diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..5730da2e237d68c33702fb2bbad55a357abd9fbe 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* 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 +diffrhythm/g2p/sources/chinese_lexicon.txt filter=lfs diff=lfs merge=lfs -text diff --git a/diffrhythm/.DS_Store b/diffrhythm/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..e7bf22c308ef70d139de8bdc449c65e7bcee840a Binary files /dev/null and b/diffrhythm/.DS_Store differ diff --git a/diffrhythm/config/defaults.ini b/diffrhythm/config/defaults.ini new file mode 100644 index 0000000000000000000000000000000000000000..ca6a8b08c28aa9fd058bee80b9a89d2592fd8be0 --- /dev/null +++ b/diffrhythm/config/defaults.ini @@ -0,0 +1,94 @@ + +[DEFAULTS] + +#name of the run +exp_name = F5 + +# the batch size +batch_size = 8 + +# the chunk size +max_frames = 3000 +min_frames = 10 + +# number of CPU workers for the DataLoader +num_workers = 4 + +# the random seed +seed = 42 + +# Batches for gradient accumulation +accum_batches = 1 + +# Number of steps between checkpoints +checkpoint_every = 10000 + +# trainer checkpoint file to restart training from +ckpt_path = '' + +# model checkpoint file to start a new training run from +pretrained_ckpt_path = '' + +# Checkpoint path for the pretransform model if needed +pretransform_ckpt_path = '' + +# configuration model specifying model hyperparameters +model_config = '' + +# configuration for datasets +dataset_config = '' + +# directory to save the checkpoints in +save_dir = '' + +# grad norm +max_grad_norm = 1.0 + +# grad accu +grad_accumulation_steps = 1 + +# lr +learning_rate = 7.5e-5 + +# epoch +epochs = 110 + +# warmup steps +num_warmup_updates = 2000 + +# save checkpoint per steps +save_per_updates = 5000 + +# save last checkpoint per steps +last_per_steps = 5000 + +prompt_path = "/mnt/sfs/music/lance/style-lance-full|/mnt/sfs/music/lance/style-lance-cnen-music-second" +lrc_path = "/mnt/sfs/music/lance/lrc-lance-emb-full|/mnt/sfs/music/lance/lrc-lance-cnen-second" +latent_path = "/mnt/sfs/music/lance/latent-lance|/mnt/sfs/music/lance/latent-lance-cnen-music-second-1|/mnt/sfs/music/lance/latent-lance-cnen-music-second-2" + +audio_drop_prob = 0.3 +cond_drop_prob = 0.0 +style_drop_prob = 0.1 +lrc_drop_prob = 0.1 + +align_lyrics = 0 +lyrics_slice = 0 +parse_lyrics = 1 +skip_empty_lyrics = 0 +lyrics_shift = -1 + +use_style_prompt = 1 + +tokenizer_type = gpt2 + +reset_lr = 0 + +resumable_with_seed = 666 + +downsample_rate = 2048 + +grad_ckpt = 0 + +dataset_path = "/mnt/sfs/music/hkchen/workspace/F5-TTS-HW/filelists/music123latent_asred_bpmstyle_cnen_pure1" + +pure_prob = 0.0 \ No newline at end of file diff --git a/diffrhythm/config/diffrhythm-1b.json b/diffrhythm/config/diffrhythm-1b.json new file mode 100644 index 0000000000000000000000000000000000000000..c83c317a84eae49ad2c811e7c59141b95894b2a3 --- /dev/null +++ b/diffrhythm/config/diffrhythm-1b.json @@ -0,0 +1,13 @@ +{ + "model_type": "diffrhythm", + "model": { + "dim": 2048, + "depth": 16, + "heads": 32, + "ff_mult": 4, + "text_dim": 512, + "conv_layers": 4, + "mel_dim": 64, + "text_num_embeds": 363 + } +} \ No newline at end of file diff --git a/diffrhythm/g2p/__pycache__/g2p_generation.cpython-310.pyc b/diffrhythm/g2p/__pycache__/g2p_generation.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a2d2a9e187b65a0a2db6db23efbf8e772c6ebfac Binary files /dev/null and b/diffrhythm/g2p/__pycache__/g2p_generation.cpython-310.pyc differ diff --git a/diffrhythm/g2p/__pycache__/g2p_generation.cpython-311.pyc b/diffrhythm/g2p/__pycache__/g2p_generation.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..49dd6f71526b374aaa8a7b83622983928985bd8e Binary files /dev/null and b/diffrhythm/g2p/__pycache__/g2p_generation.cpython-311.pyc differ diff --git a/diffrhythm/g2p/g2p/__init__.py b/diffrhythm/g2p/g2p/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a926893b1b3473fbc8363238a517781e59092365 --- /dev/null +++ b/diffrhythm/g2p/g2p/__init__.py @@ -0,0 +1,87 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from diffrhythm.g2p.g2p import cleaners +from tokenizers import Tokenizer +from diffrhythm.g2p.g2p.text_tokenizers import TextTokenizer +import LangSegment +import json +import re + + +class PhonemeBpeTokenizer: + + def __init__(self, vacab_path="./diffrhythm/g2p/g2p/vocab.json"): + self.lang2backend = { + "zh": "cmn", + "ja": "ja", + "en": "en-us", + "fr": "fr-fr", + "ko": "ko", + "de": "de", + } + self.text_tokenizers = {} + self.int_text_tokenizers() + + with open(vacab_path, "r") as f: + json_data = f.read() + data = json.loads(json_data) + self.vocab = data["vocab"] + LangSegment.setfilters(["en", "zh", "ja", "ko", "fr", "de"]) + + def int_text_tokenizers(self): + for key, value in self.lang2backend.items(): + self.text_tokenizers[key] = TextTokenizer(language=value) + + def tokenize(self, text, sentence, language): + + # 1. convert text to phoneme + phonemes = [] + if language == "auto": + seglist = LangSegment.getTexts(text) + tmp_ph = [] + for seg in seglist: + tmp_ph.append( + self._clean_text( + seg["text"], sentence, seg["lang"], ["cjekfd_cleaners"] + ) + ) + phonemes = "|_|".join(tmp_ph) + else: + phonemes = self._clean_text(text, sentence, language, ["cjekfd_cleaners"]) + # print('clean text: ', phonemes) + + # 2. tokenize phonemes + phoneme_tokens = self.phoneme2token(phonemes) + # print('encode: ', phoneme_tokens) + + # # 3. decode tokens [optional] + # decoded_text = self.tokenizer.decode(phoneme_tokens) + # print('decoded: ', decoded_text) + + return phonemes, phoneme_tokens + + def _clean_text(self, text, sentence, language, cleaner_names): + for name in cleaner_names: + cleaner = getattr(cleaners, name) + if not cleaner: + raise Exception("Unknown cleaner: %s" % name) + text = cleaner(text, sentence, language, self.text_tokenizers) + return text + + def phoneme2token(self, phonemes): + tokens = [] + if isinstance(phonemes, list): + for phone in phonemes: + phone = phone.split("\t")[0] + phonemes_split = phone.split("|") + tokens.append( + [self.vocab[p] for p in phonemes_split if p in self.vocab] + ) + else: + phonemes = phonemes.split("\t")[0] + phonemes_split = phonemes.split("|") + tokens = [self.vocab[p] for p in phonemes_split if p in self.vocab] + return tokens diff --git a/diffrhythm/g2p/g2p/__pycache__/__init__.cpython-310.pyc b/diffrhythm/g2p/g2p/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c9f11e88f64aa267f63c0a55ea587c137edaa21f Binary files /dev/null and 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numpy as np +import torch +from torch.utils.data import DataLoader +import json +from transformers import BertTokenizer +from torch.utils.data import Dataset +from transformers.models.bert.modeling_bert import * +import torch +import torch.nn.functional as F +from onnxruntime import InferenceSession, GraphOptimizationLevel, SessionOptions + + +class PolyDataset(Dataset): + def __init__(self, words, labels, word_pad_idx=0, label_pad_idx=-1): + self.dataset = self.preprocess(words, labels) + self.word_pad_idx = word_pad_idx + self.label_pad_idx = label_pad_idx + + def preprocess(self, origin_sentences, origin_labels): + """ + Maps tokens and tags to their indices and stores them in the dict data. + examples: + word:['[CLS]', '浙', '商', '银', '行', '企', '业', '信', '贷', '部'] + sentence:([101, 3851, 1555, 7213, 6121, 821, 689, 928, 6587, 6956], + array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])) + label:[3, 13, 13, 13, 0, 0, 0, 0, 0] + """ + data = [] + labels = [] + sentences = [] + # tokenize + for line in origin_sentences: + # replace each token by its index + # we can not use encode_plus because our sentences are aligned to labels in list type + words = [] + word_lens = [] + for token in line: + words.append(token) + word_lens.append(1) + token_start_idxs = 1 + np.cumsum([0] + word_lens[:-1]) + sentences.append(((words, token_start_idxs), 0)) + ### + for tag in origin_labels: + labels.append(tag) + + for sentence, label in zip(sentences, labels): + data.append((sentence, label)) + return data + + def __getitem__(self, idx): + """sample data to get batch""" + word = self.dataset[idx][0] + label = self.dataset[idx][1] + return [word, label] + + def __len__(self): + """get dataset size""" + return len(self.dataset) + + def collate_fn(self, batch): + + sentences = [x[0][0] for x in batch] + ori_sents = [x[0][1] for x in batch] + labels = [x[1] for x in batch] + batch_len = len(sentences) + + # compute length of longest sentence in batch + max_len = max([len(s[0]) for s in sentences]) + max_label_len = 0 + batch_data = np.ones((batch_len, max_len)) + batch_label_starts = [] + + # padding and aligning + for j in range(batch_len): + cur_len = len(sentences[j][0]) + batch_data[j][:cur_len] = sentences[j][0] + label_start_idx = sentences[j][-1] + label_starts = np.zeros(max_len) + label_starts[[idx for idx in label_start_idx if idx < max_len]] = 1 + batch_label_starts.append(label_starts) + max_label_len = max(int(sum(label_starts)), max_label_len) + + # padding label + batch_labels = self.label_pad_idx * np.ones((batch_len, max_label_len)) + batch_pmasks = self.label_pad_idx * np.ones((batch_len, max_label_len)) + for j in range(batch_len): + cur_tags_len = len(labels[j]) + batch_labels[j][:cur_tags_len] = labels[j] + batch_pmasks[j][:cur_tags_len] = [ + 1 if item > 0 else 0 for item in labels[j] + ] + + # convert data to torch LongTensors + batch_data = torch.tensor(batch_data, dtype=torch.long) + batch_label_starts = torch.tensor(batch_label_starts, dtype=torch.long) + batch_labels = torch.tensor(batch_labels, dtype=torch.long) + batch_pmasks = torch.tensor(batch_pmasks, dtype=torch.long) + return [batch_data, batch_label_starts, batch_labels, batch_pmasks, ori_sents] + + +class BertPolyPredict: + def __init__(self, bert_model, jsonr_file, json_file): + self.tokenizer = BertTokenizer.from_pretrained(bert_model, do_lower_case=True) + with open(jsonr_file, "r", encoding="utf8") as fp: + self.pron_dict = json.load(fp) + with open(json_file, "r", encoding="utf8") as fp: + self.pron_dict_id_2_pinyin = json.load(fp) + self.num_polyphone = len(self.pron_dict) + self.device = "cpu" + self.polydataset = PolyDataset + options = SessionOptions() # initialize session options + options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL + print(os.path.join(bert_model, "poly_bert_model.onnx")) + self.session = InferenceSession( + os.path.join(bert_model, "poly_bert_model.onnx"), + sess_options=options, + providers=[ + "CoreMLExecutionProvider", # Replace CUDA with CoreML + "CPUExecutionProvider", + ], # CPUExecutionProvider #CUDAExecutionProvider + ) + # self.session.set_providers(['CUDAExecutionProvider', "CPUExecutionProvider"], [ {'device_id': 0}]) + + # disable session.run() fallback mechanism, it prevents for a reset of the execution provider + self.session.disable_fallback() + + def predict_process(self, txt_list): + word_test, label_test, texts_test = self.get_examples_po(txt_list) + data = self.polydataset(word_test, label_test) + predict_loader = DataLoader( + data, batch_size=1, shuffle=False, collate_fn=data.collate_fn + ) + pred_tags = self.predict_onnx(predict_loader) + return pred_tags + + def predict_onnx(self, dev_loader): + pred_tags = [] + with torch.no_grad(): + for idx, batch_samples in enumerate(dev_loader): + # [batch_data, batch_label_starts, batch_labels, batch_pmasks, ori_sents] + batch_data, batch_label_starts, batch_labels, batch_pmasks, _ = ( + batch_samples + ) + # shift tensors to GPU if available + batch_data = batch_data.to(self.device) + batch_label_starts = batch_label_starts.to(self.device) + batch_labels = batch_labels.to(self.device) + batch_pmasks = batch_pmasks.to(self.device) + batch_data = np.asarray(batch_data, dtype=np.float32) + batch_pmasks = np.asarray(batch_pmasks, dtype=np.float32) + # batch_output = self.session.run(output_names=['outputs'], input_feed={"input_ids":batch_data, "input_pmasks": batch_pmasks})[0][0] + batch_output = self.session.run( + output_names=["outputs"], input_feed={"input_ids": batch_data} + )[0] + label_masks = batch_pmasks == 1 + batch_labels = batch_labels.to("cpu").numpy() + for i, indices in enumerate(np.argmax(batch_output, axis=2)): + for j, idx in enumerate(indices): + if label_masks[i][j]: + # pred_tag.append(idx) + pred_tags.append(self.pron_dict_id_2_pinyin[str(idx + 1)]) + return pred_tags + + def get_examples_po(self, text_list): + + word_list = [] + label_list = [] + sentence_list = [] + id = 0 + for line in [text_list]: + sentence = line[0] + words = [] + tokens = line[0] + index = line[-1] + front = index + back = len(tokens) - index - 1 + labels = [0] * front + [1] + [0] * back + words = ["[CLS]"] + [item for item in sentence] + words = self.tokenizer.convert_tokens_to_ids(words) + word_list.append(words) + label_list.append(labels) + sentence_list.append(sentence) + + id += 1 + # mask_list.append(masks) + assert len(labels) + 1 == len(words), print( + ( + poly, + sentence, + words, + labels, + sentence, + len(sentence), + len(words), + len(labels), + ) + ) + assert len(labels) + 1 == len( + words + ), "Number of labels does not match number of words" + assert len(labels) == len( + sentence + ), "Number of labels does not match number of sentences" + assert len(word_list) == len( + label_list + ), "Number of label sentences does not match number of word sentences" + return word_list, label_list, text_list diff --git a/diffrhythm/g2p/g2p/cleaners.py b/diffrhythm/g2p/g2p/cleaners.py new file mode 100644 index 0000000000000000000000000000000000000000..f9f6782eb01cef3a30fe0952ec262be6534d273a --- /dev/null +++ b/diffrhythm/g2p/g2p/cleaners.py @@ -0,0 +1,31 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re +from diffrhythm.g2p.g2p.japanese import japanese_to_ipa +from diffrhythm.g2p.g2p.mandarin import chinese_to_ipa +from diffrhythm.g2p.g2p.english import english_to_ipa +from diffrhythm.g2p.g2p.french import french_to_ipa +from diffrhythm.g2p.g2p.korean import korean_to_ipa +from diffrhythm.g2p.g2p.german import german_to_ipa + + +def cjekfd_cleaners(text, sentence, language, text_tokenizers): + + if language == "zh": + return chinese_to_ipa(text, sentence, text_tokenizers["zh"]) + elif language == "ja": + return japanese_to_ipa(text, text_tokenizers["ja"]) + elif language == "en": + return english_to_ipa(text, text_tokenizers["en"]) + elif language == "fr": + return french_to_ipa(text, text_tokenizers["fr"]) + elif language == "ko": + return korean_to_ipa(text, text_tokenizers["ko"]) + elif language == "de": + return german_to_ipa(text, text_tokenizers["de"]) + else: + raise Exception("Unknown language: %s" % language) + return None diff --git a/diffrhythm/g2p/g2p/english.py b/diffrhythm/g2p/g2p/english.py new file mode 100644 index 0000000000000000000000000000000000000000..f8f349fd621ba3d6aa110f447238249642d80326 --- /dev/null +++ b/diffrhythm/g2p/g2p/english.py @@ -0,0 +1,202 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re +from unidecode import unidecode +import inflect + +""" + Text clean time +""" +_inflect = inflect.engine() +_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])") +_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)") +_percent_number_re = re.compile(r"([0-9\.\,]*[0-9]+%)") +_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)") +_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)") +_fraction_re = re.compile(r"([0-9]+)/([0-9]+)") +_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)") +_number_re = re.compile(r"[0-9]+") + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = [ + (re.compile("\\b%s\\b" % x[0], re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ("etc", "et cetera"), + ("btw", "by the way"), + ] +] + +_special_map = [ + ("t|ɹ", "tɹ"), + ("d|ɹ", "dɹ"), + ("t|s", "ts"), + ("d|z", "dz"), + ("ɪ|ɹ", "ɪɹ"), + ("ɐ", "ɚ"), + ("ᵻ", "ɪ"), + ("əl", "l"), + ("x", "k"), + ("ɬ", "l"), + ("ʔ", "t"), + ("n̩", "n"), + ("oː|ɹ", "oːɹ"), +] + + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + + +def _remove_commas(m): + return m.group(1).replace(",", "") + + +def _expand_decimal_point(m): + return m.group(1).replace(".", " point ") + + +def _expand_percent(m): + return m.group(1).replace("%", " percent ") + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split(".") + if len(parts) > 2: + return " " + match + " dollars " # Unexpected format + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = "dollar" if dollars == 1 else "dollars" + cent_unit = "cent" if cents == 1 else "cents" + return " %s %s, %s %s " % (dollars, dollar_unit, cents, cent_unit) + elif dollars: + dollar_unit = "dollar" if dollars == 1 else "dollars" + return " %s %s " % (dollars, dollar_unit) + elif cents: + cent_unit = "cent" if cents == 1 else "cents" + return " %s %s " % (cents, cent_unit) + else: + return " zero dollars " + + +def fraction_to_words(numerator, denominator): + if numerator == 1 and denominator == 2: + return " one half " + if numerator == 1 and denominator == 4: + return " one quarter " + if denominator == 2: + return " " + _inflect.number_to_words(numerator) + " halves " + if denominator == 4: + return " " + _inflect.number_to_words(numerator) + " quarters " + return ( + " " + + _inflect.number_to_words(numerator) + + " " + + _inflect.ordinal(_inflect.number_to_words(denominator)) + + " " + ) + + +def _expand_fraction(m): + numerator = int(m.group(1)) + denominator = int(m.group(2)) + return fraction_to_words(numerator, denominator) + + +def _expand_ordinal(m): + return " " + _inflect.number_to_words(m.group(0)) + " " + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return " two thousand " + elif num > 2000 and num < 2010: + return " two thousand " + _inflect.number_to_words(num % 100) + " " + elif num % 100 == 0: + return " " + _inflect.number_to_words(num // 100) + " hundred " + else: + return ( + " " + + _inflect.number_to_words(num, andword="", zero="oh", group=2).replace( + ", ", " " + ) + + " " + ) + else: + return " " + _inflect.number_to_words(num, andword="") + " " + + +# Normalize numbers pronunciation +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r"\1 pounds", text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_fraction_re, _expand_fraction, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_percent_number_re, _expand_percent, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text + + +def _english_to_ipa(text): + # text = unidecode(text).lower() + text = expand_abbreviations(text) + text = normalize_numbers(text) + return text + + +# special map +def special_map(text): + for regex, replacement in _special_map: + regex = regex.replace("|", "\|") + while re.search(r"(^|[_|]){}([_|]|$)".format(regex), text): + text = re.sub( + r"(^|[_|]){}([_|]|$)".format(regex), r"\1{}\2".format(replacement), text + ) + # text = re.sub(r'([,.!?])', r'|\1', text) + return text + + +# Add some special operation +def english_to_ipa(text, text_tokenizer): + if type(text) == str: + text = _english_to_ipa(text) + else: + text = [_english_to_ipa(t) for t in text] + phonemes = text_tokenizer(text) + if phonemes[-1] in "p⁼ʰmftnlkxʃs`ɹaoəɛɪeɑʊŋiuɥwæjː": + phonemes += "|_" + if type(text) == str: + return special_map(phonemes) + else: + result_ph = [] + for phone in phonemes: + result_ph.append(special_map(phone)) + return result_ph diff --git a/diffrhythm/g2p/g2p/french.py b/diffrhythm/g2p/g2p/french.py new file mode 100644 index 0000000000000000000000000000000000000000..bd9400cdfc6598e7d642480cbfc1f990fc78cddf --- /dev/null +++ b/diffrhythm/g2p/g2p/french.py @@ -0,0 +1,149 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re + +""" + Text clean time +""" +# List of (regular expression, replacement) pairs for abbreviations in french: +_abbreviations = [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("M", "monsieur"), + ("Mlle", "mademoiselle"), + ("Mlles", "mesdemoiselles"), + ("Mme", "Madame"), + ("Mmes", "Mesdames"), + ("N.B", "nota bene"), + ("M", "monsieur"), + ("p.c.q", "parce que"), + ("Pr", "professeur"), + ("qqch", "quelque chose"), + ("rdv", "rendez-vous"), + ("max", "maximum"), + ("min", "minimum"), + ("no", "numéro"), + ("adr", "adresse"), + ("dr", "docteur"), + ("st", "saint"), + ("co", "companie"), + ("jr", "junior"), + ("sgt", "sergent"), + ("capt", "capitain"), + ("col", "colonel"), + ("av", "avenue"), + ("av. J.-C", "avant Jésus-Christ"), + ("apr. J.-C", "après Jésus-Christ"), + ("art", "article"), + ("boul", "boulevard"), + ("c.-à-d", "c’est-à-dire"), + ("etc", "et cetera"), + ("ex", "exemple"), + ("excl", "exclusivement"), + ("boul", "boulevard"), + ] +] + [ + (re.compile("\\b%s" % x[0]), x[1]) + for x in [ + ("Mlle", "mademoiselle"), + ("Mlles", "mesdemoiselles"), + ("Mme", "Madame"), + ("Mmes", "Mesdames"), + ] +] + +rep_map = { + "：": ",", + "；": ",", + "，": ",", + "。": ".", + "！": "!", + "？": "?", + "\n": ".", + "·": ",", + "、": ",", + "...": ".", + "…": ".", + "$": ".", + "“": "", + "”": "", + "‘": "", + "’": "", + "（": "", + "）": "", + "(": "", + ")": "", + "《": "", + "》": "", + "【": "", + "】": "", + "[": "", + "]": "", + "—": "", + "～": "-", + "~": "-", + "「": "", + "」": "", + "¿": "", + "¡": "", +} + + +def collapse_whitespace(text): + # Regular expression matching whitespace: + _whitespace_re = re.compile(r"\s+") + return re.sub(_whitespace_re, " ", text).strip() + + +def remove_punctuation_at_begin(text): + return re.sub(r"^[,.!?]+", "", text) + + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"\«\»]+", "", text) + return text + + +def replace_symbols(text): + text = text.replace(";", ",") + text = text.replace("-", " ") + text = text.replace(":", ",") + text = text.replace("&", " et ") + return text + + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + + +def replace_punctuation(text): + pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys())) + replaced_text = pattern.sub(lambda x: rep_map[x.group()], text) + return replaced_text + + +def text_normalize(text): + text = expand_abbreviations(text) + text = replace_punctuation(text) + text = replace_symbols(text) + text = remove_aux_symbols(text) + text = remove_punctuation_at_begin(text) + text = collapse_whitespace(text) + text = re.sub(r"([^\.,!\?