R OW0 D AH1 B S T EH2 V -STARON S T AE1 R AH0 N -STARPLEX S T AA1 R P L EH2 K S -STARPOINTE S T AA1 R P OY2 N T -STARR S T AA1 R -STARR'S S T AA1 R Z -STARRED S T AA1 R D -STARRETT S T AE1 R IH0 T -STARRING S T AA1 R IH0 NG -STARRS S T AA1 R Z -STARRY S T AA1 R IY2 -STARS S T AA1 R Z -STARS' S T AA1 R Z -STARSHIP S T AA1 R SH IH2 P -STARSIGHT S T AA1 R S AY2 T -STARSTREAM S T AA1 R S T R IY2 M -STARSTRUCK S T AA1 R S T R AH2 K -START S T AA1 R T -START-UP S T AA1 R T AH2 P -START-UPS S T AA1 R T AH1 P S -STARTED S T AA1 R T IH0 D -STARTER S T AA1 R T ER0 -STARTERS S T AA1 R T ER0 Z -STARTING S T AA1 R T IH0 NG -STARTLE S T AA1 R T AH0 L -STARTLED S T AA1 R T AH0 L D -STARTLES S T AA1 R T AH0 L Z -STARTLING S T AA1 R T L IH0 NG -STARTLINGLY S T AA1 R T L IH0 NG L IY2 -STARTS S T AA1 R T S -STARTUP S T AA1 R T AH2 P -STARTUPS S T AA1 R T AH2 P S -STARVATION S T AA0 R V EY1 SH AH0 N -STARVE S T AA1 R V -STARVED S T AA1 R V D -STARVES S T AA1 R V Z -STARVING S T AA1 R V IH0 NG 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AE1 CH AH0 T AO2 R IY0 -STATZ S T AE1 T S -STATZER S T AE1 T Z ER0 -STATZER(2) S T EY1 T Z ER0 -STAUB S T AO1 B -STAUBER S T AW1 B ER0 -STAUBIN S T AW1 B IH0 N -STAUBS S T AO1 B Z -STAUCH S T AO1 CH -STAUDACHER S T AW1 D AH0 K ER0 -STAUDE S T AO1 D -STAUDER S T AW1 D ER0 -STAUDINGER S T AW1 D IH0 NG ER0 -STAUDT S T AO1 D T -STAUFF S T AO1 F -STAUFFACHER S T AW1 F AH0 K ER0 -STAUFFER S T AO1 F ER0 -STAUFFER'S S T AO1 F ER0 Z -STAUNCH S T AO1 N CH -STAUNCHEST S T AO1 N CH AH0 S T -STAUNCHLY S T AO1 N CH L IY0 -STAUNTON S T AO1 N T AH0 N -STAUP S T AO1 P -STAUSS S T AO1 S -STAUTER S T AW1 T ER0 -STAVE S T EY1 V -STAVED S T EY1 V D -STAVELY S T EY1 V L IY0 -STAVER S T EY1 V ER0 -STAVES S T EY1 V Z -STAVING S T EY1 V IH0 NG -STAVINOHA S T AE0 V IH0 N OW1 HH AH0 -STAVOLA S T AA0 V OW1 L AH0 -STAVROPOULOS S T AH0 V R AA1 P AH0 L IH0 S -STAVROS S T AH0 V R OW1 Z -STAWICKI S T AA0 V IH1 T S K IY0 -STAWSKI S T AA1 F S K IY0 -STAY S T EY1 -STAYED S T EY1 D -STAYER S T EY1 ER0 -STAYING S T EY1 IH0 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S T IY1 B ER0 -STEED S T IY1 D -STEED'S S T IY1 D Z -STEEDLEY S T IY1 D L IY0 -STEEDMAN S T IY1 D M AH0 N -STEEDS S T IY1 D Z -STEEG S T IY1 G -STEEGE S T IY1 JH -STEEGO S T IY1 G OW0 -STEEL S T IY1 L -STEEL'S S T IY1 L Z -STEELCASE S T IY1 L K EY2 S -STEELE S T IY1 L -STEELED S T IY1 L D -STEELER S T IY1 L ER0 -STEELERS S T IY1 L ER0 Z -STEELEY S T IY1 L IY0 -STEELHEAD S T IY1 L HH EH2 D -STEELMAKER S T IY1 L M EY2 K ER0 -STEELMAKER'S S T IY1 L M EY2 K ER0 Z -STEELMAKERS S T IY1 L M EY2 K ER0 Z -STEELMAKERS' S T IY1 L M AH0 K ER0 Z -STEELMAKING S T IY1 L M EY2 K IH0 NG -STEELMAN S T IY1 L M AH0 N -STEELS S T IY1 L Z -STEELWORKER S T IY1 L W ER2 K ER0 -STEELWORKERS S T IY1 L W ER2 K ER0 Z -STEELWORKERS' S T IY1 L W ER0 K ER0 Z -STEELWORKS S T IY1 L W ER2 K S -STEELY S T IY1 L IY0 -STEEN S T IY1 N -STEENBERGEN S T IY1 N B ER0 G AH0 N -STEENBURGEN S T IY1 N B ER0 G AH0 N -STEENKAMP S T IY1 N K AE2 M P -STEENKISTE S T IY1 N K IH2 S T -STEENROD S T IY1 N R AH0 D -STEENSMA S T IY1 N Z M AH0 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N S K IY0 -STEFFAN S T EH1 F AH0 N -STEFFANCI S T EH2 F AA1 N S IY0 -STEFFE S T EH1 F -STEFFEK S T EH1 F IH0 K -STEFFEL S T EH1 F AH0 L -STEFFEN S T EH1 F AH0 N -STEFFENHAGEN S T EH1 F IH0 N HH AH0 G AH0 N -STEFFENS S T EH1 F AH0 N Z -STEFFENSEN S T EH1 F IH0 N S AH0 N -STEFFENSMEIER S T EH1 F IH0 N S M AY0 ER0 -STEFFENSON S T EH1 F IH0 N S AH0 N -STEFFES S T EH1 F S -STEFFEY S T EH1 F IY0 -STEFFI S T EH1 F IY0 -STEFFIE S T EH1 F IY0 -STEFFLER S T EH1 F L ER0 -STEFFY S T EH1 F IY0 -STEFKO S T EH1 F K OW0 -STEFL S T EH1 F AH0 L -STEGALL S T EH1 G AH0 L -STEGAR S T IY1 G ER0 -STEGAR'S S T IY1 G ER0 Z -STEGE S T IY1 JH -STEGEMAN S T IY1 G M AH0 N -STEGEMANN S T IY1 G M AH0 N -STEGEMEIER S T EH1 G M AY2 R -STEGENGA S T EH0 JH EH1 NG G AH0 -STEGER S T IY1 G ER0 -STEGMAIER S T EH1 G M AY0 ER0 -STEGMAN S T EH1 G M AH0 N -STEGMANN S T EH1 G M AH0 N -STEGNER S T EH1 G N ER0 -STEGOSAURUS S T EH2 G AH0 S AO1 R AH0 S -STEHLE S T EH1 HH AH0 L -STEHLIK S T EH1 L IH0 K -STEHLIN S T EH1 L IH0 N 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N B R EH2 K ER0 -STEINBRENNER S T AY1 N B R EH2 N ER0 -STEINBRINK S T AY1 N B R IH2 NG K -STEINEM S T AY1 N AH0 M -STEINER S T AY1 N ER0 -STEINER'S S T AY1 N ER0 Z -STEINERT S T AY1 N ER0 T -STEINES S T AY1 N Z -STEINFELD S T AY1 N F EH2 L D -STEINFELDT S T AY1 N F EH2 L T -STEINGUT S T AY1 N G AH2 T -STEINHAGEN S T AY1 N HH AE0 G AH0 N -STEINHARDT S T AY1 N HH AA2 R T -STEINHARDT'S S T AY1 N HH AA0 R T S -STEINHART S T AY1 N HH AA2 R T -STEINHAUER S T AY1 N HH AW0 ER0 -STEINHAUS S T AY1 N HH AW2 S -STEINHAUSER S T AY1 N HH AW2 Z ER0 -STEINHILBER S T AY1 N HH IH2 L B ER0 -STEINHOFF S T AY1 N HH AO2 F -STEINHORST S T AY1 N HH AO0 R S T -STEININGER S T AY1 N IH0 NG ER0 -STEINKAMP S T AY1 NG K AE0 M P -STEINKE S T AY1 NG K -STEINKRAUS S T AY1 NG K R AW0 Z -STEINKRAUSS S T AY1 N K R AW2 S -STEINKUEHLER S T AY1 N K Y UW2 L ER0 -STEINLE S T AY1 N AH0 L -STEINMAN S T AY1 N M AH0 N -STEINMANN S T AY1 N M AH0 N -STEINMETZ S T AY1 N M EH0 T S -STEINMEYER S T AY1 N M AY0 ER0 -STEINMILLER S T AY1 N M IH2 L ER0 -STEINROE S T AY1 N R OW2 -STEINWAY S T AY1 N W EY2 -STEINWAY'S S T AY1 N W EY2 Z -STEITZ S T IY1 T S -STEJSKAL S T EH1 JH S K AH0 L -STEKETEE S T EH1 K IH0 T IY0 -STEKLY S T EH1 K L IY0 -STELCO S T EH1 L K OW0 -STELIAN S T IY1 L IY0 AH0 N -STELL S T EH1 L -STELLA S T EH1 L AH0 -STELLA'S S T EH1 L AH0 Z -STELLAR S T EH1 L ER0 -STELLARTON S T EH1 L ER0 T AH0 N -STELLATO S T EH0 L AA1 T OW0 -STELLE S T EH1 L -STELLENBOSCH S T EH1 L AH0 N B AO2 SH -STELLER S T EH1 L ER0 -STELLHORN S T EH1 L HH ER0 N -STELLING S T EH1 L IH0 NG -STELLJES S T EY1 L Y EH0 S -STELLMACH S T EH1 L M AH0 K -STELLMACHER S T EH1 L M AH0 K ER0 -STELLMAN S T EH1 L M AH0 N -STELLO S T EH1 L OW0 -STELLY S T EH1 L IY0 -STELMACH S T EH1 L M AH0 K -STELMACK S T EH1 L M AH0 K -STELOFF S T EH1 L AO0 F -STELTER S T EH1 L T ER0 -STELTZ S T EH1 L T S -STELZER S T EH1 L Z ER0 -STELZNER S T EH1 L Z N ER0 -STEM S T EH1 M -STEMBERG S T EH1 M B ER0 G -STEMBRIDGE S T EH1 M B R IH2 JH -STEMEN S T EH1 M AH0 N -STEMLER S T EH1 M L ER0 -STEMLIKE S T EH1 M L AY2 K -STEMM S T EH1 M -STEMMED S T EH1 M D -STEMMER S T EH1 M ER0 -STEMMING S T EH1 M IH0 NG -STEMMLER S T EH1 M L ER0 -STEMPEL S T EH1 M P AH0 L -STEMPEL'S S T EH1 M P AH0 L Z -STEMPER S T EH1 M P ER0 -STEMPIEN S T EH1 M P IY0 N -STEMPLE S T EH1 M P AH0 L -STEMPLER S T EH1 M P L ER0 -STEMPLER'S S T EH1 M P L ER0 Z -STEMPOSTS S T EH1 M P OW2 S T S -STEMS S T EH1 M Z -STEN S T EH1 N -STENA S T IY1 N AH0 -STENA(2) S T EH1 N AH0 -STENA(3) S T EY1 N AH0 -STENBERG S T EH1 N B ER0 G -STENCEL S T EH1 N S AH0 L -STENCH S T EH1 N CH -STENCIL S T EH1 N S IH0 L -STENCILED S T EH1 N S IH0 L D -STENCILING S T EH1 N S IH0 L IH0 NG -STENCILING(2) S T EH1 N S L IH0 NG -STENDAL S T EH1 N D AH0 L -STENDER S T EH1 N D ER0 -STENDHAL S T EH1 N D AA0 L -STENDIG S T EH1 N D IH0 G -STENE S T IY1 N -STENERSON S T EH1 N ER0 S AH0 N -STENGEL S T EH1 NG G AH0 L -STENGER S T EH1 N JH ER0 -STENGLEIN S T IH1 NG L AY0 N -STENHOLM S T EH1 N HH OW2 L M -STENHOUSE S T EH1 N HH AW2 S -STENNER S T EH1 N ER0 -STENNETT S T EH1 N IH0 T -STENNIS S T EH1 N IH0 S -STENO S T EH1 N OW0 -STENOGRAPHER S T EH0 N AH1 G R AH0 F ER0 -STENOGRAPHIC S T EH2 N AH0 G R AE1 F IH0 K -STENOSIS S T EH2 N OW1 S IH0 S -STENQUIST S T EH1 N K W IH2 S T -STENSETH S T EH1 N S IH0 TH -STENSLAND S T EH1 N S L AH0 N D -STENSON S T EH1 N S AH0 N -STENSRUD S T EH1 N S R AH0 D -STENSTROM S T EH1 N S T R AH0 M -STENT S T EH1 N T -STENTOR S T EH1 N T ER0 -STENTORS S T EH1 N T ER0 Z -STENTZ S T EH1 N T S -STENY S T EH1 N IY0 -STENZ S T EH1 N Z -STENZEL S T EH1 N Z AH0 L -STEP S T EH1 P -STEP-BY-STEP S T EH1 P B IY0 S T EH2 P -STEPAN S T EH1 P AH0 N -STEPANAKERT S T AH0 P AE1 N AH0 K ER0 T -STEPANEK S T EH1 P AH0 N IH0 K -STEPANIAN S T IH0 P EY1 N IY0 AH0 N -STEPANIAN'S S T IH0 P EY1 N IY0 AH0 N Z -STEPANSKI S T IH0 P AE1 N S K IY0 -STEPCHILD S T EH1 P CH AY2 L D -STEPCHILDREN S T EH1 P CH IH1 L D R AH0 N -STEPDAUGHTER S T EH1 P D AO2 T ER0 -STEPDAUGHTERS S T EH1 P D AO2 T ER0 Z -STEPFATHER S T EH1 P F AA2 DH ER0 -STEPH S T EH1 F -STEPHA S T EH1 F AH0 -STEPHAN S T EH1 F AH0 N -STEPHANA S T EH0 F AA1 N AH0 -STEPHANE S T EH0 F AA1 N -STEPHANI S T EH1 F AH0 N IY0 -STEPHANIA S T IH0 F AE1 N IY0 AH0 -STEPHANIE S T EH1 F AH0 N IY0 -STEPHANOPOULOS S T EH2 F AH0 N AA1 P AH0 L AH0 S -STEPHANOPOULOS' S T EH2 F AH0 N AA1 P AH0 L AH0 S -STEPHANOPOULOS'S S T EH2 F AH0 N AA1 P AH0 L AH0 S IH0 S -STEPHANS S T EH1 F AH0 N Z -STEPHANY S T EH1 F AH0 N IY0 -STEPHEN S T IY1 V AH0 N -STEPHEN'S S T IY1 V AH0 N Z -STEPHENS S T IY1 V AH0 N Z -STEPHENS(2) S T EH1 F AH0 N Z -STEPHENS'S S T IY1 V IH0 N Z IH0 Z -STEPHENSON S T IY1 V AH0 N S AH0 N -STEPHENVILLE S T IY1 V IH0 N V IH2 L -STEPIEN S T EH1 P IY0 AH0 N -STEPKA S T EH1 P K AA2 -STEPLADDER S T EH1 P L AE2 D ER0 -STEPLADDERS S T EH1 P L AE2 D ER0 Z -STEPMOM S T EH1 P M AA2 M -STEPMOTHER S T EH1 P M AH2 DH ER0 -STEPNEY S T EH1 P N IY0 -STEPP S T EH1 P -STEPPE S T EH1 P -STEPPED S T EH1 P T -STEPPEL S T EH1 P AH0 L -STEPPENWOLF S T EH1 P AH0 N W UH2 L F 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B ER0 G ER0 -STERNE S T ER1 N -STERNEM S T ER1 N EH0 M -STERNER S T ER1 N ER0 -STERNEST S T ER1 N AH0 S T -STERNFELD S T ER1 N F EH0 L D -STERNHAGEN S T ER1 N HH AH0 G AH0 N -STERNLY S T ER1 N L IY0 -STERNNESS S T ER1 N N AH0 S -STERNNESS(2) S T ER1 N AH0 S -STERNPOST S T ER1 N P OW2 S T -STERNPOSTS S T ER1 N P OW2 S T S -STERNS S T ER1 N Z -STEROID S T ER0 OY1 D -STEROIDS S T EH1 R OY0 D Z -STERR S T EH1 R -STERRETT S T EH1 R IH0 T -STERRY S T EH1 R IY0 -STET S T EH1 T -STETHEM S T EH1 TH AH0 M -STETHEM(2) S T EH1 T AH0 M -STETHOSCOPE S T EH1 TH AH0 S K OW2 P -STETHOSCOPES S T EH1 TH AH0 S K OW2 P S -STETLER S T EH1 T L ER0 -STETSON S T EH1 T S AH0 N -STETTER S T EH1 T ER0 -STETTLER S T EH1 T L ER0 -STETTNER S T EH1 T N ER0 -STETZ S T EH1 T S -STETZEL S T EH1 T Z AH0 L -STETZER S T EH1 T Z ER0 -STEUART S T OY1 AA0 R T -STEUBEN S T Y UW1 B IH0 N -STEUBEN(2) S T UW1 B IH0 N -STEUBENVILLE S T UW1 B AH0 N V IH2 L -STEUBER S T OY1 B ER0 -STEUCK S T UW1 K -STEUER S T OY1 ER0 -STEUERWALD S T OY1 ER0 W AO0 L D -STEURER S T ER1 ER0 -STEUVER S T UW1 V ER0 -STEVANA S T IH0 V AE1 N AH0 -STEVE S T IY1 V -STEVE'S S T IY1 V Z -STEVEDORE S T IY1 V AH0 D AO2 R -STEVEDORING S T IY1 V AH0 D AO2 R IH0 NG -STEVEN S T IY1 V AH0 N -STEVEN'S S T IY1 V AH0 N Z -STEVENA S T EH1 V IH0 N AH0 -STEVENS S T IY1 V AH0 N Z -STEVENS' S T IY1 V AH0 N Z -STEVENS'S S T IY1 V AH0 N Z AH0 Z -STEVENS'S(2) S T IY1 V AH0 N Z IH0 Z -STEVENSON S T IY1 V AH0 N S AH0 N -STEVER S T IY1 V ER0 -STEVERSON S T EH1 V ER0 S AH0 N -STEVES S T IY1 V Z -STEVESON S T EH1 V IH0 S AH0 N -STEVICK S T EH1 V IH0 K -STEVIE S T IY1 V IY0 -STEVISON S T EH1 V IH0 S AH0 N -STEW S T UW1 -STEW'S S T UW1 Z -STEWARD S T UW1 ER0 D -STEWARDESS S T UW1 ER0 D AH0 S -STEWARDESSES S T UW1 ER0 D AH0 S IH0 Z -STEWARDS S T UW1 ER0 D Z -STEWARDSHIP S T UW1 ER0 D SH IH2 P -STEWART S T UW1 ER0 T -STEWART'S S T UW1 ER0 T S -STEWED S T UW1 D -STEWING S T UW1 IH0 NG -STEWS S T UW1 Z -STEYER S T EY1 ER0 -STIBEL S T IH1 B AH0 L -STICE S T AY1 S -STICH S T IH1 CH -STICHA S T IH1 CH AH0 -STICHNOTH S T IH1 K N AA2 TH -STICHT S T IH1 K T -STICHTER S T IH1 K T ER0 -STICK S T IH1 K -STICKA S T IH1 K AH0 -STICKBALL S T IH1 K B AO2 L -STICKEL S T IH1 K AH0 L -STICKELS S T IH1 K AH0 L Z -STICKER S T IH1 K ER0 -STICKERS S T IH1 K ER0 Z -STICKIER S T IH1 K IY0 ER0 -STICKIEST S T IH1 K IY0 AH0 S T -STICKING S T IH1 K IH0 NG -STICKLAND S T IH1 K L AH0 N D -STICKLE S T IH1 K AH0 L -STICKLER S T IH1 K AH0 L ER0 -STICKLER(2) S T IH1 K L ER0 -STICKLES S T IH1 K AH0 L Z -STICKLEY S T IH1 K L IY0 -STICKNEY S T IH1 K N IY0 -STICKS S T IH1 K S -STICKTIGHT S T IH1 K T AY2 T -STICKTIGHTS S T IH1 K T AY2 T S -STICKY S T IH1 K IY0 -STIDD S T IH1 D -STIDHAM S T IH1 D HH AH0 M -STIEBEL S T IY1 B AH0 L -STIEBER S T IY1 B ER0 -STIEF S T IY1 F -STIEFEL S T IY1 F AH0 L -STIEFELHAGEN S T IY1 F AH0 L HH EY2 G AH0 N -STIEFELHAGEN(2) S T AY1 F AH0 L HH EY2 G AH0 N -STIEFVATER S T IY1 F V AH0 T ER0 -STIEG S T IY1 G -STIEGEMEIER S T IY1 JH AH0 M AY2 R -STIEGLER S T IY1 G L ER0 -STIEGLITZ S T IY1 G L IH0 T S -STIEHL S T IY1 L -STIENS S T IY1 N Z -STIER S T AY1 ER0 -STIERS S T AY1 ER0 Z -STIERWALT S T IH1 R W AH0 L T -STIEVE S T IY1 V -STIFEL S T IH1 F AH0 L -STIFF S T IH1 F -STIFFED S T IH1 F T -STIFFEL S T IH1 F AH0 L -STIFFEN S T IH1 F AH0 N -STIFFENED S T IH1 F AH0 N D -STIFFENER S T IH1 F AH0 N ER0 -STIFFENING S T IH1 F AH0 N IH0 NG -STIFFENING(2) S T IH1 F N IH0 NG -STIFFENS S T IH1 F AH0 N Z -STIFFER S T IH1 F ER0 -STIFFEST S T IH1 F AH0 S T -STIFFLER S T IH1 F L ER0 -STIFFLY S T IH1 F L IY0 -STIFFNESS S T IH1 F N AH0 S -STIFFS S T IH1 F S -STIFLE S T AY1 F AH0 L -STIFLED S T AY1 F AH0 L D -STIFLER S T AY1 F L ER0 -STIFLES S T AY1 F AH0 L Z -STIFLING S T AY1 F L IH0 NG -STIFLING(2) S T AY1 F AH0 L IH0 NG -STIFTER S T IH1 F T ER0 -STIG S T IH1 G -STIGALL S T IH1 G AH0 L -STIGER S T AY1 G ER0 -STIGERS S T AY1 G ER0 Z -STIGLER S T IH1 G L ER0 -STIGLITZ S T IH1 G L IH0 T S -STIGMA S T IH1 G M AH0 -STIGMATA S T IH0 G M AA1 T AH0 -STIGMATISM S T IH1 G M AH0 T IH2 Z AH0 M -STIGMATIZE S T IH1 G M AH0 T AY2 Z -STIGMATIZED S T IH1 G M AH0 T AY2 Z D -STIGMATIZING S T IH1 G M AH0 T AY2 Z IH0 NG -STIHL S T IH1 L -STIKA S T IH1 K AH0 -STIKELEATHER S T IH1 K IH0 L EH0 DH ER0 -STIKELEATHER(2) S T IH1 K L EH0 DH ER0 -STIKELEATHER(3) S T AY1 K L EH0 DH ER0 -STIL S T IH1 L -STILE S T AY1 L -STILES S T AY1 L Z -STILETTO S T AH0 L EH1 T OW0 -STILETTOS S T AH0 L EH1 T OW0 Z -STILL S T IH1 L -STILLBORN S T IH1 L B AO1 R N -STILLE S T IH1 L -STILLED S T IH1 L D -STILLER S T IH1 L ER0 -STILLER'S S T IH1 L ER0 Z -STILLEY S T IH1 L IY0 -STILLINGER S T IH1 L IH0 NG ER0 -STILLINGS S T IH1 L IH0 NG Z -STILLION S T IH1 L Y AH0 N -STILLMAN S T IH1 L M AH0 N -STILLNESS S T IH1 L N AH0 S -STILLS S T IH1 L Z -STILLSON S T IH1 L S AH0 N -STILLWAGON S T IH1 L W AE2 G AH0 N -STILLWATER S T IH1 L W AO2 T ER0 -STILLWELL S T IH1 L W EH2 L -STILLWELL'S S T IH1 L W EH2 L Z -STILS S T IH1 L Z -STILSON S T IH1 L S AH0 N -STILT S T IH1 L T -STILTED S T IH1 L T IH0 D -STILTNER S T IH1 L T N ER0 -STILTS S T IH1 L T S -STILWELL S T IH1 L W EH2 L -STIMAC S T IH1 M AH0 K -STIMMEL S T IH1 M AH0 L -STIMPERT S T IH1 M P ER0 T -STIMPSON S T IH1 M P S AH0 N -STIMPY S T IH1 M P IY0 -STIMSON S T IH1 M S AH0 N -STIMULANT S T IH1 M Y AH0 L AH0 N T -STIMULANTS S T IH1 M Y AH0 L AH0 N T S -STIMULATE S T IH1 M Y AH0 L EY2 T -STIMULATED S T IH1 M Y AH0 L EY2 T IH0 D -STIMULATES S T IH1 M Y AH0 L EY2 T S -STIMULATING S T IH1 M Y AH0 L EY2 T IH0 NG -STIMULATION S T IH2 M Y AH0 L EY1 SH AH0 N -STIMULATIVE S T IH1 M Y AH0 L EY2 T IH0 V -STIMULATOR S T IH1 M Y AH0 L EY2 T ER0 -STIMULATORS S T IH1 M Y AH0 L EY2 T ER0 Z -STIMULI S T IH1 M Y AH0 L AY2 -STIMULUS S T IH1 M Y AH0 L AH0 S -STINAR S T AY1 N AA0 R -STINCHCOMB S T IH1 N CH K AH0 M -STINCHFIELD S T IH1 N CH F IY0 L D -STINE S T AY1 N -STINEBAUGH S T IH1 N IH0 B AO0 -STINEL S T IH1 N AH0 L -STINEL(2) S T IH2 N EH1 L -STINEL'S S T IH1 N AH0 L Z -STINEL'S(2) S T IH2 N EH1 L Z -STINEMAN S T AY1 N M AH0 N -STINER S T AY1 N ER0 -STINES S T AY1 N Z -STING S T IH1 NG -STING'S S T IH1 NG Z -STINGER S T IH1 NG ER0 -STINGERS S T IH1 NG ER0 Z -STINGIER S T IH1 N JH IY0 ER0 -STINGINESS S T IH1 N JH IY0 N IH0 S -STINGING S T IH1 NG IH0 NG -STINGLEY S T IH1 NG G L IY0 -STINGS S T IH1 NG Z -STINGY S T IH1 N JH IY0 -STINK S T IH1 NG K -STINKBUG S T IH1 NG K B AH0 G -STINKBUGS S T IH1 NG K B AH0 G Z -STINKERS S T IH1 NG K ER0 Z -STINKING S T IH1 NG K IH0 NG -STINKS S T IH1 NG K S -STINKY S T IH1 NG K IY0 -STINNETT S T IH1 N IH0 T -STINNETTE S T IH0 N EH1 T -STINSON S T IH1 N S AH0 N -STINT S T IH1 N T -STINTS S T IH1 N T S -STIPANOVICH S T IH0 P AE1 N AH0 V IH0 CH -STIPE S T AY1 P -STIPEND S T AY1 P AH0 N D -STIPENDS S T AY1 P AH0 N D Z -STIPES S T AY1 P S -STIPP S T IH1 P -STIPULATE S T IH1 P Y AH0 L EY2 T -STIPULATED S T IH1 P Y AH0 L EY2 T IH0 D -STIPULATES S T IH1 P Y AH0 L EY2 T S -STIPULATING S T IH1 P Y AH0 L EY2 T IH0 NG -STIPULATION S T IH2 P Y AH0 L EY1 SH AH0 N -STIPULATIONS S T IH2 P Y AH0 L EY1 SH AH0 N Z -STIR S T ER1 -STIRES S T AY1 R Z -STIREWALT S T ER1 W AA2 L T -STIRLING S T ER1 L IH0 NG -STIRLING'S S T ER1 L IH0 NG Z -STIRN S T ER1 N -STIRRED S T ER1 D -STIRRING S T ER1 IH0 NG -STIRRINGS S T ER1 IH0 NG Z -STIRRUP S T ER1 AH0 P -STIRRUPS S T ER1 AH0 P S -STIRS S T ER1 Z -STITCH S T IH1 CH -STITCHED S T IH1 CH T -STITCHES S T IH1 CH IH0 Z -STITCHING S T IH1 CH IH0 NG -STITELER S T AY1 T L ER0 -STITELY S T AY1 T L IY0 -STITES S T AY1 T S -STITH S T IH1 TH -STITT S T IH1 T -STITZ S T IH1 T S -STITZEL S T IH1 T Z AH0 L -STITZER S T IH1 T Z ER0 -STIVER S T AY1 V ER0 -STIVERS S T AY1 V ER0 Z -STIVERSON S T IH1 V ER0 S AH0 N -STIVORIC S T IH0 V AO1 R IH0 K -STJOHN S EY1 N T JH AA1 N -STOBAUGH S T AA1 B AO0 -STOBBE S T AA1 B -STOBER S T OW1 B ER0 -STOBIE S T AA1 B IY0 -STOCHASTIC S T OW0 K AE1 S T IH0 K -STOCHASTICALLY S T OW0 K AE1 S T IH0 K AH0 L IY2 -STOCHASTICS S T OW0 K AE1 S T IH0 K S -STOCK S T AA1 K -STOCK'S S T AA1 K S -STOCKADE S T AA1 K EY2 D -STOCKADE'S S T AA1 K EY2 D Z -STOCKADES S T AA1 K EY2 D Z -STOCKARD S T AA1 K ER0 D -STOCKBRIDGE S T AA1 K B R IH0 JH -STOCKBROKER S T AA1 K B R OW2 K ER0 -STOCKBROKER'S S T AA1 K B R OW2 K ER0 Z -STOCKBROKERAGE S T AA1 K B R OW2 K ER0 IH0 JH -STOCKBROKERAGES S T AA1 K B R OW2 K ER0 IH0 JH IH0 Z -STOCKBROKERS S T AA1 K B R OW2 K ER0 Z -STOCKBROKERS' S T AA1 K B R OW2 K ER0 Z -STOCKBROKING S T AA1 K B R OW2 K IH0 NG -STOCKBURGER S T AA1 K B ER0 G ER0 -STOCKDALE S T AA1 K D EY2 L -STOCKDALE'S S T AA1 K D EY2 L Z -STOCKE S T AA1 K -STOCKED S T AA1 K T -STOCKEL S T AA1 K AH0 L -STOCKER S T AA1 K ER0 -STOCKERT S T AA1 K ER0 T -STOCKETT S T AA1 K IH0 T -STOCKFORD S T AA1 K F ER0 D -STOCKHAM S T AA1 K HH AH0 M -STOCKHAUSEN S T AA1 K HH AW2 Z AH0 N -STOCKHOLDER S T AA1 K HH OW2 L D ER0 -STOCKHOLDERS S T AA1 K HH OW2 L D ER0 Z -STOCKHOLDERS' S T AA1 K HH OW2 L D ER0 Z -STOCKHOLDING S T AA1 K HH OW2 L D IH0 NG -STOCKHOLDINGS S T AA1 K HH OW2 L D IH0 NG Z -STOCKHOLM S T AA1 K HH OW2 L M -STOCKHOLM'S S T AA1 K HH OW2 L M Z -STOCKING S T AA1 K IH0 NG -STOCKINGER S T AA1 K IH0 NG ER0 -STOCKINGS S T AA1 K IH0 NG Z -STOCKLEY S T AA1 K L IY0 -STOCKMAN S T AA1 K M AH0 N -STOCKMAN'S S T AA1 K M AE2 N Z -STOCKMARKET S T AA1 K M AA2 R K IH0 T -STOCKPILE S T AA1 K P AY2 L -STOCKPILED S T AA1 K P AY2 L D -STOCKPILES S T AA1 K P AY2 L Z -STOCKPILING S T AA1 K P AY2 L IH0 NG -STOCKROOM S T AA1 K R UW2 M -STOCKS S T AA1 K S -STOCKS' S T AA1 K S -STOCKSDALE S T AA1 K S D EY2 L -STOCKSLAGER S T AA1 K S L EY0 G ER0 -STOCKSTILL S T AA1 K S T IH2 L -STOCKTON S T AA1 K T AH0 N -STOCKWELL S T AA1 K W EH2 L -STOCKY S T AA1 K IY0 -STOCKYARD S T AA1 K Y AA2 R D -STOCKYARDS S T AA1 K Y AA2 R D Z -STOCUM S T OW1 K AH0 M -STODDARD S T AA1 D ER0 D -STODDARD'S S T AA1 D ER0 D Z -STODGHILL S T AA1 JH HH IH2 L -STODGY S T AA1 JH IY0 -STODOLA S T OW0 D OW1 L AA2 -STODOLSKY S T AH0 D AO1 L S K IY2 -STOEBER S T OW1 B ER0 -STOECKEL S T OW1 K AH0 L -STOECKER S T OW1 K ER0 -STOECKLE S T OW1 K AH0 L -STOECKLEIN S T OW1 K L AY2 N -STOEGER S T OW1 G ER0 -STOEHR S T AO1 R -STOELTING S T OW1 L T IH0 NG -STOELTZE S T OW1 L T S -STOERMER S T AO1 R M ER0 -STOESSEL S T OW1 S AH0 L -STOEVER S T AA1 EH0 V ER0 -STOFER S T OW1 F ER0 -STOFF S T AO1 F -STOFFEL S T AA1 F AH0 L -STOFFELS S T AA1 F AH0 L Z -STOFFER S T AO1 F ER0 -STOFFERS S T AO1 F ER0 Z -STOFFREGEN S T AA1 F R IH0 G AH0 N -STOFKO S T OW1 F K OW2 -STOGA S T OW1 G AH0 -STOGDILL S T AA1 G D IH0 L -STOGIE S T OW1 G IY2 -STOGIES S T OW1 G IY2 Z -STOGNER S T AA1 G N ER0 -STOGSDILL S T AA1 G Z D IH0 L -STOHL S T OW1 L -STOHLER S T OW1 L ER0 -STOHR S T AO1 R -STOIA S T OW1 Y AA2 -STOIBER S T OY1 B ER0 -STOIC S T OW1 IH0 K -STOICALLY S T OW1 IH0 K L IY0 -STOICISM S T OW1 AH0 S IH2 Z AH0 M -STOICS S T OW1 IH0 K S -STOKE S T OW1 K -STOKED S T OW1 K T -STOKELY