\-…])$", r"\1", text) + return text + + +def french_to_ipa(text, text_tokenizer): + if type(text) == str: + text = text_normalize(text) + phonemes = text_tokenizer(text) + return phonemes + else: + for i, t in enumerate(text): + text[i] = text_normalize(t) + return text_tokenizer(text) diff --git a/diffrhythm/g2p/g2p/german.py b/diffrhythm/g2p/g2p/german.py new file mode 100644 index 0000000000000000000000000000000000000000..bd82eeabc44cc891acd98daa982cd2be1e991e3a --- /dev/null +++ b/diffrhythm/g2p/g2p/german.py @@ -0,0 +1,94 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re + +""" + Text clean time +""" +rep_map = { + "：": ",", + "；": ",", + "，": ",", + "。": ".", + "！": "!", + "？": "?", + "\n": ".", + "·": ",", + "、": ",", + "...": ".", + "…": ".", + "$": ".", + "“": "", + "”": "", + "‘": "", + "’": "", + "（": "", + "）": "", + "(": "", + ")": "", + "《": "", + "》": "", + "【": "", + "】": "", + "[": "", + "]": "", + "—": "", + "～": "-", + "~": "-", + "「": "", + "」": "", + "¿": "", + "¡": "", +} + + +def collapse_whitespace(text): + # Regular expression matching whitespace: + _whitespace_re = re.compile(r"\s+") + return re.sub(_whitespace_re, " ", text).strip() + + +def remove_punctuation_at_begin(text): + return re.sub(r"^[,.!?]+", "", text) + + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"\«\»]+", "", text) + return text + + +def replace_symbols(text): + text = text.replace(";", ",") + text = text.replace("-", " ") + text = text.replace(":", ",") + return text + + +def replace_punctuation(text): + pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys())) + replaced_text = pattern.sub(lambda x: rep_map[x.group()], text) + return replaced_text + + +def text_normalize(text): + text = replace_punctuation(text) + text = replace_symbols(text) + text = remove_aux_symbols(text) + text = remove_punctuation_at_begin(text) + text = collapse_whitespace(text) + text = re.sub(r"([^\.,!\?\-…])$", r"\1", text) + return text + + +def german_to_ipa(text, text_tokenizer): + if type(text) == str: + text = text_normalize(text) + phonemes = text_tokenizer(text) + return phonemes + else: + for i, t in enumerate(text): + text[i] = text_normalize(t) + return text_tokenizer(text) diff --git a/diffrhythm/g2p/g2p/japanese.py b/diffrhythm/g2p/g2p/japanese.py new file mode 100644 index 0000000000000000000000000000000000000000..f01fec575214bb939834924e165f9df748a6a89a --- /dev/null +++ b/diffrhythm/g2p/g2p/japanese.py @@ -0,0 +1,816 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import io, re, os, sys, time, argparse, pdb, json +from io import StringIO +from typing import Optional +import numpy as np +import traceback +import pyopenjtalk +from pykakasi import kakasi + +punctuation = [",", ".", "!", "?", ":", ";", "'", "…"] + +jp_xphone2ipa = [ + " a a", + " i i", + " u ɯ", + " e e", + " o o", + " a: aː", + " i: iː", + " u: ɯː", + " e: eː", + " o: oː", + " k k", + " s s", + " t t", + " n n", + " h ç", + " f ɸ", + " m m", + " y j", + " r ɾ", + " w ɰᵝ", + " N ɴ", + " g g", + " j d ʑ", + " z z", + " d d", + " b b", + " p p", + " q q", + " v v", + " : :", + " by b j", + " ch t ɕ", + " dy d e j", + " ty t e j", + " gy g j", + " gw g ɯ", + " hy ç j", + " ky k j", + " kw k ɯ", + " my m j", + " ny n j", + " py p j", + " ry ɾ j", + " sh ɕ", + " ts t s ɯ", +] + +_mora_list_minimum: list[tuple[str, Optional[str], str]] = [ + ("ヴォ", "v", "o"), + ("ヴェ", "v", "e"), + ("ヴィ", "v", "i"), + ("ヴァ", "v", "a"), + ("ヴ", "v", "u"), + ("ン", None, "N"), + ("ワ", "w", "a"), + ("ロ", "r", "o"), + ("レ", "r", "e"), + ("ル", "r", "u"), + ("リョ", "ry", "o"), + ("リュ", "ry", "u"), + ("リャ", "ry", "a"), + ("リェ", "ry", "e"), + ("リ", "r", "i"), + ("ラ", "r", "a"), + ("ヨ", "y", "o"), + ("ユ", "y", "u"), + ("ヤ", "y", "a"), + ("モ", "m", "o"), + ("メ", "m", "e"), + ("ム", "m", "u"), + ("ミョ", "my", "o"), + ("ミュ", "my", "u"), + ("ミャ", "my", "a"), + ("ミェ", "my", "e"), + ("ミ", "m", "i"), + ("マ", "m", "a"), + ("ポ", "p", "o"), + ("ボ", "b", "o"), + ("ホ", "h", "o"), + ("ペ", "p", "e"), + ("ベ", "b", "e"), + ("ヘ", "h", "e"), + ("プ", "p", "u"), + ("ブ", "b", "u"), + ("フォ", "f", "o"), + ("フェ", "f", "e"), + ("フィ", "f", "i"), + ("ファ", "f", "a"), + ("フ", "f", "u"), + ("ピョ", "py", "o"), + ("ピュ", "py", "u"), + ("ピャ", "py", "a"), + ("ピェ", "py", "e"), + ("ピ", "p", "i"), + ("ビョ", "by", "o"), + ("ビュ", "by", "u"), + ("ビャ", "by", "a"), + ("ビェ", "by", "e"), + ("ビ", "b", "i"), + ("ヒョ", "hy", "o"), + ("ヒュ", "hy", "u"), + ("ヒャ", "hy", "a"), + ("ヒェ", "hy", "e"), + ("ヒ", "h", "i"), + ("パ", "p", "a"), + ("バ", "b", "a"), + ("ハ", "h", "a"), + ("ノ", "n", "o"), + ("ネ", "n", "e"), + ("ヌ", "n", "u"), + ("ニョ", "ny", "o"), + ("ニュ", "ny", "u"), + ("ニャ", "ny", "a"), + ("ニェ", "ny", "e"), + ("ニ", "n", "i"), + ("ナ", "n", "a"), + ("ドゥ", "d", "u"), + ("ド", "d", "o"), + ("トゥ", "t", "u"), + ("ト", "t", "o"), + ("デョ", "dy", "o"), + ("デュ", "dy", "u"), + ("デャ", "dy", "a"), + # ("デェ", "dy", "e"), + ("ディ", "d", "i"), + ("デ", "d", "e"), + ("テョ", "ty", "o"), + ("テュ", "ty", "u"), + ("テャ", "ty", "a"), + ("ティ", "t", "i"), + ("テ", "t", "e"), + ("ツォ", "ts", "o"), + ("ツェ", "ts", "e"), + ("ツィ", "ts", "i"), + ("ツァ", "ts", "a"), + ("ツ", "ts", "u"), + ("ッ", None, "q"), # 「cl」から「q」に変更 + ("チョ", "ch", "o"), + ("チュ", "ch", "u"), + ("チャ", "ch", "a"), + ("チェ", "ch", "e"), + ("チ", "ch", "i"), + ("ダ", "d", "a"), + ("タ", "t", "a"), + ("ゾ", "z", "o"), + ("ソ", "s", "o"), + ("ゼ", "z", "e"), + ("セ", "s", "e"), + ("ズィ", "z", "i"), + ("ズ", "z", "u"), + ("スィ", "s", "i"), + ("ス", "s", "u"), + ("ジョ", "j", "o"), + ("ジュ", "j", "u"), + ("ジャ", "j", "a"), + ("ジェ", "j", "e"), + ("ジ", "j", "i"), + ("ショ", "sh", "o"), + ("シュ", "sh", "u"), + ("シャ", "sh", "a"), + ("シェ", "sh", "e"), + ("シ", "sh", "i"), + ("ザ", "z", "a"), + ("サ", "s", "a"), + ("ゴ", "g", "o"), + ("コ", "k", "o"), + ("ゲ", "g", "e"), + ("ケ", "k", "e"), + ("グヮ", "gw", "a"), + ("グ", "g", "u"), + ("クヮ", "kw", "a"), + ("ク", "k", "u"), + ("ギョ", "gy", "o"), + ("ギュ", "gy", "u"), + ("ギャ", "gy", "a"), + ("ギェ", "gy", "e"), + ("ギ", "g", "i"), + ("キョ", "ky", "o"), + ("キュ", "ky", "u"), + ("キャ", "ky", "a"), + ("キェ", "ky", "e"), + ("キ", "k", "i"), + ("ガ", "g", "a"), + ("カ", "k", "a"), + ("オ", None, "o"), + ("エ", None, "e"), + ("ウォ", "w", "o"), + ("ウェ", "w", "e"), + ("ウィ", "w", "i"), + ("ウ", None, "u"), + ("イェ", "y", "e"), + ("イ", None, "i"), + ("ア", None, "a"), +] + +_mora_list_additional: list[tuple[str, Optional[str], str]] = [ + ("ヴョ", "by", "o"), + ("ヴュ", "by", "u"), + ("ヴャ", "by", "a"), + ("ヲ", None, "o"), + ("ヱ", None, "e"), + ("ヰ", None, "i"), + ("ヮ", "w", "a"), + ("ョ", "y", "o"), + ("ュ", "y", "u"), + ("ヅ", "z", "u"), + ("ヂ", "j", "i"), + ("ヶ", "k", "e"), + ("ャ", "y", "a"), + ("ォ", None, "o"), + ("ェ", None, "e"), + ("ゥ", None, "u"), + ("ィ", None, "i"), + ("ァ", None, "a"), +] + +# 例: "vo" -> "ヴォ", "a" -> "ア" +mora_phonemes_to_mora_kata: dict[str, str] = { + (consonant or "") + vowel: kana for [kana, consonant, vowel] in _mora_list_minimum +} + +# 例: "ヴォ" -> ("v", "o"), "ア" -> (None, "a") +mora_kata_to_mora_phonemes: dict[str, tuple[Optional[str], str]] = { + kana: (consonant, vowel) + for [kana, consonant, vowel] in _mora_list_minimum + _mora_list_additional +} + + +# 正規化で記号を変換するための辞書 +rep_map = { + "：": ":", + "；": ";", + "，": ",", + "。": ".", + "！": "!", + "？": "?", + "\n": ".", + "．": ".", + "⋯": "…", + "···": "…", + "・・・": "…", + "·": ",", + "・": ",", + "•": ",", + "、": ",", + "$": ".", + # "“": "'", + # "”": "'", + # '"': "'", + "‘": "'", + "’": "'", + # "（": "'", + # "）": "'", + # "(": "'", + # ")": "'", + # "《": "'", + # "》": "'", + # "【": "'", + # "】": "'", + # "[": "'", + # "]": "'", + # "——": "-", + # "−": "-", + # "-": "-", + # "『": "'", + # "』": "'", + # "〈": "'", + # "〉": "'", + # "«": "'", + # "»": "'", + # # "～": "-", # これは長音記号「ー」として扱うよう変更 + # # "~": "-", # これは長音記号「ー」として扱うよう変更 + # "「": "'", + # "」": "'", +} + + +def _numeric_feature_by_regex(regex, s): + match = re.search(regex, s) + if match is None: + return -50 + return int(match.group(1)) + + +def replace_punctuation(text: str) -> str: + """句読点等を「.」「,」「!」「?」「'」「-」に正規化し、OpenJTalkで読みが取得できるもののみ残す： + 漢字・平仮名・カタカナ、アルファベット、ギリシャ文字 + """ + pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys())) + # print("before: ", text) + # 句読点を辞書で置換 + replaced_text = pattern.sub(lambda x: rep_map[x.group()], text) + + replaced_text = re.sub( + # ↓ ひらがな、カタカナ、漢字 + r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005" + # ↓ 半角アルファベット（大文字と小文字） + + r"\u0041-\u005A\u0061-\u007A" + # ↓ 全角アルファベット（大文字と小文字） + + r"\uFF21-\uFF3A\uFF41-\uFF5A" + # ↓ ギリシャ文字 + + r"\u0370-\u03FF\u1F00-\u1FFF" + # ↓ "!", "?", "…", ",", ".", "'", "-", 但し`…`はすでに`...`に変換されている + + "".join(punctuation) + r"]+", + # 上述以外の文字を削除 + "", + replaced_text, + ) + # print("after: ", replaced_text) + return replaced_text + + +def fix_phone_tone(phone_tone_list: list[tuple[str, int]]) -> list[tuple[str, int]]: + """ + `phone_tone_list`のtone（アクセントの値）を0か1の範囲に修正する。 + 例: [(a, 0), (i, -1), (u, -1)] → [(a, 1), (i, 0), (u, 0)] + """ + tone_values = set(tone for _, tone in phone_tone_list) + if len(tone_values) == 1: + assert tone_values == {0}, tone_values + return phone_tone_list + elif len(tone_values) == 2: + if tone_values == {0, 1}: + return phone_tone_list + elif tone_values == {-1, 0}: + return [ + (letter, 0 if tone == -1 else 1) for letter, tone in phone_tone_list + ] + else: + raise ValueError(f"Unexpected tone values: {tone_values}") + else: + raise ValueError(f"Unexpected tone values: {tone_values}") + + +def fix_phone_tone_wplen(phone_tone_list, word_phone_length_list): + phones = [] + tones = [] + w_p_len = [] + p_len = len(phone_tone_list) + idx = 0 + w_idx = 0 + while idx < p_len: + offset = 0 + if phone_tone_list[idx] == "▁": + w_p_len.append(w_idx + 1) + + curr_w_p_len = word_phone_length_list[w_idx] + for i in range(curr_w_p_len): + p, t = phone_tone_list[idx] + if p == ":" and len(phones) > 0: + if phones[-1][-1] != ":": + phones[-1] += ":" + offset -= 1 + else: + phones.append(p) + tones.append(str(t)) + idx += 1 + if idx >= p_len: + break + w_p_len.append(curr_w_p_len + offset) + w_idx += 1 + # print(w_p_len) + return phones, tones, w_p_len + + +def g2phone_tone_wo_punct(prosodies) -> list[tuple[str, int]]: + """ + テキストに対して、音素とアクセント（0か1）のペアのリストを返す。 + ただし「!」「.」「?」等の非音素記号(punctuation)は全て消える（ポーズ記号も残さない）。 + 非音素記号を含める処理は`align_tones()`で行われる。 + また「っ」は「cl」でなく「q」に変換される（「ん」は「N」のまま）。 + 例: "こんにちは、世界ー。。元気？！" → + [('k', 0), ('o', 0), ('N', 1), ('n', 1), ('i', 1), ('ch', 1), ('i', 1), ('w', 1), ('a', 1), ('s', 1), ('e', 1), ('k', 0), ('a', 0), ('i', 0), ('i', 0), ('g', 1), ('e', 1), ('N', 0), ('k', 0), ('i', 0)] + """ + result: list[tuple[str, int]] = [] + current_phrase: list[tuple[str, int]] = [] + current_tone = 0 + last_accent = "" + for i, letter in enumerate(prosodies): + # 特殊記号の処理 + + # 文頭記号、無視する + if letter == "^": + assert i == 0, "Unexpected ^" + # アクセント句の終わりに来る記号 + elif letter in ("$", "?", "_", "#"): + # 保持しているフレーズを、アクセント数値を0-1に修正し結果に追加 + result.extend(fix_phone_tone(current_phrase)) + # 末尾に来る終了記号、無視（文中の疑問文は`_`になる） + if letter in ("$", "?"): + assert i == len(prosodies) - 1, f"Unexpected {letter}" + # あとは"_"（ポーズ）と"#"（アクセント句の境界）のみ + # これらは残さず、次のアクセント句に備える。 + + current_phrase = [] + # 0を基準点にしてそこから上昇・下降する（負の場合は上の`fix_phone_tone`で直る） + current_tone = 0 + last_accent = "" + # アクセント上昇記号 + elif letter == "[": + if last_accent != letter: + current_tone = current_tone + 1 + last_accent = letter + # アクセント下降記号 + elif letter == "]": + if last_accent != letter: + current_tone = current_tone - 1 + last_accent = letter + # それ以外は通常の音素 + else: + if letter == "cl": # 「っ」の処理 + letter = "q" + current_phrase.append((letter, current_tone)) + return result + + +def handle_long(sep_phonemes: list[list[str]]) -> list[list[str]]: + for i in range(len(sep_phonemes)): + if sep_phonemes[i][0] == "ー": + # sep_phonemes[i][0] = sep_phonemes[i - 1][-1] + sep_phonemes[i][0] = ":" + if "ー" in sep_phonemes[i]: + for j in range(len(sep_phonemes[i])): + if sep_phonemes[i][j] == "ー": + # sep_phonemes[i][j] = sep_phonemes[i][j - 1][-1] + sep_phonemes[i][j] = ":" + return sep_phonemes + + +def handle_long_word(sep_phonemes: list[list[str]]) -> list[list[str]]: + res = [] + for i in range(len(sep_phonemes)): + if sep_phonemes[i][0] == "ー": + sep_phonemes[i][0] = sep_phonemes[i - 1][-1] + # sep_phonemes[i][0] = ':' + if "ー" in sep_phonemes[i]: + for j in range(len(sep_phonemes[i])): + if sep_phonemes[i][j] == "ー": + sep_phonemes[i][j] = sep_phonemes[i][j - 1][-1] + # sep_phonemes[i][j] = ':' + res.append(sep_phonemes[i]) + res.append("▁") + return res + + +def align_tones( + phones_with_punct: list[str], phone_tone_list: list[tuple[str, int]] +) -> list[tuple[str, int]]: + """ + 例: + …私は、、そう思う。 + phones_with_punct: + [".", ".", ".", "w", "a", "t", "a", "sh", "i", "w", "a", ",", ",", "s", "o", "o", "o", "m", "o", "u", "."] + phone_tone_list: + [("w", 0), ("a", 0), ("t", 1), ("a", 1), ("sh", 1), ("i", 1), ("w", 1), ("a", 1), ("s", 0), ("o", 0), ("o", 1), ("o", 1), ("m", 1), ("o", 1), ("u", 0))] + Return: + [(".", 0), (".", 0), (".", 0), ("w", 0), ("a", 0), ("t", 1), ("a", 1), ("sh", 1), ("i", 1), ("w", 1), ("a", 1), (",", 0), (",", 0), ("s", 0), ("o", 0), ("o", 1), ("o", 1), ("m", 1), ("o", 1), ("u", 0), (".", 0)] + """ + result: list[tuple[str, int]] = [] + tone_index = 0 + for phone in phones_with_punct: + if tone_index >= len(phone_tone_list): + # 余ったpunctuationがある場合 → (punctuation, 0)を追加 + result.append((phone, 0)) + elif phone == phone_tone_list[tone_index][0]: + # phone_tone_listの現在の音素と一致する場合 → toneをそこから取得、(phone, tone)を追加 + result.append((phone, phone_tone_list[tone_index][1])) + # 探すindexを1つ進める + tone_index += 1 + elif phone in punctuation or phone == "▁": + # phoneがpunctuationの場合 → (phone, 0)を追加 + result.append((phone, 0)) + else: + print(f"phones: {phones_with_punct}") + print(f"phone_tone_list: {phone_tone_list}") + print(f"result: {result}") + print(f"tone_index: {tone_index}") + print(f"phone: {phone}") + raise ValueError(f"Unexpected phone: {phone}") + return result + + +def kata2phoneme_list(text: str) -> list[str]: + """ + 原則カタカナの`text`を受け取り、それをそのままいじらずに音素記号のリストに変換。 + 注意点： + - punctuationが来た場合（punctuationが1文字の場合がありうる）、処理せず1文字のリストを返す + - 冒頭に続く「ー」はそのまま「ー」のままにする（`handle_long()`で処理される） + - 文中の「ー」は前の音素記号の最後の音素記号に変換される。 + 例： + `ーーソーナノカーー` → ["ー", "ー", "s", "o", "o", "n", "a", "n", "o", "k", "a", "a", "a"] + `?` → ["?"] + """ + if text in punctuation: + return [text] + # `text`がカタカナ（`ー`含む）のみからなるかどうかをチェック + if re.fullmatch(r"[\u30A0-\u30FF]+", text) is None: + raise ValueError(f"Input must be katakana only: {text}") + sorted_keys = sorted(mora_kata_to_mora_phonemes.keys(), key=len, reverse=True) + pattern = "|".join(map(re.escape, sorted_keys)) + + def mora2phonemes(mora: str) -> str: + cosonant, vowel = mora_kata_to_mora_phonemes[mora] + if cosonant is None: + return f" {vowel}" + return f" {cosonant} {vowel}" + + spaced_phonemes = re.sub(pattern, lambda m: mora2phonemes(m.group()), text) + + # 長音記号「ー」の処理 + long_pattern = r"(\w)(ー*)" + long_replacement = lambda m: m.group(1) + (" " + m.group(1)) * len(m.group(2)) + spaced_phonemes = re.sub(long_pattern, long_replacement, spaced_phonemes) + # spaced_phonemes += ' ▁' + return spaced_phonemes.strip().split(" ") + + +def frontend2phoneme(labels, drop_unvoiced_vowels=False): + N = len(labels) + + phones = [] + for n in range(N): + lab_curr = labels[n] + # print(lab_curr) + # 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 + # print(p3, a1, a2, a3, f1, a2_next, lab_curr) + 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("[") + + # phones = ' '.join(phones) + return phones + + +class JapanesePhoneConverter(object): + def __init__(self, lexicon_path=None, ipa_dict_path=None): + # lexicon_lines = open(lexicon_path, 'r', encoding='utf-8').readlines() + # self.lexicon = {} + # self.single_dict = {} + # self.double_dict = {} + # for curr_line in lexicon_lines: + # k,v = curr_line.strip().split('+',1) + # self.lexicon[k] = v + # if len(k) == 2: + # self.double_dict[k] = v + # elif len(k) == 1: + # self.single_dict[k] = v + self.ipa_dict = {} + for curr_line in jp_xphone2ipa: + k, v = curr_line.strip().split(" ", 1) + self.ipa_dict[k] = re.sub("\s", "", v) + # kakasi1 = kakasi() + # kakasi1.setMode("H","K") + # kakasi1.setMode("J","K") + # kakasi1.setMode("r","Hepburn") + self.japan_JH2K = kakasi() + self.table = {ord(f): ord(t) for f, t in zip("67", "_¯")} + + def text2sep_kata(self, parsed) -> tuple[list[str], list[str]]: + """ + `text_normalize`で正規化済みの`norm_text`を受け取り、それを単語分割し、 + 分割された単語リストとその読み（カタカナor記号1文字）のリストのタプルを返す。 + 単語分割結果は、`g2p()`の`word2ph`で1文字あたりに割り振る音素記号の数を決めるために使う。 + 例: + `私はそう思う!って感じ?` → + ["私", "は", "そう", "思う", "!", "って", "感じ", "?"], ["ワタシ", "ワ", "ソー", "オモウ", "!", "ッテ", "カンジ", "?"] + """ + # parsed: OpenJTalkの解析結果 + sep_text: list[str] = [] + sep_kata: list[str] = [] + fix_parsed = [] + i = 0 + while i <= len(parsed) - 1: + # word: 実際の単語の文字列 + # yomi: その読み、但し無声化サインの`’`は除去 + # print(parsed) + yomi = parsed[i]["pron"] + tmp_parsed = parsed[i] + if i != len(parsed) - 1 and parsed[i + 1]["string"] in [ + "々", + "ゝ", + "ヽ", + "ゞ", + "ヾ", + "゛", + ]: + word = parsed[i]["string"] + parsed[i + 1]["string"] + i += 1 + else: + word = parsed[i]["string"] + word, yomi = replace_punctuation(word), yomi.replace("’", "") + """ + ここで`yomi`の取りうる値は以下の通りのはず。 + - `word`が通常単語 → 通常の読み（カタカナ） + （カタカナからなり、長音記号も含みうる、`アー` 等） + - `word`が`ー` から始まる → `ーラー` や `ーーー` など + - `word`が句読点や空白等 → `、` + - `word`が`?` → `？`（全角になる） + 他にも`word`が読めないキリル文字アラビア文字等が来ると`、`になるが、正規化でこの場合は起きないはず。 + また元のコードでは`yomi`が空白の場合の処理があったが、これは起きないはず。 + 処理すべきは`yomi`が`、`の場合のみのはず。 + """ + assert yomi != "", f"Empty yomi: {word}" + if yomi == "、": + # wordは正規化されているので、`.`, `,`, `!`, `'`, `-`のいずれか + if word not in ( + ".", + ",", + "!", + "'", + "-", + "?", + ":", + ";", + "…", + "", + ): + # ここはpyopenjtalkが読めない文字等のときに起こる + #print( + # "{}Cannot read:{}, yomi:{}, new_word:{};".format( + # parsed, word, yomi, self.japan_JH2K.convert(word)[0]["kana"] + # ) + #) + # raise ValueError(word) + word = self.japan_JH2K.convert(word)[0]["kana"] + # print(word, self.japan_JH2K.convert(word)[0]['kana'], kata2phoneme_list(self.japan_JH2K.convert(word)[0]['kana'])) + tmp_parsed["pron"] = word + # yomi = "-" + # word = ',' + # yomiは元の記号のままに変更 + # else: + # parsed[i]['pron'] = parsed[i]["string"] + yomi = word + elif yomi == "？": + assert word == "?", f"yomi `？` comes from: {word}" + yomi = "?" + if word == "": + i += 1 + continue + sep_text.append(word) + sep_kata.append(yomi) + # print(word, yomi, parts) + fix_parsed.append(tmp_parsed) + i += 1 + # print(sep_text, sep_kata) + return sep_text, sep_kata, fix_parsed + + def getSentencePhone(self, sentence, blank_mode=True, phoneme_mode=False): + # print("origin:", sentence) + words = [] + words_phone_len = [] + short_char_flag = False + output_duration_flag = [] + output_before_sil_flag = [] + normed_text = [] + sentence = sentence.strip().strip("'") + sentence = re.sub(r"\s+", "", sentence) + output_res = [] + failed_words = [] + last_long_pause = 4 + last_word = None + frontend_text = pyopenjtalk.run_frontend(sentence) + # print("frontend_text: ", frontend_text) + try: + frontend_text = pyopenjtalk.estimate_accent(frontend_text) + except: + pass + # print("estimate_accent: ", frontend_text) + # sep_text: 単語単位の単語のリスト + # sep_kata: 単語単位の単語のカタカナ読みのリスト + sep_text, sep_kata, frontend_text = self.text2sep_kata(frontend_text) + # print("sep_text: ", sep_text) + # print("sep_kata: ", sep_kata) + # print("frontend_text: ", frontend_text) + # sep_phonemes: 各単語ごとの音素のリストのリスト + sep_phonemes = handle_long_word([kata2phoneme_list(i) for i in sep_kata]) + # print("sep_phonemes: ", sep_phonemes) + + pron_text = [x["pron"].strip().replace("’", "") for x in frontend_text] + # pdb.set_trace() + prosodys = pyopenjtalk.make_label(frontend_text) + prosodys = frontend2phoneme(prosodys, drop_unvoiced_vowels=True) + # print("prosodys: ", ' '.join(prosodys)) + # print("pron_text: ", pron_text) + normed_text = [x["string"].strip() for x in frontend_text] + # punctuationがすべて消えた、音素とアクセントのタプルのリスト + phone_tone_list_wo_punct = g2phone_tone_wo_punct(prosodys) + # print("phone_tone_list_wo_punct: ", phone_tone_list_wo_punct) + + # phone_w_punct: sep_phonemesを結合した、punctuationを元のまま保持した音素列 + phone_w_punct: list[str] = [] + w_p_len = [] + for i in sep_phonemes: + phone_w_punct += i + w_p_len.append(len(i)) + phone_w_punct = phone_w_punct[:-1] + # punctuation無しのアクセント情報を使って、punctuationを含めたアクセント情報を作る + # print("phone_w_punct: ", phone_w_punct) + # print("phone_tone_list_wo_punct: ", phone_tone_list_wo_punct) + phone_tone_list = align_tones(phone_w_punct, phone_tone_list_wo_punct) + + jp_item = {} + jp_p = "" + jp_t = "" + # mye rye pye bye nye + # je she + # print(phone_tone_list) + for p, t in phone_tone_list: + if p in self.ipa_dict: + curr_p = self.ipa_dict[p] + jp_p += curr_p + jp_t += str(t + 6) * len(curr_p) + elif p in punctuation: + jp_p += p + jp_t += "0" + elif p == "▁": + jp_p += p + jp_t += " " + else: + print(p, t) + jp_p += "|" + jp_t += "0" + # return phones, tones, w_p_len + jp_p = jp_p.replace("▁", " ") + jp_t = jp_t.translate(self.table) + jp_l = "" + for t in jp_t: + if t == " ": + jp_l += " " + else: + jp_l += "2" + # print(jp_p) + # print(jp_t) + # print(jp_l) + # print(len(jp_p_len), sum(w_p_len), len(jp_p), sum(jp_p_len)) + assert len(jp_p) == len(jp_t) and len(jp_p) == len(jp_l) + + jp_item["jp_p"] = jp_p.replace("| |", "|").rstrip("|") + jp_item["jp_t"] = jp_t + jp_item["jp_l"] = jp_l + jp_item["jp_normed_text"] = " ".join(normed_text) + jp_item["jp_pron_text"] = " ".join(pron_text) + # jp_item['jp_ruoma'] = sep_phonemes + # print(len(normed_text), len(sep_phonemes)) + # print(normed_text) + return jp_item + + +jpc = JapanesePhoneConverter() + + +def japanese_to_ipa(text, text_tokenizer): + # phonemes = text_tokenizer(text) + if type(text) == str: + return jpc.getSentencePhone(text)["jp_p"] + else: + result_ph = [] + for t in text: + result_ph.append(jpc.getSentencePhone(t)["jp_p"]) + return result_ph diff --git a/diffrhythm/g2p/g2p/korean.py b/diffrhythm/g2p/g2p/korean.py new file mode 100644 index 0000000000000000000000000000000000000000..c7c540b47d98ccf6e0db5f938e52834abf679b59 --- /dev/null +++ b/diffrhythm/g2p/g2p/korean.py @@ -0,0 +1,81 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re + +""" + Text clean time +""" +english_dictionary = { + "KOREA": "코리아", + "IDOL": "아이돌", + "IT": "아이티", + "IQ": "아이큐", + "UP": "업", + "DOWN": "다운", + "PC": "피씨", + "CCTV": "씨씨티비", + "SNS": "에스엔에스", + "AI": "에이아이", + "CEO": "씨이오", + "A": "에이", + "B": "비", + "C": "씨", + "D": "디", + "E": "이", + "F": "에프", + "G": "지", + "H": "에이치", + "I": "아이", + "J": "제이", + "K": "케이", + "L": "엘", + "M": "엠", + "N": "엔", + "O": "오", + "P": "피", + "Q": "큐", + "R": "알", + "S": "에스", + "T": "티", + "U": "유", + "V": "브이", + "W": "더블유", + "X": "엑스", + "Y": "와이", + "Z": "제트", +} + + +def