S T OW1 K L IY0 -STOKER S T OW1 K ER0 -STOKERS S T OW1 K ER0 Z -STOKES S T OW1 K S -STOKING S T OW1 K IH0 NG -STOKKE S T AA1 K -STOKLEY S T AA1 K L IY0 -STOKLOSA S T AH0 K L OW1 S AA2 -STOL S T OW1 L -STOLAR S T OW1 L ER0 -STOLARSKI S T AH0 L AA1 R S K IY2 -STOLARZ S T OW1 L AA0 R Z -STOLBERG S T OW1 L B ER0 G -STOLDT S T OW1 L T -STOLE S T OW1 L -STOLEN S T OW1 L AH0 N -STOLER S T OW1 L ER0 -STOLFI S T OW1 L F IY0 -STOLICHNAYA S T OW2 L IH0 K N AY1 AH0 -STOLID S T AA1 L AH0 D -STOLL S T OW1 L -STOLLAR S T AA1 L ER0 -STOLLE S T AA1 L -STOLLEN S T AA1 L AH0 N -STOLLER S T OW1 L ER0 -STOLLEY S T AA1 L IY0 -STOLLINGS S T OW1 L IH0 NG Z -STOLP S T OW1 L P -STOLPE S T OW1 L P -STOLPER S T OW1 L P ER0 -STOLT S T OW1 L T -STOLTE S T OW1 L T -STOLTENBERG S T OW1 L T AH0 N B ER0 G -STOLTMAN S T OW1 L T M AH0 N -STOLTZ S T OW1 L T S -STOLTZFUS S T OW1 L T S F AH0 S -STOLTZMAN S T OW1 L T S M AH0 N -STOLZ S T OW1 L Z -STOLZE S T OW1 L Z -STOMA S T OW1 M AH0 -STOMACH S T AH1 M AH0 K -STOMACHS S T AH1 M AH0 K S -STOMATA S T OW1 M AH0 T AH0 -STOMBAUGH S T AA1 M B AO2 -STOMP S T AA1 M P -STOMPED S T AA1 M P T -STOMPING S T AA1 M P IH0 NG -STONE S T OW1 N -STONE'S S T OW1 N Z -STONEBACK S T OW1 N B AE2 K -STONEBERG S T OW1 N B ER0 G -STONEBERGER S T OW1 N B ER0 G ER0 -STONEBRAKER S T AA1 N IH0 B R AH0 K ER0 -STONEBRAKER(2) S T OW1 N B R EY0 K ER0 -STONEBURNER S T OW1 N B ER2 N ER0 -STONECIPHER S T OW1 N S AY2 F ER0 -STONECUTTER S T OW1 N K AH2 T ER0 -STONECUTTERS S T OW1 N K AH2 T ER0 Z -STONECYPHER S T AA1 N IH0 S IH0 F ER0 -STONECYPHER(2) S T OW1 N S AY0 F ER0 -STONED S T OW1 N D -STONEHAM S T OW1 N AH0 M -STONEHENGE S T OW1 N HH EH2 N JH -STONEHILL S T OW1 N HH IH2 L -STONEHOCKER S T OW1 N HH AA2 K ER0 -STONEHOUSE S T OW1 N HH AW2 S -STONEKING S T OW1 N K IH2 NG -STONEMAN S T OW1 N M AH0 N -STONER S T OW1 N ER0 -STONERIDGE S T OW1 N R IH2 JH -STONEROCK S T OW1 N R AA2 K -STONES S T OW1 N Z -STONES' S T OW1 N Z -STONESIFER S T OW1 N S AY2 F ER0 -STONESTREET S T OW1 N S T R IY2 T -STONEWALL S T OW1 N W AO2 L -STONEWALLED S T OW1 N W AO2 L D -STONEWALLING S T OW1 N W AO2 L IH0 NG -STONEWARE S T OW1 N W EH2 R -STONEY S T OW1 N IY0 -STONG S T AO1 NG -STONGE S T AA1 N JH -STONING S T OW1 N IH0 NG -STONY S T OW1 N IY0 -STONYFIELD S T OW1 N IY0 F IY2 L D -STOOD S T UH1 D -STOOGE S T UW1 JH -STOOGES S T UW1 JH IH0 Z -STOOKEY S T UW1 K IY0 -STOOKSBURY S T UW1 K S B EH0 R IY0 -STOOL S T UW1 L -STOOLS S T UW1 L Z -STOOP S T UW1 P -STOOPED S T UW1 P T -STOOPING S T UW1 P IH0 NG -STOOPS S T UW1 P S -STOOTS S T UW1 T S -STOP S T AA1 P -STOP-GAP S T AA1 P G AE2 P -STOPA S T OW1 P AH0 -STOPGAP S T AA1 P G AE2 P -STOPHER S T AA1 F ER0 -STOPKA S T OW1 P K AH0 -STOPLIGHT S T AA1 P L AY2 T -STOPLIGHTS S T AA1 P L AY2 T S -STOPOVER S T AA1 P OW2 V ER0 -STOPOVERS S T AA1 P OW2 V ER0 Z -STOPPAGE S T AA1 P IH0 JH -STOPPAGES S T AA1 P IH0 JH IH0 Z -STOPPED S T AA1 P T -STOPPEL S T AA1 P AH0 L -STOPPER S T AA1 P ER0 -STOPPERS S T AA1 P ER0 Z -STOPPING S T AA1 P IH0 NG -STOPS S T AA1 P S -STOPWATCH S T AA1 P W AA2 CH -STOPWATCHES S T AA1 P W AA2 CH IH0 Z -STORA S T AO1 R AH0 -STORA'S S T AO1 R AH0 Z -STORAGE S T AO1 R AH0 JH -STORAGE(2) S T AO1 R IH0 JH -STORAGETEK S T AO1 R AH0 JH T EH2 K -STORASKA S T AO2 R AE1 S AH0 -STORBECK S T AO1 R B EH0 K -STORCH S T AO1 R K -STORCK S T AO1 R K -STORDAHL S T AO1 R D AA0 L -STORE S T AO1 R -STORE'S S T AO1 R Z -STORED S T AO1 R D -STOREFRONT S T AO1 R F R AA2 N T -STOREFRONTS S T AO1 R F R AA2 N T S -STOREHOUSE S T AO1 R HH AW2 S -STOREHOUSE'S S T AO1 R HH AW2 S IH0 Z -STOREHOUSES S T AO1 R HH AW2 Z AH0 Z -STOREKEEPER S T AO1 R K IY2 P ER0 -STOREKEEPERS S T AO1 R K IY2 P ER0 Z -STORER S T AO1 R ER0 -STORER'S S T AO1 R ER0 Z -STOREROOM S T AO1 R R UW2 M -STORES S T AO1 R Z -STORES' S T AO1 R Z -STOREY S T AO1 R IY0 -STORFER S T AO1 R F ER0 -STORIE S T AO1 R IY0 -STORIED S T AO1 R IY0 D -STORIES S T AO1 R IY0 Z -STORING S T AO1 R IH0 NG -STORK S T AO1 R K -STORLIE S T AO1 R L IY0 -STORM S T AO1 R M -STORM'S S T AO1 R M Z -STORMED S T AO1 R M D -STORMENT S T AO1 R M AH0 N T -STORMER S T AO1 R M ER0 -STORMES S T AO1 R M Z -STORMIEST S T AO1 R M IY0 IH0 S T -STORMINESS S T AO1 R M IY0 N IH0 S -STORMING S T AO1 R M IH0 NG -STORMONT S T AO1 R M OW0 N T -STORMS S T AO1 R M Z -STORMY S T AO1 R M IY0 -STORR S T AO1 R -STORROW S T AA1 R OW0 -STORRS S T AO1 R Z -STORTI S T AO1 R T IY0 -STORTING S T AO1 R T IH0 NG -STORTS S T AO1 R T S -STORTZ S T AO1 R T S -STORY S T AO1 R IY0 -STORY'S S T AO1 R IY0 Z -STORYBOARD S T AO1 R IY0 B AO2 R D -STORYBOOK S T AO1 R IY0 B UH2 K -STORYBOOKS S T AO1 R IY0 B UH2 K S -STORYLINE S T AO1 R IY0 L AY2 N -STORYLINES S T AO1 R IY0 L AY2 N Z -STORYTELLER S T AO1 R IY0 T EH2 L ER0 -STORYTELLERS S T AO1 R IY0 T EH2 L ER0 Z -STORYTELLING S T AO1 R IY0 T EH2 L IH0 NG -STORZ S T AO1 R Z -STOSSEL S T AA1 S AH0 L -STOSSEL'S S T AA1 S AH0 L Z -STOTLER S T AA1 T L ER0 -STOTT S T AA1 T -STOTTLEMYER S T AA1 T AH0 L M AY0 ER0 -STOTTS S T AA1 T S -STOTZ S T AA1 T S -STOUDEMIRE S T UW1 D AH0 M AY2 R -STOUDENMIRE S T UW1 D AH0 N M AY2 R -STOUDER S T AH1 D ER0 -STOUDT S T AH1 D T -STOUFFER S T AH1 F ER0 -STOUFFER(2) S T OW1 F ER0 -STOUFFS S T AH1 F S -STOUFFS(2) S T OW1 F S -STOUGH S T AH1 F -STOUGHTON S T OW1 T AH0 N -STOUP S T UW1 P -STOUT S T AW1 T -STOUTE S T AW1 T -STOUTENBURG S T AW1 T AH0 N B ER0 G -STOUTLY S T AW1 T L IY0 -STOUTNESS S T AW1 T N AH0 S -STOVALL S T OW1 V AA0 L -STOVE S T OW1 V -STOVER S T OW1 V ER0 -STOVES S T OW1 V Z -STOW S T OW1 -STOWAWAY S T OW1 AH0 W EY2 -STOWAWAY'S S T OW1 AH0 W EY2 Z -STOWAWAYS S T OW1 AH0 W EY2 Z -STOWE S T OW1 -STOWED S T OW1 D -STOWELL S T AA1 W EH0 L -STOWER S T OW1 ER0 -STOWERS S T OW1 ER0 Z -STOWING S T OW1 W IH0 NG -STOY S T OY1 -STOYER S T OY1 ER0 -STRACENER S T R AE1 S IY0 N ER0 -STRACHAN S T R AE1 CH AH0 N -STRACK S T R AE1 K -STRACKE S T R AE1 K -STRADA S T R AA1 D AH0 -STRADDLE S T R AE1 D AH0 L -STRADDLED S T R AE1 D AH0 L D -STRADDLES S T R AE1 D AH0 L Z -STRADDLING S T R AE1 D L IH0 NG -STRADER S T R EY1 D ER0 -STRADFORD S T R AE1 D F ER0 D -STRADIVARIUS S T R AE2 D IH0 V EH1 R IY0 AH0 S -STRADLEY S T R AE1 D L IY0 -STRADLING S T R AE1 D L IH0 NG -STRAFE S T R EY1 F -STRAFING S T R EY1 F IH0 NG -STRAGGLE S T R AE1 G AH0 L -STRAGGLED S T R AE1 G AH0 L D -STRAGGLER S T R AE1 G L ER0 -STRAGGLERS S T R AE1 G L ER0 Z -STRAHAN S T R AE1 HH AH0 N -STRAHL S T R AA1 L -STRAHLE S T R EY1 HH AH0 L -STRAHLER S T R AA1 L ER0 -STRAHM S T R AA1 M -STRAIGHT S T R EY1 T -STRAIGHTAWAY S T R EY1 T AH0 W EY2 -STRAIGHTAWAYS S T R EY1 T AH0 W EY2 Z -STRAIGHTEDGE S T R EY1 T EH2 JH -STRAIGHTEN S T R EY1 T AH0 N -STRAIGHTENED S T R EY1 T AH0 N D -STRAIGHTENING S T R EY1 T AH0 N IH0 NG -STRAIGHTENING(2) S T R EY1 T N IH0 NG -STRAIGHTENS S T R EY1 T AH0 N Z -STRAIGHTER S T R EY1 T ER0 -STRAIGHTFORWARD S T R EY1 T F AO1 R W ER0 D -STRAIGHTFORWARDLY S T R EY2 T F AO1 R W ER0 D L IY0 -STRAIGHTS S T R EY1 T S -STRAIN S T R EY1 N -STRAINED S T R EY1 N D -STRAINING S T R EY1 N IH0 NG -STRAINS S T R EY1 N Z -STRAIT S T R EY1 T -STRAITJACKET S T R EY1 T JH AE2 K AH0 T -STRAITS S T R EY1 T S -STRAKA S T R AA1 K AH0 -STRAKER S T R EY1 K ER0 -STRALEY S T R AE1 L IY0 -STRAM S T R AE1 M -STRANAHAN S T R AE1 N AH0 HH AE0 N -STRAND S T R AE1 N D -STRANDBERG S T R AE1 N D B ER0 G -STRANDE S T R AE1 N D -STRANDED S T R AE1 N D IH0 D -STRANDING S T R AE1 N D IH0 NG -STRANDLINE S T R AE1 N D L AY2 N -STRANDLINE(2) S T R AE1 N L AY2 N -STRANDLINES S T R AE1 N D L AY2 N Z -STRANDLINES(2) S T R AE1 N L AY2 N Z -STRANDS S T R AE1 N D Z -STRANG S T R AE1 NG -STRANGE S T R EY1 N JH -STRANGELOVE S T R EY1 N JH L AH2 V -STRANGELY S T R EY1 N JH L IY0 -STRANGENESS S T R EY1 N JH N AH0 S -STRANGER S T R EY1 N JH ER0 -STRANGER'S S T R EY1 N JH ER0 Z -STRANGERS S T R EY1 N JH ER0 Z -STRANGEST S T R EY1 N JH IH0 S T -STRANGIS S T R AE1 N JH IH0 S -STRANGLE S T R AE1 NG G AH0 L -STRANGLED S T R AE1 NG G AH0 L D -STRANGLEHOLD S T R AE1 NG G AH0 L HH OW2 L D -STRANGLER S T R AE1 NG G L ER0 -STRANGLING S T R AE1 NG G AH0 L IH0 NG -STRANGLING(2) S T R AE1 NG G L IH0 NG -STRANGULATE S T R AE1 NG G Y AH0 L EY2 T -STRANGULATION S T R AE2 NG G Y AH0 L EY1 SH AH0 N -STRANGULATIONS S T R AE2 NG G Y AH0 L EY1 SH AH0 N Z -STRANGWAYES S T R AE1 NG W EY2 Z -STRANO S T R AA1 N OW0 -STRANSKY S T R AE1 N S K IY0 -STRAP S T R AE1 P -STRAPPED S T R AE1 P T -STRAPPING S T R AE1 P IH0 NG -STRAPS S T R AE1 P S -STRASBERG S T R AE1 S B ER0 G -STRASBOURG S T R AE1 S B AO2 R G -STRASBURG S T R AE1 S B ER0 G -STRASBURGER S T R AE1 S B ER0 G ER0 -STRASSBURG S T R AE1 S B ER0 G -STRASSBURGER S T R AE1 S B ER0 G ER0 -STRASSER S T R AE1 S ER0 -STRASSMAN S T R AE1 S M AH0 N -STRASSNER S T R AE1 S N ER0 -STRASZHEIM S T R AE1 S HH AY2 M -STRATA S T R AE1 T AH0 -STRATACOM S T R AE1 T AH0 K AA0 M -STRATAGEM S T R AE1 T AH0 JH AH0 M -STRATAGEMS S T R AE1 T AH0 JH AH0 M Z -STRATAS S T R AE1 T AH0 Z -STRATE S T R EY1 T -STRATEGEM S T R AE1 T IH0 JH EH0 M -STRATEGIC S T R AH0 T IY1 JH IH0 K -STRATEGICAL S T R AH0 T IY1 JH IH0 K AH0 L -STRATEGICALLY S T R AH0 T IY1 JH IH0 K L IY0 -STRATEGIES S T R AE1 T AH0 JH IY0 Z -STRATEGIST S T R AE1 T IH0 JH IH0 S T -STRATEGISTS S T R AE1 T IH0 JH IH0 S T S -STRATEGIZE S T R AE1 T AH0 JH AY0 Z -STRATEGIZING S T R AE1 T AH0 JH AY0 Z IH0 NG -STRATEGY S T R AE1 T AH0 JH IY0 -STRATEGY'S S T R AE1 T AH0 JH IY0 Z -STRATER S T R EY1 T ER0 -STRATFORD S T R AE1 T F ER0 D -STRATHMAN S T R AE1 TH M AH0 N -STRATIFICATION S T R AE2 T AH0 F IH0 K EY1 SH AH0 N -STRATIFIED S T R AE1 T AH0 F AY2 D -STRATIFY S T R AE1 T AH0 F AY2 -STRATIGRAPHIC S T R AE2 T AH0 G R AE1 F IH0 K -STRATMAN S T R AE1 T M AH0 N -STRATMANN S T R AE1 T M AH0 N -STRATOCASTER S T R AE1 T OW0 K AE2 S T ER0 -STRATOFLEX S T R AE1 T OW0 F L EH2 K S -STRATOSPHERE S T R AE1 T AH0 S F IH2 R -STRATOSPHERIC S T R AE2 T AH0 S F IH1 R IH0 K -STRATTON S T R AE1 T AH0 N -STRATUM S T R AE1 T AH0 M -STRATUS S T R AE1 T AH0 S -STRATUS'S S T R AE1 T AH0 S IH0 Z -STRAUB S T R AW1 B -STRAUBE S T R AW1 B -STRAUCH S T R AW1 K -STRAUGHAN S T R AO1 AH0 N -STRAUGHN S T R AO1 N -STRAUGHTER S T R AO1 T ER0 -STRAUM S T R AW1 M -STRAUM(2) S T R AA1 M -STRAUS S T R AW1 S -STRAUSBAUGH S T R AW1 S B AW0 -STRAUSE S T R AW1 S -STRAUSER S T R AW1 S ER0 -STRAUSS S T R AW1 S -STRAUSS'S S T R AW1 S IH0 Z -STRAUSSER S T R AW1 S ER0 -STRAVINSKY S T R AH0 V IH1 N S K IY0 -STRAVINSKY'S S T R AH0 V IH1 N S K IY0 Z -STRAW S T R AO1 -STRAWBERRIES S T R AO1 B EH2 R IY0 Z -STRAWBERRY S T R AO1 B EH2 R IY0 -STRAWBRIDGE S T R AO1 B R IH2 JH -STRAWDER S T R AO1 D ER0 -STRAWDERMAN S T R AO1 D ER0 M AH0 N -STRAWN S T R AO1 N -STRAWS S T R AO1 Z -STRAWSER S T R AO1 Z ER0 -STRAY S T R EY1 -STRAYED S T R EY1 D -STRAYER S T R EY1 ER0 -STRAYHORN S T R EY1 HH ER0 N -STRAYING S T R EY1 IH0 NG -STRAYS S T R EY1 Z -STREAK S T R IY1 K -STREAKED S T R IY1 K T -STREAKER S T R IY1 K ER0 -STREAKING S T R IY1 K IH0 NG -STREAKS S T R IY1 K S -STREAM S T R IY1 M -STREAMED S T R IY1 M D -STREAMER S T R IY1 M ER0 -STREAMERS S T R IY1 M ER0 Z -STREAMING S T R IY1 M IH0 NG -STREAMLINE S T R IY1 M L AY2 N -STREAMLINED S T R IY1 M L AY2 N D -STREAMLINING S T R IY1 M L AY2 N IH0 NG -STREAMS S T R IY1 M Z -STREATER S T R IY1 T ER0 -STREB S T R EH1 B -STREBE S T R IY1 B -STREBECK S T R IY1 B EH0 K -STREBEL S T R EH1 B AH0 L -STRECK S T R EH1 K -STRECKER S T R EH1 K ER0 -STREED S T R IY1 D -STREEP S T R IY1 P -STREEPER S T R IY1 P ER0 -STREET S T R IY1 T -STREET'S S T R IY1 T S -STREETCAR S T R IY1 T K AA2 R -STREETER S T R IY1 T ER0 -STREETERS S T R IY1 T ER0 Z -STREETMAN S T R IY1 T M AH0 N -STREETS S T R IY1 T S -STREETT S T R IY1 T -STREETWISE S T R IY1 T W AY2 Z -STREETY S T R IY1 T IY0 -STREFF S T R EH1 F -STREGE S T R IY1 JH -STREHL S T R EH1 L -STREHLE S T R EH1 L -STREHLOW S T R EH1 L OW0 -STREIB S T R AY1 B -STREIBER S T R AY1 B ER0 -STREICH S T R AY1 K -STREICHER S T R AY1 K ER0 -STREIFF S T R AY1 F -STREIGHT S T R EY1 T -STREIKER S T R AY1 K ER0 -STREISAND S T R AY1 Z AH0 N D -STREISAND(2) S T R AY1 S AE2 N D -STREISAND'S S T R AY1 Z AH0 N D Z -STREISAND'S(2) S T R AY1 S AE2 N D Z -STREIT S T R AY1 T -STREITMATTER S T R AY1 T M AH0 T ER0 -STRELOW S T R EH1 L OW0 -STRENG S T R EH1 NG -STRENGER S T R EH1 NG G ER0 -STRENGTH S T R EH1 NG K TH -STRENGTH(2) S T R EH1 NG TH -STRENGTHEN S T R EH1 NG TH AH0 N -STRENGTHENED S T R EH1 NG TH AH0 N D -STRENGTHENING S T R EH1 NG TH AH0 N IH0 NG -STRENGTHENS S T R EH1 NG TH AH0 N Z -STRENGTHS S T R EH1 NG K TH S -STRENGTHS(2) S T R EH1 NG TH S -STRENIO S T R IY1 N IY0 OW0 -STRENUOUS S T R EH1 N Y UW0 AH0 S -STRENUOUSLY S T R EH1 N Y UW0 AH0 S L IY0 -STREP S T R EH1 P -STREPS S T R EH1 P S -STREPTOCOCCUS S T R EH2 P T AH0 K AO1 K AH0 S -STREPTOKINASE S T R EH2 P T AH0 K AY1 N EY2 S -STREPTOKINASE(2) S T R EH2 P T OW0 K AY1 N EY2 Z -STRESS S T R EH1 S -STRESSED S T R EH1 S T -STRESSES S T R EH1 S AH0 Z -STRESSES(2) S T R EH1 S IH0 Z -STRESSFUL S T R EH1 S F AH0 L -STRESSING S T R EH1 S IH0 NG -STRESSOR S T R EH1 S ER0 -STRESSORS S T R EH1 S ER0 Z -STRETCH S T R EH1 CH -STRETCHED S T R EH1 CH T -STRETCHER S T R EH1 CH ER0 -STRETCHER-BEARER S T R EH1 CH ER0 B EH1 R ER0 -STRETCHER-BEARERS S T R EH1 CH ER0 B EH1 R ER0 Z -STRETCHERS S T R EH1 CH ER0 Z -STRETCHES S T R EH1 CH AH0 Z -STRETCHES(2) S T R EH1 CH IH0 Z -STRETCHING S T R EH1 CH IH0 NG -STRETTO S T R EH1 T OW2 -STREVIG S T R EH1 V IH0 G -STREW S T R UW1 -STREWN S T R UW1 N -STREY S T R EY1 -STRIAR S T R AY1 R -STRIBLING S T ER1 AH0 B AH0 L IH0 NG -STRIBLING(2) S T R IH1 B L IH0 NG -STRICK S T R IH1 K -STRICKEN S T R IH1 K AH0 N -STRICKER S T R IH1 K ER0 -STRICKLAND S T R IH1 K L AH0 N D -STRICKLEN S T R IH1 K AH0 L AH0 N -STRICKLER S T R IH1 K L ER0 -STRICKLIN S T R IH1 K L IH0 N -STRICKLING S T R IH1 K L IH0 NG -STRICT S T R IH1 K T -STRICTER S T R IH1 K T ER0 -STRICTEST S T R IH1 K T AH0 S T -STRICTLY S T R IH1 K T L IY0 -STRICTURE S T R IH1 K CH ER0 -STRICTURES S T R IH1 K CH ER0 Z -STRIDE S T R AY1 D -STRIDENCY S T R AY1 D AH0 N S IY0 -STRIDENT S T R AY1 D AH0 N T -STRIDENTLY S T R AY1 D AH0 N T L IY0 -STRIDER S T R AY1 D ER0 -STRIDES S T R AY1 D Z -STRIDING S T R AY1 D IH0 NG -STRIEBER S T R AY1 B ER0 -STRIEGEL S T R IY1 G AH0 L -STRIEKER S T R IY1 K ER0 -STRIEKER'S S T R IY1 K ER0 Z -STRIETER S T R IY1 T ER0 -STRIFE S T R AY1 F -STRIFES S T R AY1 F S -STRIKE S T R AY1 K -STRIKE'S S T R AY1 K S -STRIKEBREAKER S T R AY1 K B R EY2 K ER0 -STRIKEBREAKERS S T R AY1 K B R EY2 K ER0 Z -STRIKEOUT S T R AY1 K AW2 T -STRIKEOUTS S T R AY1 K AW2 T S -STRIKER S T R AY1 K ER0 -STRIKER'S S T R AY1 K ER0 Z -STRIKERS S T R AY1 K ER0 Z -STRIKERS' S T R AY1 K ER0 Z -STRIKES S T R AY1 K S -STRIKES' S T R AY1 K S -STRIKING S T R AY1 K IH0 NG -STRIKINGLY S T R AY1 K IH0 NG L IY0 -STRIMPLE S T R IH1 M P AH0 L -STRINDEN S T R IH1 N D AH0 N -STRINE S T R AY1 N -STRING S T R IH1 NG -STRINGED S T R IH1 NG D -STRINGENCY S T R IH1 N JH AH0 N S IY0 -STRINGENT S T R IH1 N JH AH0 N T -STRINGENTLY S T R IH1 N JH AH0 N T L IY0 -STRINGER S T R IH1 NG ER0 -STRINGERS S T R IH1 NG ER0 Z -STRINGFELLOW S T R IH1 NG F EH2 L OW0 -STRINGFIELD S T R IH1 NG F IY2 L D -STRINGHAM S T R IH1 NG HH AE2 M -STRINGING S T R IH1 NG IH0 NG -STRINGS S T R IH1 NG Z -STRINGY S T R IH1 NG IY0 -STRIP S T R IH1 P -STRIP'S S T R IH1 P S -STRIPE S T R AY1 P -STRIPED S T R AY1 P T -STRIPER S T R AY1 P ER0 -STRIPERS S T R AY1 P ER0 Z -STRIPES S T R AY1 P S -STRIPLIN S T R IH1 P L IH0 N -STRIPLING S T R IH1 P L IH0 NG -STRIPPED S T R IH1 P T -STRIPPER S T R IH1 P ER0 -STRIPPERS S T R IH1 P ER0 Z -STRIPPING S T R IH1 P IH0 NG -STRIPS S T R IH1 P S -STRIPTEASE S T R IH1 P T IY2 Z -STRITE S T R AY1 T -STRITTMATTER S T R IH1 T M AH0 T ER0 -STRIVE S T R AY1 V -STRIVEN S T R IH1 V AH0 N -STRIVES S T R AY1 V Z -STRIVING S T R AY1 V IH0 NG -STRIVINGS S T R AY1 V IH0 NG Z -STRNAD S T ER1 N AE0 D -STROBE S T R OW1 B -STROBEL S T R OW1 B AH0 L -STROBEL'S S T R OW1 B AH0 L Z -STROBER S T R OW1 B ER0 -STROBL S T R AA1 B AH0 L -STROBLE S T R OW1 B AH0 L -STROBRIDGE S T R AA1 B R IH0 JH -STROCK S T R AA1 K -STRODE S T R OW1 D -STRODER S T R OW1 D ER0 -STROEBEL S T R OW1 B AH0 L -STROESSNER S T R OW1 S N ER0 -STROGANOFF S T R OW1 G AH0 N AO2 F -STROH S T R OW1 -STROHECKER S T R OW1 IH0 K ER0 -STROHL S T R OW1 L -STROHM S T R OW1 M -STROHMAIER S T R OW1 M AY0 ER0 -STROHMAN S T R OW1 M AH0 N -STROHMEIER S T R OW1 M AY0 ER0 -STROHMEYER S T R OW1 M AY0 ER0 -STROIK S T R OY1 K -STROJNY S T R OW1 Y N IY0 -STROKE S T R OW1 K -STROKED S T R OW1 K T -STROKES S T R OW1 K S -STROKING S T R OW1 K IH0 NG -STROLE S T R OW1 L -STROLL S T R OW1 L -STROLLED S T R OW1 L D -STROLLER S T R OW1 L ER0 -STROLLERS S T R OW1 L ER0 Z -STROLLING S T R OW1 L IH0 NG -STROLLO S T R AA1 L OW0 -STROLLS S T R OW1 L Z -STROM S T R AA1 M -STROMA S T R OW1 M AH0 -STROMAIN S T R AA1 M AY0 N -STROMAN S T R OW1 M AH0 N -STROMBECK S T R AA1 M B EH2 K -STROMBERG S T R AA1 M B ER0 G -STROMBOLI S T R AA2 M B OW1 L IY0 -STROMBOLI'S S T R AA2 M B OW1 L IY0 Z -STROME S T R OW1 M -STROMER S T R OW1 M ER0 -STROMGREN S T R AA1 M G R EH0 N -STROMME S T R AA1 M -STROMMEN S T R AA1 M AH0 N -STROMQUIST S T R AA1 M K W IH2 S T -STRONACH S T R AA1 N AH0 K -STRONG S T R AO1 NG -STRONGER S T R AO1 NG ER0 -STRONGER(2) S T R AO1 NG G ER0 -STRONGEST S T R AO1 NG G AH0 S T -STRONGHOLD S T R AO1 NG HH OW2 L D -STRONGHOLDS S T R AO1 NG HH OW2 L D Z -STRONGLY S T R AO1 NG L IY0 -STRONGMAN S T R AO1 NG M AE2 N -STRONTIUM S T R AA1 N T IY0 AH0 M -STROOCK S T R UH1 K -STROOP S T R UW1 P -STROOPE S T R UW1 P -STROOT S T R UW1 T -STROPE S T R OW1 P -STROSCHEIN S T R AO1 SH AY0 N -STROSNIDER S T R AA1 S N AY0 D ER0 -STROSSEN S T R AO1 S EH0 N -STROSSEN(2) S T R AO1 S IH0 N -STROTHER S T R AA1 DH ER0 -STROTHERS S T R AH1 DH ER0 Z -STROTHMAN S T R AA1 TH M AH0 N -STROUD S T R AW1 D -STROUGH S T R AW1 -STROUP S T R UW1 P -STROUPE S T R UW1 P -STROUSE S T R AW1 S -STROUT S T R AW1 T -STROUTH S T R AW1 TH -STROVE S T R OW1 V -STROW S T R OW1 -STROZIER S T R OW1 Z IY0 ER0 -STRUB S T R AH1 B -STRUBBE S T R AH1 B -STRUBE S T R UW1 B -STRUBEL S T R UW1 B AH0 L -STRUBLE S T R UW1 B AH0 L -STRUCHEN S T R AH1 K AH0 N -STRUCK S T R AH1 K -STRUCKMAN S T R AH1 K M AH0 N -STRUCTURAL S T R AH1 K CH ER0 AH0 L -STRUCTURALLY S T R AH1 K CH ER0 AH0 L IY0 -STRUCTURE S T R AH1 K CH ER0 -STRUCTURE'S S T R AH1 K CH ER0 Z -STRUCTURED S T R AH1 K CH ER0 D -STRUCTURES S T R AH1 K CH ER0 Z -STRUCTURING S T R AH1 K CH ER0 IH0 NG -STRUEBING S T R UH1 B IH0 NG -STRUGGLE S T R AH1 G AH0 L -STRUGGLE'S S T R AH1 G AH0 L Z -STRUGGLED S T R AH1 G AH0 L D -STRUGGLES S T R AH1 G AH0 L Z -STRUGGLING S T R AH1 G AH0 L IH0 NG -STRUGGLING(2) S T R AH1 G L IH0 NG -STRUM S T R AH1 M -STRUMMING S T R AH1 M IH0 NG -STRUMS S T R AH1 M Z -STRUNG S T R AH1 NG -STRUNK S T R AH1 NG K -STRUNK'S S T R AH1 NG K S -STRUPP S T R AH1 P -STRUSS S T R AH1 S -STRUT S T R AH1 T -STRUTHERS S T R AH1 DH ER0 Z -STRUTS S T R AH1 T S -STRUTTING S T R AH1 T IH0 NG -STRUTTON S T R AH1 T AH0 N -STRUTZ S T R AH1 T S -STRUVE S T R UW1 V -STRYCHARZ S T R IH1 HH ER0 Z -STRYCHNINE S T R IH1 K N AY2 N -STRYKER S T R AY1 K ER0 -STRZELECKI S T R EH2 Z IH0 L EH1 T S K IY0 -STRZELECKI(2) S T ER2 Z IH0 L EH1 T S K IY0 -STU S T UW1 -STUARD S T UW1 ER0 D -STUART S T UW1 ER0 T -STUART(2) S T Y UW1 ER0 T -STUART(3) S T AO1 R T -STUART'S S T UW1 ER0 T S -STUART'S(2) S T Y UW1 ER0 T S -STUART'S(3) S T AO1 R T S -STUARTS S T UW1 ER0 T S -STUB S T AH1 B -STUBBE S T AH1 B -STUBBED S T AH1 B D -STUBBINS S T AH1 B IH0 N Z -STUBBLE S T AH1 B AH0 L -STUBBLEFIELD S T AH1 B AH0 L F IY2 L D -STUBBORN S T AH1 B ER0 N -STUBBORNLY S T AH1 B ER0 N L IY0 -STUBBORNNESS S T AH1 B ER0 N N AH0 S -STUBBORNNESS(2) S T AH1 B ER0 N AH0 S -STUBBS S T AH1 B Z -STUBBY S T AH1 B IY0 -STUBER S T UW1 B ER0 -STUBS S T AH1 B Z -STUCCO S T AH1 K OW0 -STUCCOED S T AH1 K OW0 D -STUCHELL S T AH1 K AH0 L -STUCK S T AH1 K -STUCKE S T AH1 K -STUCKER S T AH1 K ER0 -STUCKERT S T AH1 K ER0 T -STUCKEY S T AH1 K IY0 -STUCKI S T AH1 K IY0 -STUCKMAN S T AH1 K M AH0 N -STUCKY S T AH1 K IY0 -STUD S T AH1 D -STUDDARD S T AH1 D ER0 D -STUDDED S T AH1 D IH0 D -STUDDS S T AH1 D Z -STUDE S T UW1 D -STUDEBAKER S T UW1 D AH0 B EY2 K ER0 -STUDEMAN S T UW1 D M AH0 N -STUDEMAN(2) S T UW1 D AH0 M AH0 N -STUDENT S T UW1 D AH0 N T -STUDENT'S S T UW1 D AH0 N T S -STUDENTS S T UW1 D AH0 N T S -STUDENTS' S T UW1 D AH0 N T S 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B AE2 N Z -TALIBANS T AE1 L IH0 B AE2 N Z -TALIGENT T AE1 L IH0 JH EH0 N T -TALISMAN T AE1 L IH0 S M AH0 N -TALITHA T AE1 L IH0 DH AH0 -TALK T AO1 K -TALK'S T AO1 K S -TALKABLE T AO1 K AH0 B AH0 L -TALKATIVE T AO1 K AH0 T IH0 V -TALKBACK T AO1 K B AE2 K -TALKED T AO1 K T -TALKER T AO1 K ER0 -TALKERS T AO1 K ER0 Z -TALKIE T AO1 K IY0 -TALKIES T AO1 K IY0 Z -TALKIN T AA1 K AH0 N -TALKIN' T AO1 K IH0 N -TALKING T AO1 K IH0 NG -TALKING'S T AO1 K IH0 NG Z -TALKINGTON T AO1 K IH0 NG T AH0 N -TALKS T AO1 K S -TALKY T AO1 K IY0 -TALL T AO1 L -TALLADEGA T AE2 L AH0 D EY1 G AH0 -TALLAHASSEAN T AE2 L AH0 HH AE1 S IY0 AH0 N -TALLAHASSEANS T AE2 L AH0 HH AE1 S IY0 AH0 N Z -TALLAHASSEE T AE2 L AH0 HH AE1 S IY0 -TALLAHASSEE'S T AE2 L AH0 HH AE1 S IY0 Z -TALLANT T AA1 L AH0 N T -TALLARICO T AA0 L AA0 R IY1 K OW0 -TALLENT T AA1 L AH0 N T -TALLER T AO1 L ER0 -TALLERICO T AA0 L ER0 IY1 K OW0 -TALLEST T AO1 L IH0 S T -TALLEY T AE1 L IY0 -TALLGRASS T AA1 L G R AE2 S -TALLIE T AO1 L IY0 -TALLIED T AE1 L IY0 D -TALLIES T AE1 L IY0 Z -TALLIL T AH0 L IH1 L -TALLMADGE T AE1 L M AE0 JH -TALLMAN T AO1 L M AH0 N -TALLO T AE1 L OW0 -TALLON T AE1 L AH0 N -TALLOW T AE1 L OW0 -TALLULA T AA0 L UW1 L AH0 -TALLULAH T AE2 L UW1 L AH0 -TALLY T AE1 L IY0 -TALLYHO T AE2 L IY0 HH OW1 -TALLYING T AE1 L IY0 IH0 NG -TALMADGE T AE1 L M AE0 JH -TALMAGE T AE1 L M IH0 JH -TALMAN T AE1 L M AH0 N -TALMOR T AE1 L M AO2 R -TALMUD T AE1 L M AH0 D -TALON T AE1 L AH0 N -TALONS T AE1 L AH0 N Z -TALSMA T AA1 L S M AH0 -TALTON T AE1 L T AH0 N -TALTOS T AA1 L T OW0 Z -TALTY T AO1 L T IY0 -TAM T AE1 M -TAMA T AA1 M AH0 -TAMALES T AH0 M AA1 L IY0 Z -TAMANAHA T AA0 M AA0 N AA1 HH AH0 -TAMARA T AH0 M AA1 R AH0 -TAMARAC T AE1 M ER0 AE0 K -TAMARGO T AA0 M AA1 R G OW0 -TAMARIN T AE1 M ER0 AH0 N -TAMARINS T AE1 M ER0 AH0 N Z -TAMARISK T AE1 M ER0 IH0 S K -TAMARISKS T AE1 M ER0 IH0 S K S -TAMAS T AA1 M AH0 Z -TAMASHIRO T AA0 M AA0 SH IH1 R OW0 -TAMAYO T AA0 M AA1 Y OW0 -TAMBLYN T AE1 M B L IH0 N -TAMBO T AE1 M B OW0 -TAMBOCOR T AE1 M B OW0 K AO2 R -TAMBOURINE T AE2 M B ER0 IY1 N -TAMBRANDS T AE1 M B R AE2 N D Z -TAMBS T AE1 M Z -TAMBURELLO T AA0 M B UH0 R EH1 L OW0 -TAMBURO T AA0 M B UH1 R OW0 -TAMBURRI T AA0 M B UH1 R IY0 -TAMBURRINO T AA0 M B UH0 R IY1 N OW0 -TAMBURRO T AA0 M B UH1 R OW0 -TAME T EY1 M -TAMED T EY1 M D -TAMER T EY1 M ER0 -TAMES T EY1 M Z -TAMEZ T AA0 M EH1 Z -TAMI T AE1 M IY0 -TAMIAMI T AE2 M IY0 AE1 M IY0 -TAMIL T AH0 M IH1 L -TAMIL(2) T AE1 M IH0 L -TAMILS T AH0 M IH1 L Z -TAMILS(2) T AE1 M IH0 L Z -TAMING T EY1 M IH0 NG -TAMKE T AE1 M K IY0 -TAMM T AE1 M -TAMMANY T AE1 M AH0 N IY0 -TAMMARO T AA0 M AA1 R OW0 -TAMMEN T AE1 M AH0 N -TAMMIE T AE1 M IY0 -TAMMINGA T AA0 M IY1 NG G AH0 -TAMMO T AE1 M OW0 -TAMMY T AE1 M IY0 -TAMOXIFEN T AE2 M AA1 K S AH0 F AH0 N -TAMP T AE1 M P -TAMPA T AE1 M P AH0 -TAMPA'S T AE1 M P AH0 Z -TAMPAX T AE1 M P AE2 K S -TAMPER T AE1 M P ER0 -TAMPERED T AE1 M P ER0 D -TAMPERING T AE1 M P ER0 IH0 NG -TAMPING T AE1 M P IH0 NG -TAMPLIN T AE1 M P L IH0 N -TAMPON T AE1 M P AA0 N 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T AA1 R P L IY0 -TARPON T AA1 R P AH0 N -TARPS T AA1 R P S -TARPY T AA1 R P IY0 -TARQUINIO T AA0 R K W IY1 N IY0 OW0 -TARR T AA1 R -TARRANCE T AA1 R AH0 N S -TARRANT T AA1 R AH0 N T -TARRED T AA1 R D -TARRING T AA1 R IH0 NG -TARRY T EH1 R IY0 -TARRYTOWN T EH1 R IY0 T AW2 N -TARSES T AA1 R S IH0 S -TARSUS T AA1 R S AH0 S -TART T AA1 R T -TARTAGLIA T AA0 R T AE1 G L IY0 AH0 -TARTAGLIONE T AA0 R T AE0 G L IY0 OW1 N IY0 -TARTAN T AA1 R T AH0 N -TARTAR T AA1 R T ER0 -TARTARS T AA1 R T ER0 Z -TARTE T AA1 R T -TARTER T AA1 R T ER0 -TARTIKOFF T AA1 R T IH0 K AO2 F -TARTIKOFF'S T AA1 R T IH0 K AO2 F S -TARTIKOFFS T AA1 R T IH0 K AO2 F S -TARTLY T AA1 R T L IY0 -TARTNESS T AA1 R T N AH0 S -TARTS T AA1 R T S -TARTT T AA1 R T -TARTU T AA2 R T UW1 -TARVER T AA1 R V ER0 -TARVIN T AA1 R V IH0 N -TARZAN T AA1 R Z AE2 N -TARZAN(2) T AA1 R Z AH0 N -TARZAN'S T AA1 R Z AE2 N Z -TARZAN'S(2) T AA1 R Z AH0 N Z -TASADAY T AE1 S AH0 D EY0 -TASCA T AA1 S K AH0 -TASCH T AE1 SH -TASCHNER T AE1 SH N ER0 -TASER T EY1 Z ER0 -TASH T AE1 SH -TASHA T AE1 SH AH0 -TASHIN T AE1 SH IH0 N -TASHIRO T AA0 SH IH1 R OW0 -TASHJIAN T AE1 SH JH IY0 AH0 N -TASHKENT T AE2 SH K EH1 N T -TASK T AE1 S K -TASKED T AE1 S K T -TASKER T AE1 S K ER0 -TASKFORCE T AE1 S K F AO2 R S -TASKING T AE1 S K IH0 NG -TASKMASTER T AE1 S K M AE2 S T ER0 -TASKS T AE1 S K S -TASM T AE1 S AH0 M -TASMAN T AE1 Z M AH0 N -TASMANIA T AE2 Z M EY1 N IY0 AH0 -TASMANIAN T AE2 Z M EY1 N IY0 AH0 N -TASS T AE1 S -TASSEL T AE1 S AH0 L -TASSELED T AE1 S AH0 L D -TASSI T AE1 S IY0 -TASSIN T AE1 S IH0 N -TASSINARI T AA0 S IY0 N AA1 R IY2 -TASSO T AE1 S OW2 -TASSONE T AA0 S OW1 N IY2 -TASTE T EY1 S T -TASTED T EY1 S T IH0 D -TASTEFUL T EY1 S T F AH0 L -TASTEFULLY T EY1 S T F AH0 L IY0 -TASTELESS T EY1 S T L AH0 S -TASTER T EY1 S T ER0 -TASTER'S T EY1 S T ER0 Z -TASTERS T EY1 S T ER0 Z -TASTES T EY1 S T S -TASTIER T EY1 S T IY0 ER0 -TASTINESS T EY1 S T IY0 N AH0 S -TASTING T EY1 S T IH0 NG -TASTINGS T EY1 S T IH0 NG Z -TASTY T EY1 S T IY0 -TAT T AE1 T 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ER0 T -TAUBES T AW1 B Z -TAUBMAN T AW1 B M AH0 N -TAUCHER T AW1 K ER0 -TAUER T AW1 ER0 -TAUGHT T AO1 T -TAUKE T AW1 K -TAUL T AO1 L -TAULBEE T AO1 L B IY2 -TAUNT T AO1 N T -TAUNTED T AO1 N T IH0 D -TAUNTING T AO1 N T IH0 NG -TAUNTON T AO1 N T AH0 N -TAUNTS T AO1 N T S -TAURUS T AO1 R AH0 S -TAURUSES T AO1 R AH0 S AH0 Z -TAUSCH T AW1 SH -TAUSCHER T AW1 SH ER0 -TAUSSIG T AW1 S IH0 G -TAUT T AO1 T -TAUZIN T AW1 Z IH0 N -TAVANO T AA0 V AA1 N OW0 -TAVARES T AA0 V AA1 R EH0 S -TAVAREZ T AA0 V AA1 R EH0 Z -TAVEL T AA0 V EH1 L -TAVENNER T AE1 V IH0 N ER0 -TAVERA T AA0 V EH1 R AH0 -TAVERAS T AA0 V EH1 R AA0 Z -TAVERN T AE1 V ER0 N -TAVERNA T AA0 V EH1 R N AH0 -TAVERNA'S T AA0 V EH1 R N AH0 Z -TAVERNAS T AA0 V EH1 R N AH0 Z -TAVERNIER T AE1 V ER0 N IY0 ER0 -TAVERNS T AE1 V ER0 N Z -TAVES T EY1 V Z -TAVIE T EY1 V IY0 -TAVIS T AA1 V IH0 S -TAVISH T EY1 V IH0 SH -TAVIST T AE1 V IH0 S T -TAVLIN T AE1 V L IH0 N -TAVOULAREAS T AA0 V UW0 L EH1 R IY0 AH0 S -TAVY T EY1 V IY0 -TAWANA T AA2 W AA1 N AH0 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IY1 Z ER0 -TEASES T IY1 Z IH0 Z -TEASING T IY1 Z IH0 NG -TEASLEY T IY1 Z L IY0 -TEASON T IY1 S AO0 N -TEASPOON T IY1 S P UW2 N -TEASPOONFUL T IY1 S P UW2 N F AH0 L -TEASPOONS T IY1 S P UW2 N Z -TEAST T IY1 S T -TEASTER T IY1 S T ER0 -TEAT T IY1 T -TEATE T IY1 EY2 T -TEATER T IY1 T ER0 -TEATRO T IY1 T R OW0 -TEATS T IY1 T S -TEBBE T EH1 B -TEBBEN T EH1 B AH0 N -TEBBETTS T EH1 B IH0 T S -TEBBIT T EH1 B IH0 T -TEBBS T EH1 B Z -TEBEAU T IH0 B OW1 -TEBELSKIS T AH0 B EH1 L S K IY0 S -TEBO T EH1 B OW0 -TEBUTHIURON T EH2 B AH0 TH Y UW1 R AA0 N -TEC T EH1 K -TECH T EH1 K -TECH'S T EH1 K S -TECHCRUNCH T EH1 CH K R AH1 N CH -TECHIE T EH1 K IY0 -TECHIES T EH1 K IY0 Z -TECHINT T EH1 CH IH0 N T -TECHINT(2) T EH1 K IH2 N T -TECHNIC T EH1 K N IH0 K -TECHNICAL T EH1 K N IH0 K AH0 L -TECHNICAL'S T EH1 K N IH0 K AH0 L Z -TECHNICALITIES T EH2 K N IH0 K AE1 L AH0 T IY0 Z -TECHNICALITY T EH2 K N IH0 K AE1 L IH0 T IY0 -TECHNICALLY T EH1 K N IH0 K AH0 L IY0 -TECHNICALLY(2) T EH1 K N IH0 K L IY0 -TECHNICALS T EH1 K N IH0 K AH0 L Z -TECHNICIAN T EH0 K N IH1 SH AH0 N -TECHNICIAN'S T EH0 K N IH1 SH AH0 N Z -TECHNICIANS T EH0 K N IH1 SH AH0 N Z -TECHNICOLOR T EH1 K N IH0 K AH2 L ER0 -TECHNICON T EH1 K N IH0 K AA2 N -TECHNICS T EH1 K N IH0 K S -TECHNIQUE T EH0 K N IY1 K -TECHNIQUES T EH0 K N IY1 K S -TECHNITROL T EH1 K N IH0 T R OW2 L -TECHNO T EH1 K N OW0 -TECHNOCRAT T EH1 K N AH0 K R AE2 T -TECHNOCRATIC T EH2 K N AH0 K R AE1 T IH0 K -TECHNOCRATS T EH1 K N AH0 K R AE2 T S -TECHNODYNE T EH1 K N OW0 D AY2 N -TECHNOLOGIC T EH2 K N AH0 L AA1 JH IH0 K -TECHNOLOGICAL T EH2 K N AH0 L AA1 JH IH0 K AH0 L -TECHNOLOGICALLY T EH2 K N AH0 L AA1 JH IH0 K AH0 L IY0 -TECHNOLOGICALLY(2) T EH2 K N AH0 L AA1 JH IH0 K L IY0 -TECHNOLOGIES T EH0 K N AA1 L AH0 JH IY0 Z -TECHNOLOGIES' T EH2 K N AA1 L AH0 JH IY0 Z -TECHNOLOGIST T EH2 K N AA1 L AH0 JH IH0 S T -TECHNOLOGISTS T EH2 K N AA1 L AH0 JH IH0 S T S -TECHNOLOGY T EH0 K N AA1 L AH0 JH IY0 -TECHNOLOGY'S T EH0 K N AA1 L AH0 JH IY0 Z -TECHNOMIC T EH2 K N AA1 M IH0 K -TECHNOPHOBE T EH1 K N OW0 F OW2 B -TECHNOPHOBE'S T EH1 K N OW0 F OW2 B Z -TECHNOPHOBES T EH1 K N OW0 F OW2 B Z -TECHS T EH1 K S -TECHSYSTEM T EH1 K S IH2 S T AH0 M -TECHSYSTEMS T EH1 K S IH2 S T AH0 M Z -TECHY T EH1 CH IY0 -TECK T EH1 K -TECK'S T EH1 K S -TECLA T EH1 K L AH0 -TECO T IY1 K OW0 -TECOGEN T EH1 K OW0 G AH0 N -TECOGEN(2) T EH1 K OW0 JH EH0 N -TECOS T IY1 K OW0 S -TECTONIC T EH0 K T AA1 N IH0 K -TECTONICS T EH0 K T AA1 N IH0 K S -TECUMSEH T AH0 K AH1 M S AH0 -TED T EH1 D -TED'S T EH1 D Z -TEDDER T EH1 D ER0 -TEDDIE T EH1 D IY0 -TEDDY T EH1 D IY0 -TEDDY'S T EH1 D IY0 Z -TEDESCHI T EH0 D EH1 S K IY0 -TEDESCO T EH0 D EH1 S K OW0 -TEDESCO'S T EH0 D EH1 S K OW0 Z -TEDFORD T EH1 D F ER0 D -TEDIOUS T IY1 D IY0 AH0 S -TEDIOUSLY T IY1 D IY0 AH0 S L IY0 -TEDIUM T IY1 D IY0 AH0 M -TEDMAN T EH1 D M AH0 N -TEDMOND T EH1 D M AH0 N D -TEDMUND T EH1 D M AH0 N D -TEDRICK T EH1 D R IH0 K -TEDROW T EH1 D R OW2 -TEE T IY1 -TEED T IY1 D -TEEGARDEN T IY1 G AA2 R D AH0 N -TEEGARDIN T IY0 G AA1 R D IH0 N -TEEHAN T IY1 AH0 N -TEEING T IY1 IH0 NG -TEEL T IY1 L -TEELE T IY1 L -TEELEY T IY1 L IY0 -TEELING T IY1 L IH0 NG -TEEM T IY1 M -TEEMING T IY1 M IH0 NG -TEEMS T IY1 M Z -TEEN T IY1 N -TEENA T IY1 N AH0 -TEENAGE T IY1 N EY2 JH -TEENAGED T IY1 N EY2 JH D -TEENAGER T IY1 N EY2 JH ER0 -TEENAGER'S T IY1 N EY2 JH ER0 Z -TEENAGERS T IY1 N EY2 JH ER0 Z -TEENIE T IY1 N IY0 -TEENS T IY1 N Z -TEENSY T IY1 N S IY0 -TEENY T IY1 N IY0 -TEEPLE T IY1 P AH0 L -TEEPLES T IY1 P AH0 L Z -TEER T IH1 R -TEES T IY1 Z -TEET T IY1 T -TEETER T IY1 T ER0 -TEETER'S T IY1 T ER0 Z -TEETERED T IY1 T ER0 D -TEETERING T IY1 T ER0 IH0 NG -TEETERS T IY1 T ER0 Z -TEETH T IY1 TH -TEETHE T IY1 DH -TEETHING T IY1 DH IH0 NG -TEETOTALER T IY2 T OW2 T EY1 L ER0 -TEETS T IY1 T S -TEFFETELLER T EH1 F IH0 T EH2 L ER0 -TEFFT T EH1 F T -TEFLON T EH1 F L AH0 N -TEFRA T EH1 F R AH0 -TEGELER T EH1 G AH0 L ER0 -TEGETHOFF T EH1 G IH0 T AO2 F -TEGGE T EH1 G -TEGTMEIER T EH1 T M AY0 ER0 -TEGTMEYER T EH1 T M AY0 ER0 -TEGUCIGALPA T EH0 G UW0 S IY0 G AE1 L P AH0 -TEGUCIGALPA(2) T EH0 G UW0 CH IY0 G AA1 L P AH0 -TEHAN T EY1 AH0 N -TEHERAN T EH2 HH ER0 AA1 N -TEHERAN'S T EH2 HH ER0 AA1 N Z -TEHERANI T EH2 HH ER0 AA1 N IY0 -TEHERANI'S T EH2 HH ER0 AA1 N IY0 Z -TEHERANIS T EH2 HH ER0 AA1 N IY0 Z -TEHERANS T EH2 HH ER0 AA1 N Z -TEHRAN T EY2 R AA1 N -TEHRAN'S T EY2 R AA1 N Z -TEHRANI T EY2 R AA1 N IY0 -TEHRANI'S T EY2 R AA1 N IY0 Z -TEHRANIS T EY2 R AA1 N IY0 Z -TEHRANS T EY2 R AA1 N Z -TEICH T AY1 K -TEICHER T AY1 K ER0 -TEICHERT T AY1 K ER0 T -TEICHMAN T AY1 K M AH0 N -TEICHMANN T AY1 K M AH0 N -TEICHOLZ T AY1 K HH AO2 L T S -TEIG T IY1 G -TEIGE T AY1 JH -TEIGEN T AY1 G AH0 N -TEIKOKU T EY2 K OW1 K UW2 -TEITEL T AY1 T AH0 L -TEITELBAUM T AY1 T AH0 L B AW0 M -TEITELL T AY1 T EH2 L -TEIXEIRA T AH0 K S EH1 R AH0 -TEJADA T EY0 Y AA1 D AH0 -TEJAN T EY0 JH AH0 N -TEJANO T EY0 Y AA1 N OW0 -TEJAS T IY1 JH AH0 S -TEJEDA T EY0 Y EY1 D AH0 -TEJERA T EY0 IH1 R AH0 -TEJON T EH1 JH AA0 N -TEK T EH1 K -TEKNOWLEDGE T EH2 K N AA1 L EH0 JH -TEKTRONIX T EH2 K T R AA1 N IH0 K S -TEL T EH1 L -TEL-AVIV T EH1 L AA0 V IY1 V -TELACTION T EH0 L AE1 K SH AH0 N -TELAMON T EH1 L AH0 M AH0 N -TELANDER T IY1 L AE0 N D ER0 -TELANDER(2) T IH0 L AE1 N D ER0 -TELCO T EH1 L K OW0 -TELE T EH1 L IY0 -TELE(2) T EH1 L AH0 -TELECABLE T EH1 L AH0 K EY2 B AH0 L -TELECARD T EH1 L AH0 K AA2 R D -TELECAST T EH1 L AH0 K AE2 S T -TELECASTS T EH1 L AH0 K AE2 S T S -TELECHARGE T EH1 L AH0 CH AA1 R JH -TELECHECK T EH1 L AH0 CH EH2 K -TELECOM T EH1 L AH0 K AA0 M -TELECOM'S T EH1 L AH0 K AA0 M Z -TELECOMMUNICATION T EH2 L AH0 K AH0 M Y UW2 N IH0 K EY1 SH AH0 N -TELECOMMUNICATIONS T EH2 L AH0 K AH0 M Y UW2 N AH0 K EY1 SH AH0 N Z -TELECOMMUNICATIONS' T EH2 L AH0 K AH0 M Y UW2 N AH0 K EY1 SH AH0 N Z -TELECOMMUTE T EH1 L AH0 K AH0 M Y UW2 T -TELECOMMUTER T EH1 L AH0 K AH0 M Y UW2 T ER0 -TELECOMMUTER'S T EH1 L AH0 K AH0 M Y UW2 T ER0 Z -TELECOMMUTERS T EH1 L AH0 K AH0 M Y UW2 T ER0 Z -TELECOMMUTING T EH0 L AH0 K AH0 M Y UW1 T IH0 NG -TELECOMS T EH1 L AH0 K AA0 M Z -TELECONFERENCE T EH2 L AH0 K AA1 N F R AH0 N S -TELECONFERENCING T EH2 L AH0 K AA1 N F R AH0 N S IH0 NG -TELECONNECT T EH2 L AH0 K AH0 N EH1 K T -TELECRAFTER T EH1 L AH0 K R AE2 F T ER0 -TELECREDIT T EH1 L AH0 K R EH2 D IH0 T -TELECTRON T EH1 L AH0 K T R AA0 N -TELECTRONIC T EH2 L AH0 K T R AA1 N IH0 K -TELECTRONICS T EH2 L AH0 K T R AA1 N IH0 K S -TELEDESIC T EH2 L AH0 D EH1 S IH0 K -TELEDYNE T EH1 L AH0 D AY2 N -TELEDYNE'S T EH1 L AH0 D AY2 N Z -TELEFLEX T EH1 L AH0 F L EH2 K S -TELEFON T EH1 L AH0 F AA2 N -TELEFONICA T EH2 L AH0 F AA1 N IH0 K AH0 -TELEFONOS T EH2 L EH0 F OW1 N OW0 S -TELEFUNKEN T EH2 L AH0 F AH1 NG K AH0 N -TELEGENIC T EH2 L AH0 JH EH1 N IH0 K -TELEGLOBE T EH1 L AH0 G L OW2 B -TELEGRAM T EH1 L AH0 G R AE2 M -TELEGRAMS T EH1 L AH0 G R AE2 M Z -TELEGRAPH T EH1 L AH0 G R AE2 F -TELEGRAPH'S T EH1 L AH0 G R AE2 F S -TELEGRAPHED T EH1 L AH0 G R AE2 F T -TELEKOM T EH1 L AH0 K AA0 M -TELEKOM'S T EH1 L AH0 K AA0 M Z -TELEMACHO T EH1 L AH0 M AA2 CH OW0 -TELEMANAGEMENT T EH1 L AH0 M AE2 N IH0 JH M AH0 N T -TELEMARKET T EH2 L AH0 M AA1 R K IH0 T -TELEMARKETER T EH2 L AH0 M AA1 R K IH0 T ER0 -TELEMARKETERS T EH2 L AH0 M AA1 R K IH0 T ER0 Z -TELEMARKETING T EH2 L AH0 M AA1 R K AH0 T IH0 NG -TELEMATIC T EH2 L AH0 M AE1 T IH0 K -TELEMATICS T EH2 L AH0 M AE1 T IH0 K S -TELEMECANIQUE T EH2 L AH0 M AH0 K AE1 N IH0 K -TELEMEDIA T EH2 L AH0 M IY1 D IY0 AH0 -TELEMEDICINE T EH2 L IH0 M EH1 D IH0 S AH0 N -TELEMETRY T AH0 L EH1 M AH0 T R IY0 -TELEMUNDO T EH2 L AH0 M UW1 N D OW0 -TELENET T EH1 L AH0 N EH2 T -TELEOLOGICAL T IY2 L IY0 AH0 L AO1 JH IH0 K AH0 L -TELEOLOGY T IY1 L IY0 AO2 L AH0 JH IY2 -TELEPATH T EH2 L AH0 P AE1 TH -TELEPATHIC T EH2 L AH0 P AE1 TH AH0 K -TELEPATHY T AH0 L EH1 P AH0 TH IY0 -TELEPHONE T EH1 L AH0 F OW2 N -TELEPHONE'S T EH1 L AH0 F OW2 N Z -TELEPHONED T EH1 L AH0 F OW2 N D -TELEPHONES T EH1 L AH0 F OW2 N Z -TELEPHONIC T EH2 L AH0 F AA1 N IH0 K -TELEPHONICS T EH2 L AH0 F AA1 N IH0 K S -TELEPHONING T EH1 L AH0 F OW2 N IH0 NG -TELEPHONIQUES T EH2 L AH0 F AA0 N IY1 K S -TELEPHONY T EH1 L AH0 F OW2 N IY0 -TELEPHOTO T EH1 L AH0 F OW2 T OW0 -TELEPICTURE T EH1 L AH0 P IH1 K CH ER0 -TELEPICTURES T EH1 L AH0 P IH1 K CH ER0 Z -TELEPORT T EH1 L AH0 P AO1 R T -TELEPORT'S T EH1 L AH0 P AO1 R T S -TELEPROBE T EH1 L AH0 P R OW1 B -TELEPROMPTER T EH1 L AH0 P R AA2 M P T ER0 -TELEQUEST T EH1 L AH0 K W EH1 S T -TELERATE T EH1 L ER0 EY1 T -TELESAT T EH1 L AH0 S AE0 T -TELESCIENCE T EH1 L AH0 S AY2 AH0 N S -TELESCIENCES T EH1 L AH0 S AY2 AH0 N S IH0 Z -TELESCO T EH0 L EH1 S K OW0 -TELESCOPE T EH1 L AH0 S K OW2 P -TELESCOPE'S T EH1 L AH0 S K OW2 P S -TELESCOPES T EH1 L AH0 S K OW2 P S -TELESCOPIC T EH2 L AH0 S K AO1 P IH0 K -TELESCRIPT T EH1 L AH0 S K R IH2 P T -TELESIS T EH1 L AH0 S IH0 S -TELESIS' T EH1 L AH0 S IH2 S -TELESIS'S T EH1 L AH0 S IH0 S IH0 Z -TELESPHERE T EH1 L AH0 S F IH2 R -TELESTRATOR T EH1 L AH0 S T R EY2 T ER0 -TELESYSTEM T EH1 L AH0 S IH2 S T AH0 M -TELETEXT T EH1 L AH0 T EH1 K S T -TELETHON T EH1 L AH0 TH AA0 N -TELETRON T EH1 L AH0 T R AA2 N -TELETTRA T EH1 L EH0 T R AH0 -TELETYPE T EH1 L AH0 T AY2 P -TELETYPES T EH1 L AH0 T AY2 P S -TELEVANGELIST T EH2 L AH0 V AE1 N JH AH0 L AH0 S T -TELEVANGELISTS T EH2 L AH0 V AE1 N JH AH0 L AH0 S T S -TELEVIDEO T EH2 L AH0 V IH1 D IY0 OW0 -TELEVISA T EH1 L AH0 V IY1 Z AH0 -TELEVISA'S T EH2 L AH0 V IY1 Z AH0 Z -TELEVISE T EH1 L AH0 V AY2 Z -TELEVISED T EH1 L AH0 V AY2 Z D -TELEVISING T EH1 L AH0 V AY2 Z IH0 NG -TELEVISION T EH1 L AH0 V IH2 ZH AH0 N -TELEVISION'S T EH1 L AH0 V IH2 ZH AH0 N Z -TELEVISIONS T EH1 L AH0 V IH2 ZH AH0 N Z -TELEVISON T EH1 L IH0 V IH0 ZH AH0 N -TELEWEST T EH1 L AH0 W EH1 S T -TELEX T EH1 L EH2 K S -TELEX'S T EH1 L EH2 K S IH0 Z -TELEXED T EH1 L EH2 K S T -TELEXES T EH1 L EH2 K S IH0 S -TELFAIR T EH1 L F EH2 R -TELFER T EH1 L F ER0 -TELFOR T EH1 L F ER0 -TELFORD T EH1 L F ER0 D -TELFOUR T EH1 L F ER0 -TELIT T EH1 L IH2 T -TELL T EH1 L -TELLABS T EH1 L AE2 B Z -TELLEFSEN T EH1 L IH0 F S AH0 N -TELLEP T EH1 L AH0 P -TELLER T EH1 L ER0 -TELLER'S T EH1 L ER0 Z -TELLERS T EH1 L ER0 Z -TELLES T EH1 L Z -TELLEZ T EY0 L EH1 Z -TELLIER T EH1 L IY0 ER0 -TELLIN' T EH1 L IH0 N -TELLING T EH1 L IH0 NG -TELLINGLY T EH1 L IH0 NG L IY0 -TELLIS T EH1 L IH0 S -TELLO T EH1 L OW0 -TELLS T EH1 L Z -TELLTALE T EH1 L T EY2 L -TELLTALES T EH1 L T EY2 L Z -TELLURIDE T EH1 L Y ER0 AY2 D -TELLURIDES T EH1 L Y ER0 AY2 D Z -TELLURIUM T EH0 L UH1 R IY0 AH0 M -TELLY T EH1 L IY0 -TELMEX T EH1 L M EH2 K S -TELMEX'S T EH1 L M EH2 K S IH0 Z -TELOMERASE T AH0 L OW1 M ER0 EY2 Z -TELOMERE T EH1 L AH0 M IY0 R -TELOMERES T EH1 L AH0 M IY0 R Z -TELOS T EH1 L OW0 S -TELSOURCE T EH1 L S AO2 R S -TELSTAR T EH1 L S T AA2 R -TELSTRA T EH1 L S T R AH0 -TELTSCHIK T EH1 L CH IH0 K -TELUGU T EH1 L UH0 G UW0 -TELUSTRATOR T EH1 L AH0 S T R EY2 T ER0 -TELUSTRATOR(2) T EH1 L Y AH0 S T R EY2 T ER0 -TELXON T EH1 L Z AA0 N -TELXON'S T EH1 L Z AH0 N Z -TELZROW T EH1 L Z R OW0 -TEMBLOR T EH1 M B L ER0 -TEMBLORS T EH1 M B L ER0 Z -TEMCO T EH1 M K OW0 -TEMECULA T AH0 M EH1 K Y AH0 L AH0 -TEMERITY T AH0 M EH1 R AH0 T IY0 -TEMERLIN T EH1 M ER0 L IH0 N -TEMEROUS T EH1 M ER0 AH0 S -TEMKIN T EH1 M K IH0 N -TEMME T EH1 M -TEMP T EH1 M P -TEMPE T EH1 M P IY0 -TEMPEL T EH1 M P AH0 L -TEMPELSMAN T EH1 M P AH0 L Z M AH0 N -TEMPER T EH1 M P ER0 -TEMPERA T EH1 M P ER0 AH0 -TEMPERAMENT T EH1 M P R AH0 M AH0 N T -TEMPERAMENT(2) T EH1 M P ER0 M AH0 N T -TEMPERAMENTAL T EH2 M P R AH0 M EH1 N T AH0 L -TEMPERAMENTAL(2) T EH2 M P ER0 M EH1 N T AH0 L -TEMPERAMENTALLY T EH2 M P R AH0 M EH1 N T AH0 L IY0 -TEMPERAMENTALLY(2) T EH2 M P ER0 M EH1 N T AH0 L IY0 -TEMPERAMENTALLY(3) T EH2 M P R AH0 M EH1 N AH0 L IY0 -TEMPERAMENTALLY(4) T EH2 M P ER0 M EH1 N AH0 L IY0 -TEMPERAMENTS T EH1 M P R AH0 M AH0 N T S -TEMPERAMENTS(2) T EH1 M P ER0 M AH0 N T S -TEMPERANCE T EH1 M P ER0 AH0 N S -TEMPERANCE(2) T EH1 M P R AH0 N S -TEMPERATE T EH1 M P R AH0 T -TEMPERATE(2) T EH1 M P ER0 AH0 T -TEMPERATURE T EH1 M P R AH0 CH ER0 -TEMPERATURE(2) T EH1 M P ER0 AH0 CH ER0 -TEMPERATURES T EH1 M P R AH0 CH ER0 Z -TEMPERATURES(2) T EH1 M P ER0 AH0 CH ER0 Z -TEMPERED T EH1 M P ER0 D -TEMPERING T EH1 M P ER0 IH0 NG -TEMPERS T EH1 M P ER0 Z -TEMPEST T EH1 M P AH0 S T -TEMPESTA T EH2 M P EH1 S T AH0 -TEMPESTUOUS T EH2 M P EH1 S CH UW0 AH0 S -TEMPLAR T EH1 M P L ER0 -TEMPLARS T EH1 M P L ER0 Z -TEMPLATE T EH1 M P L AH0 T -TEMPLATE(2) T EH1 M P L EY0 T -TEMPLATES T EH1 M P L AH0 T S -TEMPLE T EH1 M P AH0 L -TEMPLE'S T EH1 M P AH0 L Z -TEMPLEMAN T EH1 M P AH0 L M AH0 N -TEMPLER T EH1 M P AH0 L ER0 -TEMPLER(2) T EH1 M P L ER0 -TEMPLERS T EH1 M P AH0 L ER0 Z -TEMPLERS(2) T EH1 M P L ER0 Z -TEMPLES T EH1 M P AH0 L Z -TEMPLET T EH1 M P L IH0 T -TEMPLETON T EH1 M P AH0 L T AH0 N -TEMPLETON'S T EH1 M P AH0 L T AH0 N Z -TEMPLIN T EH1 M P L IH0 N -TEMPO T EH1 M P OW2 -TEMPORAL T EH1 M P ER0 AH0 L -TEMPORALLY T EH1 M P ER0 AH0 L IY0 -TEMPORARIES T EH1 M P ER0 EH2 R IY0 Z -TEMPORARILY T EH2 M P ER0 EH1 R AH0 L IY0 -TEMPORARY T EH1 M P ER0 EH2 R IY0 -TEMPORE T EH1 M P AO0 R -TEMPORE(2) T EH2 M P AO1 R IY0 -TEMPORIZE T EH1 M P ER0 AY2 Z -TEMPORIZING T EH1 M P ER0 AY2 Z IH0 NG -TEMPOS T EH1 M P OW2 Z -TEMPS T EH1 M P S -TEMPT T EH1 M P T -TEMPTATION T EH0 M T EY1 SH AH0 N -TEMPTATIONS T EH0 M T EY1 SH AH0 N Z -TEMPTED T EH1 M P T IH0 D -TEMPTING T EH1 M P T IH0 NG -TEMPTRESS T EH1 M P T R IH0 S -TEMPTS T EH1 M P T S -TEN T EH1 N -TEN'S T EH1 N Z -TENA T EH1 N AH0 -TENABLE T EH1 N AH0 B AH0 L -TENACIOUS T AH0 N EY1 SH AH0 S -TENACIOUSLY T AH0 N EY1 SH AH0 S L IY0 -TENACITY T AH0 N AE1 S IH0 T IY0 -TENAGLIA T EH0 N AA1 G L IY0 AH0 -TENANCY T EH1 N AH0 N S IY0 -TENANT T EH1 N AH0 N T -TENANT'S T EH1 N