normalize(text): + text = text.strip() + text = re.sub( + "[⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]", "", text + ) + text = normalize_english(text) + text = text.lower() + return text + + +def normalize_english(text): + def fn(m): + word = m.group() + if word in english_dictionary: + return english_dictionary.get(word) + return word + + text = re.sub("([A-Za-z]+)", fn, text) + return text + + +def korean_to_ipa(text, text_tokenizer): + if type(text) == str: + text = normalize(text) + phonemes = text_tokenizer(text) + return phonemes + else: + for i, t in enumerate(text): + text[i] = normalize(t) + return text_tokenizer(text) diff --git a/diffrhythm/g2p/g2p/mandarin.py b/diffrhythm/g2p/g2p/mandarin.py new file mode 100644 index 0000000000000000000000000000000000000000..f52a80d88a736f1e41c83b58865ea3322a8623a2 --- /dev/null +++ b/diffrhythm/g2p/g2p/mandarin.py @@ -0,0 +1,600 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re +import jieba +import cn2an +from pypinyin import lazy_pinyin, BOPOMOFO +from typing import List +from diffrhythm.g2p.g2p.chinese_model_g2p import BertPolyPredict +from diffrhythm.g2p.utils.front_utils import * +import os +from huggingface_hub import hf_hub_download + +# from g2pw import G2PWConverter + + +# set blank level, {0："none",1:"char", 2:"word"} +BLANK_LEVEL = 0 + +# conv = G2PWConverter(style='pinyin', enable_non_tradional_chinese=True) +resource_path = r"./diffrhythm/g2p" +poly_all_class_path = os.path.join( + resource_path, "sources", "g2p_chinese_model", "polychar.txt" +) +if not os.path.exists(poly_all_class_path): + print( + "Incorrect path for polyphonic character class dictionary: {}, please check...".format( + poly_all_class_path + ) + ) + exit() +poly_dict = generate_poly_lexicon(poly_all_class_path) + +# Set up G2PW model parameters +g2pw_poly_model_path = os.path.join(resource_path, "sources", "g2p_chinese_model") +if not os.path.exists(g2pw_poly_model_path): + print( + "Incorrect path for g2pw polyphonic character model: {}, please check...".format( + g2pw_poly_model_path + ) + ) + exit() + +json_file_path = os.path.join( + resource_path, "sources", "g2p_chinese_model", "polydict.json" +) +if not os.path.exists(json_file_path): + print( + "Incorrect path for g2pw id to pinyin dictionary: {}, please check...".format( + json_file_path + ) + ) + exit() + +jsonr_file_path = os.path.join( + resource_path, "sources", "g2p_chinese_model", "polydict_r.json" +) +if not os.path.exists(jsonr_file_path): + print( + "Incorrect path for g2pw pinyin to id dictionary: {}, please check...".format( + jsonr_file_path + ) + ) + exit() + +g2pw_poly_predict = BertPolyPredict( + g2pw_poly_model_path, jsonr_file_path, json_file_path +) + + +""" + Text clean time +""" +# List of (Latin alphabet, bopomofo) pairs: +_latin_to_bopomofo = [ + (re.compile("%s" % x[0], re.IGNORECASE), x[1]) + for x in [ + ("a", "ㄟˉ"), + ("b", "ㄅㄧˋ"), + ("c", "ㄙㄧˉ"), + ("d", "ㄉㄧˋ"), + ("e", "ㄧˋ"), + ("f", "ㄝˊㄈㄨˋ"), + ("g", "ㄐㄧˋ"), + ("h", "ㄝˇㄑㄩˋ"), + ("i", "ㄞˋ"), + ("j", "ㄐㄟˋ"), + ("k", "ㄎㄟˋ"), + ("l", "ㄝˊㄛˋ"), + ("m", "ㄝˊㄇㄨˋ"), + ("n", "ㄣˉ"), + ("o", "ㄡˉ"), + ("p", "ㄆㄧˉ"), + ("q", "ㄎㄧㄡˉ"), + ("r", "ㄚˋ"), + ("s", "ㄝˊㄙˋ"), + ("t", "ㄊㄧˋ"), + ("u", "ㄧㄡˉ"), + ("v", "ㄨㄧˉ"), + ("w", "ㄉㄚˋㄅㄨˋㄌㄧㄡˋ"), + ("x", "ㄝˉㄎㄨˋㄙˋ"), + ("y", "ㄨㄞˋ"), + ("z", "ㄗㄟˋ"), + ] +] + +# List of (bopomofo, ipa) pairs: +_bopomofo_to_ipa = [ + (re.compile("%s" % x[0]), x[1]) + for x in [ + ("ㄅㄛ", "p⁼wo"), + ("ㄆㄛ", "pʰwo"), + ("ㄇㄛ", "mwo"), + ("ㄈㄛ", "fwo"), + ("ㄧㄢ", "|jɛn"), + ("ㄩㄢ", "|ɥæn"), + ("ㄧㄣ", "|in"), + ("ㄩㄣ", "|ɥn"), + ("ㄧㄥ", "|iŋ"), + ("ㄨㄥ", "|ʊŋ"), + ("ㄩㄥ", "|jʊŋ"), + # Add + ("ㄧㄚ", "|ia"), + ("ㄧㄝ", "|iɛ"), + ("ㄧㄠ", "|iɑʊ"), + ("ㄧㄡ", "|ioʊ"), + ("ㄧㄤ", "|iɑŋ"), + ("ㄨㄚ", "|ua"), + ("ㄨㄛ", "|uo"), + ("ㄨㄞ", "|uaɪ"), + ("ㄨㄟ", "|ueɪ"), + ("ㄨㄢ", "|uan"), + ("ㄨㄣ", "|uən"), + ("ㄨㄤ", "|uɑŋ"), + ("ㄩㄝ", "|ɥɛ"), + # End + ("ㄅ", "p⁼"), + ("ㄆ", "pʰ"), + ("ㄇ", "m"), + ("ㄈ", "f"), + ("ㄉ", "t⁼"), + ("ㄊ", "tʰ"), + ("ㄋ", "n"), + ("ㄌ", "l"), + ("ㄍ", "k⁼"), + ("ㄎ", "kʰ"), + ("ㄏ", "x"), + ("ㄐ", "tʃ⁼"), + ("ㄑ", "tʃʰ"), + ("ㄒ", "ʃ"), + ("ㄓ", "ts`⁼"), + ("ㄔ", "ts`ʰ"), + ("ㄕ", "s`"), + ("ㄖ", "ɹ`"), + ("ㄗ", "ts⁼"), + ("ㄘ", "tsʰ"), + ("ㄙ", "|s"), + ("ㄚ", "|a"), + ("ㄛ", "|o"), + ("ㄜ", "|ə"), + ("ㄝ", "|ɛ"), + ("ㄞ", "|aɪ"), + ("ㄟ", "|eɪ"), + ("ㄠ", "|ɑʊ"), + ("ㄡ", "|oʊ"), + ("ㄢ", "|an"), + ("ㄣ", "|ən"), + ("ㄤ", "|ɑŋ"), + ("ㄥ", "|əŋ"), + ("ㄦ", "əɹ"), + ("ㄧ", "|i"), + ("ㄨ", "|u"), + ("ㄩ", "|ɥ"), + ("ˉ", "→|"), + ("ˊ", "↑|"), + ("ˇ", "↓↑|"), + ("ˋ", "↓|"), + ("˙", "|"), + ] +] +must_not_er_words = {"女儿", "老儿", "男儿", "少儿", "小儿"} + + +chinese_lexicon_path = hf_hub_download( + repo_id="ASLP-lab/DiffRhythm", + filename="diffrhythm/g2p/sources/chinese_lexicon.txt", + repo_type="space" + ) +word_pinyin_dict = {} +with open(chinese_lexicon_path, "r", encoding="utf-8") as fread: + txt_list = fread.readlines() + for txt in txt_list: + word, pinyin = txt.strip().split("\t") + word_pinyin_dict[word] = pinyin + fread.close() + +pinyin_2_bopomofo_dict = {} +with open( + r"./diffrhythm/g2p/sources/pinyin_2_bpmf.txt", "r", encoding="utf-8" +) as fread: + txt_list = fread.readlines() + for txt in txt_list: + pinyin, bopomofo = txt.strip().split("\t") + pinyin_2_bopomofo_dict[pinyin] = bopomofo + fread.close() + +tone_dict = { + "0": "˙", + "5": "˙", + "1": "", + "2": "ˊ", + "3": "ˇ", + "4": "ˋ", +} + +bopomofos2pinyin_dict = {} +with open( + r"./diffrhythm/g2p/sources/bpmf_2_pinyin.txt", "r", encoding="utf-8" +) as fread: + txt_list = fread.readlines() + for txt in txt_list: + v, k = txt.strip().split("\t") + bopomofos2pinyin_dict[k] = v + fread.close() + + +def bpmf_to_pinyin(text): + bopomofo_list = text.split("|") + pinyin_list = [] + for info in bopomofo_list: + pinyin = "" + for c in info: + if c in bopomofos2pinyin_dict: + pinyin += bopomofos2pinyin_dict[c] + if len(pinyin) == 0: + continue + if pinyin[-1] not in "01234": + pinyin += "1" + if pinyin[:-1] == "ve": + pinyin = "y" + pinyin + if pinyin[:-1] == "sh": + pinyin = pinyin[:-1] + "i" + pinyin[-1] + if pinyin == "sh": + pinyin = pinyin[:-1] + "i" + if pinyin[:-1] == "s": + pinyin = "si" + pinyin[-1] + if pinyin[:-1] == "c": + pinyin = "ci" + pinyin[-1] + if pinyin[:-1] == "i": + pinyin = "yi" + pinyin[-1] + if pinyin[:-1] == "iou": + pinyin = "you" + pinyin[-1] + if pinyin[:-1] == "ien": + pinyin = "yin" + pinyin[-1] + if "iou" in pinyin and pinyin[-4:-1] == "iou": + pinyin = pinyin[:-4] + "iu" + pinyin[-1] + if "uei" in pinyin: + if pinyin[:-1] == "uei": + pinyin = "wei" + pinyin[-1] + elif pinyin[-4:-1] == "uei": + pinyin = pinyin[:-4] + "ui" + pinyin[-1] + if "uen" in pinyin and pinyin[-4:-1] == "uen": + if pinyin[:-1] == "uen": + pinyin = "wen" + pinyin[-1] + elif pinyin[-4:-1] == "uei": + pinyin = pinyin[:-4] + "un" + pinyin[-1] + if "van" in pinyin and pinyin[-4:-1] == "van": + if pinyin[:-1] == "van": + pinyin = "yuan" + pinyin[-1] + elif pinyin[-4:-1] == "van": + pinyin = pinyin[:-4] + "uan" + pinyin[-1] + if "ueng" in pinyin and pinyin[-5:-1] == "ueng": + pinyin = pinyin[:-5] + "ong" + pinyin[-1] + if pinyin[:-1] == "veng": + pinyin = "yong" + pinyin[-1] + if "veng" in pinyin and pinyin[-5:-1] == "veng": + pinyin = pinyin[:-5] + "iong" + pinyin[-1] + if pinyin[:-1] == "ieng": + pinyin = "ying" + pinyin[-1] + if pinyin[:-1] == "u": + pinyin = "wu" + pinyin[-1] + if pinyin[:-1] == "v": + pinyin = "yv" + pinyin[-1] + if pinyin[:-1] == "ing": + pinyin = "ying" + pinyin[-1] + if pinyin[:-1] == "z": + pinyin = "zi" + pinyin[-1] + if pinyin[:-1] == "zh": + pinyin = "zhi" + pinyin[-1] + if pinyin[0] == "u": + pinyin = "w" + pinyin[1:] + if pinyin[0] == "i": + pinyin = "y" + pinyin[1:] + pinyin = pinyin.replace("ien", "in") + + pinyin_list.append(pinyin) + return " ".join(pinyin_list) + + +# Convert numbers to Chinese pronunciation +def number_to_chinese(text): + # numbers = re.findall(r'\d+(?:\.?\d+)?', text) + # for number in numbers: + # text = text.replace(number, cn2an.an2cn(number), 1) + text = cn2an.transform(text, "an2cn") + return text + + +def normalization(text): + text = text.replace("，", ",") + text = text.replace("。", ".") + text = text.replace("！", "!") + text = text.replace("？", "?") + text = text.replace("；", ";") + text = text.replace("：", ":") + text = text.replace("、", ",") + text = text.replace("‘", "'") + text = text.replace("’", "'") + text = text.replace("⋯", "…") + text = text.replace("···", "…") + text = text.replace("・・・", "…") + text = text.replace("...", "…") + text = re.sub(r"\s+", "", text) + text = re.sub(r"[^\u4e00-\u9fff\s_,\.\?!;:\'…]", "", text) + text = re.sub(r"\s*([,\.\?!;:\'…])\s*", r"\1", text) + return text + + +def change_tone(bopomofo: str, tone: str) -> str: + if bopomofo[-1] not in "˙ˊˇˋ": + bopomofo = bopomofo + tone + else: + bopomofo = bopomofo[:-1] + tone + return bopomofo + + +def er_sandhi(word: str, bopomofos: List[str]) -> List[str]: + if len(word) > 1 and word[-1] == "儿" and word not in must_not_er_words: + bopomofos[-1] = change_tone(bopomofos[-1], "˙") + return bopomofos + + +def bu_sandhi(word: str, bopomofos: List[str]) -> List[str]: + valid_char = set(word) + if len(valid_char) == 1 and "不" in valid_char: + pass + elif word in ["不字"]: + pass + elif len(word) == 3 and word[1] == "不" and bopomofos[1][:-1] == "ㄅㄨ": + bopomofos[1] = bopomofos[1][:-1] + "˙" + else: + for i, char in enumerate(word): + if ( + i + 1 < len(bopomofos) + and char == "不" + and i + 1 < len(word) + and 0 < len(bopomofos[i + 1]) + and bopomofos[i + 1][-1] == "ˋ" + ): + bopomofos[i] = bopomofos[i][:-1] + "ˊ" + return bopomofos + + +def yi_sandhi(word: str, bopomofos: List[str]) -> List[str]: + punc = "：，；。？！“”‘’':,;.?!