AH0 N T S -TENANTS T EH1 N AH0 N T S -TENANTS' T EH1 N AH0 N T S -TENBRINK T EH1 N B R IH2 NG K -TENCH T EH1 N CH -TENCZA T EH1 N CH AH0 -TEND T EH1 N D -TENDED T EH1 N D IH0 D -TENDENCIES T EH1 N D AH0 N S IY0 Z -TENDENCY T EH1 N D AH0 N S IY0 -TENDENTIOUS T EH2 N D EH1 N SH AH0 S -TENDER T EH1 N D ER0 -TENDERED T EH1 N D ER0 D -TENDERING T EH1 N D ER0 IH0 NG -TENDERLOIN T EH1 N D ER0 L OY2 N -TENDERLY T EH1 N D ER0 L IY0 -TENDERNESS T EH1 N D ER0 N AH0 S -TENDERS T EH1 N D ER0 Z -TENDING T EH1 N D IH0 NG -TENDLER T EH1 N D L ER0 -TENDON T EH1 N D AH0 N -TENDONS T EH1 N D AH0 N Z -TENDS T EH1 N D Z -TENEBROUS T EH1 N AH0 B R AH0 S -TENEMENT T EH1 N AH0 M AH0 N T -TENEMENTS T EH1 N AH0 M AH0 N T S -TENENBAUM T EH1 N AH0 N B AW2 M -TENER T EH1 N ER0 -TENERA T EH2 N EH1 R AH0 -TENET T EH1 N AH0 T -TENETS T EH1 N AH0 T S -TENEYCK T EH1 N IY0 K -TENFOLD T EH1 N F OW2 L D -TENG T EH1 NG -TENG-WEN T EH1 NG W EH1 N -TENGELMANN T EH1 NG G AH0 L M AH0 N -TENGIZ T EH1 NG G IH0 Z -TENGLEMANN T EH1 NG G AH0 L M AH0 N -TENN T EH1 N -TENNANT T EH1 N AH0 N T -TENNCARE T EH1 N K EH2 R -TENNCARE'S T EH1 N K EH2 R Z -TENNECO T EH1 N AH0 K OW0 -TENNECO'S T EH1 N AH0 K OW0 Z -TENNELL T EH1 N AH0 L -TENNENBAUM T EH1 N AH0 N B AW2 M -TENNENT T EH1 N AH0 N T -TENNER T EH1 N ER0 -TENNESSEAN T EH2 N IH0 S IY1 AH0 N -TENNESSEANS T EH2 N IH0 S IY1 AH0 N Z -TENNESSEE T EH2 N AH0 S IY1 -TENNESSEE'S T EH2 N AH0 S IY1 Z -TENNEY T EH1 N IY0 -TENNIS T EH1 N AH0 S -TENNIS(2) T EH1 N IH0 S -TENNIS'S T EH1 N AH0 S IH0 Z -TENNISON T EH1 N IH0 S AH0 N -TENNY T EH1 N IY0 -TENNYSON T EH1 N IH0 S AH0 N -TENOR T EH1 N ER0 -TENORE T EH1 N AO2 R -TENORIO T EH0 N AO1 R IY0 OW0 -TENORS T EH1 N ER0 Z -TENPAS T EH1 N P AH0 Z -TENPENNY T EH1 N P EH2 N IY0 -TENRECS T EH1 N R EH2 K S -TENS T EH1 N Z -TENSE T EH1 N S -TENSELY T EH1 N S L IY0 -TENSILE T EH1 N S AH0 L -TENSIOMETER T EH2 N S IY0 AA1 M IH0 T ER0 -TENSION T EH1 N SH AH0 N -TENSIONS T EH1 N CH AH0 N Z -TENSOR T EH1 N S ER0 -TENSORS T EH1 N S ER0 Z -TENT T EH1 N T -TENTACLE T EH1 N T AH0 K AH0 L -TENTACLES T EH1 N T AH0 K AH0 L Z -TENTATIVE T EH1 N T AH0 T IH0 V -TENTATIVE(2) T EH1 N AH0 T IH0 V -TENTATIVELY T EH1 N T AH0 T IH0 V L IY0 -TENTATIVELY(2) T EH1 N T AH0 V L IY0 -TENTH T EH1 N TH -TENTHS T EH1 N TH S -TENTING T EH1 N T IH0 NG -TENTS T EH1 N T S -TENUOUS T EH1 N Y AH0 W AH0 S -TENURE T EH1 N Y ER0 -TENURED T EH1 N Y ER0 D -TENURES T EH1 N Y ER0 Z -TENUTA T EH0 N UW1 T AH0 -TENZER T EH1 N Z ER0 -TEO T EY1 OW0 -TEODORO T IY2 OW0 D AO1 R OW0 -TEPE T IY1 P -TEPER T IY1 P ER0 -TEPID T EH1 P IH0 D -TEPLY T EH1 P L IY0 -TEPOZTLAN T IH0 P AO1 S T L AH0 N -TEPPER T EH1 P ER0 -TEPPERMAN T EH1 P ER0 M AH0 N -TEQUILA T AH0 K IY1 L AH0 -TEQUILIU T AH0 K IY1 L Y UW0 -TER T ER1 -TERABYTE T EH2 R AH0 B AY1 T -TERABYTES T EH2 R AH0 B AY1 T S -TERADA T ER0 AA1 D AH0 -TERADATA T EH2 R AH0 D AA1 T AH0 -TERADYNE T EH1 R AH0 D AY2 N -TERADYNE'S T EH1 R AH0 D AY2 N Z -TERAN T EH1 R AH0 N -TERASAWA T EH2 R AH0 S AA1 W AH0 -TERBUSH T ER1 B AH0 SH -TERBUSH(2) T ER1 B UH0 SH -TERCEL T ER1 S AH0 L -TERCERO T ER0 CH EH1 R OW0 -TERENCE T EH1 R AH0 N S -TERENCE'S T EH1 R AH0 N S IH0 Z -TERENTIA T ER0 EH1 N SH AH0 -TERESA T ER0 IY1 S AH0 -TERESA(2) T ER0 EY1 S AH0 -TERESA'S T ER0 IY1 S AH0 Z -TERESA'S(2) T ER0 EY1 S AH0 Z -TERESE T EH1 R IY0 Z -TERESI T ER0 EH1 S IY0 -TERESITA T ER0 EH0 S IY1 T AH0 -TERESSA T ER0 EH1 S AH0 -TEREX T EH1 R AH0 K S -TEREZA T ER0 EY1 Z AH0 -TEREZA'S T ER0 EY1 Z AH0 Z -TERHAAR T ER1 HH AA0 R -TERHORST T ER1 HH AO0 R S T -TERHUNE T ER0 HH Y UW1 N -TERI T EH1 R IY0 -TERIYAKI T EH2 R IH0 Y AA1 K IY0 -TERKEL T ER1 K AH0 L -TERKHORN T ER1 K AO2 R N -TERLECKI T ER0 L EH1 T S K IY0 -TERLIZZI T ER0 L IY1 T S IY0 -TERM T ER1 M -TERM'S T ER1 M Z -TERMAN T ER1 M AH0 N -TERMED T ER1 M D -TERMEER T ER0 M IH1 R -TERMER T ER1 M ER0 -TERMERS T ER1 M ER0 Z -TERMINABLE T ER1 M AH0 N AH0 B AH0 L -TERMINAL T ER1 M AH0 N AH0 L -TERMINALLY T ER1 M AH0 N AH0 L IY0 -TERMINALS T ER1 M AH0 N AH0 L Z -TERMINATE T ER1 M AH0 N EY2 T -TERMINATED T ER1 M AH0 N EY2 T IH0 D -TERMINATES T ER1 M IH0 N EY2 T S -TERMINATING T ER1 M AH0 N EY2 T IH0 NG -TERMINATION T ER0 M AH0 N EY1 SH AH0 N -TERMINATIONS T ER2 M AH0 N EY1 SH AH0 N Z -TERMINATOR T ER1 M AH0 N EY2 T ER0 -TERMINE T ER1 M IH0 N -TERMING T ER1 M IH0 NG -TERMINI T ER1 M IH0 N AY2 -TERMINOLOGY T ER2 M IH0 N AA1 L AH0 JH IY0 -TERMINUS T ER1 M IH0 N AH0 S 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L IY0 -TERRIE T EH1 R IY0 -TERRIEN T EH1 R IY0 AH0 N -TERRIER T EH1 R IY0 ER0 -TERRIERS T EH1 R IY0 ER0 Z -TERRIFIC T ER0 IH1 F IH0 K -TERRIFICALLY T ER0 IH1 F IH0 K L IY0 -TERRIFIED T EH1 R AH0 F AY2 D -TERRIFIES T EH1 R AH0 F AY2 Z -TERRIFY T EH1 R AH0 F AY2 -TERRIFYING T EH1 R AH0 F AY2 IH0 NG -TERRILE T EH2 R IY1 L -TERRILL T EH1 R AH0 L -TERRINGTON T EH1 R IH0 NG T AH0 N -TERRIO T EH1 R IY0 OW0 -TERRIS T EH1 R IH0 S -TERRITO T ER0 IY1 T OW0 -TERRITORIAL T EH2 R IH0 T AO1 R IY0 AH0 L -TERRITORIALISM T EH2 R AH0 T AO1 R IY0 AH0 L IH2 Z AH0 M -TERRITORIALLY T EH2 R IH0 T AO1 R IY0 AH0 L IY0 -TERRITORIES T EH1 R AH0 T AO2 R IY0 Z -TERRITORY T EH1 R IH0 T AO2 R IY0 -TERRITORY'S T EH1 R IH0 T AO2 R IY0 Z -TERRIZZI T EH2 R IH1 T S IY0 -TERRONES T EH1 R AH0 N Z -TERROR T EH1 R ER0 -TERRORISM T EH1 R ER0 IH2 Z AH0 M -TERRORIST T EH1 R ER0 IH0 S T -TERRORISTIC T EH2 R ER0 IH1 S T IH0 K -TERRORISTS T EH1 R ER0 AH0 S T S -TERRORISTS(2) T EH1 R ER0 IH0 S T S -TERRORISTS' T EH1 R ER0 IH0 S T S 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W AH0 L AY2 Z ER0 -TRANQUILIZERS T R AE1 NG K W AH0 L AY2 Z ER0 Z -TRANQUILIZING T R AE1 NG K W AH0 L AY2 Z IH0 NG -TRANQUILLITY T R AE0 NG K W IH1 L IH0 T IY0 -TRANS T R AE1 N Z -TRANSACT T R AE0 N Z AE1 K T -TRANSACTED T R AE0 N S AE1 K T IH0 D -TRANSACTED(2) T R AE0 N Z AE1 K T IH0 D -TRANSACTION T R AE0 N Z AE1 K SH AH0 N -TRANSACTION'S T R AE0 N Z AE1 K SH AH0 N Z -TRANSACTIONS T R AE0 N Z AE1 K SH AH0 N Z -TRANSAFRICA T R AE2 N Z AE1 F R IH0 K AH0 -TRANSALASKA T R AE2 N Z AH0 L AE1 S K AH0 -TRANSALTA T R AE2 N Z AO1 L T AH0 -TRANSAMERICA T R AE2 N S AH0 M EH1 R IH0 K AH0 -TRANSAMERICA'S T R AE2 N Z AH0 M EH1 R IH0 K AH0 Z -TRANSAMERICAN T R AE2 N Z AH0 M EH1 R IH0 K AH0 N -TRANSAMERICAN'S T R AE2 N Z AH0 M EH1 R IH0 K AH0 N Z -TRANSATLANTIC T R AE2 N Z AH0 T L AE1 N T IH0 K -TRANSATLANTIC(2) T R AE2 N Z AH0 T L AE1 N IH0 K -TRANSCANADA T R AE2 N Z K AE1 N AH0 T AH0 -TRANSCANADA'S T R AE2 N Z K AE1 N AH0 D AH0 Z -TRANSCAPITAL T R AE2 N Z K AE1 P IH0 T AH0 L -TRANSCEND T R AE0 N S EH1 N D -TRANSCENDED T R AE0 N S EH1 N D IH0 D -TRANSCENDENCE T R AE0 N S EH1 N D AH0 N S -TRANSCENDENT T R AE0 N S EH1 N D AH0 N T -TRANSCENDENTAL T R AE2 N S AH0 N D EH1 N T AH0 L -TRANSCENDENTAL(2) T R AE2 N S AH0 N D EH1 N AH0 L -TRANSCENDING T R AE0 N S EH1 N D IH0 NG -TRANSCENDS T R AE0 N S EH1 N D Z -TRANSCHANNEL T R AH1 N S CH AE1 N AH0 L -TRANSCHANNEL'S T R AH1 N S CH AE1 N AH0 L Z -TRANSCISCO T R AE2 N S IH1 S K OW0 -TRANSCO T R AE1 N S K OW0 -TRANSCON T R AE1 N Z K AA0 N -TRANSCONTINENTAL T R AE2 N Z K AA2 N T IH0 N EH1 N T AH0 L -TRANSCONTINENTAL(2) T R AE2 N Z K AA2 N IH0 N EH1 N T AH0 L -TRANSCONTINENTAL(3) T R AE2 N Z K AA2 N T IH0 N EH1 N AH0 L -TRANSCONTINENTAL(4) T R AE2 N Z K AA2 N IH0 N EH1 N AH0 L -TRANSCONTINENTAL'S T R AE2 N Z K AA2 N T IH0 N EH1 N T AH0 L Z -TRANSCONTINENTAL'S(2) T R AE2 N Z K AA2 N IH0 N EH1 N T AH0 L Z -TRANSCONTINENTAL'S(3) T R AE2 N Z K AA2 N T IH0 N EH1 N AH0 L Z -TRANSCONTINENTAL'S(4) T R AE2 N Z K AA2 N IH0 N EH1 N AH0 L Z -TRANSCRIBE T R AE0 N S K R AY1 B -TRANSCRIBED T R AE0 N S K R AY1 B D -TRANSCRIBER T R AE0 N S K R AY1 B ER0 -TRANSCRIBERS T R AE0 N S K R AY1 B ER0 Z -TRANSCRIBES T R AE0 N S K R AY1 B Z -TRANSCRIBING T R AE0 N S K R AY1 B IH0 NG -TRANSCRIPT T R AE1 N S K R IH2 P T -TRANSCRIPTION T R AE2 N S K R IH1 P SH AH0 N -TRANSCRIPTIONS T R AE2 N S K R IH1 P SH AH0 N Z -TRANSCRIPTS T R AE1 N S K R IH2 P T S -TRANSDUCER T R AE0 N S D UW1 S ER0 -TRANSDUCERS T R AE0 N S D UW1 S ER0 Z -TRANSECT T R AE1 N S EH2 K T -TRANSECTED T R AE1 N S EH2 K T IH0 D -TRANSECTION T R AE1 N S EH2 K SH AH0 N -TRANSFER T R AE0 N S F ER1 -TRANSFER(2) T R AE1 N S F ER0 -TRANSFERABILITY T R AE2 N S F ER0 AH0 B IH1 L IH0 T IY0 -TRANSFERABLE T R AE0 N S F ER1 AH0 B AH0 L -TRANSFERED T R AE0 N S F ER1 D -TRANSFERENCE T R AE0 N S F ER1 AH0 N S -TRANSFERING T R AE0 N S F ER1 IH0 NG -TRANSFERRABLE T R AE2 N S F ER1 AH0 B AH0 L -TRANSFERRED T R AE0 N S F ER1 D -TRANSFERRED(2) T R AE1 N S F ER0 D -TRANSFERRING T R AE0 N S F ER1 IH0 NG -TRANSFERS T R AE0 N S F ER1 Z -TRANSFERS(2) T R AE1 N S F ER0 Z -TRANSFIX T R AE0 N S F IH1 K S -TRANSFIXED T R AE0 N S F IH1 K S T -TRANSFORM T R AE0 N S F AO1 R M -TRANSFORM(2) T R AE1 N S F AO0 R M -TRANSFORMATION T R AE2 N S F ER0 M EY1 SH AH0 N -TRANSFORMATIONAL T R AE2 N S F ER0 M EY1 SH AH0 N AH0 L -TRANSFORMATIONS T R AE2 N S F ER0 M EY1 SH AH0 N Z -TRANSFORMATIVE T R AE2 N S F AO1 R M AA0 T IH2 V -TRANSFORMED T R AE0 N S F AO1 R M D -TRANSFORMER T R AE0 N S F AO1 R M ER0 -TRANSFORMERS T R AE0 N S F AO1 R M ER0 Z -TRANSFORMING T R AE0 N S F AO1 R M IH0 NG -TRANSFORMS T R AE0 N S F AO1 R M Z -TRANSFUSE T R AE0 N S F Y UW1 Z -TRANSFUSED T R AE0 N S F Y UW1 Z D -TRANSFUSION T R AE0 N S F Y UW1 ZH AH0 N -TRANSFUSIONS T R AE0 N S F Y UW1 ZH AH0 N Z -TRANSGENDER T R AE1 N Z JH EH1 N D ER0 -TRANSGENIC T R AE2 N Z JH EH1 N IH0 K -TRANSGRESS T R AE0 N Z G R EH1 S -TRANSGRESSED T R AE0 N Z G R EH1 S T -TRANSGRESSES T R AE0 N Z G R EH1 S IH0 S -TRANSGRESSING T R AE0 N Z G R EH1 S IH0 NG -TRANSGRESSION T R AE0 N Z G R EH1 SH AH0 N -TRANSGRESSIONS T R AE0 N Z G R EH1 SH AH0 N Z -TRANSGRESSOR T R AE0 N Z G R EH1 S ER0 -TRANSIENCE T R AE1 N Z IY0 AH0 N S -TRANSIENT T R AE1 N ZH AH0 N T -TRANSIENTS T R AE1 N Z IY0 AH0 N T S -TRANSILLUMINATION T R AE2 N Z AH0 L UW2 M AH0 N EY1 SH AH0 N -TRANSIMAGE T R AE2 N Z IH1 M IH0 JH -TRANSISTOR T R AE0 N Z IH1 S T ER0 -TRANSISTORS T R AE0 N Z IH1 S T ER0 Z -TRANSIT T R AE1 N Z IH0 T -TRANSITED T R AE1 N Z IH0 T IH0 D -TRANSITING T R AE1 N Z IH0 T IH0 NG -TRANSITION T R AE0 N Z IH1 SH AH0 N -TRANSITIONAL T R AE0 N S IH1 SH AH0 N AH0 L -TRANSITIONAL(2) T R AE0 N Z IH1 SH AH0 N AH0 L -TRANSITIONING T R AE0 N Z IH1 SH AH0 N IH0 NG -TRANSITIONS T R AE0 N Z IH1 SH AH0 N Z -TRANSITORY T R AE1 N Z AH0 T AO2 R IY0 -TRANSITS T R AE1 N Z IH0 T S -TRANSKEI T R AE1 N Z K EY2 -TRANSLATE T R AE0 N Z L EY1 T -TRANSLATE(2) T R AE0 N S L EY1 T -TRANSLATED T R AE0 N Z L EY1 T IH0 D -TRANSLATED(2) T R AE0 N S L EY1 T IH0 D -TRANSLATES T R AE0 N Z L EY1 T S -TRANSLATES(2) T R AE1 N S L EY2 T S -TRANSLATING T R AE0 N Z L EY1 T IH0 NG -TRANSLATING(2) T R AE1 N S L EY2 T IH0 NG -TRANSLATION T R AE0 N Z L EY1 SH AH0 N -TRANSLATION(2) T R AE0 N S L EY1 SH AH0 N -TRANSLATIONS T R AE0 N Z L EY1 SH AH0 N Z -TRANSLATIONS(2) T R AE0 N S L EY1 SH AH0 N Z -TRANSLATOR T R AE0 N S L EY1 T ER0 -TRANSLATOR(2) T R AE0 N Z L EY1 T ER0 -TRANSLATORS T R AE0 N S L EY1 T ER0 Z -TRANSLATORS(2) T R AE0 N Z L EY1 T ER0 Z -TRANSLOGIC T R AE2 N Z L AA1 JH IH0 K -TRANSLUCENT T R AE0 N S L UW1 S AH0 N T -TRANSMARK T R AE1 N Z M AA2 R K -TRANSMEDIA T R AE2 N Z M IY1 D IY0 AH0 -TRANSMISSION T R AE0 N S M IH1 SH AH0 N -TRANSMISSION(2) T R AE0 N Z M IH1 SH AH0 N -TRANSMISSIONS T R AE0 N Z M IH1 SH AH0 N Z -TRANSMIT T R AE0 N Z M IH1 T -TRANSMITS T R AE0 N Z M IH1 T S -TRANSMITTABLE T R AE0 N Z M IH1 T AH0 B AH0 L -TRANSMITTAL T R AE0 N S M IH1 T AH0 L -TRANSMITTED T R AE0 N S M IH1 T IH0 D -TRANSMITTED(2) T R AE0 N Z M IH1 T AH0 D -TRANSMITTER T R AE0 N S M IH1 T ER0 -TRANSMITTER'S T R AE0 N S M IH1 T ER0 Z -TRANSMITTERS T R AE0 N S M IH1 T ER0 Z -TRANSMITTING T R AE0 N S M IH1 T IH0 NG -TRANSNATIONAL T R AE0 N S N AE1 SH AH0 N AH0 L -TRANSOCEANIC T R AE2 N Z OW0 SH IY0 AE1 N IH0 K -TRANSOHIO T R AE2 N Z OW0 HH AY1 OW0 -TRANSOM T R AE1 N S AH0 M -TRANSOMS T R AE1 N S AH0 M Z -TRANSOU T R AE1 N Z UW2 -TRANSPAC T R AE1 N Z P AE2 K -TRANSPACIFIC T R AE2 N S P AH0 S IH1 F IH0 K -TRANSPARENCIES T R AE0 N S P EH1 R AH0 N S IY0 Z -TRANSPARENCY T R AE0 N S P EH1 R AH0 N S IY0 -TRANSPARENT T R AE0 N S P EH1 R AH0 N T -TRANSPARENTLY T R AE0 N S P EH1 R AH0 N T L IY0 -TRANSPARK T R AE1 N S P AA1 R K -TRANSPIRE T R AE0 N S P AY1 ER0 -TRANSPIRED T R AE0 N S P AY1 ER0 D -TRANSPIRES T R AE0 N S P AY1 ER0 Z -TRANSPIRING T R AE0 N S P AY1 ER0 IH0 NG -TRANSPLANT T R AE0 N S P L AE1 N T -TRANSPLANTATION T R AE2 N Z P L AE0 N T EY1 SH AH0 N -TRANSPLANTED T R AE0 N S P L AE1 N T IH0 D -TRANSPLANTING T R AE0 N S P L AE1 N T IH0 NG -TRANSPLANTS T R AE0 N S P L AE1 N T S -TRANSPONDER T R AE0 N S P AA1 N D ER0 -TRANSPONDERS T R AE0 N S P AA1 N D ER0 Z -TRANSPORT T R AE0 N S P AO1 R T -TRANSPORT(2) T R AE1 N S P AO0 R T -TRANSPORTABLE T R AE0 N S P AO1 R T AH0 B AH0 L -TRANSPORTATION T R AE2 N S P ER0 T EY1 SH AH0 N -TRANSPORTATION'S T R AE2 N S P ER0 T EY1 SH AH0 N Z -TRANSPORTED T R AE0 N S P AO1 R T IH0 D -TRANSPORTER T R AE0 N S P AO1 R T ER0 -TRANSPORTERS T R AE0 N S P AO1 R T ER0 Z -TRANSPORTING T R AE0 N S P AO1 R T IH0 NG -TRANSPORTS T R AE0 N S P AO1 R T S -TRANSPORTS(2) T R AE1 N S P AO0 R T S -TRANSPOSE T R AE0 N S P OW1 Z -TRANSPOSED T R AE0 N S P OW1 Z D -TRANSRACIAL T R AE2 N Z R EY1 SH AH0 L -TRANSRAPID T R AE1 N Z R AE1 P IH0 D -TRANSSEXUAL T R AE0 N S EH1 K SH Y UW0 AH0 L -TRANSSEXUALS T R AE0 N S EH1 K SH Y UW0 AH0 L Z -TRANSTAR T R AE0 N S T AA1 R -TRANSTECHNOLOGY T R AE2 N Z T AH0 K N AA1 L AH0 JH IY0 -TRANSTECTOR T R AE2 N Z T EH1 K T ER0 -TRANSUE T R AE1 N Z UW0 -TRANSVAAL T R AE0 N Z V AA1 L -TRANSVERSE T R AE0 N Z V ER1 S -TRANSVESTITE T R AE0 N Z V EH1 S T AY0 T -TRANSVESTITES T R AE0 N Z V EH1 S T AY0 T S -TRANSWAY T R AE1 N Z W EY2 -TRANSWESTERN T R AE2 N Z W EH1 S T ER0 N -TRANSWORLD T R AE0 N S W ER1 L D -TRANSYLVANIA T R AE2 N Z IY0 L V EY1 N IY0 AH0 -TRANSYLVANIA(2) T R AE2 N S IH0 L V EY1 N Y AH0 -TRANT T R AE1 N T -TRANTER T R AE1 N T ER0 -TRANTHAM T R AE1 N TH AH0 M -TRANUM T R AE1 N AH0 M -TRANZONIC T R AE0 N Z AA1 N IH0 K -TRAP T R AE1 P -TRAPANI T R AA0 P AA1 N IY0 -TRAPASSO T R AA0 P AA1 S OW0 -TRAPELO T R AH0 P EH1 L OW0 -TRAPEZE T R AH0 P IY1 Z -TRAPHAGEN T R AE1 F AH0 G AH0 N -TRAPNELL T R AE1 P N AH0 L -TRAPP T R AE1 P -TRAPPE T R AE1 P -TRAPPED T R AE1 P T -TRAPPER T R AE1 P ER0 -TRAPPERS T R AE1 P ER0 Z -TRAPPING T R AE1 P IH0 NG -TRAPPINGS T R AE1 P IH0 NG Z -TRAPPIST T R AE1 P IH0 S T -TRAPS T R AE1 P S -TRASH T R AE1 SH -TRASHED T R AE1 SH T -TRASHES T R AE1 SH IH0 Z -TRASHING T R AE1 SH IH0 NG -TRASHY T R AE1 SH IY0 -TRASK T R AE1 S K -TRAUB T R AO1 B -TRAUDT T R AO1 D T -TRAUGER T R AW1 G ER0 -TRAUGH T R AO1 -TRAUGHBER T R AO1 B ER0 -TRAUGOTT T R AW1 G AH0 T -TRAUM T R AO1 M -TRAUMA T R AO1 M AH0 -TRAUMAS T R AO1 M AH0 Z -TRAUMATIC T R AO0 M AE1 T IH0 K -TRAUMATIZE T R AO1 M AH0 T AY2 Z -TRAUMATIZED T R AO1 M AH0 T AY2 Z D -TRAUSCH T R AW1 SH -TRAUT T R AO1 T -TRAUTH T R AO1 TH -TRAUTMAN T R AW1 T M AH0 N -TRAUTMANN T R AW1 T M AH0 N -TRAUTNER T R AW1 T N ER0 -TRAUTWEIN T R AW1 T W AY2 N -TRAVAGLINI T R AA0 V AA0 G L IY1 N IY0 -TRAVAIL T R AH0 V EY1 L -TRAVAILS T R AH0 V EY1 L Z -TRAVEL T R AE1 V AH0 L -TRAVELDAY T R AE1 V AH0 L D EY2 -TRAVELDAYS T R AE1 V AH0 L D EY2 Z -TRAVELED T R AE1 V AH0 L D -TRAVELER T R AE1 V AH0 L ER0 -TRAVELER(2) T R AE1 V L ER0 -TRAVELER'S T R AE1 V AH0 L ER0 Z -TRAVELERS T R AE1 V AH0 L ER0 Z -TRAVELERS(2) T R AE1 V L ER0 Z -TRAVELERS' T R AE1 V AH0 L ER0 Z -TRAVELGATE T R AE1 V AH0 L G EY2 T -TRAVELING T R AE1 V AH0 L IH0 NG -TRAVELING(2) T R AE1 V L IH0 NG -TRAVELLED T R AE1 V AH0 L D -TRAVELLER T R AE1 V AH0 L ER0 -TRAVELLERS T R AE1 V AH0 L ER0 Z -TRAVELLING T R AE1 V AH0 L IH0 NG -TRAVELLING(2) T R AE1 V L IH0 NG -TRAVELOCITY T R AE1 V AH0 L AA1 S AH0 T IY0 -TRAVELODGE T R AE1 V AH0 L AA1 JH -TRAVELOGUE T R AE1 V AH0 L AO2 G -TRAVELS T R AE1 V AH0 L Z -TRAVELSTEAD T R AE1 V AH0 L S T EH2 D -TRAVENOL T R AE1 V AH0 N AH0 L -TRAVER T R EY1 V ER0 -TRAVERS T R AE1 V ER0 Z -TRAVERSE T R AE1 V ER0 S -TRAVERSE(2) T R AH0 V ER1 S -TRAVERSED T R AE1 V ER0 S T -TRAVERSED(2) T R AH0 V ER1 S T -TRAVERSING T R AH0 V ER1 S IH0 NG -TRAVERSO T R AA0 V EH1 R S OW0 -TRAVESTIES T R AE1 V AH0 S T IY0 Z -TRAVESTY T R AE1 V AH0 S T IY0 -TRAVIATA T R AA0 V IY0 AA1 T AH0 -TRAVIESO T R AA0 V IY1 S OW0 -TRAVIS T R AE1 V IH0 S -TRAVISANO T R AE2 V IH0 S AA1 N OW0 -TRAVNIK T R AE1 V N IH0 K -TRAVOLTA T R AH0 V OW1 L T AH0 -TRAVOLTA'S T R AH0 V OW1 L T AH0 Z -TRAVOLTAS T R AH0 V OW1 L T AH0 Z -TRAWEEK T R AO1 IY2 K -TRAWICK T R AO1 IH0 K -TRAWLER T R AO1 L ER0 -TRAWLERS T R AO1 L ER0 Z -TRAX T R AE1 K S -TRAXLER T R AE1 K S L ER0 -TRAY T R EY1 -TRAYER T R EY1 ER0 -TRAYLOR T R EY1 L ER0 -TRAYNHAM T R EY1 N HH AH0 M -TRAYNOR T R EY1 N ER0 -TRAYS T R EY1 Z -TRAYWICK T R EY1 W IH2 K -TRBOVICH T ER0 B AA1 V IH0 CH -TRE T R EY1 -TREACHEROUS T R EH1 CH ER0 AH0 S -TREACHERY T R EH1 CH ER0 IY0 -TREACY T R EY1 S IY0 -TREAD T R EH1 D -TREADAWAY T R EH1 D AH0 W EY2 -TREADING T R EH1 D IH0 NG -TREADMILL T R EH1 D M IH2 L -TREADMILLS T R EH1 D M IH2 L Z -TREADS T R EH1 D Z -TREADSTONE T R EH1 D S T OW2 N -TREADWAY T R EH1 D W EY2 -TREADWELL T R EH1 D W EH2 L -TREADWHEEL T R EH1 D W IY2 L -TREANOR T R IY1 N ER0 -TREASE T R IY1 Z -TREASON T R IY1 Z AH0 N -TREASONOUS T R IY1 Z AH0 N AH2 S -TREASONS T R IY1 Z AH0 N S -TREASTER T R IY1 S T ER0 -TREASURE T R EH1 ZH ER0 -TREASURED T R EH1 ZH ER0 D -TREASURER T R EH1 ZH ER0 ER0 -TREASURER'S T R EH1 ZH ER0 ER0 Z -TREASURERS T R EH1 ZH ER0 ER0 Z -TREASURES T R EH1 ZH ER0 Z -TREASURIES T R EH1 ZH ER0 IY0 Z -TREASURY T R EH1 ZH ER0 IY0 -TREASURY'S T R EH1 ZH ER0 IY0 Z -TREASURYS T R EH1 ZH ER0 IY0 Z -TREAT T R IY1 T -TREATABLE T R IY1 T AH0 B AH0 L -TREATED T R IY1 T IH0 D -TREATER T R IY1 T ER0 -TREATERS T R IY1 T ER0 Z -TREATIES T R IY1 T IY0 Z -TREATING T R IY1 T IH0 NG -TREATISE T R IY1 T AH0 S -TREATISES T R IY1 T AH0 S AH0 Z -TREATMENT T R IY1 T M AH0 N T -TREATMENTS T R IY1 T M AH0 N T S -TREATS T R IY1 T S -TREATY T R IY1 T IY0 -TREATY'S T R IY1 T IY0 Z -TREBILCOCK T R IH0 B IH1 L K AH0 K -TREBLE T R EH1 B AH0 L -TREBLED T R EH1 B AH0 L D -TREBLINKA T R EH0 B L IH1 NG K ER0 -TREBLINKA(2) T R EH0 B L IH1 NG K AH0 -TRECKER T R EH1 K ER0 -TREDER T R IY1 D ER0 -TREDWAY T R EH1 D W EY2 -TREE T R IY1 -TREECE T R IY1 S -TREELESS T R IY1 L AH0 S -TREEN T R IY1 N -TREES T R IY1 Z -TREESE T R IY1 Z -TREESH T R IY1 SH -TREESWEET T R IY1 S W IY2 T -TREETOP T R IY1 T AO2 P -TREETOPS T R IY1 T AO2 P S -TREFETHEN T R EH1 F IH0 TH AH0 N -TREFGARNE T R EH1 F G AA0 R N -TREFRY T R EH1 F R IY0 -TREFZ T R EH1 F Z -TREGLIA T R EH1 G L IY0 AH0 -TREGO T R EH1 G OW0 -TREGONING T R EH1 G AH0 N IH0 NG -TREGRE T R EH1 G ER0 -TREGURTHA T R EH0 G ER1 TH AH0 -TREHARNE T R EH1 HH AA0 R N -TREIBER T R AY1 B ER0 -TREICHEL T R AY1 K AH0 -TREICHLER T R AY1 K AH0 L ER0 -TREICHLER(2) T R AY1 K L ER0 -TREINEN T R AY1 N AH0 N -TREJO T R EY1 Y OW0 -TREK T R EH1 K -TREKKED T R EH1 K T -TREKKING T R EH1 K IH0 NG -TREKS T R EH1 K S -TRELA T R EH1 L AH0 -TRELLA T R EH1 L AH0 -TRELLEBORG T R EH1 L AH0 B AO0 R G -TRELLEBORG'S T R EH1 L AH0 B AO0 R G Z -TRELLIS T R EH1 L AH0 S -TRELOAR T R EH1 L AO0 R -TREMAIN T R EH1 M AY0 N -TREMAINE T R IH0 M EY1 N -TREMAYNE T R EH1 M EY0 N -TREMBATH T R EH1 M B AH0 TH -TREMBLAY T R EH1 M B L EY0 -TREMBLE T R EH1 M B AH0 L -TREMBLED T R EH1 M B AH0 L D -TREMBLEY T R EH1 M B L IY0 -TREMBLING T R EH1 M B AH0 L IH0 NG -TREMBLING(2) T R EH1 M B L IH0 NG -TREMBLY T R EH1 M B L IY0 -TREMEL T R EH1 M AH0 L -TREMENDOUS T R AH0 M EH1 N D AH0 S -TREMENDOUS(2) T R IH0 M EH1 N D AH0 S -TREMENDOUSLY T R AH0 M EH1 N D AH0 S L IY0 -TREMENDOUSLY(2) T R IH0 M EH1 N D AH0 S L IY0 -TREMENS T R EH1 M AH0 N Z -TREML T R EH1 M AH0 L -TREMMEL T R EH1 M AH0 L -TREMONT T R EH1 M AH0 N T -TREMOR T R EH1 M ER0 -TREMORS T R EH1 M ER0 Z -TREMPER T R EH1 M P ER0 -TREMULOUS T R EH1 M Y AH0 L AH0 S -TREMULOUSLY T R EH1 M Y AH0 L AH0 S L IY0 -TRENARY T R EH1 N EH0 R IY0 -TRENCH T R EH1 N CH -TRENCHANT T R EH1 N CH AH0 N T -TRENCHARD T R EH1 NG K ER0 D -TRENCHER T R EH1 N CH ER0 -TRENCHES T R EH1 N CH IH0 Z -TREND T R EH1 N D -TRENDED T R EH1 N D IH0 D -TRENDIER T R EH1 N D Y ER0 -TRENDIER(2) T R EH1 N D IY0 ER0 -TRENDIEST T R EH0 N D IY1 S T -TRENDIEST(2) T R EH1 N D IY0 AH0 S T -TRENDING T R EH1 N D IH0 NG -TRENDLESS T R EH1 N D L AH0 S -TRENDLINE T R EH1 N D L AY2 N -TRENDS T R EH1 N D Z -TRENDS(2) T R EH1 N Z -TRENDSETTER T R EH1 N D S EH2 T ER0 -TRENDY T R EH1 N D IY0 -TRENHOLM T R EH1 N HH OW2 L M -TRENKAMP T R EH1 N K AE2 M P -TRENKLE T R EH1 NG K AH0 L -TRENT T R EH1 N T -TRENT'S T R EH1 N T S -TRENTE-ET-QUARANTE T R EY1 N T EY0 K W AA2 R EH1 N T EY0 -TRENTHAM T R EH1 N TH AH0 M -TRENTMAN T R EH1 N T M AH0 N -TRENTON T R EH1 N T AH0 N -TRENTON'S T R EH1 N T AH0 N Z -TREON T R IY1 AH0 N -TREPAGNIER T R EH1 P AH0 G N IY2 ER0 -TREPANIER T R EH1 P AH0 N IY2 ER0 -TREPIDATION T R EH2 P IH0 D EY1 SH AH0 N -TREPPEL T R EH1 P AH0 L -TREPTOW T R EH1 P T OW0 -TRESCH T R EH1 SH -TRESCOTT T R EH1 S K AH0 T -TRESPASS T R EH1 S P AE2 S -TRESPASS(2) T R EH1 S P AH0 S -TRESPASSING T R EH1 S P AE2 S IH0 NG -TRESPASSING(2) T R EH1 S P AH0 S IH0 NG -TRESS T R EH1 S -TRESSEL T R EH1 S AH0 L -TRESSES T R EH1 S IH0 Z -TRESSLER T R EH1 S L ER0 -TREST T R EH1 S T -TRESTER T R EH1 S T ER0 -TRESTLE T R EH1 S AH0 L -TRETHEWEY T R EH1 TH Y UW0 IY0 -TRETINOIN T R EH1 T IH0 N OY2 N -TRETTEL T R EH1 T AH0 L -TRETTER T R EH1 T ER0 -TRETTIN T R EH1 T IH0 N -TREU T R UW1 -TREUHAND T R UW1 HH AE2 N D -TREUHANDANSTALT T R UW2 HH AE1 N D AH0 N S T AA2 L T -TREURNICHT T R UW1 R N IH0 K T -TREVATHAN T R EH1 V AH0 TH AH0 N -TREVELYAN T R AH0 V EH1 L Y AH0 N -TREVINO T R AH0 V IY1 N OW0 -TREVISO T R EH0 V IY1 S OW0 -TREVIZO T R EH0 V IY1 Z OW0 -TREVOR T R EH1 V ER0 -TREW T R UW1 -TREXLER T R EH1 K S L ER0 -TREY T R EY1 -TREYBIG T R EY1 B IH0 G -TREZISE T R EH1 Z AY0 Z -TREZZA T R EH1 Z AH0 -TRI T R AY1 -TRI-STATE T R AY1 S T EY1 T -TRIAD T R AY1 AE2 D -TRIAD'S T R AY1 AE2 D Z -TRIADS T R AY1 AE2 D Z -TRIAGE T R AY1 IH0 JH -TRIAL T R AY1 AH0 L -TRIAL(2) T R AY1 L -TRIAL'S T R AY1 AH0 L Z -TRIALS T R AY1 AH0 L Z -TRIALS(2) T R AY1 L Z -TRIANA T R IY0 AE1 N AH0 -TRIANGLE T R AY1 AE2 NG G AH0 L -TRIANGLE'S T R AY1 AE2 NG G AH0 L Z -TRIANGLES T R AY1 AE2 NG G AH0 L Z -TRIANGULAR T R AY0 AE1 NG G Y AH0 L ER0 -TRIANGULATION T R AY0 AE2 NG G Y UW0 L EY1 SH AH0 N -TRIANO T R IY0 AA1 N OW0 -TRIARC T R AY1 AA2 R K -TRIASSIC T R AY2 AE1 S IH2 K -TRIATHLON T R AY2 AE1 TH L AH0 N -TRIB T R IH1 B -TRIBAL T R AY1 B AH0 L -TRIBALISM T R AY1 B AH0 L IH0 Z M -TRIBALISM(2) T R AY1 B AH0 L IH0 Z AH0 M -TRIBASA T R AY1 B AE1 S AH0 -TRIBBETT T R IH1 B IH0 T -TRIBBEY T R IH1 B IY0 -TRIBBLE T R IH1 B AH0 L -TRIBBLES T R IH1 B AH0 L Z -TRIBBY T R IH1 B IY0 -TRIBE T R AY1 B -TRIBE'S T R AY1 B Z -TRIBECA T R IH0 B EH1 K AH0 -TRIBES T R AY1 B Z -TRIBESMAN T R AY1 B Z M AE0 N -TRIBESMEN T R AY1 B Z M IH0 N -TRIBLE T R IH1 B AH0 L -TRIBOROUGH T R AY1 B ER0 OW0 -TRIBULATION T R IH2 B Y AH0 L EY1 SH AH0 N -TRIBULATIONS T R IH2 B Y AH0 L EY1 SH AH0 N Z -TRIBULL T R IH1 B AH0 L -TRIBUNAL T R AH0 B Y UW1 N AH0 L -TRIBUNALS T R AY2 B Y UW1 N AH0 L Z -TRIBUNE T R IH1 B Y UW0 N -TRIBUNE'S T R IH1 B Y UW0 N Z -TRIBUTARIES T R IH1 B Y AH0 T EH2 R IY0 Z -TRIBUTARY T R IH1 B Y AH0 T EH2 R IY0 -TRIBUTE T R IH1 B Y UW0 T -TRIBUTE'S T R IH1 B Y UW0 T S -TRIBUTES T R IH1 B Y UW0 T S -TRICARICO T R IY0 K AA0 R IY1 K OW0 -TRICE T R AY1 S -TRICENTROL T R AY2 S EH1 N T R AA0 L -TRICENTROL'S T R AY2 S EH1 N T R AA0 L Z -TRICEPS T R AY1 S EH2 P S -TRICERATOPS T R AY2 S EH1 R AH0 T AO2 P S -TRICHE T R IH1 CH -TRICHET T R IH1 CH IH0 T -TRICIA T R IH1 SH AH0 -TRICIA'S T R IH1 SH AH0 Z -TRICIL T R IH1 S IH0 L -TRICK T R IH1 K -TRICKED T R IH1 K T -TRICKEL T R IH1 K AH0 L -TRICKERY T R IH1 K ER0 IY0 -TRICKETT T R IH1 K IH0 T -TRICKEY T R IH1 K IY0 -TRICKIER T R IH1 K IY0 ER0 -TRICKIEST T R IH1 K IY0 AH0 S T -TRICKING T R IH1 K IH0 NG -TRICKLE T R IH1 K AH0 L -TRICKLED T R IH1 K AH0 L D -TRICKLES T R IH1 K AH0 L Z -TRICKLING T R IH1 K L IH0 NG -TRICKS T R IH1 K S -TRICKSTER T R IH1 K S T ER0 -TRICKSTERS T R IH1 K S T ER0 Z -TRICKY T R IH1 K IY0 -TRICO T R IY1 K OW0 -TRICUSPID T R AY0 K AH1 S P AH0 D -TRICYCLE T R IH1 S IH0 K AH0 L -TRIDENT T R AY1 D AH0 N T -TRIDEX T R IH1 D EH2 K S -TRIE T R AY1 -TRIED T R AY1 D -TRIENNIAL T R AY0 EH1 N IY0 AH0 L -TRIER T R AY1 ER0 -TRIERWEILER T R IH1 R W AY0 L ER0 -TRIES T R AY1 Z -TRIESTE T R IY1 S T -TRIEU T R UW1 -TRIFARI T R IH0 F AA1 R IY0 -TRIFECTA T R AY0 F EH1 K T AH0 -TRIFLE T R AY1 F AH0 L -TRIFLES T R AY1 F AH0 L Z -TRIFLING T R AY1 F L IH0 NG -TRIG T R IH1 G -TRIGG T R IH1 G -TRIGGER T R IH1 G ER0 -TRIGGERED T R IH1 G ER0 D 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T R IH0 NG G AA1 L IY0 -TRINH T R IH1 N -TRINIDAD T R IH1 N IH0 D AE2 D -TRINITY T R IH1 N AH0 T IY0 -TRINITY(2) T R IH1 N IH0 T IY0 -TRINITY'S T R IH1 N IH0 T IY0 Z -TRINKA T R IH1 NG K AH0 -TRINKET T R IH1 NG K AH0 T -TRINKETS T R IH1 NG K AH0 T S -TRINKLE T R IH1 NG K AH0 L -TRINOVA T R AY2 N OW1 V AH0 -TRINTEX T R IH1 N T EH2 K S -TRIO T R IY1 OW2 -TRIO'S T R IY1 OW2 Z -TRIOLA T R IY0 OW1 L AH0 -TRIOLO T R IY0 OW1 L OW0 -TRIOMPHE T R IY0 OW1 M F -TRIP T R IH1 P -TRIP'S T R IH1 P S -TRIPARTITE T R AY0 P AA1 R T AY2 T -TRIPE T R AY1 P -TRIPI T R IY1 P IY0 -TRIPLE T R IH1 P AH0 L -TRIPLECAST T R IH1 P AH0 L K AE2 S T -TRIPLED T R IH1 P AH0 L D -TRIPLES T R IH1 P AH0 L Z -TRIPLET T R IH1 P L AH0 T -TRIPLETS T R IH1 P L AH0 T S -TRIPLETT T R IH1 P L IH0 T -TRIPLEX T R IH0 P L EH1 K S -TRIPLICATE T R IH1 P L IH0 K AH0 T -TRIPLING T R IH1 P AH0 L IH0 NG -TRIPLING(2) T R IH1 P L IH0 NG -TRIPOD T R AY1 P AA2 D -TRIPODI T R IY0 P OW1 D IY0 -TRIPODS T R AY1 P AA2 D Z -TRIPOLI T R IH1 P AH0 L IY0 -TRIPOLI'S T R IH1 P AH0 L IY0 Z -TRIPP T R IH1 P -TRIPP'S T R IH1 P S -TRIPPE T R IH1 P -TRIPPED T R IH1 P T -TRIPPEL T R IH1 P AH0 L -TRIPPER T R IH1 P ER0 -TRIPPERS T R IH1 P ER0 Z -TRIPPETT T R IH1 P IH0 T -TRIPPIE T R IH1 P IY0 -TRIPPING T R IH1 P IH0 NG -TRIPPLE T R IH1 P AH0 L -TRIPPY T R IH1 P IY2 -TRIPS T R IH1 P S -TRIPTYCH T R IH1 P T IH0 K -TRIPTYCHS T R IH1 P T IH0 K S -TRIREME T R AY1 R IY2 M -TRIREMES T R AY1 R IY2 M Z -TRISH T R IH1 SH -TRISHA T R IH1 SH AH0 -TRISHA'S T R IH1 SH AH0 Z -TRISKA T R IH1 S K AH0 -TRISLER T R IH1 S AH0 L ER0 -TRISLER(2) T R IH1 S L ER0 -TRISM T R IH1 Z AH0 M -TRISTA T R IH1 S T AH0 -TRISTAN T R IH1 S T AE2 N -TRISTAR T R AY1 S T AA2 R -TRISTATE T R AY1 S T EY2 T -TRISTRAM T R IH1 S T R AH0 M -TRITCH T R IH1 CH -TRITE T R AY1 T -TRITES T R AY1 T S -TRITIUM T R IH1 T IY0 AH0 M -TRITON T R AY1 T AH0 N -TRITON'S T R AY1 T AH0 N Z -TRITSCH T R IH1 CH -TRITSCHLER T R IH1 CH L ER0 -TRITT T R IH1 T -TRITZ T R IH1 T S -TRIUMPH T R AY1 AH0 M F 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-TROESTER T R OW1 S T ER0 -TROGDON T R AA1 G D AH0 N -TROHA T R OW1 HH AH0 -TROIA T R OW1 Y AH0 -TROIANI T R OW0 Y AA1 N IY0 -TROIANO T R OW0 IY0 AA1 N OW0 -TROIKA T R OY1 K AH0 -TROIS T W AA1 -TROISE T R OY1 Z -TROISI T R OY1 S IY0 -TROJAN T R OW1 JH AH0 N -TROJANOWSKI T R AH0 Y AH0 N AO1 F S K IY0 -TROJANS T R OW1 JH AH0 N Z -TROKEL T R OW1 K AH0 L -TROLINGER T R OW1 L IH0 NG ER0 -TROLL T R OW1 L -TROLL'S T R OW1 L Z -TROLLEY T R AA1 L IY0 -TROLLEYS T R AA1 L IY0 Z -TROLLING T R OW1 L IH0 NG -TROLLINGER T R OW1 L IH0 NG ER0 -TROLLS T R OW1 L Z -TROMA T R OW1 M AH0 -TROMBINO T R OW0 M B IY1 N OW0 -TROMBLE T R AA1 M B AH0 L -TROMBLEY T R AA1 M B L IY0 -TROMBLY T R AA1 M B L IY0 -TROMBONE T R AA0 M B OW1 N -TROMBONES T R AA0 M B OW1 N Z -TROMBONIST T R AA2 M B OW1 N IH0 S T -TROMP T R AA1 M P -TROMPETER T R AA1 M P IY0 T ER0 -TRON T R AA1 N -TRONCOSO T R OW0 N K OW1 S OW0 -TRONE T R OW1 N -TRONIC T R AA1 N IH0 K -TROON T R UW1 N -TROOP T R UW1 P -TROOP'S T R UW1 P S -TROOPED T R UW1 P T -TROOPER T R UW1 P ER0 -TROOPER'S T R UW1 P ER0 Z -TROOPERS T R UW1 P ER0 Z -TROOPERS' T R UW1 P ER0 Z -TROOPING T R UW1 P IH0 NG -TROOPS T R UW1 P S -TROOPS' T R UW1 P S -TROOST T R UW1 S T -TROPEA T R OW1 P IY0 AH0 -TROPEANO T R OW2 P IY1 N OW0 -TROPHIES T R OW1 F IY0 Z -TROPHY T R OW1 F IY0 -TROPIC T R AA1 P IH0 K -TROPICAL T R AA1 P IH0 K AH0 L -TROPICANA T R AA2 P IH0 K AE1 N AH0 -TROPICANA'S T R AA2 P IH0 K AE1 N AH0 Z -TROPICS T R AA1 P IH0 K S -TROPOPAUSE T R AA1 P AH0 P AO2 Z -TROPP T R AA1 P -TROPWORLD T R AA1 P W ER0 L D -TROSCH T R AA1 SH -TROSCLAIR T R AH0 S K L EH1 R -TROSPER T R AA1 S P ER0 -TROST T R AA1 S T -TROSTEL T R AA1 S T AH0 L -TROSTLE T R AA1 S AH0 L -TROT T R AA1 T -TROTH T R OW1 TH -TROTH(2) T R AO1 TH -TROTMAN T R AA1 T M AH0 N -TROTS T R AA1 T S -TROTSKY T R AA1 T S K IY2 -TROTT T R AA1 T -TROTTED T R AA1 T IH0 D -TROTTEN T R AA1 T AH0 N -TROTTER T R AA1 T ER0 -TROTTIER T R AA1 T IY0 ER0 -TROTTING T R AA1 T IH0 NG -TROUBADOUR T R UW1 B AH0 D AO2 R -TROUBH T R UW1 B -TROUBLE T R AH1 B AH0 L -TROUBLED T R AH1 B AH0 L D -TROUBLEFIELD T R AH1 B AH0 L F IY2 L D -TROUBLEMAKER T R AH1 B AH0 L M EY2 K ER0 -TROUBLEMAKERS T R AH1 B AH0 L M EY2 K ER0 Z -TROUBLES T R AH1 B AH0 L Z -TROUBLESHOOT T R AH1 B AH0 L SH UW2 T -TROUBLESHOOTER T R AH1 B AH0 L SH UW2 T ER0 -TROUBLESHOOTING T R AH1 B AH0 L SH UW2 T IH0 NG -TROUBLESHOOTS T R AH1 B AH0 L SH UW2 T S -TROUBLESOME T R AH1 B AH0 L S AH0 M -TROUBLING T R AH1 B AH0 L IH0 NG -TROUBLING(2) T R AH1 B L IH0 NG -TROUDT T R AW1 D T -TROUGH T R AO1 F -TROUGHS T R AO1 F S -TROUNCE T R AW1 N S -TROUNCED T R AW1 N S T -TROUNCING T R AW1 N S IH0 NG -TROUNG T R AW1 NG -TROUP T R UW1 P -TROUPE T R UW1 P -TROUPE'S T R UW1 P S -TROUPES T R UW1 P S -TROUSDALE T ER1 AH0 S D EY0 L -TROUSER T R AW1 Z ER0 -TROUSERS T R AW1 Z ER0 Z -TROUT T R AW1 T -TROUT'S T R AW1 T S -TROUTMAN T R AW1 T M AH0 N -TROUTNER T R AW1 T N ER0 -TROUTT T R AW1 T -TROUTWINE T R AW1 T W AY2 N -TROVATO T R OW0 V AA1 T OW0 -TROVATORE T R OW1 V AH0 T AO2 R -TROVE T R OW1 V -TROW T R OW1 -TROWBRIDGE T R OW1 B R IH0 JH -TROWEL T R AW1 W EH0 L -TROWELL T R AA1 W EH0 L -TROWER T R AW1 ER0 -TROXEL T R AA1 K S AH0 L -TROXELL T R AA1 K S AH0 L -TROXLER T R AA1 K S L ER0 -TROY T R OY1 -TROYAN T R OY1 AH0 N -TROYANOS T R OY2 AA1 N OW0 S -TROYAT T R OY1 AE0 T -TROYER T R OY1 ER0 -TROYKA T R OY1 K AH0 -TROYU T R OY0 UW1 -TRUANCY T R UW1 AH0 N S IY0 -TRUANT T R UW1 AH0 N T -TRUAX T R UW1 AE0 K S -TRUBEY T R UW1 B IY0 -TRUBY T R UW1 B IY0 -TRUCCO T R UW1 K OW0 -TRUCE T R UW1 S -TRUCHAN T R AH1 CH AH0 N -TRUCK T R AH1 K -TRUCK'S T R AH1 K S -TRUCKED T R AH1 K T -TRUCKEE T R AH1 K IY0 -TRUCKER T R AH1 K ER0 -TRUCKERS T R AH1 K ER0 Z -TRUCKERS' T R AH1 K ER0 Z -TRUCKING T R AH1 K IH0 NG -TRUCKLOAD T R AH1 K L OW2 D -TRUCKLOADS T R AH1 K L OW2 D Z -TRUCKS T R AH1 K S -TRUCKS' T R AH1 K S -TRUCULENT T R AH1 K Y AH0 L AH0 N T -TRUDA T R UW1 D AH0 -TRUDE T R UW1 D -TRUDEAU T R UW0 D OW1 -TRUDEL T R UW1 D AH0 L -TRUDELL T R AH1 D AH0 L -TRUDGE T R AH1 JH -TRUDGED T R AH1 JH D -TRUDGEN T R AH1 JH AH0 N -TRUDGES T R AH1 JH IH0 Z -TRUDGING T R AH1 JH IH0 NG -TRUDIA T R UW1 D IY0 AH0 -TRUDIE T R UW1 D IY0 -TRUDO T R UW1 D OW0 -TRUDY T R UW1 D IY0 -TRUE T R UW1 -TRUE-VIEW T R UW1 V Y UW1 -TRUEBLOOD T R UW1 B L AH2 D -TRUELL T R UW1 AH0 L -TRUELOVE T R UW1 L AH2 V -TRUEMAN T R UH1 M AH0 N -TRUER T R UW1 ER0 -TRUESDALE T R UW1 Z D EY2 L -TRUESDELL T R UW1 Z D EH2 L -TRUEST T R UW1 IH0 S T -TRUETT T R UW1 T -TRUEX T R UW1 EH2 K S -TRUFFAUT T R UW0 F OW1 -TRUFFAUT'S T R UW0 F OW1 Z -TRUFFLE T R AH1 F AH0 L -TRUFFLES T R AH1 F AH0 L Z -TRUGLIO T R AH1 G L IY0 OW0 -TRUICKO T R UW1 K OW0 -TRUICKO(2) T R UW2 IY1 K OW0 -TRUISM T R UW1 IH0 Z AH0 M -TRUITT T R UW1 T -TRUJILLO T R UW0 HH IY1 OW0 -TRULL T R AH1 L -TRULLINGER T R AH1 L IH0 NG ER0 -TRULOCK T R AH1 L AH0 K -TRULOVE T R UW0 L OW1 V IY0 -TRULSON T R AH1 L S AH0 N -TRULUCK T R AH1 L AH0 K -TRULY T R UW1 L IY0 -TRUMAN T R UW1 M AH0 N -TRUMAN'S T R UW1 M AH0 N Z -TRUMBLE T R AH1 M B AH0 L 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AA2 R T -TRYBA T R IH1 B AH0 -TRYBUS T R IH1 B IH0 S -TRYGG T R IH1 G -TRYGSTAD T R IH1 G S T AH0 D -TRYGVE T R IH1 G V IY0 -TRYIN' T R AY1 IH0 N -TRYING T R AY1 IH0 NG -TRYING(2) T R AY1 NG -TRYON T R AY1 AH0 N -TRYOUT T R AY1 AW2 T -TRYOUTS T R AY1 AW2 T S -TRYPHENA T R IH1 F IH0 N AH0 -TRYST T R IH1 S T -TRYST(2) T R AY1 S T -TRZASKA T ER0 Z AA1 S K AH0 -TRZCINSKI T ER0 ZH IH1 N S K IY0 -TRZECIAK T ER0 Z EH1 CH IY0 AE0 K -TS T IY1 EH1 S -TSAI T S AY1 -TSAI(2) S AY1 -TSAI'S T S AY1 Z -TSAI'S(2) S AY1 Z -TSAKOS T S AA1 K OW0 S -TSANG T S AE1 NG -TSANG(2) S AE1 NG -TSAO T S AW1 -TSAO(2) S AW1 -TSAR Z AA1 R -TSAR(2) T S AA1 R -TSCHANTZ CH AE1 N T S -TSCHETTER CH EH1 T ER0 -TSCHIDA CH AY1 D AH0 -TSCHIRHART CH ER1 HH AA0 R T -TSCHOPP CH AA1 P -TSCHUDY CH UW1 D IY0 -TSCHUMI CH UW1 M IY0 -TSE T S IY1 -TSE(2) S IY1 -TSEMEL T S EH1 M AH0 L -TSENG T S EH1 NG -TSENG(2) S EH1 NG -TSETSE T S IY1 T S IY0 -TSETSE(2) T IY1 T S IY0 -TSHISEKEDI SH IH0 S EH1 K AH0 D IY0 -TSIANG T S Y AE1 NG -TSINGTAO S 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EH1 R IY0 NG -WESTWARDS W EH1 S T W ER0 D Z -WESTWOOD W EH1 S T W UH2 D -WESTWOOD'S W EH1 S T W UH2 D Z -WESTWORLD W EH1 S T W ER2 L D -WESUN W IY1 S AH2 N -WET W EH1 T -WETHERBEE W EH1 DH ER0 B IY2 -WETHERBY W EH1 TH ER0 B IY0 -WETHERELL W EH1 TH ER0 AH0 L -WETHERILL W EH1 TH ER0 AH0 L -WETHERINGTON W EH1 DH ER0 IH0 NG T AH0 N -WETHERLY W EH1 DH ER0 L IY0 -WETHINGTON W EH1 TH IH0 NG T AH0 N -WETLAND W EH1 T L AE2 N D -WETLANDS W EH1 T L AE2 N D Z -WETLANDS' W EH1 T L AE2 N D Z -WETMORE W EH1 T M AO0 R -WETNESS W EH1 T N AH0 S -WETSEL W EH1 T S AH0 L -WETSTEIN W EH1 T S T IY2 N -WETSTEIN(2) W EH1 T S T AY2 N -WETSUIT W EH1 T S UW2 T -WETTENGEL W EH1 T IH0 NG G AH0 L -WETTER W EH1 T ER0 -WETTERAU W EH1 T ER0 AW0 -WETTERGREEN W EH1 T ER0 G R IY2 N -WETTEST W EH1 T AH0 S T -WETTING W EH1 T IH0 NG -WETTING(2) HH W EH1 T IH0 NG -WETTLAUFER W EH1 T L AW0 F ER0 -WETTSTEIN W EH1 T S T AY0 N -WETTSTEIN(2) W EH1 T S T IY0 N -WETZ W EH1 T S -WETZEL W EH1 T Z AH0 L -WETZLER W EH1 T S L ER0 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W AH1 T AH0 V -WHAT-WHAT W AH1 T W AH1 T -WHATE W EY1 T -WHATEVER W AH2 T EH1 V ER0 -WHATEVER(2) HH W AH2 T EH1 V ER0 -WHATEVER'S W AH2 T EH1 V ER0 Z -WHATEVER'S(2) HH W AH2 T EH1 V ER0 Z -WHATLEY W AH1 T L IY0 -WHATLEY(2) HH W AH1 T L IY0 -WHATNOT W AH1 T N AA2 T -WHATNOT(2) HH W AH1 T N AA2 T -WHATS W AH0 T S -WHATS(2) HH W AH0 T S -WHATSAPP W AH2 T S AA1 P -WHATSOEVER W AH2 T S OW0 EH1 V ER0 -WHATSOEVER(2) HH W AH2 T S OW0 EH1 V ER0 -WHAY-YU W EY1 Y UW1 -WHAY-YU(2) HH W EY1 Y UW1 -WHEAT W IY1 T -WHEAT(2) HH W IY1 T -WHEATEN W IY1 T AH0 N -WHEATEN(2) HH W IY1 T AH0 N -WHEATIE W IY1 T IY0 -WHEATIE(2) HH W IY1 T IY0 -WHEATIES W IY1 T IY0 Z -WHEATIES(2) HH W IY1 T IY0 Z -WHEATLEY W IY1 T L IY0 -WHEATLEY(2) HH W IY1 T L IY0 -WHEATLY W IY1 T L IY0 -WHEATLY(2) HH W IY1 T L IY0 -WHEATON W IY1 T AH0 N -WHEATON(2) HH W IY1 T AH0 N -WHEDBEE W EH1 D B IY2 -WHEDBEE(2) HH W EH1 D B IY2 -WHEDON W EH1 D AH0 N -WHEDON(2) HH W EH1 D AH0 N -WHEEL W IY1 L -WHEEL(2) HH W IY1 L -WHEELABRATOR W IY2 L AH0 B R EY1 T ER0 -WHEELABRATOR(2) HH W IY2 L AH0 B R EY1 T ER0 -WHEELAN W IY1 L AH0 N -WHEELAN(2) HH W IY1 L AH0 N -WHEELAND W IY1 L AH0 N D -WHEELAND(2) HH W IY1 L AH0 N D -WHEELBARROW W IY1 L B EH2 R OW0 -WHEELBARROW(2) HH W IY1 L B EH2 R OW0 -WHEELBARROWS W IY1 L B EH2 R OW0 Z -WHEELBARROWS(2) HH W IY1 L B EH2 R OW0 Z -WHEELBASE W IY1 L B EY2 S -WHEELBASE(2) HH W IY1 L B EY2 S -WHEELCHAIR W IY1 L CH EH2 R -WHEELCHAIR(2) HH W IY1 L CH EH2 R -WHEELCHAIRS W IY1 L CH EH2 R Z -WHEELCHAIRS(2) HH W IY1 L CH EH2 R Z -WHEELDON W IY1 L D AH0 N -WHEELDON(2) HH W IY1 L D AH0 N -WHEELED W IY1 L D -WHEELED(2) HH W IY1 L D -WHEELER W IY1 L ER0 -WHEELER(2) HH W IY1 L ER0 -WHEELER'S W IY1 L ER0 Z -WHEELER'S(2) HH W IY1 L ER0 Z -WHEELERS W IY1 L ER0 Z -WHEELERS(2) HH W IY1 L ER0 Z -WHEELESS W IY1 L AH0 S -WHEELESS(2) HH W IY1 L AH0 S -WHEELING W IY1 L IH0 NG -WHEELING(2) HH W IY1 L IH0 NG -WHEELING'S W IY1 L IH0 NG Z -WHEELING'S(2) HH W IY1 L IH0 NG Z -WHEELIS W IY1 L IH0 S -WHEELIS(2) HH W IY1 L IH0 S 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Z -WHENCE W EH1 N S -WHENCE(2) HH W EH1 N S -WHENEVER W EH0 N EH1 V ER0 -WHENEVER(2) HH W EH0 N EH1 V ER0 -WHERE W EH1 R -WHERE(2) HH W EH1 R -WHERE'D W EH1 R D -WHERE'D(2) HH W EH1 R D -WHERE'RE W EH1 R ER0 -WHERE'S W EH1 R Z -WHERE'S(2) HH W EH1 R Z -WHERE'VE W EH1 R AH0 V -WHEREABOUTS W EH1 R AH0 B AW2 T S -WHEREABOUTS(2) HH W EH1 R AH0 B AW2 T S -WHEREAS W EH0 R AE1 Z -WHEREAS(2) HH W EH0 R AE1 Z -WHEREBY W EH0 R B AY1 -WHEREBY(2) HH W EH0 R B AY1 -WHEREFORE W EH0 R F AO1 R -WHEREIN W EH0 R IH1 N -WHEREIN(2) HH W EH0 R IH1 N -WHEREUPON W EH1 R AH0 P AA1 N -WHEREUPON(2) HH W EH1 R AH0 P AA1 N -WHEREVER W EH0 R EH1 V ER0 -WHEREVER(2) HH W EH0 R EH1 V ER0 -WHEREWITHAL W EH1 R W IH0 DH AO2 L -WHEREWITHAL(2) HH W EH1 R W IH0 DH AO2 L -WHERLEY W ER1 L IY0 -WHERLEY(2) HH W ER1 L IY0 -WHERRY W EH1 R IY0 -WHERRY(2) HH W EH1 R IY0 -WHET W EH1 T -WHET(2) HH W EH1 T -WHETHER W EH1 DH ER0 -WHETHER(2) HH W EH1 DH ER0 -WHETSEL W EH1 T S AH0 L -WHETSEL(2) HH W EH1 T S AH0 L -WHETSELL W EH1 T S AH0 L -WHETSELL(2) HH W EH1 T S AH0 L -WHETSTINE W EH1 T S T IY0 N -WHETSTINE(2) HH W EH1 T S T IY0 N -WHETSTONE W EH1 T S T OW2 N -WHETSTONE(2) HH W EH1 T S T OW2 N -WHETTED W EH1 T IH0 D -WHETTED(2) HH W EH1 T IH0 D -WHETZEL W EH1 T Z AH0 L -WHETZEL(2) HH W EH1 T Z AH0 L -WHEW W UW1 -WHEW(2) HH W UW1 -WHEW(3) HH Y UW1 -WHEY W EY1 -WHEY(2) HH W EY1 -WHICH W IH1 CH -WHICH(2) HH W IH1 CH -WHICH'RE W IH1 CH ER0 -WHICH'RE(2) HH W IH1 CH ER0 -WHICH'S W IH1 CH IH0 Z -WHICH'S(2) HH W IH1 CH IH0 Z -WHICHARD W IH1 CH ER0 D -WHICHARD(2) HH W IH1 CH ER0 D -WHICHEVER W IH0 CH EH1 V ER0 -WHICHEVER(2) HH W IH0 CH EH1 V ER0 -WHICKER W IH1 K ER0 -WHICKER(2) HH W IH1 K ER0 -WHIDBY W IH1 D B IY0 -WHIDBY(2) HH W IH1 D B IY0 -WHIDDEN W IH1 D AH0 N -WHIDDEN(2) HH W IH1 D AH0 N -WHIDDON W IH1 D AH0 N -WHIDDON(2) HH W IH1 D AH0 N -WHIFF W IH1 F -WHIFF(2) HH W IH1 F -WHIG W IH1 G -WHIG(2) HH W IH1 G -WHIGHAM W IH1 G AH0 M -WHIGHAM(2) HH W IH1 G AH0 M -WHIGS W IH1 G Z -WHIGS(2) HH W IH1 G Z -WHILDEN W AY1 L D AH0 N -WHILDEN(2) HH W AY1 L D AH0 N -WHILE W AY1 L -WHILE(2) HH W AY1 L -WHILES W AY1 L Z -WHILES(2) HH W AY1 L Z -WHILST W AY1 L S T -WHIM W IH1 M -WHIM(2) HH W IH1 M -WHIMPER W IH1 M P ER0 -WHIMPER(2) HH W IH1 M P ER0 -WHIMPERING W IH1 M P ER0 IH0 NG -WHIMPERING(2) HH W IH1 M P ER0 IH0 NG -WHIMS W IH1 M Z -WHIMS(2) HH W IH1 M Z -WHIMSICAL W IH1 M Z IH0 K AH0 L -WHIMSICAL(2) HH W IH1 M Z IH0 K AH0 L -WHIMSICALLY W IH1 M Z IH0 K L IY0 -WHIMSICALLY(2) HH W IH1 M Z IH0 K L IY0 -WHIMSY W IH1 M S IY0 -WHIMSY(2) HH W IH1 M S IY0 -WHINE W AY1 N -WHINE(2) HH W AY1 N -WHINER W AY1 N ER0 -WHINER(2) HH W AY1 N ER0 -WHINERS W AY1 N ER0 Z -WHINERS(2) HH W AY1 N ER0 Z -WHINERY W AY1 N ER0 IY0 -WHINERY(2) HH W AY1 N ER0 IY0 -WHINES W AY1 N Z -WHINES(2) HH W AY1 N Z -WHINING W AY1 N IH0 NG -WHINING(2) HH W AY1 N IH0 NG -WHINNERY W IH1 N ER0 IY0 -WHINNERY(2) HH W IH1 N ER0 IY0 -WHINNEY W IH1 N IY0 -WHINNEY(2) HH W IH1 N IY0 -WHINY W AY1 N IY0 -WHINY(2) HH W AY1 N IY0 -WHIP W IH1 P -WHIP(2) HH W IH1 P -WHIPKEY W IH1 P K IY2 -WHIPKEY(2) HH W IH1 P K IY2 -WHIPLASH W IH1 P L AE2 SH -WHIPLASH(2) HH W IH1 P L AE2 SH -WHIPLASHES W IH1 P L AE2 SH AH0 Z -WHIPLASHES(2) HH W IH1 P L AE2 SH AH0 Z -WHIPP W IH1 P -WHIPP(2) HH W IH1 P -WHIPPANY W IH1 P AH0 N IY0 -WHIPPANY(2) HH W IH1 P AH0 N IY0 -WHIPPED W IH1 P T -WHIPPED(2) HH W IH1 P T -WHIPPING W IH1 P IH0 NG -WHIPPING(2) HH W IH1 P IH0 NG -WHIPPLE W IH1 P AH0 L -WHIPPLE(2) HH W IH1 P AH0 L -WHIPPLE'S W IH1 P AH0 L Z -WHIPPLE'S(2) HH W IH1 P AH0 L Z -WHIPPOORWILLS W IH1 P ER0 W IH2 L Z -WHIPPOORWILLS(2) HH W IH1 P ER0 W IH2 L Z -WHIPPS W IH1 P S -WHIPPS(2) HH W IH1 P S -WHIPS W IH1 P S -WHIPS(2) HH W IH1 P S -WHIPSAW W IH1 P S AO2 -WHIPSAW(2) HH W IH1 P S AO2 -WHIPSAWED W IH1 P S AO2 D -WHIPSAWED(2) HH W IH1 P S AO2 D -WHIPSAWING W IH1 P S AO2 IH0 NG -WHIPSAWING(2) HH W IH1 P S AO2 IH0 NG -WHIRL W ER1 L -WHIRL(2) HH W ER1 L -WHIRLED W ER1 L D -WHIRLED(2) HH W ER1 L D -WHIRLEY W ER1 L IY0 -WHIRLEY(2) HH W ER1 L IY0 -WHIRLING W ER1 L IH0 NG 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-WHISLER W IH1 S L ER0 -WHISLER(2) HH W IH1 S L ER0 -WHISMAN W IH1 S M AH0 N -WHISMAN(2) HH W IH1 S M AH0 N -WHISNANT W IH1 S N AH0 N T -WHISNANT(2) HH W IH1 S N AH0 N T -WHISNER W IH1 S N ER0 -WHISNER(2) HH W IH1 S