()（）{}【】[]-~`、 " + if word.find("一") != -1 and any( + [item.isnumeric() for item in word if item != "一"] + ): + for i in range(len(word)): + if ( + i == 0 + and word[0] == "一" + and len(word) > 1 + and word[1] + not in [ + "零", + "一", + "二", + "三", + "四", + "五", + "六", + "七", + "八", + "九", + "十", + ] + ): + if len(bopomofos[0]) > 0 and bopomofos[1][-1] in ["ˋ", "˙"]: + bopomofos[0] = change_tone(bopomofos[0], "ˊ") + else: + bopomofos[0] = change_tone(bopomofos[0], "ˋ") + elif word[i] == "一": + bopomofos[i] = change_tone(bopomofos[i], "") + return bopomofos + elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]: + bopomofos[1] = change_tone(bopomofos[1], "˙") + elif word.startswith("第一"): + bopomofos[1] = change_tone(bopomofos[1], "") + elif word.startswith("一月") or word.startswith("一日") or word.startswith("一号"): + bopomofos[0] = change_tone(bopomofos[0], "") + else: + for i, char in enumerate(word): + if char == "一" and i + 1 < len(word): + if ( + len(bopomofos) > i + 1 + and len(bopomofos[i + 1]) > 0 + and bopomofos[i + 1][-1] in {"ˋ"} + ): + bopomofos[i] = change_tone(bopomofos[i], "ˊ") + else: + if word[i + 1] not in punc: + bopomofos[i] = change_tone(bopomofos[i], "ˋ") + else: + pass + return bopomofos + + +def merge_bu(seg: List) -> List: + new_seg = [] + last_word = "" + for word in seg: + if word != "不": + if last_word == "不": + word = last_word + word + new_seg.append(word) + last_word = word + return new_seg + + +def merge_er(seg: List) -> List: + new_seg = [] + for i, word in enumerate(seg): + if i - 1 >= 0 and word == "儿": + new_seg[-1] = new_seg[-1] + seg[i] + else: + new_seg.append(word) + return new_seg + + +def merge_yi(seg: List) -> List: + new_seg = [] + # function 1 + for i, word in enumerate(seg): + if ( + i - 1 >= 0 + and word == "一" + and i + 1 < len(seg) + and seg[i - 1] == seg[i + 1] + ): + if i - 1 < len(new_seg): + new_seg[i - 1] = new_seg[i - 1] + "一" + new_seg[i - 1] + else: + new_seg.append(word) + new_seg.append(seg[i + 1]) + else: + if i - 2 >= 0 and seg[i - 1] == "一" and seg[i - 2] == word: + continue + else: + new_seg.append(word) + seg = new_seg + new_seg = [] + isnumeric_flag = False + for i, word in enumerate(seg): + if all([item.isnumeric() for item in word]) and not isnumeric_flag: + isnumeric_flag = True + new_seg.append(word) + else: + new_seg.append(word) + seg = new_seg + new_seg = [] + # function 2 + for i, word in enumerate(seg): + if new_seg and new_seg[-1] == "一": + new_seg[-1] = new_seg[-1] + word + else: + new_seg.append(word) + return new_seg + + +# Word Segmentation, and convert Chinese pronunciation to pinyin (bopomofo) +def chinese_to_bopomofo(text_short, sentence): + # bopomofos = conv(text_short) + words = jieba.lcut(text_short, cut_all=False) + words = merge_yi(words) + words = merge_bu(words) + words = merge_er(words) + text = "" + + char_index = 0 + for word in words: + bopomofos = [] + if word in word_pinyin_dict and word not in poly_dict: + pinyin = word_pinyin_dict[word] + for py in pinyin.split(" "): + if py[:-1] in pinyin_2_bopomofo_dict and py[-1] in tone_dict: + bopomofos.append( + pinyin_2_bopomofo_dict[py[:-1]] + tone_dict[py[-1]] + ) + if BLANK_LEVEL == 1: + bopomofos.append("_") + else: + bopomofos_lazy = lazy_pinyin(word, BOPOMOFO) + bopomofos += bopomofos_lazy + if BLANK_LEVEL == 1: + bopomofos.append("_") + else: + for i in range(len(word)): + c = word[i] + if c in poly_dict: + poly_pinyin = g2pw_poly_predict.predict_process( + [text_short, char_index + i] + )[0] + py = poly_pinyin[2:-1] + bopomofos.append( + pinyin_2_bopomofo_dict[py[:-1]] + tone_dict[py[-1]] + ) + if BLANK_LEVEL == 1: + bopomofos.append("_") + elif c in word_pinyin_dict: + py = word_pinyin_dict[c] + bopomofos.append( + pinyin_2_bopomofo_dict[py[:-1]] + tone_dict[py[-1]] + ) + if BLANK_LEVEL == 1: + bopomofos.append("_") + else: + bopomofos.append(c) + if BLANK_LEVEL == 1: + bopomofos.append("_") + if BLANK_LEVEL == 2: + bopomofos.append("_") + char_index += len(word) + + if ( + len(word) == 3 + and bopomofos[0][-1] == "ˇ" + and bopomofos[1][-1] == "ˇ" + and bopomofos[-1][-1] == "ˇ" + ): + bopomofos[0] = bopomofos[0] + "ˊ" + bopomofos[1] = bopomofos[1] + "ˊ" + if len(word) == 2 and bopomofos[0][-1] == "ˇ" and bopomofos[-1][-1] == "ˇ": + bopomofos[0] = bopomofos[0][:-1] + "ˊ" + bopomofos = bu_sandhi(word, bopomofos) + bopomofos = yi_sandhi(word, bopomofos) + bopomofos = er_sandhi(word, bopomofos) + if not re.search("[\u4e00-\u9fff]", word): + text += "|" + word + continue + for i in range(len(bopomofos)): + bopomofos[i] = re.sub(r"([\u3105-\u3129])$", r"\1ˉ", bopomofos[i]) + if text != "": + text += "|" + text += "|".join(bopomofos) + return text + + +# Convert latin pronunciation to pinyin (bopomofo) +def latin_to_bopomofo(text): + for regex, replacement in _latin_to_bopomofo: + text = re.sub(regex, replacement, text) + return text + + +# Convert pinyin (bopomofo) to IPA +def bopomofo_to_ipa(text): + for regex, replacement in _bopomofo_to_ipa: + text = re.sub(regex, replacement, text) + return text + + +def _chinese_to_ipa(text, sentence): + text = number_to_chinese(text.strip()) + text = normalization(text) + text = chinese_to_bopomofo(text, sentence) + # pinyin = bpmf_to_pinyin(text) + text = latin_to_bopomofo(text) + text = bopomofo_to_ipa(text) + text = re.sub("([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)", r"\1ɹ\2", text) + text = re.sub("([s][⁼ʰ]?)([→↓↑ ]+|$)", r"\1ɹ\2", text) + text = re.sub(r"^\||[^\w\s_,\.\?!;:\'…\|→↓↑⁼ʰ`]", "", text) + text = re.sub(r"([,\.\?!;:\'…])", r"|\1|", text) + text = re.sub(r"\|+", "|", text) + text = text.rstrip("|") + return text + + +# Convert Chinese to IPA +def chinese_to_ipa(text, sentence, text_tokenizer): + # phonemes = text_tokenizer(text.strip()) + if type(text) == str: + return _chinese_to_ipa(text, sentence) + else: + result_ph = [] + for t in text: + result_ph.append(_chinese_to_ipa(t, sentence)) + return result_ph diff --git a/diffrhythm/g2p/g2p/text_tokenizers.py b/diffrhythm/g2p/g2p/text_tokenizers.py new file mode 100644 index 0000000000000000000000000000000000000000..33d5752a98209393514cc6d6706f4f5ebd909d2c --- /dev/null +++ b/diffrhythm/g2p/g2p/text_tokenizers.py @@ -0,0 +1,85 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re +import os +from typing import List, Pattern, Union +from phonemizer.utils import list2str, str2list +from phonemizer.backend import EspeakBackend +from phonemizer.backend.espeak.language_switch import LanguageSwitch +from phonemizer.backend.espeak.words_mismatch import WordMismatch +from phonemizer.punctuation import Punctuation +# from zer.separator import Separator +from phonemizer.separator import Separator + + +class TextTokenizer: + """Phonemize Text.""" + + def __init__( + self, + language="en-us", + backend="espeak", + separator=Separator(word="|_|", syllable="-", phone="|"), + preserve_punctuation=True, + with_stress: bool = False, + tie: Union[bool, str] = False, + language_switch: LanguageSwitch = "remove-flags", + words_mismatch: WordMismatch = "ignore", + ) -> None: + self.preserve_punctuation_marks = ",.?!;:'…" + self.backend = EspeakBackend( + language, + punctuation_marks=self.preserve_punctuation_marks, + preserve_punctuation=preserve_punctuation, + with_stress=with_stress, + tie=tie, + language_switch=language_switch, + words_mismatch=words_mismatch, + ) + + self.separator = separator + + # convert chinese punctuation to english punctuation + def convert_chinese_punctuation(self, text: str) -> str: + text = text.replace("，", ",") + text = text.replace("。", ".") + text = text.replace("！", "!") + text = text.replace("？", "?") + text = text.replace("；", ";") + text = text.replace("：", ":") + text = text.replace("、", ",") + text = text.replace("‘", "'") + text = text.replace("’", "'") + text = text.replace("⋯", "…") + text = text.replace("···", "…") + text = text.replace("・・・", "…") + text = text.replace("...", "…") + return text + + def __call__(self, text, strip=True) -> List[str]: + + text_type = type(text) + normalized_text = [] + for line in str2list(text): + line = self.convert_chinese_punctuation(line.strip()) + line = re.sub(r"[^\w\s_,\.\?!;:\'…]", "", line) + line = re.sub(r"\s*([,\.\?!;:\'…])\s*", r"\1", line) + line = re.sub(r"\s+", " ", line) + normalized_text.append(line) + # print("Normalized test: ", normalized_text[0]) + phonemized = self.backend.phonemize( + normalized_text, separator=self.separator, strip=strip, njobs=1 + ) + if text_type == str: + phonemized = re.sub(r"([,\.\?!;:\'…])", r"|\1|", list2str(phonemized)) + phonemized = re.sub(r"\|+", "|", phonemized) + phonemized = phonemized.rstrip("|") + else: + for i in range(len(phonemized)): + phonemized[i] = re.sub(r"([,\.\?!;:\'…])", r"|\1|", phonemized[i]) + phonemized[i] = re.sub(r"\|+", "|", phonemized[i]) + phonemized[i] = phonemized[i].rstrip("|") + return phonemized diff --git a/diffrhythm/g2p/g2p/vocab.json b/diffrhythm/g2p/g2p/vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..28d32aaf01881c6ff5449aaaf942d94b753a4e91 --- /dev/null +++ b/diffrhythm/g2p/g2p/vocab.json @@ -0,0 +1,372 @@ +{ + "vocab": { + ",": 0, + ".": 1, + "?": 2, + "!": 3, + "_": 4, + "iː": 5, + "ɪ": 6, + "ɜː": 7, + "ɚ": 8, + "oːɹ": 9, + "ɔː": 10, + "ɔːɹ": 11, + "ɑː": 12, + "uː": 13, + "ʊ": 14, + "ɑːɹ": 15, + "ʌ": 16, + "ɛ": 17, + "æ": 18, + "eɪ": 19, + "aɪ": 20, + "ɔɪ": 21, + "aʊ": 22, + "oʊ": 23, + "ɪɹ": 24, + "ɛɹ": 25, + "ʊɹ": 26, + "p": 27, + "b": 28, + "t": 29, + "d": 30, + "k": 31, + "ɡ": 32, + "f": 33, + "v": 34, + "θ": 35, + "ð": 36, + "s": 37, + "z": 38, + "ʃ": 39, + "ʒ": 40, + "h": 41, + "tʃ": 42, + "dʒ": 43, + "m": 44, + "n": 45, + "ŋ": 46, + "j": 47, + "w": 48, + "ɹ": 49, + "l": 50, + "tɹ": 51, + "dɹ": 52, + "ts": 53, + "dz": 54, + "i": 55, + "ɔ": 56, + "ə": 57, + "ɾ": 58, + "iə": 59, + "r": 60, + "u": 61, + "oː": 62, + "ɛː": 63, + "ɪː": 64, + "aɪə": 65, + "aɪɚ": 66, + "ɑ̃": 67, + "ç": 68, + "ɔ̃": 69, + "ææ": 70, + "ɐɐ": 71, + "ɡʲ": 72, + "nʲ": 73, + "iːː": 74, + + "p⁼": 75, + "pʰ": 76, + "t⁼": 77, + "tʰ": 78, + "k⁼": 79, + "kʰ": 80, + "x": 81, + "tʃ⁼": 82, + "tʃʰ": 83, + "ts`⁼": 84, + "ts`ʰ": 85, + "s`": 86, + "ɹ`": 87, + "ts⁼": 88, + "tsʰ": 89, + "p⁼wo": 90, + "p⁼wo→": 91, + "p⁼wo↑": 92, + "p⁼wo↓↑": 93, + "p⁼wo↓": 94, + "pʰwo": 95, + "pʰwo→": 96, + "pʰwo↑": 97, + "pʰwo↓↑": 98, + "pʰwo↓": 99, + "mwo": 100, + "mwo→": 101, + "mwo↑": 102, + "mwo↓↑": 103, + "mwo↓": 104, + "fwo": 105, + "fwo→": 106, + "fwo↑": 107, + "fwo↓↑": 108, + "fwo↓": 109, + "jɛn": 110, + "jɛn→": 111, + "jɛn↑": 112, + "jɛn↓↑": 113, + "jɛn↓": 114, + "ɥæn": 115, + "ɥæn→": 116, + "ɥæn↑": 117, + "ɥæn↓↑": 118, + "ɥæn↓": 119, + "in": 120, + "in→": 121, + "in↑": 122, + "in↓↑": 123, + "in↓": 124, + "ɥn": 125, + "ɥn→": 126, + "ɥn↑": 127, + "ɥn↓↑": 128, + "ɥn↓": 129, + "iŋ": 130, + "iŋ→": 131, + "iŋ↑": 132, + "iŋ↓↑": 133, + "iŋ↓": 134, + "ʊŋ": 135, + "ʊŋ→": 136, + "ʊŋ↑": 137, + "ʊŋ↓↑": 138, + "ʊŋ↓": 139, + "jʊŋ": 140, + "jʊŋ→": 141, + "jʊŋ↑": 142, + "jʊŋ↓↑": 143, + "jʊŋ↓": 144, + "ia": 145, + "ia→": 146, + "ia↑": 147, + "ia↓↑": 148, + "ia↓": 149, + "iɛ": 150, + "iɛ→": 151, + "iɛ↑": 152, + "iɛ↓↑": 153, + "iɛ↓": 154, + "iɑʊ": 155, + "iɑʊ→": 156, + "iɑʊ↑": 157, + "iɑʊ↓↑": 158, + "iɑʊ↓": 159, + "ioʊ": 160, + "ioʊ→": 161, + "ioʊ↑": 162, + "ioʊ↓↑": 163, + "ioʊ↓": 164, + "iɑŋ": 165, + "iɑŋ→": 166, + "iɑŋ↑": 167, + "iɑŋ↓↑": 168, + "iɑŋ↓": 169, + "ua": 170, + "ua→": 171, + "ua↑": 172, + "ua↓↑": 173, + "ua↓": 174, + "uo": 175, + "uo→": 176, + "uo↑": 177, + "uo↓↑": 178, + "uo↓": 179, + "uaɪ": 180, + "uaɪ→": 181, + "uaɪ↑": 182, + "uaɪ↓↑": 183, + "uaɪ↓": 184, + "ueɪ": 185, + "ueɪ→": 186, + "ueɪ↑": 187, + "ueɪ↓↑": 188, + "ueɪ↓": 189, + "uan": 190, + "uan→": 191, + "uan↑": 192, + "uan↓↑": 193, + "uan↓": 194, + "uən": 195, + "uən→": 196, + "uən↑": 197, + "uən↓↑": 198, + "uən↓": 199, + "uɑŋ": 200, + "uɑŋ→": 201, + "uɑŋ↑": 202, + "uɑŋ↓↑": 203, + "uɑŋ↓": 204, + "ɥɛ": 205, + "ɥɛ→": 206, + "ɥɛ↑": 207, + "ɥɛ↓↑": 208, + "ɥɛ↓": 209, + "a": 210, + "a→": 211, + "a↑": 212, + "a↓↑": 213, + "a↓": 214, + "o": 215, + "o→": 216, + "o↑": 217, + "o↓↑": 218, + "o↓": 219, + "ə→": 220, + "ə↑": 221, + "ə↓↑": 222, + "ə↓": 223, + "ɛ→": 224, + "ɛ↑": 225, + "ɛ↓↑": 226, + "ɛ↓": 227, + "aɪ→": 228, + "aɪ↑": 229, + "aɪ↓↑": 230, + "aɪ↓": 231, + "eɪ→": 232, + "eɪ↑": 233, + "eɪ↓↑": 234, + "eɪ↓": 235, + "ɑʊ": 236, + "ɑʊ→": 237, + "ɑʊ↑": 238, + "ɑʊ↓↑": 239, + "ɑʊ↓": 240, + "oʊ→": 241, + "oʊ↑": 242, + "oʊ↓↑": 243, + "oʊ↓": 244, + "an": 245, + "an→": 246, + "an↑": 247, + "an↓↑": 248, + "an↓": 249, + "ən": 250, + "ən→": 251, + "ən↑": 252, + "ən↓↑": 253, + "ən↓": 254, + "ɑŋ": 255, + "ɑŋ→": 256, + "ɑŋ↑": 257, + "ɑŋ↓↑": 258, + "ɑŋ↓": 259, + "əŋ": 260, + "əŋ→": 261, + "əŋ↑": 262, + "əŋ↓↑": 263, + "əŋ↓": 264, + "əɹ": 265, + "əɹ→": 266, + "əɹ↑": 267, + "əɹ↓↑": 268, + "əɹ↓": 269, + "i→": 270, + "i↑": 271, + "i↓↑": 272, + "i↓": 273, + "u→": 274, + "u↑": 275, + "u↓↑": 276, + "u↓": 277, + "ɥ": 278, + "ɥ→": 279, + "ɥ↑": 280, + "ɥ↓↑": 281, + "ɥ↓": 