N ER0 -WHISONANT W IH1 S AH0 N AH0 N T -WHISPER W IH1 S P ER0 -WHISPER(2) HH W IH1 S P ER0 -WHISPERED W IH1 S P ER0 D -WHISPERED(2) HH W IH1 S P ER0 D -WHISPERER W IH1 S P AH0 R ER2 -WHISPERER'S W IH1 S P AH0 R ER2 Z -WHISPERERS W IH1 S P AH0 R ER2 Z -WHISPERING W IH1 S P ER0 IH0 NG -WHISPERING(2) HH W IH1 S P ER0 IH0 NG -WHISPERS W IH1 S P ER0 Z -WHISPERS(2) HH W IH1 S P ER0 Z -WHISTLE W IH1 S AH0 L -WHISTLE(2) HH W IH1 S AH0 L -WHISTLEBLOWER W IH1 S AH0 L B L OW2 ER0 -WHISTLEBLOWER(2) HH W IH1 S AH0 L B L OW2 ER0 -WHISTLEBLOWERS W IH1 S AH0 L B L OW2 ER0 Z -WHISTLEBLOWERS(2) HH W IH1 S AH0 L B L OW2 ER0 Z -WHISTLED W IH1 S AH0 L D -WHISTLED(2) HH W IH1 S AH0 L D -WHISTLER W IH1 S L ER0 -WHISTLER(2) HH W IH1 S L ER0 -WHISTLERS W IH1 S L ER0 Z -WHISTLERS(2) HH W IH1 S L ER0 Z -WHISTLES W IH1 S AH0 L Z -WHISTLES(2) HH W IH1 S AH0 L Z -WHISTLING W IH1 S L IH0 NG -WHISTLING(2) HH W IH1 S L IH0 NG -WHISTON W IH1 S T AH0 N -WHISTON(2) HH W IH1 S T AH0 N -WHIT W IH1 T -WHIT(2) HH W IH1 T -WHITACRE W IH1 T AH0 K ER0 -WHITACRE(2) HH W IH1 T AH0 K ER0 -WHITAKER W IH1 T AH0 K ER0 -WHITAKER(2) HH W IH1 T AH0 K ER0 -WHITBECK W IH1 T B EH2 K -WHITBECK(2) HH W IH1 T B EH2 K -WHITBREAD W IH1 T B R EH2 D -WHITBREAD(2) HH W IH1 T B R EH2 D -WHITBY W IH1 T B IY0 -WHITBY(2) HH W IH1 T B IY0 -WHITCHER W IH1 CH ER0 -WHITCHER(2) HH W IH1 CH ER0 -WHITCHURCH W IH1 T CH ER2 CH -WHITCHURCH(2) HH W IH1 T CH ER2 CH -WHITCOMB W IH1 T K AH0 M -WHITCOMB(2) HH W IH1 T K AH0 M -WHITCRAFT W IH1 T K R AE2 F T -WHITCRAFT(2) HH W IH1 T K R AE2 F T -WHITE W AY1 T -WHITE(2) HH W AY1 T -WHITE'S W AY1 T S -WHITE'S(2) HH W AY1 T S -WHITEAKER W IH1 T AH0 K ER0 -WHITEAKER(2) HH W IH1 T AH0 K ER0 -WHITEBREAD W AY1 T B R EH2 D -WHITEBREAD(2) HH W AY1 T B R EH2 D -WHITECOTTON W AY1 T K AA2 T AH0 N -WHITECOTTON(2) HH W AY1 T K AA2 T AH0 N -WHITED W AY1 T IH0 D -WHITED(2) HH W AY1 T IH0 D -WHITEFIELD W AY1 T F IY2 L D -WHITEFIELD(2) HH W AY1 T F IY2 L D -WHITEFISH W AY1 T F IH2 SH -WHITEFISH(2) HH W AY1 T F IH2 SH -WHITEFORD W AY1 T F AO0 R D -WHITEFORD(2) HH W AY1 T F AO0 R D -WHITEHAIR W AY1 T HH EH1 R -WHITEHAIR(2) HH W AY1 T HH EH1 R -WHITEHALL W AY1 T HH AO2 L -WHITEHALL(2) HH W AY1 T HH AO2 L -WHITEHALL'S W AY1 T HH AO2 L Z -WHITEHALL'S(2) HH W AY1 T HH AO2 L Z -WHITEHEAD W AY1 T HH EH2 D -WHITEHEAD(2) HH W AY1 T HH EH2 D -WHITEHEAD'S W AY1 T HH EH2 D Z -WHITEHEAD'S(2) HH W AY1 T HH EH2 D Z -WHITEHILL W AY1 T HH IH2 L -WHITEHILL(2) HH W AY1 T HH IH2 L -WHITEHORN W AY1 T HH AO2 R N -WHITEHORN(2) HH W AY1 T HH AO2 R N -WHITEHORSE W AY1 T HH AO2 R S -WHITEHORSE(2) HH W AY1 T HH AO2 R S -WHITEHOUSE W AY1 T HH AW2 S -WHITEHOUSE(2) HH W AY1 T HH AW2 S -WHITEHURST W AY1 T HH ER2 S T -WHITEHURST(2) HH W AY1 T HH ER2 S T -WHITEHURST'S W AY1 T HH ER2 S T S -WHITELAW W AY1 T L AO2 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HH W IH1 TH AH0 M -WHITHAM(3) HH W IH1 T AH0 M -WHITHER W IH1 DH ER0 -WHITHER(2) HH W IH1 DH ER0 -WHITING W AY1 T IH0 NG -WHITING(2) HH W AY1 T IH0 NG -WHITINGS W AY1 T IH0 NG Z -WHITINGS(2) HH W AY1 T IH0 NG Z -WHITIS W AY1 T IH0 S -WHITIS(2) HH W AY1 T IH0 S -WHITISH W AY1 T IH0 SH -WHITISH(2) HH W AY1 T IH0 SH -WHITLATCH W IH1 T L AE2 CH -WHITLATCH(2) HH W IH1 T L AE2 CH -WHITLEDGE W IH1 T L EH2 JH -WHITLEDGE(2) HH W IH1 T L EH2 JH -WHITLEY W IH1 T L IY0 -WHITLEY(2) HH W IH1 T L IY0 -WHITLING W IH1 T L IH0 NG -WHITLING(2) HH W IH1 T L IH0 NG -WHITLOCK W IH1 T L AA2 K -WHITLOCK(2) HH W IH1 T L AA2 K -WHITLOW W IH1 T L OW2 -WHITLOW(2) HH W IH1 T L OW2 -WHITLY W IH1 T L IY0 -WHITLY(2) HH W IH1 T L IY0 -WHITMAN W IH1 T M AH0 N -WHITMAN(2) HH W IH1 T M AH0 N -WHITMAN'S W IH1 T M AH0 N Z -WHITMAN'S(2) HH W IH1 T M AH0 N Z -WHITMARSH W IH1 T M AA2 R SH -WHITMARSH(2) HH W IH1 T M AA2 R SH -WHITMER W IH1 T M ER0 -WHITMER(2) HH W IH1 T M ER0 -WHITMILL W IH1 T M IH2 L -WHITMILL(2) HH W IH1 T M IH2 L -WHITMIRE W IH1 T M AY2 R -WHITMIRE(2) HH W IH1 T M AY2 R -WHITMORE W IH1 T M AO0 R -WHITMORE(2) HH W IH1 T M AO0 R -WHITMOYER W IH1 T M OY2 ER0 -WHITMOYER(2) HH W IH1 T M OY2 ER0 -WHITMYER W IH1 T M AY2 ER0 -WHITMYER(2) HH W IH1 T M AY2 ER0 -WHITNER W IH1 T N ER0 -WHITNER(2) HH W IH1 T N ER0 -WHITNEY W IH1 T N IY0 -WHITNEY(2) HH W IH1 T N IY0 -WHITNEY'S W IH1 T N IY0 Z -WHITNEY'S(2) HH W IH1 T N IY0 Z -WHITON W IH1 T AH0 N -WHITON(2) HH W IH1 T AH0 N -WHITROW W IH1 T R OW2 -WHITROW(2) HH W IH1 T R OW2 -WHITSEL W IH1 T S AH0 L -WHITSEL(2) HH W IH1 T S AH0 L -WHITSELL W IH1 T S AH0 L -WHITSELL(2) HH W IH1 T S AH0 L -WHITSETT W IH1 T S IH0 T -WHITSETT(2) HH W IH1 T S IH0 T -WHITSITT W IH1 T S IH0 T -WHITSITT(2) HH W IH1 T S IH0 T -WHITSON W IH1 T S AH0 N -WHITSON(2) HH W IH1 T S AH0 N -WHITT W IH1 T -WHITT(2) HH W IH1 T -WHITTAKER W IH1 T AH0 K ER0 -WHITTAKER(2) HH W IH1 T AH0 K ER0 -WHITTED W IH1 T IH0 D -WHITTED(2) HH W IH1 T IH0 D -WHITTEMORE W IH1 T M AO0 R -WHITTEMORE(2) HH W IH1 T M AO0 R -WHITTEN W IH1 T AH0 N -WHITTEN(2) HH W IH1 T AH0 N -WHITTENBERG W IH1 T AH0 N B ER0 G -WHITTENBERG(2) HH W IH1 T AH0 N B ER0 G -WHITTENBURG W IH1 T AH0 N B ER0 G -WHITTENBURG(2) HH W IH1 T AH0 N B ER0 G -WHITTET W IH1 T IH0 T -WHITTET(2) HH W IH1 T IH0 T -WHITTIER W IH1 T IY0 ER0 -WHITTIER(2) HH W IH1 T IY0 ER0 -WHITTING W IH1 T IH0 NG -WHITTING(2) HH W IH1 T IH0 NG -WHITTINGHAM W IH1 T IH0 NG HH AE2 M -WHITTINGHAM(2) HH W IH1 T IH0 NG HH AE2 M -WHITTINGHILL W IH1 T IH0 NG HH IH2 L -WHITTINGHILL(2) HH W IH1 T IH0 NG HH IH2 L -WHITTINGTON W IH1 T IH0 NG T AH0 N -WHITTINGTON(2) HH W IH1 T IH0 NG T AH0 N -WHITTLE W IH1 T AH0 L -WHITTLE(2) HH W IH1 T AH0 L -WHITTLE'S W IH1 T AH0 L Z -WHITTLE'S(2) HH W IH1 T AH0 L Z -WHITTLED W IH1 T AH0 L D -WHITTLED(2) HH W IH1 T AH0 L D -WHITTLESEY W IH1 T AH0 L S IY0 -WHITTLESEY(2) HH W IH1 T AH0 L S IY0 -WHITTLING W IH1 T L IH0 NG -WHITTLING(2) HH W IH1 T L IH0 NG -WHITTON W IH1 T AH0 N -WHITTON(2) HH W IH1 T AH0 N -WHITTY W IH1 T IY0 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AO1 R Z -WHORL W ER1 L -WHORL(2) W AO1 R L -WHORL(3) HH W AO1 R L -WHORLEY HH AO1 R L IY0 -WHORLEY(2) W AO1 R L IY0 -WHORLEY(3) HH W AO1 R L IY0 -WHORLS W ER1 L Z -WHORLS(2) W AO1 R L Z -WHORLS(3) HH W AO1 R L Z -WHORTON HH AO1 R T AH0 N -WHOSE HH UW1 Z -WHOSOEVER HH UW2 S OW0 EH1 V ER0 -WHY W AY1 -WHY(2) HH W AY1 -WHY'D W AY1 D -WHY'D(2) HH W AY1 D -WHY'RE W AY1 ER0 -WHY'S W AY1 Z -WHY'S(2) HH W AY1 Z -WHYS W AY1 Z -WHYS(2) HH W AY1 Z -WHYTE W AY1 T -WHYTE(2) HH W AY1 T -WI-FI W AY1 F AY2 -WIACEK V AY1 AH0 CH EH2 K -WIAN W AY1 AH0 N -WIANT W AY1 AH0 N T -WIARD W AY1 AA0 R D -WIATER W AY1 AH0 T ER0 -WIATROWSKI V IY0 AH0 T R AO1 F S K IY2 -WIATT W AY1 AH0 T -WIBBELSMAN W IH1 B AH0 L Z M AH0 N -WIBBENMEYER W IH1 B IH0 N M AY2 ER0 -WIBERG W AY1 B ER0 G -WIBLE W AY1 B AH0 L -WICAT W IH1 K AE0 T -WICCA W IH1 K AH0 -WICCAN W IH1 K AH0 N -WICE W AY1 S -WICH W IH1 CH -WICHERN W IH1 K ER0 N -WICHERT W IH1 CH ER0 T -WICHITA W IH1 CH AH0 T AO2 -WICHITA'S W IH1 CH AH0 T AO2 Z -WICHMAN W IH1 CH M 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M D -ZOOMING Z UW1 M IH0 NG -ZOOMS Z UW1 M Z -ZOOS Z UW1 Z -ZOOT-SUIT Z UW1 T S UW1 T -ZOPFI Z OW1 P F IY0 -ZORA Z AO1 R AH0 -ZORAH Z AO1 R AH0 -ZORAN Z AO1 R AH0 N -ZORANA Z AO0 R AE1 N AH0 -ZORICH Z AO1 R IH0 K -ZORINA Z AO0 R IY1 N AH0 -ZORINE Z AO0 R IY1 N IY0 -ZORINSKY Z AO0 R IH1 N S K IY0 -ZORKIN Z AO1 R K IH0 N -ZORN Z AO1 R N -ZORNES Z AO1 R N Z -ZORNOW Z AO1 R N AW0 -ZORRO Z AO1 R OW2 -ZORTMAN Z AO1 R T M AH0 N -ZORZ Z AO1 R Z -ZORZI Z AO1 R Z IY0 -ZOSEN Z OW1 Z AH0 N -ZOSS Z AO1 S -ZOSTER Z AA1 S T ER0 -ZOTOS Z OW1 T OW0 Z -ZOTTER Z AA1 T ER0 -ZOUABRI Z UW2 W AA1 B R IY2 -ZOUCHA Z UW1 HH AH0 -ZOUNDS Z AW1 N D Z -ZOVIRAX Z OW1 V IH0 R AE0 K S -ZOVUT Z OW1 V AH0 T -ZSA ZH AA1 -ZSA-ZSA ZH AA1 ZH AA2 -ZSAZSA ZH AA1 ZH AA2 -ZSCHAU ZH AW1 -ZUBA Z UW1 B AH0 -ZUBE Z UW1 B -ZUBER Z UW1 B ER0 -ZUBIA Z UW1 B IY0 AH0 -ZUBILLAGA Z UW2 B IH0 L AA1 G AH0 -ZUBIN Z UW1 B IH0 N -ZUBOFF Z UW1 B AO0 F -ZUBROD Z AH1 B R AH0 D -ZUCCARELLI Z UW0 K ER0 EH1 L IY0 -ZUCCARO Z UW0 K AA1 R OW0 -ZUCCHINI Z UW0 K IY1 N IY0 -ZUCCHINIS Z UW0 K IY1 N IY0 Z -ZUCCO Z UW1 K OW0 -ZUCH Z AH1 CH -ZUCHOWSKI Z AH0 HH AO1 F S K IY0 -ZUCHOWSKI(2) Z UW0 K AO1 F S K IY0 -ZUCK Z AH1 K -ZUCKER Z AH1 K ER0 -ZUCKER(2) Z UW1 K ER0 -ZUCKER'S Z AH1 K ER0 Z -ZUCKER'S(2) Z UW1 K ER0 Z -ZUCKERBERG Z AH1 K ER0 B ER2 G -ZUCKERBERG'S Z AH1 K ER0 B ER2 G Z -ZUCKERMAN Z AH1 K ER0 M AH0 N -ZUCKERMAN(2) Z UW1 K ER0 M AH0 N -ZUCKERMAN'S Z AH1 K ER0 M AH0 N Z -ZUCKERMAN'S(2) Z UW1 K ER0 M AH0 N Z -ZUE Z UW1 -ZUE'S Z UW1 Z -ZUEGE Z UW1 JH -ZUEHLKE Z UH1 L K -ZUELKE Z UW1 L K -ZUERCHER Z UH1 R K ER0 -ZUFALL Z UW1 F AH0 L -ZUFELT Z AH1 F IH0 L T -ZUG Z AH1 G -ZUG'S Z AH1 G Z -ZUGANOV Z UW1 G AH0 N AO2 V -ZUGANOV(2) Z UW1 G AH0 N AO2 F -ZUGANOV'S Z UW1 G AH0 N AO2 V S -ZUGANOV'S(2) Z UW1 G AH0 N AO2 F S -ZUHLKE Z UW1 L K -ZUIDEMA Z UW0 IH0 D EH1 M AH0 -ZUK Z AH1 K -ZUKAS Z UW1 K AH0 Z -ZUKER Z UW1 K ER0 -ZUKERMAN Z UW1 K ER0 M AH0 N -ZUKIN Z UW1 K IH0 N -ZUKOWSKI Z AH0 K AO1 F S K IY0 -ZULAUF Z UW1 L AW0 F -ZULEGER Z UW1 L IH0 G ER0 -ZULFIKAR Z AH2 L F IH0 K AA1 R -ZULLI Z UW1 L IY0 -ZULLO Z UW1 L OW0 -ZULMA Z UH1 L M AH0 -ZULU Z UW1 L UW2 -ZULUAGA Z UW2 L UW0 AA1 G AH0 -ZULUS Z UW1 L UW0 Z -ZUMBRO Z AH1 M B R OW0 -ZUMBRUN Z AH1 M B R AH0 N -ZUMEL Z UW2 M EH1 L -ZUMMO Z UW1 M OW0 -ZUMPANO Z UW0 M P AA1 N OW0 -ZUMSTEIN Z AH1 M S T IY2 N -ZUMSTEIN(2) Z AH1 M S T AY2 N -ZUMWALT Z AH1 M W AH0 L T -ZUNDEL Z AH1 N D AH0 L -ZUNGU Z AH2 NG UW1 -ZUNI Z UW1 N IY2 -ZUNIGA Z UW0 N IY1 G AH0 -ZUNINO Z UW0 N IY1 N OW0 -ZUNKER Z AH1 NG K ER0 -ZUPAN Z UW1 P AH0 N -ZUPANCIC Z AH0 P AE1 NG K IH0 K -ZUPANJA Z AH0 P AE1 N JH AH0 -ZUPKO Z AH1 P K OW0 -ZURAWSKI Z ER0 AA1 F S K IY0 -ZURCHER Z ER1 K ER0 -ZUREK Z UH1 R EH0 K -ZURFLUH Z ER1 F L UW0 -ZURI Z UH1 R IY0 -ZURICH Z UH1 R IH0 K -ZURICH'S Z UH1 R IH0 K S -ZURITA Z ER0 AY1 T AH0 -ZURKUHLEN Z ER0 K Y UW1 L AH0 N -ZURN Z ER1 N -ZUROWSKI Z UW2 R OW1 S K IY2 -ZURVE Z ER1 V -ZUVER Z UW1 V ER0 -ZUZANA Z UW2 Z AA1 N AH0 -ZUZU Z UW1 Z UW2 -ZVORNIK Z V AO1 R N IH0 K -ZWACK Z W AO1 K -ZWAHLEN Z W AA1 L AH0 N -ZWART Z W AO1 R T -ZWEBER Z W IY1 B ER0 -ZWEIBEL Z W AY1 B AH0 L -ZWEIFEL Z W AY1 F AH0 L -ZWEIG Z W AY1 G -ZWERDLING Z W ER1 D L IH0 NG -ZWERDLING'S Z W ER1 D L IH0 NG Z -ZWETCHKENBAUM Z W EH1 CH K AH0 N B AA0 M -ZWICK Z W IH1 K -ZWICKER Z W IH1 K ER0 -ZWICKY Z W IH1 K IY0 -ZWIEBEL Z W IY1 B AH0 L -ZWIEFELHOFER Z W IY1 F AH0 L HH AA2 F ER0 -ZWIEG Z W IY1 G -ZWILLING Z W IH1 L IH0 NG -ZWOLINSKI Z V AH0 L IH1 N S K IY0 -ZYCAD Z IH1 K AE2 D -ZYCH Z AY1 CH -ZYCHER Z IH1 K ER0 -ZYDECO Z AY1 D AH0 K OW2 -ZYGMUNT Z IH1 G M AH0 N T -ZYGOTE Z AY1 G OW0 T -ZYLA Z IH1 L AH0 -ZYLKA Z IH1 L K AH0 -ZYLSTRA Z IH1 L S T R AH0 -ZYMAN Z AY1 M AH0 N -ZYNDA Z IH1 N D AH0 -ZYSK Z AY1 S K -ZYSKOWSKI Z IH0 S K AO1 F S K IY0 -ZYUGANOV Z Y UW1 G AA0 N AA0 V -ZYUGANOV(2) Z UW1 G AA0 N AA0 V -ZYUGANOV'S Z Y UW1 G AA0 N AA0 V Z -ZYUGANOV'S(2) Z UW1 G AA0 N AA0 V Z -ZYWICKI Z IH0 W IH1 K IY0 diff --git a/text/engdict-hot.rep b/text/engdict-hot.rep deleted file mode 100644 index 22d4cd289d983755cb3e44fb3eab413d25b8b522..0000000000000000000000000000000000000000 --- a/text/engdict-hot.rep +++ /dev/null @@ -1 +0,0 @@ -CHATGPT CH AE1 T JH IY1 P IY1 T IY1 \ No newline at end of file diff --git a/text/engdict_cache.pickle b/text/engdict_cache.pickle deleted file mode 100644 index 337f12deeba794930bec929bad2c47ceff7fb6d7..0000000000000000000000000000000000000000 --- a/text/engdict_cache.pickle +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:36ea4c34f2ca534437a39db0fc4092efa0595093d3587bafc33fc50468ef53f9 -size 6530139 diff --git a/text/english.py b/text/english.py deleted file mode 100644 index 0a5d6c21dc64ffe2246d94692c583de353b63771..0000000000000000000000000000000000000000 --- a/text/english.py +++ /dev/null @@ -1,234 +0,0 @@ -import pickle -import os -import re -from g2p_en import G2p - -from string import punctuation - -from text import symbols - -current_file_path = os.path.dirname(__file__) -CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep") -CMU_DICT_FAST_PATH = os.path.join(current_file_path, "cmudict-fast.rep") -CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep") -CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle") -_g2p = G2p() - -arpa = { - "AH0", - "S", - "AH1", - "EY2", - "AE2", - "EH0", - "OW2", - "UH0", - "NG", - "B", - "G", - "AY0", - "M", - "AA0", - "F", - "AO0", - "ER2", - "UH1", - "IY1", - "AH2", - "DH", - "IY0", - "EY1", - "IH0", - "K", - "N", - "W", - "IY2", - "T", - "AA1", - "ER1", - "EH2", - "OY0", - "UH2", - "UW1", - "Z", - "AW2", - "AW1", - "V", - "UW2", - "AA2", - "ER", - "AW0", - "UW0", - "R", - "OW1", - "EH1", - "ZH", - "AE0", - "IH2", - "IH", - "Y", - "JH", - "P", - "AY1", - "EY0", - "OY2", - "TH", - "HH", - "D", - "ER0", - "CH", - "AO1", - "AE1", - "AO2", - "OY1", - "AY2", - "IH1", - "OW0", - "L", - "SH", -} - - -def replace_phs(phs): - rep_map = {";": ",", ":": ",", "'": "-", '"': "-"} - phs_new = [] - for ph in phs: - if ph in symbols: - phs_new.append(ph) - elif ph in rep_map.keys(): - phs_new.append(rep_map[ph]) - else: - print("ph not in symbols: ", ph) - return phs_new - - -def read_dict(): - g2p_dict = {} - start_line = 49 - with open(CMU_DICT_PATH) as f: - line = f.readline() - line_index = 1 - while line: - if line_index >= start_line: - line = line.strip() - word_split = line.split(" ") - word = word_split[0] - - syllable_split = word_split[1].split(" - ") - g2p_dict[word] = [] - for syllable in syllable_split: - phone_split = syllable.split(" ") - g2p_dict[word].append(phone_split) - - line_index = line_index + 1 - line = f.readline() - - return g2p_dict - - -def read_dict_new(): - g2p_dict = {} - with open(CMU_DICT_PATH) as f: - line = f.readline() - line_index = 1 - while line: - if line_index >= 49: - line = line.strip() - word_split = line.split(" ") - word = word_split[0] - - syllable_split = word_split[1].split(" - ") - g2p_dict[word] = [] - for syllable in syllable_split: - phone_split = syllable.split(" ") - g2p_dict[word].append(phone_split) - - line_index = line_index + 1 - line = f.readline() - - with open(CMU_DICT_FAST_PATH) as f: - line = f.readline() - line_index = 1 - while line: - if line_index >= 0: - line = line.strip() - word_split = line.split(" ") - word = word_split[0] - if word not in g2p_dict: - g2p_dict[word] = [] - g2p_dict[word].append(word_split[1:]) - - line_index = line_index + 1 - line = f.readline() - - with open(CMU_DICT_HOT_PATH) as f: - line = f.readline() - line_index = 1 - while line: - if line_index >= 0: - line = line.strip() - word_split = line.split(" ") - word = word_split[0] - if word not in g2p_dict: - g2p_dict[word] = [] - g2p_dict[word].append(word_split[1:]) - - line_index = line_index + 1 - line = f.readline() - - return g2p_dict - - -def cache_dict(g2p_dict, file_path): - with open(file_path, "wb") as pickle_file: - pickle.dump(g2p_dict, pickle_file) - - -def get_dict(): - if os.path.exists(CACHE_PATH): - with open(CACHE_PATH, "rb") as pickle_file: - g2p_dict = pickle.load(pickle_file) - else: - g2p_dict = read_dict_new() - cache_dict(g2p_dict, CACHE_PATH) - - return g2p_dict - - -eng_dict = get_dict() - - -def text_normalize(text): - # todo: eng text normalize - return text.replace(";", ",") - - -def g2p(text): - phones = [] - words = re.split(r"([,;.\-\?\!\s+])", text) - for w in words: - if w.upper() in eng_dict: - phns = eng_dict[w.upper()] - for ph in phns: - phones += ph - else: - phone_list = list(filter(lambda p: p != " ", _g2p(w))) - for ph in phone_list: - if ph in arpa: - phones.append(ph) - else: - phones.append(ph) - - return replace_phs(phones) - - -if __name__ == "__main__": - # print(get_dict()) - print(g2p("hello")) - print(g2p("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")) - # all_phones = set() - # for k, syllables in eng_dict.items(): - # for group in syllables: - # for ph in group: - # all_phones.add(ph) - # print(all_phones) diff --git a/text/japanese.py b/text/japanese.py deleted file mode 100644 index a571467c2295c2815dbb0024726d965d59081ac2..0000000000000000000000000000000000000000 --- a/text/japanese.py +++ /dev/null @@ -1,191 +0,0 @@ -# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py -import re -import sys - -import pyopenjtalk - - -from text import symbols -# Regular expression matching Japanese without punctuation marks: -_japanese_characters = re.compile( - r"[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]" -) - -# Regular expression matching non-Japanese characters or punctuation marks: -_japanese_marks = re.compile( - r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]" -) - -# List of (symbol, Japanese) pairs for marks: -_symbols_to_japanese = [(re.compile("%s" % x[0]), x[1]) for x in [("％", "パーセント")]] - - -# List of (consonant, sokuon) pairs: -_real_sokuon = [ - (re.compile("%s" % x[0]), x[1]) - for x in [ - (r"Q([↑↓]*[kg])", r"k#\1"), - (r"Q([↑↓]*[tdjʧ])", r"t#\1"), - (r"Q([↑↓]*[sʃ])", r"s\1"), - (r"Q([↑↓]*[pb])", r"p#\1"), - ] -] - -# List of (consonant, hatsuon) pairs: -_real_hatsuon = [ - (re.compile("%s" % x[0]), x[1]) - for x in [ - (r"N([↑↓]*[pbm])", r"m\1"), - (r"N([↑↓]*[ʧʥj])", r"n^\1"), - (r"N([↑↓]*[tdn])", r"n\1"), - (r"N([↑↓]*[kg])", r"ŋ\1"), - ] -] - - -def post_replace_ph(ph): - rep_map = { - "：": ",", - "；": ",", - "，": ",", - "。": ".", - "！": "!", - "？": "?", - "\n": ".", - "·": ",", - "、": ",", - "...": "…", - } - if ph in rep_map.keys(): - ph = rep_map[ph] - if ph in symbols: - return ph - if ph not in symbols: - ph = "UNK" - return ph - - -def symbols_to_japanese(text): - for regex, replacement in _symbols_to_japanese: - text = re.sub(regex, replacement, text) - return text - - -def preprocess_jap(text, with_prosody=False): - """Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html""" - text = symbols_to_japanese(text) - sentences = re.split(_japanese_marks, text) - marks = re.findall(_japanese_marks, text) - text = [] - for i, sentence in enumerate(sentences): - if re.match(_japanese_characters, sentence): - if with_prosody: - text += pyopenjtalk_g2p_prosody(sentence)[1:-1] - else: - p = pyopenjtalk.g2p(sentence) - text += p.split(" ") - - if i < len(marks): - if marks[i] == " ":# 防止意外的UNK - continue - text += [marks[i].replace(" ", "")] - return text - - -def text_normalize(text): - # todo: jap text normalize - return text - -# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py -def pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True): - """Extract phoneme + prosoody symbol sequence from input full-context labels. - - The algorithm is based on `Prosodic features control by symbols as input of - sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks. - - Args: - text (str): Input text. - drop_unvoiced_vowels (bool): whether to drop unvoiced vowels. - - Returns: - List[str]: List of phoneme + prosody symbols. - - Examples: - >>> from espnet2.text.phoneme_tokenizer import pyopenjtalk_g2p_prosody - >>> pyopenjtalk_g2p_prosody("こんにちは。") - ['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$'] - - .. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic - modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104 - - """ - labels = pyopenjtalk.make_label(pyopenjtalk.run_frontend(text)) - N = len(labels) - - phones = [] - for n in range(N): - lab_curr = labels[n] - - # current phoneme - p3 = re.search(r"\-(.*?)\+", lab_curr).group(1) - # deal unvoiced vowels as normal vowels - if drop_unvoiced_vowels and p3 in "AEIOU": - p3 = p3.lower() - - # deal with sil at the beginning and the end of text - if p3 == "sil": - assert n == 0 or n == N - 1 - if n == 0: - phones.append("^") - elif n == N - 1: - # check question form or not - e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr) - if e3 == 0: - phones.append("$") - elif e3 == 1: - phones.append("?") - continue - elif p3 == "pau": - phones.append("_") - continue - else: - phones.append(p3) - - # accent type and position info (forward or backward) - a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr) - a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr) - a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr) - - # number of mora in accent phrase - f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr) - - a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1]) - # accent phrase border - if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl": - phones.append("#") - # pitch falling - elif a1 == 0 and a2_next == a2 + 1 and a2 != f1: - phones.append("]") - # pitch rising - elif a2 == 1 and a2_next == 2: - phones.append("[") - - return phones - -# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py -def _numeric_feature_by_regex(regex, s): - match = re.search(regex, s) - if match is None: - return -50 - return int(match.group(1)) - -def g2p(norm_text, with_prosody=False): - phones = preprocess_jap(norm_text, with_prosody) - phones = [post_replace_ph(i) for i in phones] - # todo: implement tones and word2ph - return phones - - -if __name__ == "__main__": - phones = g2p("こんにちは, hello, AKITOです,よろしくお願いしますね！") - print(phones) \ No newline at end of file diff --git a/text/symbols.py b/text/symbols.py deleted file mode 100644 index 70499492c3a2fac668847b1317e6e213cb086ece..0000000000000000000000000000000000000000 --- a/text/symbols.py +++ /dev/null @@ -1,401 +0,0 @@ -import os - -# punctuation = ['!', '?', '…', ",", ".","@"]#@是SP停顿 -punctuation = ["!", "?", "…", ",", "."] # @是SP停顿 -punctuation.append("-") -pu_symbols = punctuation + ["SP", "SP2", "SP3", "UNK"] -# pu_symbols = punctuation + ["SP", 'SP2', 'SP3','SP4', "UNK"] -pad = "_" - -c = [ - "AA", - "EE", - "OO", - "b", - "c", - "ch", - "d", - "f", - "g", - "h", - "j", - "k", - "l", - "m", - "n", - "p", - "q", - "r", - "s", - "sh", - "t", - "w", - "x", - "y", - "z", - "zh", -] -v = [ - "E1", - "En1", - "a1", - "ai1", - "an1", - "ang1", - "ao1", - "e1", - "ei1", - "en1", - "eng1", - "er1", - "i1", - "i01", - "ia1", - "ian1", - "iang1", - "iao1", - "ie1", - "in1", - "ing1", - "iong1", - "ir1", - "iu1", - "o1", - "ong1", - "ou1", - "u1", - "ua1", - "uai1", - "uan1", - "uang1", - "ui1", - "un1", - "uo1", - "v1", - "van1", - "ve1", - "vn1", - "E2", - "En2", - "a2", - "ai2", - "an2", - "ang2", - "ao2", - "e2", - "ei2", - "en2", - "eng2", - "er2", - "i2", - "i02", - "ia2", - "ian2", - "iang2", - "iao2", - "ie2", - "in2", - "ing2", - "iong2", - "ir2", - "iu2", - "o2", - "ong2", - "ou2", - "u2", - "ua2", - "uai2", - "uan2", - "uang2", - "ui2", - "un2", - "uo2", - "v2", - "van2", - "ve2", - "vn2", - "E3", - "En3", - "a3", - "ai3", - "an3", - "ang3", - "ao3", - "e3", - "ei3", - "en3", - "eng3", - "er3", - "i3", - "i03", - "ia3", - "ian3", - "iang3", - "iao3", - "ie3", - "in3", - "ing3", - "iong3", - "ir3", - "iu3", - "o3", - "ong3", - "ou3", - "u3", - "ua3", - "uai3", - "uan3", - "uang3", - "ui3", - "un3", - "uo3", - "v3", - "van3", - "ve3", - "vn3", - "E4", - "En4", - "a4", - "ai4", - "an4", - "ang4", - "ao4", - "e4", - "ei4", - "en4", - "eng4", - "er4", - "i4", - "i04", - "ia4", - "ian4", - "iang4", - "iao4", - "ie4", - "in4", - "ing4", - "iong4", - "ir4", - "iu4", - "o4", - "ong4", - "ou4", - "u4", - "ua4", - "uai4", - "uan4", - "uang4", - "ui4", - "un4", - "uo4", - "v4", - "van4", - "ve4", - "vn4", - "E5", - "En5", - "a5", - "ai5", - "an5", - "ang5", - "ao5", - "e5", - "ei5", - "en5", - "eng5", - "er5", - "i5", - "i05", - "ia5", - "ian5", - "iang5", - "iao5", - "ie5", - "in5", - "ing5", - "iong5", - "ir5", - "iu5", - "o5", - "ong5", - "ou5", - "u5", - "ua5", - "uai5", - "uan5", - "uang5", - "ui5", - "un5", - "uo5", - "v5", - "van5", - "ve5", - "vn5", -] - -v_without_tone = [ - "E", - "En", - "a", - "ai", - "an", - "ang", - "ao", - "e", - "ei", - "en", - "eng", - "er", - "i", - "i0", - "ia", - "ian", - "iang", - "iao", - "ie", - "in", - "ing", - "iong", - "ir", - "iu", - "o", - "ong", - "ou", - "u", - "ua", - "uai", - "uan", - "uang", - "ui", - "un", - "uo", - "v", - "van", - "ve", - "vn", -] - -# japanese -ja_symbols = [ - "I", - "N", - "U", - "a", - "b", - "by", - "ch", - "cl", - "d", - "dy", - "e", - "f", - "g", - "gy", - "h", - "hy", - "i", - "j", - "k", - "ky", - "m", - "my", - "n", - "ny", - "o", - "p", - "py", - "r", - "ry", - "s", - "sh", - "t", - "ts", - "u", - "v", - "w", - "y", - "z", - # "[", #上升调型 - # "]", #下降调型 - # "$", #结束符 - # "^", #开始符 -] - -arpa = { - "AH0", - "S", - "AH1", - "EY2", - "AE2", - "EH0", - "OW2", - "UH0", - "NG", - "B", - "G", - "AY0", - "M", - "AA0", - "F", - "AO0", - "ER2", - "UH1", - "IY1", - "AH2", - "DH", - "IY0", - "EY1", - "IH0", - "K", - "N", - "W", - "IY2", - "T", - "AA1", - "ER1", - "EH2", - "OY0", - "UH2", - "UW1", - "Z", - "AW2", - "AW1", - "V", - "UW2", - "AA2", - "ER", - "AW0", - "UW0", - "R", - "OW1", - "EH1", - "ZH", - "AE0", - "IH2", - "IH", - "Y", - "JH", - "P", - "AY1", - "EY0", - "OY2", - "TH", - "HH", - "D", - "ER0", - "CH", - "AO1", - "AE1", - "AO2", - "OY1", - "AY2", - "IH1", - "OW0", - "L", - "SH", -} - -symbols = [pad] + c + v + ja_symbols + pu_symbols + list(arpa) -symbols = sorted(set(symbols)) -if __name__ == "__main__": - print(len(symbols)) diff --git a/text/tone_sandhi.py b/text/tone_sandhi.py deleted file mode 100644 index 9f62abe937b72f25eb97762d705dfc4aa1271c9a..0000000000000000000000000000000000000000 --- a/text/tone_sandhi.py +++ /dev/null @@ -1,805 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import List -from typing import Tuple - -import jieba_fast as jieba -from pypinyin import lazy_pinyin -from pypinyin import Style - - -class ToneSandhi: - def __init__(self): - self.must_neural_tone_words = { - "麻烦", - "麻利", - "鸳鸯", - "高粱", - "骨头", - "骆驼", - "马虎", - "首饰", - "馒头", - "馄饨", - "风筝", - "难为", - "队伍", - "阔气", - "闺女", - "门道", - "锄头", - "铺盖", - "铃铛", - "铁匠", - "钥匙", - "里脊", - "里头", - "部分", - "那么", - "道士", - "造化", - "迷糊", - "连累", - "这么", - "这个", - "运气", - "过去", - "软和", - "转悠", - "踏实", - "跳蚤", - "跟头", - "趔趄", - "财主", - "豆腐", - "讲究", - "记性", - "记号", - "认识", - "规矩", - "见识", - "裁缝", - "补丁", - "衣裳", - "衣服", - "衙门", - "街坊", - "行李", - "行当", - "蛤蟆", - "蘑菇", - "薄荷", - "葫芦", - "葡萄", - "萝卜", - "荸荠", - "苗条", - "苗头", - "苍蝇", - "芝麻", - "舒服", - "舒坦", - "舌头", - "自在", - "膏药", - "脾气", - "脑袋", - "脊梁", - "能耐", - "胳膊", - "胭脂", - "胡萝", - "胡琴", - "胡同", - "聪明", - "耽误", - "耽搁", - "耷拉", - "耳朵", - "老爷", - "老实", - "老婆", - "老头", - "老太", - "翻腾", - "罗嗦", - "罐头", - "编辑", - "结实", - "红火", - "累赘", - "糨糊", - "糊涂", - "精神", - "粮食", - "簸箕", - "篱笆", - "算计", - "算盘", - "答应", - "笤帚", - "笑语", - "笑话", - "窟窿", - "窝囊", - "窗户", - "稳当", - "稀罕", - "称呼", - "秧歌", - "秀气", - "秀才", - "福气", - "祖宗", - "砚台", - "码头", - "石榴", - "石头", - "石匠", - "知识", - "眼睛", - "眯缝", - "眨巴", - "眉毛", - "相声", - "盘算", - "白净", - "痢疾", - "痛快", - "疟疾", - "疙瘩", - "疏忽", - "畜生", - "生意", - "甘蔗", - "琵琶", - "琢磨", - "琉璃", - "玻璃", - "玫瑰", - "玄乎", - "狐狸", - "状元", - "特务", - "牲口", - "牙碜", - "牌楼", - "爽快", - "爱人", - "热闹", - "烧饼", - "烟筒", - "烂糊", - "点心", - "炊帚", - "灯笼", - "火候", - "漂亮", - "滑溜", - "溜达", - "温和", - "清楚", - "消息", - "浪头", - "活泼", - "比方", - "正经", - "欺负", - "模糊", - "槟榔", - "棺材", - "棒槌", - "棉花", - "核桃", - "栅栏", - "柴火", - "架势", - "枕头", - "枇杷", - "机灵", - "本事", - "木头", - "木匠", - "朋友", - "月饼", - "月亮", - "暖和", - "明白", - "时候", - "新鲜", - "故事", - "收拾", - "收成", - "提防", - "挖苦", - "挑剔", - "指甲", - "指头", - "拾掇", - "拳头", - "拨弄", - "招牌", - "招呼", - "抬举", - "护士", - "折腾", - "扫帚", - "打量", - "打算", - "打点", - "打扮", - "打听", - "打发", - "扎实", - "扁担", - "戒指", - "懒得", - "意识", - "意思", - "情形", - "悟性", - "怪物", - "思量", - "怎么", - "念头", - "念叨", - "快活", - "忙活", - "志气", - "心思", - "得罪", - "张罗", - "弟兄", - "开通", - "应酬", - "庄稼", - "干事", - "帮手", - "帐篷", - "希罕", - "师父", - "师傅", - "巴结", - "巴掌", - "差事", - "工夫", - "岁数", - "屁股", - "尾巴", - "少爷", - "小气", - "小伙", - "将就", - "对头", - "对付", - "寡妇", - "家伙", - "客气", - "实在", - "官司", - "学问", - "学生", - "字号", - "嫁妆", - "媳妇", - "媒人", - "婆家", - "娘家", - "委屈", - "姑娘", - "姐夫", - "妯娌", - "妥当", - "妖精", - "奴才", - "女婿", - "头发", - "太阳", - "大爷", - "大方", - "大意", - "大夫", - "多少", - "多么", - "外甥", - "壮实", - "地道", - "地方", - "在乎", - "困难", - "嘴巴", - "嘱咐", - "嘟囔", - "嘀咕", - "喜欢", - "喇嘛", - "喇叭", - "商量", - "唾沫", - "哑巴", - "哈欠", - "哆嗦", - "咳嗽", - "和尚", - "告诉", - "告示", - "含糊", - "吓唬", - "后头", - "名字", - "名堂", - "合同", - "吆喝", - "叫唤", - "口袋", - "厚道", - "厉害", - "千斤", - "包袱", - "包涵", - "匀称", - "勤快", - "动静", - "动弹", - "功夫", - "力气", - "前头", - "刺猬", - "刺激", - "别扭", - "利落", - "利索", - "利害", - "分析", - "出息", - "凑合", - "凉快", - "冷战", - "冤枉", - "冒失", - "养活", - "关系", - "先生", - "兄弟", - "便宜", - "使唤", - "佩服", - "作坊", - "体面", - "位置", - "似的", - "伙计", - "休息", - "什么", - "人家", - "亲戚", - "亲家", - "交情", - "云彩", - "事情", - "买卖", - "主意", - "丫头", - "丧气", - "两口", - "东西", - "东家", - "世故", - "不由", - "不在", - "下水", - "下巴", - "上头", - "上司", - "丈夫", - "丈人", - "一辈", - "那个", - "菩萨", - "父亲", - "母亲", - "咕噜", - "邋遢", - "费用", - "冤家", - "甜头", - "介绍", - "荒唐", - "大人", - "泥鳅", - "幸福", - "熟悉", - "计划", - "扑腾", - "蜡烛", - "姥爷", - "照顾", - "喉咙", - "吉他", - "弄堂", - "蚂蚱", - "凤凰", - "拖沓", - "寒碜", - "糟蹋", - "倒腾", - "报复", - "逻辑", - "盘缠", - "喽啰", - "牢骚", - "咖喱", - "扫把", - "惦记", - } - self.must_not_neural_tone_words = { - "男子", - "女子", - "分子", - "原子", - "量子", - "莲子", - "石子", - "瓜子", - "电子", - "人人", - "虎虎", - "幺幺", - "干嘛", - "学子", - "哈哈", - "数数", - "袅袅", - "局地", - "以下", - "娃哈哈", - "花花草草", - "留得", - "耕地", - "想想", - "熙熙", - "攘攘", - "卵子", - "死死", - "冉冉", - "恳恳", - "佼佼", - "吵吵", - "打打", - "考考", - "整整", - "莘莘", - "落地", - "算子", - "家家户户", - "青青", - } - self.punc = "：，；。？！“”‘’':,;.?!" - - # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041 - # e.g. - # word: "家里" - # pos: "s" - # finals: ['ia1', 'i3'] - def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]: - # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺 - for j, item in enumerate(word): - if ( - j - 1 >= 0 - and item == word[j - 1] - and pos[0] in {"n", "v", "a"} - and word not in self.must_not_neural_tone_words - ): - finals[j] = finals[j][:-1] + "5" - ge_idx = word.find("个") - if len(word) >= 1 and word[-1] in "吧呢哈啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶": - finals[-1] = finals[-1][:-1] + "5" - elif len(word) >= 1 and word[-1] in "的地得": - finals[-1] = finals[-1][:-1] + "5" - # e.g. 走了, 看着, 去过 - elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}: - finals[-1] = finals[-1][:-1] + "5" - elif ( - len(word) > 1 - and word[-1] in "们子" - and pos in {"r", "n"} - and word not in self.must_not_neural_tone_words - ): - finals[-1] = finals[-1][:-1] + "5" - # e.g. 桌上, 地下, 家里 - elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}: - finals[-1] = finals[-1][:-1] + "5" - # e.g. 上来, 下去 - elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开": - finals[-1] = finals[-1][:-1] + "5" - # 个做量词 - elif ( - ge_idx >= 1 - and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是") - ) or word == "个": - finals[ge_idx] = finals[ge_idx][:-1] + "5" - else: - if ( - word in self.must_neural_tone_words - or word[-2:] in self.must_neural_tone_words - ): - finals[-1] = finals[-1][:-1] + "5" - - word_list = self._split_word(word) - finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]] - for i, word in enumerate(word_list): - # conventional neural in Chinese - if ( - word in self.must_neural_tone_words - or word[-2:] in self.must_neural_tone_words - ): - finals_list[i][-1] = finals_list[i][-1][:-1] + "5" - finals = sum(finals_list, []) - return finals - - def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]: - # e.g. 看不懂 - if len(word) == 3 and word[1] == "不": - finals[1] = finals[1][:-1] + "5" - else: - for i, char in enumerate(word): - # "不" before tone4 should be bu2, e.g. 不怕 - if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4": - finals[i] = finals[i][:-1] + "2" - return finals - - def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]: - # "一" in number sequences, e.g. 一零零, 二一零 - if word.find("一") != -1 and all( - [item.isnumeric() for item in word if item != "一"] - ): - return finals - # "一" between reduplication words shold be yi5, e.g. 看一看 - elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]: - finals[1] = finals[1][:-1] + "5" - # when "一" is ordinal word, it should be yi1 - elif word.startswith("第一"): - finals[1] = finals[1][:-1] + "1" - else: - for i, char in enumerate(word): - if char == "一" and i + 1 < len(word): - # "一" before tone4 should be yi2, e.g. 一段 - if finals[i + 1][-1] == "4": - finals[i] = finals[i][:-1] + "2" - # "一" before non-tone4 should be yi4, e.g. 一天 - else: - # "一" 后面如果是标点，还读一声 - if word[i + 1] not in self.punc: - finals[i] = finals[i][:-1] + "4" - return finals - - def _split_word(self, word: str) -> List[str]: - word_list = jieba.cut_for_search(word) - word_list = sorted(word_list, key=lambda i: len(i), reverse=False) - first_subword = word_list[0] - first_begin_idx = word.find(first_subword) - if first_begin_idx == 0: - second_subword = word[len(first_subword) :] - new_word_list = [first_subword, second_subword] - else: - second_subword = word[: -len(first_subword)] - new_word_list = [second_subword, first_subword] - return new_word_list - - def _three_sandhi(self, word: str, finals: List[str]) -> List[str]: - if len(word) == 2 and self._all_tone_three(finals): - finals[0] = finals[0][:-1] + "2" - elif len(word) == 3: - word_list = self._split_word(word) - if self._all_tone_three(finals): - # disyllabic + monosyllabic, e.g. 蒙古/包 - if len(word_list[0]) == 2: - finals[0] = finals[0][:-1] + "2" - finals[1] = finals[1][:-1] + "2" - # monosyllabic + disyllabic, e.g. 纸/老虎 - elif len(word_list[0]) == 1: - finals[1] = finals[1][:-1] + "2" - else: - finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]] - if len(finals_list) == 2: - for i, sub in enumerate(finals_list): - # e.g. 所有/人 - if self._all_tone_three(sub) and len(sub) == 2: - finals_list[i][0] = finals_list[i][0][:-1] + "2" - # e.g. 好/喜欢 - elif ( - i == 1 - and not self._all_tone_three(sub) - and finals_list[i][0][-1] == "3" - and finals_list[0][-1][-1] == "3" - ): - finals_list[0][-1] = finals_list[0][-1][:-1] + "2" - finals = sum(finals_list, []) - # split idiom into two words who's length is 2 - elif len(word) == 4: - finals_list = [finals[:2], finals[2:]] - finals = [] - for sub in finals_list: - if self._all_tone_three(sub): - sub[0] = sub[0][:-1] + "2" - finals += sub - - return finals - - def _all_tone_three(self, finals: List[str]) -> bool: - return all(x[-1] == "3" for x in finals) - - # merge "不" and the word behind it - # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error - def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - last_word = "" - for word, pos in seg: - if last_word == "不": - word = last_word + word - if word != "不": - new_seg.append((word, pos)) - last_word = word[:] - if last_word == "不": - new_seg.append((last_word, "d")) - last_word = "" - return new_seg - - # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听" - # function 2: merge single "一" and the word behind it - # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error - # e.g. - # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')] - # output seg: [['听一听', 'v']] - def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - # function 1 - for i, (word, pos) in enumerate(seg): - if ( - i - 1 >= 0 - and word == "一" - and i + 1 < len(seg) - and seg[i - 1][0] == seg[i + 1][0] - and seg[i - 1][1] == "v" - ): - new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0] - else: - if ( - i - 2 >= 0 - and seg[i - 1][0] == "一" - and seg[i - 2][0] == word - and pos == "v" - ): - continue - else: - new_seg.append([word, pos]) - seg = new_seg - new_seg = [] - # function 2 - for i, (word, pos) in enumerate(seg): - if new_seg and new_seg[-1][0] == "一": - new_seg[-1][0] = new_seg[-1][0] + word - else: - new_seg.append([word, pos]) - return new_seg - - # the first and the second words are all_tone_three - def _merge_continuous_three_tones( - self, seg: List[Tuple[str, str]] - ) -> List[Tuple[str, str]]: - new_seg = [] - sub_finals_list = [ - lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) - for (word, pos) in seg - ] - assert len(sub_finals_list) == len(seg) - merge_last = [False] * len(seg) - for i, (word, pos) in enumerate(seg): - if ( - i - 1 >= 0 - and self._all_tone_three(sub_finals_list[i - 1]) - and self._all_tone_three(sub_finals_list[i]) - and not merge_last[i - 1] - ): - # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi - if ( - not self._is_reduplication(seg[i - 1][0]) - and len(seg[i - 1][0]) + len(seg[i][0]) <= 3 - ): - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - merge_last[i] = True - else: - new_seg.append([word, pos]) - else: - new_seg.append([word, pos]) - - return new_seg - - def _is_reduplication(self, word: str) -> bool: - return len(word) == 2 and word[0] == word[1] - - # the last char of first word and the first char of second word is tone_three - def _merge_continuous_three_tones_2( - self, seg: List[Tuple[str, str]] - ) -> List[Tuple[str, str]]: - new_seg = [] - sub_finals_list = [ - lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) - for (word, pos) in seg - ] - assert len(sub_finals_list) == len(seg) - merge_last = [False] * len(seg) - for i, (word, pos) in enumerate(seg): - if ( - i - 1 >= 0 - and sub_finals_list[i - 1][-1][-1] == "3" - and sub_finals_list[i][0][-1] == "3" - and not merge_last[i - 1] - ): - # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi - if ( - not self._is_reduplication(seg[i - 1][0]) - and len(seg[i - 1][0]) + len(seg[i][0]) <= 3 - ): - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - merge_last[i] = True - else: - new_seg.append([word, pos]) - else: - new_seg.append([word, pos]) - return new_seg - - def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - for i, (word, pos) in enumerate(seg): - if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#": - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - else: - new_seg.append([word, pos]) - return new_seg - - def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - for i, (word, pos) in enumerate(seg): - if new_seg and word == new_seg[-1][0]: - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - else: - new_seg.append([word, pos]) - return new_seg - - def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - seg = self._merge_bu(seg) - try: - seg = self._merge_yi(seg) - except: - print("_merge_yi failed") - seg = self._merge_reduplication(seg) - try: - seg = self._merge_continuous_three_tones(seg) - except: - print("_merge_continuous_three_tones failed") - try: - seg = self._merge_continuous_three_tones_2(seg) - except: - print("_merge_continuous_three_tones_2 failed") - - seg = self._merge_er(seg) - return seg - - def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]: - finals = self._bu_sandhi(word, finals) - finals = self._yi_sandhi(word, finals) - finals = self._neural_sandhi(word, pos, finals) - finals = self._three_sandhi(word, finals) - return finals diff --git a/text/zh_normalization/README.md b/text/zh_normalization/README.md deleted file mode 100644 index 92eea9f54630dfd41dfc3ce53bc511cc7595062c..0000000000000000000000000000000000000000 --- a/text/zh_normalization/README.md +++ /dev/null @@ -1,16 +0,0 @@ -## Supported NSW (Non-Standard-Word) Normalization - -|NSW type|raw|normalized| -|:--|:-|:-| -|serial number|电影中梁朝伟扮演的陈永仁的编号27149|电影中梁朝伟扮演的陈永仁的编号二七一四九| -|cardinal|这块黄金重达324.75克
我们班的最高总分为583分|这块黄金重达三百二十四点七五克
我们班的最高总分为五百八十三分| -|numeric range |12\~23
-1.5\~2|十二到二十三
负一点五到二| -|date|她出生于86年8月18日，她弟弟出生于1995年3月1日|她出生于八六年八月十八日， 她弟弟出生于一九九五年三月一日| -|time|等会请在12:05请通知我|等会请在十二点零五分请通知我 -|temperature|今天的最低气温达到-10°C|今天的最低气温达到零下十度 -|fraction|现场有7/12的观众投出了赞成票|现场有十二分之七的观众投出了赞成票| -|percentage|明天有62％的概率降雨|明天有百分之六十二的概率降雨| -|money|随便来几个价格12块5，34.5元，20.1万|随便来几个价格十二块五，三十四点五元，二十点一万| -|telephone|这是固话0421-33441122
这是手机+86 18544139121|这是固话零四二一三三四四一一二二