282, + "ts`⁼ɹ": 283, + "ts`⁼ɹ→": 284, + "ts`⁼ɹ↑": 285, + "ts`⁼ɹ↓↑": 286, + "ts`⁼ɹ↓": 287, + "ts`ʰɹ": 288, + "ts`ʰɹ→": 289, + "ts`ʰɹ↑": 290, + "ts`ʰɹ↓↑": 291, + "ts`ʰɹ↓": 292, + "s`ɹ": 293, + "s`ɹ→": 294, + "s`ɹ↑": 295, + "s`ɹ↓↑": 296, + "s`ɹ↓": 297, + "ɹ`ɹ": 298, + "ɹ`ɹ→": 299, + "ɹ`ɹ↑": 300, + "ɹ`ɹ↓↑": 301, + "ɹ`ɹ↓": 302, + "ts⁼ɹ": 303, + "ts⁼ɹ→": 304, + "ts⁼ɹ↑": 305, + "ts⁼ɹ↓↑": 306, + "ts⁼ɹ↓": 307, + "tsʰɹ": 308, + "tsʰɹ→": 309, + "tsʰɹ↑": 310, + "tsʰɹ↓↑": 311, + "tsʰɹ↓": 312, + "sɹ": 313, + "sɹ→": 314, + "sɹ↑": 315, + "sɹ↓↑": 316, + "sɹ↓": 317, + + "ɯ": 318, + "e": 319, + "aː": 320, + "ɯː": 321, + "eː": 322, + "ç": 323, + "ɸ": 324, + "ɰᵝ": 325, + "ɴ": 326, + "g": 327, + "dʑ": 328, + "q": 329, + "ː": 330, + "bj": 331, + "tɕ": 332, + "dej": 333, + "tej": 334, + "gj": 335, + "gɯ": 336, + "çj": 337, + "kj": 338, + "kɯ": 339, + "mj": 340, + "nj": 341, + "pj": 342, + "ɾj": 343, + "ɕ": 344, + "tsɯ": 345, + + "ɐ": 346, + "ɑ": 347, + "ɒ": 348, + "ɜ": 349, + "ɫ": 350, + "ʑ": 351, + "ʲ": 352, + + "y": 353, + "ø": 354, + "œ": 355, + "ʁ": 356, + "̃": 357, + "ɲ": 358, + + ":": 359, + ";": 360, + "'": 361, + "…": 362 + } +} diff --git a/diffrhythm/g2p/g2p_generation.py b/diffrhythm/g2p/g2p_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..e78f3e7076f8d55678d1705b13900950cd151adf --- /dev/null +++ b/diffrhythm/g2p/g2p_generation.py @@ -0,0 +1,133 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import sys + +from diffrhythm.g2p.g2p import PhonemeBpeTokenizer +from diffrhythm.g2p.utils.g2p import phonemizer_g2p +import tqdm +from typing import List +import json +import os +import re + + +def ph_g2p(text, language): + + return phonemizer_g2p(text=text, language=language) + + +def g2p(text, sentence, language): + + return text_tokenizer.tokenize(text=text, sentence=sentence, language=language) + + +def is_chinese(char): + if char >= "\u4e00" and char <= "\u9fa5": + return True + else: + return False + + +def is_alphabet(char): + if (char >= "\u0041" and char <= "\u005a") or ( + char >= "\u0061" and char <= "\u007a" + ): + return True + else: + return False + + +def is_other(char): + if not (is_chinese(char) or is_alphabet(char)): + return True + else: + return False + + +def get_segment(text: str) -> List[str]: + # sentence --> [ch_part, en_part, ch_part, ...] + segments = [] + types = [] + flag = 0 + temp_seg = "" + temp_lang = "" + + # Determine the type of each character. type: blank, chinese, alphabet, number, unk and point. + for i, ch in enumerate(text): + if is_chinese(ch): + types.append("zh") + elif is_alphabet(ch): + types.append("en") + else: + types.append("other") + + assert len(types) == len(text) + + for i in range(len(types)): + # find the first char of the seg + if flag == 0: + temp_seg += text[i] + temp_lang = types[i] + flag = 1 + else: + if temp_lang == "other": + if types[i] == temp_lang: + temp_seg += text[i] + else: + temp_seg += text[i] + temp_lang = types[i] + else: + if types[i] == temp_lang: + temp_seg += text[i] + elif types[i] == "other": + temp_seg += text[i] + else: + segments.append((temp_seg, temp_lang)) + temp_seg = text[i] + temp_lang = types[i] + flag = 1 + + segments.append((temp_seg, temp_lang)) + return segments + + +def chn_eng_g2p(text: str): + # now only en and ch + segments = get_segment(text) + all_phoneme = "" + all_tokens = [] + + for index in range(len(segments)): + seg = segments[index] + phoneme, token = g2p(seg[0], text, seg[1]) + all_phoneme += phoneme + "|" + all_tokens += token + + if seg[1] == "en" and index == len(segments) - 1 and all_phoneme[-2] == "_": + all_phoneme = all_phoneme[:-2] + all_tokens = all_tokens[:-1] + return all_phoneme, all_tokens + + +text_tokenizer = PhonemeBpeTokenizer() +with open("./diffrhythm/g2p/g2p/vocab.json", "r") as f: + json_data = f.read() +data = json.loads(json_data) +vocab = data["vocab"] + +if __name__ == '__main__': + phone, token = chn_eng_g2p("你好，hello world") + phone, token = chn_eng_g2p("你好，hello world, Bonjour, 테스트 해 보겠습니다, 五月雨緑") + print(phone) + print(token) + + #phone, token = text_tokenizer.tokenize("你好，hello world, Bonjour, 테스트 해 보겠습니다, 五月雨緑", "", "auto") + phone, token = text_tokenizer.tokenize("緑", "", "auto") + #phone, token = text_tokenizer.tokenize("आइए इसका परीक्षण करें", "", "auto") + #phone, token = text_tokenizer.tokenize("आइए इसका परीक्षण करें", "", "other") + print(phone) + print(token) \ No newline at end of file diff --git a/diffrhythm/g2p/sources/bpmf_2_pinyin.txt b/diffrhythm/g2p/sources/bpmf_2_pinyin.txt new file mode 100644 index 0000000000000000000000000000000000000000..474529e5d347b94a80e5052de0065347ff14b95e --- /dev/null +++ b/diffrhythm/g2p/sources/bpmf_2_pinyin.txt @@ -0,0 +1,41 @@ +b ㄅ +p ㄆ +m ㄇ +f ㄈ +d ㄉ +t ㄊ +n ㄋ +l ㄌ +g ㄍ +k ㄎ +h ㄏ +j ㄐ +q ㄑ +x ㄒ +zh ㄓ +ch ㄔ +sh ㄕ +r ㄖ +z ㄗ +c ㄘ +s ㄙ +i ㄧ +u ㄨ +v ㄩ +a ㄚ +o ㄛ +e ㄜ +e ㄝ +ai ㄞ +ei ㄟ +ao ㄠ +ou ㄡ +an ㄢ +en ㄣ +ang ㄤ +eng ㄥ +er ㄦ +2 ˊ +3 ˇ +4 ˋ +0 ˙ diff --git a/diffrhythm/g2p/sources/chinese_lexicon.txt b/diffrhythm/g2p/sources/chinese_lexicon.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d7dbf347a29d3b87c199d0e56ef7f1dbf28a6ee --- /dev/null +++ b/diffrhythm/g2p/sources/chinese_lexicon.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3a7685d1c3e68eb2fa304bfc63e90c90c3c1a1948839a5b1b507b2131b3e2fb +size 14779443 diff --git a/diffrhythm/g2p/sources/g2p_chinese_model/config.json b/diffrhythm/g2p/sources/g2p_chinese_model/config.json new file mode 100644 index 0000000000000000000000000000000000000000..5fb70ca91db27a4ad73b58a0c500a903be9bc1a9 --- /dev/null +++ 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"16": "了{liao5}", + "17": "什{shen2}", + "18": "什{shi2}", + "19": "仔{zai3}", + "20": "仔{zai5}", + "21": "仔{zi3}", + "22": "仔{zi5}", + "23": "令{ling2}", + "24": "令{ling4}", + "25": "任{ren2}", + "26": "任{ren4}", + "27": "会{hui4}", + "28": "会{hui5}", + "29": "会{kuai4}", + "30": "传{chuan2}", + "31": "传{zhuan4}", + "32": "佛{fo2}", + "33": "佛{fu2}", + "34": "供{gong1}", + "35": "供{gong4}", + "36": "便{bian4}", + "37": "便{pian2}", + "38": "倒{dao3}", + "39": "倒{dao4}", + "40": "假{jia3}", + "41": "假{jia4}", + "42": "兴{xing1}", + "43": "兴{xing4}", + "44": "冠{guan1}", + "45": "冠{guan4}", + "46": "冲{chong1}", + "47": "冲{chong4}", + "48": "几{ji1}", + "49": "几{ji2}", + "50": "几{ji3}", + "51": "分{fen1}", + "52": "分{fen4}", + "53": "分{fen5}", + "54": "切{qie1}", + "55": "切{qie4}", + "56": "划{hua2}", + "57": "划{hua4}", + "58": "划{hua5}", + "59": "创{chuang1}", + "60": "创{chuang4}", + "61": "剥{bao1}", + "62": "剥{bo1}", + "63": "勒{le4}", + "64": "勒{le5}", + "65": "勒{lei1}", + "66": "区{ou1}", + "67": "区{qu1}", + "68": "华{hua2}", + "69": "华{hua4}", + "70": "单{chan2}", + "71": "单{dan1}", + "72": "单{shan4}", + "73": "卜{bo5}", + "74": "卜{bu3}", + "75": "占{zhan1}", + "76": "占{zhan4}", + "77": "卡{ka2}", + "78": "卡{ka3}", + "79": "卡{qia3}", + "80": "卷{jvan3}", + "81": "卷{jvan4}", + "82": "厦{sha4}", + "83": "厦{xia4}", + "84": "参{can1}", + "85": "参{cen1}", + "86": "参{shen1}", + "87": "发{fa1}", + "88": "发{fa4}", + "89": "发{fa5}", + "90": "只{zhi1}", + "91": "只{zhi3}", + "92": "号{hao2}", + "93": "号{hao4}", + "94": "号{hao5}", + "95": "同{tong2}", + "96": "同{tong4}", + "97": "同{tong5}", + "98": "吐{tu2}", + "99": "吐{tu3}", + "100": "吐{tu4}", + "101": "和{he2}", + "102": "和{he4}", + "103": "和{he5}", + "104": "和{huo2}", + "105": "和{huo4}", + "106": "和{huo5}", + "107": "喝{he1}", + "108": "喝{he4}", + "109": "圈{jvan4}", + "110": "圈{qvan1}", + "111": "圈{qvan5}", + "112": "地{de5}", + "113": "地{di4}", + "114": "地{di5}", + "115": "塞{sai1}", + "116": "塞{sai2}", + "117": "塞{sai4}", + "118": "塞{se4}", + "119": "壳{ke2}", + "120": "壳{qiao4}", + "121": "处{chu3}", + "122": "处{chu4}", + "123": "奇{ji1}", + "124": "奇{qi2}", + "125": "奔{ben1}", + "126": "奔{ben4}", + "127": "好{hao3}", + "128": "好{hao4}", + "129": "好{hao5}", + "130": "宁{ning2}", + "131": "宁{ning4}", + "132": "宁{ning5}", + "133": "宿{su4}", + "134": "宿{xiu3}", + "135": "宿{xiu4}", + "136": "将{jiang1}", + "137": "将{jiang4}", + "138": "少{shao3}", + "139": "少{shao4}", + "140": "尽{jin3}", + "141": "尽{jin4}", + "142": "岗{gang1}", + "143": "岗{gang3}", + "144": "差{cha1}", + "145": "差{cha4}", + "146": "差{chai1}", + "147": "差{ci1}", + "148": "巷{hang4}", + "149": "巷{xiang4}", + "150": "帖{tie1}", + "151": "帖{tie3}", + "152": "帖{tie4}", + "153": "干{gan1}", + "154": "干{gan4}", + "155": "应{ying1}", + "156": "应{ying4}", + "157": "应{ying5}", + "158": "度{du4}", + "159": "度{du5}", + "160": "度{duo2}", + "161": "弹{dan4}", + "162": "弹{tan2}", + "163": "弹{tan5}", + "164": "强{jiang4}", + "165": "强{qiang2}", + "166": "强{qiang3}", + "167": "当{dang1}", + "168": "当{dang4}", + "169": "当{dang5}", + "170": "待{dai1}", + "171": "待{dai4}", + "172": "得{de2}", + "173": "得{de5}", + "174": "得{dei3}", + "175": "得{dei5}", + "176": "恶{e3}", + "177": "恶{e4}", + "178": "恶{wu4}", + "179": "扁{bian3}", + "180": "扁{pian1}", + "181": "扇{shan1}", + "182": "扇{shan4}", + "183": "扎{za1}", + "184": "扎{zha1}", + "185": "扎{zha2}", + "186": "扫{sao3}", + "187": "扫{sao4}", + "188": "担{dan1}", + "189": "担{dan4}", + "190": "担{dan5}", + "191": "挑{tiao1}", + "192": "挑{tiao3}", + "193": "据{jv1}", + "194": "据{jv4}", + "195": "撒{sa1}", + "196": "撒{sa3}", + "197": "撒{sa5}", + "198": "教{jiao1}", + "199": "教{jiao4}", + "200": "散{san3}", + "201": "散{san4}", + "202": "散{san5}", + "203": "数{shu3}", + "204": "数{shu4}", + "205": "数{shu5}", + "206": "斗{dou3}", + "207": "斗{dou4}", + "208": "晃{huang3}", + "209": "曝{bao4}", + "210": "曲{qu1}", + "211": "曲{qu3}", + "212": "更{geng1}", + "213": "更{geng4}", + "214": "曾{ceng1}", + "215": "曾{ceng2}", + "216": "曾{zeng1}", + "217": "朝{chao2}", + "218": "朝{zhao1}", + "219": "朴{piao2}", + "220": "朴{pu2}", + "221": "朴{pu3}", + "222": "杆{gan1}", + "223": "杆{gan3}", + "224": "查{cha2}", + "225": "查{zha1}", + "226": "校{jiao4}", + "227": "校{xiao4}", + "228": "模{mo2}", + "229": "模{mu2}", + "230": "横{heng2}", + "231": "横{heng4}", + "232": "没{mei2}", + "233": "没{mo4}", + "234": "泡{pao1}", + "235": "泡{pao4}", + "236": "泡{pao5}", + "237": "济{ji3}", + "238": "济{ji4}", + "239": "混{hun2}", + "240": "混{hun3}", + "241": "混{hun4}", + "242": "混{hun5}", + "243": "漂{piao1}", + "244": "漂{piao3}", + "245": "漂{piao4}", + "246": "炸{zha2}", + "247": "炸{zha4}", + "248": "熟{shou2}", + "249": "熟{shu2}", + "250": "燕{yan1}", + "251": "燕{yan4}", + "252": "片{pian1}", + "253": "片{pian4}", + "254": "率{lv4}", + "255": "率{shuai4}", + "256": "畜{chu4}", + "257": "畜{xu4}", + "258": "的{de5}", + "259": "的{di1}", + "260": "的{di2}", + "261": "的{di4}", + "262": "的{di5}", + "263": "盛{cheng2}", + "264": "盛{sheng4}", + "265": "相{xiang1}", + "266": "相{xiang4}", + "267": "相{xiang5}", + "268": "省{sheng3}", + "269": "省{xing3}", + "270": "看{kan1}", + "271": "看{kan4}", + "272": "看{kan5}", + "273": "着{zhao1}", + "274": "着{zhao2}", + "275": "着{zhao5}", + "276": "着{zhe5}", + "277": "着{zhuo2}", + "278": "着{zhuo5}", + "279": "矫{jiao3}", + "280": "禁{jin1}", + "281": "禁{jin4}", + "282": "种{zhong3}", + "283": "种{zhong4}", + "284": "称{chen4}", + "285": "称{cheng1}", + "286": "空{kong1}", + "287": "空{kong4}", + "288": "答{da1}", + "289": "答{da2}", + "290": "粘{nian2}", + "291": "粘{zhan1}", + "292": "糊{hu2}", + "293": "糊{hu5}", + "294": "系{ji4}", + "295": "系{xi4}", + "296": "系{xi5}", + "297": "累{lei2}", + "298": "累{lei3}", + "299": "累{lei4}", + "300": "累{lei5}", + "301