这是手机八六一八五四四一三九一二一| -## References -[Pull requests #658 of DeepSpeech](https://github.com/PaddlePaddle/DeepSpeech/pull/658/files) diff --git a/text/zh_normalization/__init__.py b/text/zh_normalization/__init__.py deleted file mode 100644 index 46b367a68b074cf02da933f1e2433b86eeffe494..0000000000000000000000000000000000000000 --- a/text/zh_normalization/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from text.zh_normalization.text_normlization import * diff --git a/text/zh_normalization/char_convert.py b/text/zh_normalization/char_convert.py deleted file mode 100644 index dcf95d72861348b36e4f21c31350f47e56934fc1..0000000000000000000000000000000000000000 --- a/text/zh_normalization/char_convert.py +++ /dev/null @@ -1,46 +0,0 @@ -# coding=utf-8 -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters. -""" -simplified_charcters = 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- -traditional_characters = 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鐔鐗馗鐙鐝鐠鐡鐦鐨鐩鐫鐬鐱鐳鐶鐻鐽鐿鑀鑅鑌鑐鑕鑚鑛鑢鑤鑥鑪鑭鑯鑱鑴鑵鑷钁钃镻閆閈閌閎閒閔閗閟閡関閤閤閧閬閲閹閺閻閼閽閿闇闉闋闐闑闒闓闘闚闞闟闠闤闥阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬騭陴険陼陾隂隃隈隒隗隞隠隣隤隩隮隰顴隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驛霂霅霈霊霑霒霓霙霝霢霣霤霨霩霪霫霮靁靆靉靑靚靣靦靪靮靰靳靷靸靺靼靿鞀鞃鞄鞌鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾韃韅韉馱韍韎韔韖韘韝韞韡韣韭韮韱韹韺頀颳頄頇頊頍頎頏頒頖頞頠頫頬顱頯頲頴頼顇顋顑顒顓顔顕顚顜顢顣顬顳颭颮颱颶颸颺颻颽颾颿飀飂飈飌飜飡飣飤飥飩飫飮飱飶餀餂餄餎餇餈餑餔餕餖餗餚餛餜餟餠餤餧餩餪餫餬餮餱餲餳餺餻餼餽餿饁饅饇饉饊饍饎饐饘饟饢馘馥馝馡馣騮騾馵馹駃駄駅駆駉駋駑駓駔駗駘駙駜駡駢駪駬駰駴駸駹駽駾騂騄騅騆騉騋騍騏驎騑騒験騕騖騠騢騣騤騧驤騵騶騸騺驀驂驃驄驆驈驊驌驍驎驏驒驔驖驙驦驩驫骺鯁骫骭骯骱骴骶骷髏骾髁髂髄髆髈髐髑髕髖髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣鬪鬫鬬鬮鬯鬰鬲鬵鬷魆魈魊魋魍魎魑魖鰾魛魟魣魦魨魬魴魵魸鮀鮁鮆鮌鮎鮑鮒鮓鮚鮞鮟鱇鮠鮦鮨鮪鮭鮶鮸鮿鯀鯄鯆鯇鯈鯔鯕鯖鯗鯙鯠鯤鯥鯫鯰鯷鯸鯿鰂鰆鶼鰉鰋鰐鰒鰕鰛鰜鰣鰤鰥鰦鰨鰩鰮鰳鰶鰷鱺鰼鰽鱀鱄鱅鱆鱈鱎鱐鱓鱔鱖鱘鱟鱠鱣鱨鱭鱮鱲鱵鱻鲅鳦鳧鳯鳲鳷鳻鴂鴃鴄鴆鴈鴎鴒鴔鴗鴛鴦鴝鵒鴟鴠鴢鴣鴥鴯鶓鴳鴴鴷鴽鵀鵁鵂鵓鵖鵙鵜鶘鵞鵟鵩鵪鵫鵵鵷鵻鵾鶂鶊鶏鶒鶖鶗鶡鶤鶦鶬鶱鶲鶵鶸鶹鶺鶿鷀鷁鷃鷄鷇鷈鷉鷊鷏鷓鷕鷖鷙鷞鷟鷥鷦鷯鷩鷫鷭鷳鷴鷽鷾鷿鸂鸇鸊鸏鸑鸒鸓鸕鸛鸜鸝鹸鹹鹺麀麂麃麄麇麋麌麐麑麒麚麛麝麤麩麪麫麮麯麰麺麾黁黈黌黢黒黓黕黙黝黟黥黦黧黮黰黱黲黶黹黻黼黽黿鼂鼃鼅鼈鼉鼏鼐鼒鼕鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌齎齏齔齕齗齙齚齜齞齟齬齠齢齣齧齩齮齯齰齱齵齾龎龑龒龔龖龘龝龡龢龤' - -assert len(simplified_charcters) == len(simplified_charcters) - -s2t_dict = {} -t2s_dict = {} -for i, item in enumerate(simplified_charcters): - s2t_dict[item] = traditional_characters[i] - t2s_dict[traditional_characters[i]] = item - - -def tranditional_to_simplified(text: str) -> str: - return "".join( - [t2s_dict[item] if item in t2s_dict else item for item in text]) - - -def simplified_to_traditional(text: str) -> str: - return "".join( - [s2t_dict[item] if item in s2t_dict else item for item in text]) - - -if __name__ == "__main__": - text = "一般是指存取一個應用程式啟動時始終顯示在網站或網頁瀏覽器中的一個或多個初始網頁等畫面存在的站點" - print(text) - text_simple = tranditional_to_simplified(text) - print(text_simple) - text_traditional = simplified_to_traditional(text_simple) - print(text_traditional) diff --git a/text/zh_normalization/chronology.py b/text/zh_normalization/chronology.py deleted file mode 100644 index ea4558e2a7abba4ff454656b82e67b3f5c483bf2..0000000000000000000000000000000000000000 --- a/text/zh_normalization/chronology.py +++ /dev/null @@ -1,134 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import re - -from .num import DIGITS -from .num import num2str -from .num import verbalize_cardinal -from .num import verbalize_digit - - -def _time_num2str(num_string: str) -> str: - """A special case for verbalizing number in time.""" - result = num2str(num_string.lstrip('0')) - if num_string.startswith('0'): - result = DIGITS['0'] + result - return result - - -# 时刻表达式 -RE_TIME = re.compile(r'([0-1]?[0-9]|2[0-3])' - r':([0-5][0-9])' - r'(:([0-5][0-9]))?') - -# 时间范围，如8:30-12:30 -RE_TIME_RANGE = re.compile(r'([0-1]?[0-9]|2[0-3])' - r':([0-5][0-9])' - r'(:([0-5][0-9]))?' - r'(~|-)' - r'([0-1]?[0-9]|2[0-3])' - r':([0-5][0-9])' - r'(:([0-5][0-9]))?') - - -def replace_time(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - - is_range = len(match.groups()) > 5 - - hour = match.group(1) - minute = match.group(2) - second = match.group(4) - - if is_range: - hour_2 = match.group(6) - minute_2 = match.group(7) - second_2 = match.group(9) - - result = f"{num2str(hour)}点" - if minute.lstrip('0'): - if int(minute) == 30: - result += "半" - else: - result += f"{_time_num2str(minute)}分" - if second and second.lstrip('0'): - result += f"{_time_num2str(second)}秒" - - if is_range: - result += "至" - result += f"{num2str(hour_2)}点" - if minute_2.lstrip('0'): - if int(minute) == 30: - result += "半" - else: - result += f"{_time_num2str(minute_2)}分" - if second_2 and second_2.lstrip('0'): - result += f"{_time_num2str(second_2)}秒" - - return result - - -RE_DATE = re.compile(r'(\d{4}|\d{2})年' - r'((0?[1-9]|1[0-2])月)?' - r'(((0?[1-9])|((1|2)[0-9])|30|31)([日号]))?') - - -def replace_date(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - year = match.group(1) - month = match.group(3) - day = match.group(5) - result = "" - if year: - result += f"{verbalize_digit(year)}年" - if month: - result += f"{verbalize_cardinal(month)}月" - if day: - result += f"{verbalize_cardinal(day)}{match.group(9)}" - return result - - -# 用 / 或者 - 分隔的 YY/MM/DD 或者 YY-MM-DD 日期 -RE_DATE2 = re.compile( - r'(\d{4})([- /.])(0[1-9]|1[012])\2(0[1-9]|[12][0-9]|3[01])') - - -def replace_date2(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - year = match.group(1) - month = match.group(3) - day = match.group(4) - result = "" - if year: - result += f"{verbalize_digit(year)}年" - if month: - result += f"{verbalize_cardinal(month)}月" - if day: - result += f"{verbalize_cardinal(day)}日" - return result diff --git a/text/zh_normalization/constants.py b/text/zh_normalization/constants.py deleted file mode 100644 index 6423ad74a5c9a92df35aebf04bebdf4050a292de..0000000000000000000000000000000000000000 --- a/text/zh_normalization/constants.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import re -import string - -from pypinyin.constants import SUPPORT_UCS4 - -# 全角半角转换 -# 英文字符全角 -> 半角映射表 (num: 52) -F2H_ASCII_LETTERS = { - ord(char) + 65248: ord(char) - for char in string.ascii_letters -} - -# 英文字符半角 -> 全角映射表 -H2F_ASCII_LETTERS = {value: key for key, value in F2H_ASCII_LETTERS.items()} - -# 数字字符全角 -> 半角映射表 (num: 10) -F2H_DIGITS = {ord(char) + 65248: ord(char) for char in string.digits} -# 数字字符半角 -> 全角映射表 -H2F_DIGITS = {value: key for key, value in F2H_DIGITS.items()} - -# 标点符号全角 -> 半角映射表 (num: 32) -F2H_PUNCTUATIONS = {ord(char) + 65248: ord(char) for char in string.punctuation} -# 标点符号半角 -> 全角映射表 -H2F_PUNCTUATIONS = {value: key for key, value in F2H_PUNCTUATIONS.items()} - -# 空格 (num: 1) -F2H_SPACE = {'\u3000': ' '} -H2F_SPACE = {' ': '\u3000'} - -# 非"有拼音的汉字"的字符串，可用于NSW提取 -if SUPPORT_UCS4: - RE_NSW = re.compile(r'(?:[^' - r'\u3007' # 〇 - r'\u3400-\u4dbf' # CJK扩展A:[3400-4DBF] - r'\u4e00-\u9fff' # CJK基本:[4E00-9FFF] - r'\uf900-\ufaff' # CJK兼容:[F900-FAFF] - r'\U00020000-\U0002A6DF' # CJK扩展B:[20000-2A6DF] - r'\U0002A703-\U0002B73F' # CJK扩展C:[2A700-2B73F] - r'\U0002B740-\U0002B81D' # CJK扩展D:[2B740-2B81D] - r'\U0002F80A-\U0002FA1F' # CJK兼容扩展:[2F800-2FA1F] - r'])+') -else: - RE_NSW = re.compile( # pragma: no cover - r'(?:[^' - r'\u3007' # 〇 - r'\u3400-\u4dbf' # CJK扩展A:[3400-4DBF] - r'\u4e00-\u9fff' # CJK基本:[4E00-9FFF] - r'\uf900-\ufaff' # CJK兼容:[F900-FAFF] - r'])+') diff --git a/text/zh_normalization/num.py b/text/zh_normalization/num.py deleted file mode 100644 index 8a54d3e63ec863a28f2650f7a77538414b096c66..0000000000000000000000000000000000000000 --- a/text/zh_normalization/num.py +++ /dev/null @@ -1,238 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Rules to verbalize numbers into Chinese characters. -https://zh.wikipedia.org/wiki/中文数字#現代中文 -""" -import re -from collections import OrderedDict -from typing import List - -DIGITS = {str(i): tran for i, tran in enumerate('零一二三四五六七八九')} -UNITS = OrderedDict({ - 1: '十', - 2: '百', - 3: '千', - 4: '万', - 8: '亿', -}) - -COM_QUANTIFIERS = '(封|艘|把|目|套|段|人|所|朵|匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|毫|厘|(公)分|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|小时|旬|纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块|元|(亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|美|)元|(亿|千万|百万|万|千|百|十|)吨|(亿|千万|百万|万|千|百|)块|角|毛|分)' - -# 分数表达式 -RE_FRAC = re.compile(r'(-?)(\d+)/(\d+)') - - -def replace_frac(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - sign = match.group(1) - nominator = match.group(2) - denominator = match.group(3) - sign: str = "负" if sign else "" - nominator: str = num2str(nominator) - denominator: str = num2str(denominator) - result = f"{sign}{denominator}分之{nominator}" - return result - - -# 百分数表达式 -RE_PERCENTAGE = re.compile(r'(-?)(\d+(\.\d+)?)%') - - -def replace_percentage(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - sign = match.group(1) - percent = match.group(2) - sign: str = "负" if sign else "" - percent: str = num2str(percent) - result = f"{sign}百分之{percent}" - return result - - -# 整数表达式 -# 带负号的整数 -10 -RE_INTEGER = re.compile(r'(-)' r'(\d+)') - - -def replace_negative_num(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - sign = match.group(1) - number = match.group(2) - sign: str = "负" if sign else "" - number: str = num2str(number) - result = f"{sign}{number}" - return result - - -# 编号-无符号整形 -# 00078 -RE_DEFAULT_NUM = re.compile(r'\d{3}\d*') - - -def replace_default_num(match): - """ - Args: - match (re.Match) - Returns: - str - """ - number = match.group(0) - return verbalize_digit(number, alt_one=True) - - -# 数字表达式 -# 纯小数 -RE_DECIMAL_NUM = re.compile(r'(-?)((\d+)(\.\d+))' r'|(\.(\d+))') -# 正整数 + 量词 -RE_POSITIVE_QUANTIFIERS = re.compile(r"(\d+)([多余几\+])?" + COM_QUANTIFIERS) -RE_NUMBER = re.compile(r'(-?)((\d+)(\.\d+)?)' r'|(\.(\d+))') - - -def replace_positive_quantifier(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - number = match.group(1) - match_2 = match.group(2) - if match_2 == "+": - match_2 = "多" - match_2: str = match_2 if match_2 else "" - quantifiers: str = match.group(3) - number: str = num2str(number) - result = f"{number}{match_2}{quantifiers}" - return result - - -def replace_number(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - sign = match.group(1) - number = match.group(2) - pure_decimal = match.group(5) - if pure_decimal: - result = num2str(pure_decimal) - else: - sign: str = "负" if sign else "" - number: str = num2str(number) - result = f"{sign}{number}" - return result - - -# 范围表达式 -# match.group(1) and match.group(8) are copy from RE_NUMBER - -RE_RANGE = re.compile( - r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))[-~]((-?)((\d+)(\.\d+)?)|(\.(\d+)))') - - -def replace_range(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - first, second = match.group(1), match.group(8) - first = RE_NUMBER.sub(replace_number, first) - second = RE_NUMBER.sub(replace_number, second) - result = f"{first}到{second}" - return result - - -def _get_value(value_string: str, use_zero: bool=True) -> List[str]: - stripped = value_string.lstrip('0') - if len(stripped) == 0: - return [] - elif len(stripped) == 1: - if use_zero and len(stripped) < len(value_string): - return [DIGITS['0'], DIGITS[stripped]] - else: - return [DIGITS[stripped]] - else: - largest_unit = next( - power for power in reversed(UNITS.keys()) if power < len(stripped)) - first_part = value_string[:-largest_unit] - second_part = value_string[-largest_unit:] - return _get_value(first_part) + [UNITS[largest_unit]] + _get_value( - second_part) - - -def verbalize_cardinal(value_string: str) -> str: - if not value_string: - return '' - - # 000 -> '零' , 0 -> '零' - value_string = value_string.lstrip('0') - if len(value_string) == 0: - return DIGITS['0'] - - result_symbols = _get_value(value_string) - # verbalized number starting with '一十*' is abbreviated as `十*` - if len(result_symbols) >= 2 and result_symbols[0] == DIGITS[ - '1'] and result_symbols[1] == UNITS[1]: - result_symbols = result_symbols[1:] - return ''.join(result_symbols) - - -def verbalize_digit(value_string: str, alt_one=False) -> str: - result_symbols = [DIGITS[digit] for digit in value_string] - result = ''.join(result_symbols) - if alt_one: - result = result.replace("一", "幺") - return result - - -def num2str(value_string: str) -> str: - integer_decimal = value_string.split('.') - if len(integer_decimal) == 1: - integer = integer_decimal[0] - decimal = '' - elif len(integer_decimal) == 2: - integer, decimal = integer_decimal - else: - raise ValueError( - f"The value string: '${value_string}' has more than one point in it." - ) - - result = verbalize_cardinal(integer) - - decimal = decimal.rstrip('0') - if decimal: - # '.22' is verbalized as '零点二二' - # '3.20' is verbalized as '三点二 - result = result if result else "零" - result += '点' + verbalize_digit(decimal) - return result diff --git a/text/zh_normalization/phonecode.py b/text/zh_normalization/phonecode.py deleted file mode 100644 index 51835112603c1a8c31052285276f138b8c5a74d6..0000000000000000000000000000000000000000 --- a/text/zh_normalization/phonecode.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import re - -from .num import verbalize_digit - -# 规范化固话/手机号码 -# 手机 -# http://www.jihaoba.com/news/show/13680 -# 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198 -# 联通：130、131、132、156、155、186、185、176 -# 电信：133、153、189、180、181、177 -RE_MOBILE_PHONE = re.compile( - r"(? str: - if mobile: - sp_parts = phone_string.strip('+').split() - result = '，'.join( - [verbalize_digit(part, alt_one=True) for part in sp_parts]) - return result - else: - sil_parts = phone_string.split('-') - result = '，'.join( - [verbalize_digit(part, alt_one=True) for part in sil_parts]) - return result - - -def replace_phone(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - return phone2str(match.group(0), mobile=False) - - -def replace_mobile(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - return phone2str(match.group(0)) diff --git a/text/zh_normalization/quantifier.py b/text/zh_normalization/quantifier.py deleted file mode 100644 index 598030e439bd2dd419900892547a550a5df96482..0000000000000000000000000000000000000000 --- a/text/zh_normalization/quantifier.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import re - -from .num import num2str - -# 温度表达式，温度会影响负号的读法 -# -3°C 零下三度 -RE_TEMPERATURE = re.compile(r'(-?)(\d+(\.\d+)?)(°C|℃|度|摄氏度)') -measure_dict = { - "cm2": "平方厘米", - "cm²": "平方厘米", - "cm3": "立方厘米", - "cm³": "立方厘米", - "cm": "厘米", - "db": "分贝", - "ds": "毫秒", - "kg": "千克", - "km": "千米", - "m2": "平方米", - "m²": "平方米", - "m³": "立方米", - "m3": "立方米", - "ml": "毫升", - "m": "米", - "mm": "毫米", - "s": "秒" -} - - -def replace_temperature(match) -> str: - """ - Args: - match (re.Match) - Returns: - str - """ - sign = match.group(1) - temperature = match.group(2) - unit = match.group(3) - sign: str = "零下" if sign else "" - temperature: str = num2str(temperature) - unit: str = "摄氏度" if unit == "摄氏度" else "度" - result = f"{sign}{temperature}{unit}" - return result - - -def replace_measure(sentence) -> str: - for q_notation in measure_dict: - if q_notation in sentence: - sentence = sentence.replace(q_notation, measure_dict[q_notation]) - return sentence diff --git a/text/zh_normalization/text_normlization.py b/text/zh_normalization/text_normlization.py deleted file mode 100644 index 1250e96ca4d1b163edca87da844fab0b173d1d02..0000000000000000000000000000000000000000 --- a/text/zh_normalization/text_normlization.py +++ /dev/null @@ -1,154 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import re -from typing import List - -from .char_convert import tranditional_to_simplified -from .chronology import RE_DATE -from .chronology import RE_DATE2 -from .chronology import RE_TIME -from .chronology import RE_TIME_RANGE -from .chronology import replace_date -from .chronology import replace_date2 -from .chronology import replace_time -from .constants import F2H_ASCII_LETTERS -from .constants import F2H_DIGITS -from .constants import F2H_SPACE -from .num import RE_DECIMAL_NUM -from .num import RE_DEFAULT_NUM -from .num import RE_FRAC -from .num import RE_INTEGER -from .num import RE_NUMBER -from .num import RE_PERCENTAGE -from .num import RE_POSITIVE_QUANTIFIERS -from .num import RE_RANGE -from .num import replace_default_num -from .num import replace_frac -from .num import replace_negative_num -from .num import replace_number -from .num import replace_percentage -from .num import replace_positive_quantifier -from .num import replace_range -from .phonecode import RE_MOBILE_PHONE -from .phonecode import RE_NATIONAL_UNIFORM_NUMBER -from .phonecode import RE_TELEPHONE -from .phonecode import replace_mobile -from .phonecode import replace_phone -from .quantifier import RE_TEMPERATURE -from .quantifier import replace_measure -from .quantifier import replace_temperature - - -class TextNormalizer(): - def __init__(self): - self.SENTENCE_SPLITOR = re.compile(r'([：、，；。？！,;?!][”’]?)') - - def _split(self, text: str, lang="zh") -> List[str]: - """Split long text into sentences with sentence-splitting punctuations. - Args: - text (str): The input text. - Returns: - List[str]: Sentences. - """ - # Only for pure Chinese here - if lang == "zh": - text = text.replace(" ", "") - # 过滤掉特殊字符 - text = re.sub(r'[——《》【】<=>{}()（）#&@“”^_|…\\]', '', text) - text = self.SENTENCE_SPLITOR.sub(r'\1\n', text) - text = text.strip() - sentences = [sentence.strip() for sentence in re.split(r'\n+', text)] - return sentences - - def _post_replace(self, sentence: str) -> str: - sentence = sentence.replace('/', '每') - sentence = sentence.replace('~', '至') - sentence = sentence.replace('～', '至') - sentence = sentence.replace('①', '一') - sentence = sentence.replace('②', '二') - sentence = sentence.replace('③', '三') - sentence = sentence.replace('④', '四') - sentence = sentence.replace('⑤', '五') - sentence = sentence.replace('⑥', '六') - sentence = sentence.replace('⑦', '七') - sentence = sentence.replace('⑧', '八') - sentence = sentence.replace('⑨', '九') - sentence = sentence.replace('⑩', '十') - sentence = sentence.replace('α', '阿尔法') - sentence = sentence.replace('β', '贝塔') - sentence = sentence.replace('γ', '伽玛').replace('Γ', '伽玛') - sentence = sentence.replace('δ', '德尔塔').replace('Δ', '德尔塔') - sentence = sentence.replace('ε', '艾普西龙') - sentence = sentence.replace('ζ', '捷塔') - sentence = sentence.replace('η', '依塔') - sentence = sentence.replace('θ', '西塔').replace('Θ', '西塔') - sentence = sentence.replace('ι', '艾欧塔') - sentence = sentence.replace('κ', '喀帕') - sentence = sentence.replace('λ', '拉姆达').replace('Λ', '拉姆达') - sentence = sentence.replace('μ', '缪') - sentence = sentence.replace('ν', '拗') - sentence = sentence.replace('ξ', '克西').replace('Ξ', '克西') - sentence = sentence.replace('ο', '欧米克伦') - sentence = sentence.replace('π', '派').replace('Π', '派') - sentence = sentence.replace('ρ', '肉') - sentence = sentence.replace('ς', '西格玛').replace('Σ', '西格玛').replace( - 'σ', '西格玛') - sentence = sentence.replace('τ', '套') - sentence = sentence.replace('υ', '宇普西龙') - sentence = sentence.replace('φ', '服艾').replace('Φ', '服艾') - sentence = sentence.replace('χ', '器') - sentence = sentence.replace('ψ', '普赛').replace('Ψ', '普赛') - sentence = sentence.replace('ω', '欧米伽').replace('Ω', '欧米伽') - # re filter special characters, have one more character "-" than line 68 - sentence = re.sub(r'[-——《》【】<=>{}()（）#&@“”^_|…\\]', '', sentence) - return sentence - - def normalize_sentence(self, sentence: str) -> str: - # basic character conversions - sentence = tranditional_to_simplified(sentence) - sentence = sentence.translate(F2H_ASCII_LETTERS).translate( - F2H_DIGITS).translate(F2H_SPACE) - - # number related NSW verbalization - sentence = RE_DATE.sub(replace_date, sentence) - sentence = RE_DATE2.sub(replace_date2, sentence) - - # range first - sentence = RE_TIME_RANGE.sub(replace_time, sentence) - sentence = RE_TIME.sub(replace_time, sentence) - - sentence = RE_TEMPERATURE.sub(replace_temperature, sentence) - sentence = replace_measure(sentence) - sentence = RE_FRAC.sub(replace_frac, sentence) - sentence = RE_PERCENTAGE.sub(replace_percentage, sentence) - sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence) - - sentence = RE_TELEPHONE.sub(replace_phone, sentence) - sentence = RE_NATIONAL_UNIFORM_NUMBER.sub(replace_phone, sentence) - - sentence = RE_RANGE.sub(replace_range, sentence) - sentence = RE_INTEGER.sub(replace_negative_num, sentence) - sentence = RE_DECIMAL_NUM.sub(replace_number, sentence) - sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier, - sentence) - sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence) - sentence = RE_NUMBER.sub(replace_number, sentence) - sentence = self._post_replace(sentence) - - return sentence - - def normalize(self, text: str) -> List[str]: - sentences = self._split(text) - sentences = [self.normalize_sentence(sent) for sent in sentences] - return sentences