": "纤{qian4}", + "302": "纤{xian1}", + "303": "结{jie1}", + "304": "结{jie2}", + "305": "结{jie5}", + "306": "给{gei3}", + "307": "给{gei5}", + "308": "给{ji3}", + "309": "缝{feng2}", + "310": "缝{feng4}", + "311": "缝{feng5}", + "312": "肖{xiao1}", + "313": "肖{xiao4}", + "314": "背{bei1}", + "315": "背{bei4}", + "316": "脏{zang1}", + "317": "脏{zang4}", + "318": "舍{she3}", + "319": "舍{she4}", + "320": "色{se4}", + "321": "色{shai3}", + "322": "落{lao4}", + "323": "落{luo4}", + "324": "蒙{meng1}", + "325": "蒙{meng2}", + "326": "蒙{meng3}", + "327": "薄{bao2}", + "328": "薄{bo2}", + "329": "薄{bo4}", + "330": "藏{cang2}", + "331": "藏{zang4}", + "332": "血{xie3}", + "333": "血{xue4}", + "334": "行{hang2}", + "335": "行{hang5}", + "336": "行{heng5}", + "337": "行{xing2}", + "338": "行{xing4}", + "339": "要{yao1}", + "340": "要{yao4}", + "341": "观{guan1}", + "342": "观{guan4}", + "343": "觉{jiao4}", + "344": "觉{jiao5}", + "345": "觉{jve2}", + "346": "角{jiao3}", + "347": "角{jve2}", + "348": "解{jie3}", + "349": "解{jie4}", + "350": "解{xie4}", + "351": "说{shui4}", + "352": "说{shuo1}", + "353": "调{diao4}", + "354": "调{tiao2}", + "355": "踏{ta1}", + "356": "踏{ta4}", + "357": "车{che1}", + "358": "车{jv1}", + "359": "转{zhuan3}", + "360": "转{zhuan4}", + "361": "载{zai3}", + "362": "载{zai4}", + "363": "还{hai2}", + "364": "还{huan2}", + "365": "遂{sui2}", + "366": "遂{sui4}", + "367": "都{dou1}", + "368": "都{du1}", + "369": "重{chong2}", + "370": "重{zhong4}", + "371": "量{liang2}", + "372": "量{liang4}", + "373": "量{liang5}", + "374": "钻{zuan1}", + "375": "钻{zuan4}", + "376": "铺{pu1}", + "377": "铺{pu4}", + "378": "长{chang2}", + "379": "长{chang3}", + "380": "长{zhang3}", + "381": "间{jian1}", + "382": "间{jian4}", + "383": "降{jiang4}", + "384": "降{xiang2}", + "385": "难{nan2}", + "386": "难{nan4}", + "387": "难{nan5}", + "388": "露{lou4}", + "389": "露{lu4}", + "390": "鲜{xian1}", + "391": "鲜{xian3}" +} \ No newline at end of file diff --git a/diffrhythm/g2p/sources/g2p_chinese_model/polydict_r.json b/diffrhythm/g2p/sources/g2p_chinese_model/polydict_r.json new file mode 100644 index 0000000000000000000000000000000000000000..aabbe6257493eaee7d3f0b77f78f0cb006e89fb6 --- /dev/null +++ b/diffrhythm/g2p/sources/g2p_chinese_model/polydict_r.json @@ -0,0 +1,393 @@ +{ + "丧{sang1}": 1, + "丧{sang4}": 2, + "中{zhong1}": 3, + "中{zhong4}": 4, + "为{wei2}": 5, + 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ㄓㄨㄟ +zhun ㄓㄨㄣ +zhuo ㄓㄨㄛ +zi ㄗ +zong ㄗㄨㄥ +zou ㄗㄡ +zu ㄗㄨ +zuan ㄗㄨㄢ +zui ㄗㄨㄟ +zun ㄗㄨㄣ +zuo ㄗㄨㄛ diff --git a/diffrhythm/g2p/utils/__pycache__/front_utils.cpython-310.pyc b/diffrhythm/g2p/utils/__pycache__/front_utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..22df02bd4faa79d79cee382fcd0ea2d69beba53b Binary files /dev/null and b/diffrhythm/g2p/utils/__pycache__/front_utils.cpython-310.pyc differ diff --git a/diffrhythm/g2p/utils/__pycache__/front_utils.cpython-311.pyc b/diffrhythm/g2p/utils/__pycache__/front_utils.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..12204d177ec80d9ff8e455aa5f58d9f284d03661 Binary files /dev/null and b/diffrhythm/g2p/utils/__pycache__/front_utils.cpython-311.pyc differ diff --git a/diffrhythm/g2p/utils/__pycache__/g2p.cpython-310.pyc b/diffrhythm/g2p/utils/__pycache__/g2p.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..145397ccf24aaf1e80529c95bc2729ea52401165 Binary files /dev/null and b/diffrhythm/g2p/utils/__pycache__/g2p.cpython-310.pyc differ diff --git a/diffrhythm/g2p/utils/__pycache__/g2p.cpython-311.pyc b/diffrhythm/g2p/utils/__pycache__/g2p.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..aab89697039771ecb2c1507222aa6c4ecafdcc8f Binary files /dev/null and b/diffrhythm/g2p/utils/__pycache__/g2p.cpython-311.pyc differ diff --git a/diffrhythm/g2p/utils/front_utils.py b/diffrhythm/g2p/utils/front_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..de9f878b5ea87868aee62b3eed5c29e3e95776b7 --- /dev/null +++ b/diffrhythm/g2p/utils/front_utils.py @@ -0,0 +1,20 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os + + +def generate_poly_lexicon(file_path: str): + """Generate poly char lexicon for Mandarin Chinese.""" + poly_dict = {} + + with open(file_path, "r", encoding="utf-8") as readf: + txt_list = readf.readlines() + for txt in txt_list: + word = txt.strip("\n") + if word not in poly_dict: + poly_dict[word] = 1 + readf.close() + return poly_dict diff --git a/diffrhythm/g2p/utils/g2p.py b/diffrhythm/g2p/utils/g2p.py new file mode 100644 index 0000000000000000000000000000000000000000..aba5e2f3fd217dd0f018ba49fa0aa298863740c8 --- /dev/null +++ b/diffrhythm/g2p/utils/g2p.py @@ -0,0 +1,139 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from phonemizer.backend import EspeakBackend +from phonemizer.separator import Separator +from phonemizer.utils import list2str, str2list +from typing import List, Union +import os +import json +import sys + +# separator=Separator(phone=' ', word=' _ ', syllable='|'), +separator = Separator(word=" _ ", syllable="|", phone=" ") + +phonemizer_zh = EspeakBackend( + "cmn", preserve_punctuation=False, with_stress=False, language_switch="remove-flags" +) +# phonemizer_zh.separator = separator + +phonemizer_en = EspeakBackend( + "en-us", + preserve_punctuation=False, + with_stress=False, + language_switch="remove-flags", +) +# phonemizer_en.separator = separator + +phonemizer_ja = EspeakBackend( + "ja", preserve_punctuation=False, with_stress=False, language_switch="remove-flags" +) +# phonemizer_ja.separator = separator + +phonemizer_ko = EspeakBackend( + "ko", preserve_punctuation=False, with_stress=False, language_switch="remove-flags" +) +# phonemizer_ko.separator = separator + +phonemizer_fr = EspeakBackend( + "fr-fr", + preserve_punctuation=False, + with_stress=False, + language_switch="remove-flags", +) +# phonemizer_fr.separator = separator + +phonemizer_de = EspeakBackend( + "de", preserve_punctuation=False, with_stress=False, language_switch="remove-flags" +) +# phonemizer_de.separator = separator + + +lang2backend = { + "zh": phonemizer_zh, + "ja": phonemizer_ja, + "en": phonemizer_en, + "fr": phonemizer_fr, + "ko": phonemizer_ko, + "de": phonemizer_de, +} + +with open("./diffrhythm/g2p/utils/mls_en.json", "r") as f: + json_data = f.read() +token = json.loads(json_data) + + +def phonemizer_g2p(text, language): + langbackend = lang2backend[language] + phonemes = _phonemize( + langbackend, + text, + separator, + strip=True, + njobs=1, + prepend_text=False, + preserve_empty_lines=False, + ) + token_id = [] + if isinstance(phonemes, list): + for phone in phonemes: + phonemes_split = phone.split(" ") + token_id.append([token[p] for p in phonemes_split if p in token]) + else: + phonemes_split = phonemes.split(" ") + token_id = [token[p] for p in phonemes_split if p in token] + return phonemes, token_id + + +def _phonemize( # pylint: disable=too-many-arguments + backend, + text: Union[str, List[str]], + separator: Separator, + strip: bool, + njobs: int, + prepend_text: bool, + preserve_empty_lines: bool, +): + """Auxiliary function to phonemize() + + Does the phonemization and returns the phonemized text. Raises a + RuntimeError on error. + + """ + # remember the text type for output (either list or string) + text_type = type(text) + + # force the text as a list + text = [line.strip(os.linesep) for line in str2list(text)] + + # if preserving empty lines, note the index of each empty line + if preserve_empty_lines: + empty_lines = [n for n, line in enumerate(text) if not line.strip()] + + # ignore empty lines + text = [line for line in text if line.strip()] + + if text: + # phonemize the text + phonemized = backend.phonemize( + text, separator=separator, strip=strip, njobs=njobs + ) + else: + phonemized = [] + + # if preserving empty lines, reinsert them into text and phonemized lists + if preserve_empty_lines: + for i in empty_lines: # noqa + if prepend_text: + text.insert(i, "") + phonemized.insert(i, "") + + # at that point, the phonemized text is a list of str. Format it as + # expected by the parameters + if prepend_text: + return list(zip(text, phonemized)) + if text_type == str: + return list2str(phonemized) + return phonemized diff --git a/diffrhythm/g2p/utils/log.py b/diffrhythm/g2p/utils/log.py new file mode 100644 index 0000000000000000000000000000000000000000..d10b887ef2e9292bd79c628e9ed7881c7a91bf52 --- /dev/null +++ b/diffrhythm/g2p/utils/log.py @@ -0,0 +1,52 @@ +# Copyright (c) 2024 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import functools +import logging + +__all__ = [ + "logger", +] + + +class Logger(object): + def __init__(self, name: str = None): + name = "PaddleSpeech" if not name else name + self.logger = logging.getLogger(name) + + log_config = { + "DEBUG": 10, + "INFO": 20, + "TRAIN": 21, + "EVAL": 22, + "WARNING": 30, + "ERROR": 40, + "CRITICAL": 50, + "EXCEPTION": 100, + } + for key, level in log_config.items(): + logging.addLevelName(level, key) + if key == "EXCEPTION": + self.__dict__[key.lower()] = self.logger.exception + else: + self.__dict__[key.lower()] = functools.partial(self.__call__, level) + + self.format = logging.Formatter( + fmt="[%(asctime)-15s] [%(levelname)8s] - %(message)s" + ) + + self.handler = logging.StreamHandler() + self.handler.setFormatter(self.format) + + self.logger.addHandler(self.handler) + self.logger.setLevel(logging.INFO) + self.logger.propagate = False + + def __call__(self, log_level: str, msg: str): + self.logger.log(log_level, msg) + + +logger = Logger() diff --git a/diffrhythm/g2p/utils/mls_en.json b/diffrhythm/g2p/utils/mls_en.json new file mode 100644 index 0000000000000000000000000000000000000000..f3aadbf144427af10ec06ca3cab8c4a2c461925d --- /dev/null +++ b/diffrhythm/g2p/utils/mls_en.json @@ -0,0 +1,335 @@ +{ + "[UNK]": 0, + "_": 1, + "b": 2, + "d": 3, + "f": 4, + "h": 5, + "i": 6, + "j": 7, + "k": 8, + "l": 9, + "m": 10, + "n": 11, + "p": 12, + "r": 13, 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0000000000000000000000000000000000000000..31d791fdd48001cbae9f39e80cd801a5877fd6fd Binary files /dev/null and b/diffrhythm/infer/__pycache__/infer_utils.cpython-310.pyc differ diff --git a/diffrhythm/infer/__pycache__/infer_utils.cpython-311.pyc b/diffrhythm/infer/__pycache__/infer_utils.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d5a04dcb0d6177fbc9c7aa0401d4cc244049f322 Binary files /dev/null and b/diffrhythm/infer/__pycache__/infer_utils.cpython-311.pyc differ diff --git a/diffrhythm/infer/infer.py b/diffrhythm/infer/infer.py new file mode 100644 index 0000000000000000000000000000000000000000..2440c1df1490f20d42f64b49b977f8c5d57f0a15 --- /dev/null +++ b/diffrhythm/infer/infer.py @@ -0,0 +1,162 @@ +import torch +import torchaudio +from einops import rearrange +import argparse +import json +import os +from tqdm import tqdm +import random +import numpy as np +import time +import io +import pydub + +from diffrhythm.infer.infer_utils import ( + get_reference_latent, + get_lrc_token, + get_audio_style_prompt, + prepare_model, + get_negative_style_prompt +) + +def decode_audio(latents, vae_model, chunked=False, overlap=32, chunk_size=128): + downsampling_ratio = 2048 + io_channels = 2 + if not chunked: + # default behavior. Decode the entire latent in parallel + return vae_model.decode_export(latents) + else: + # chunked decoding + hop_size = chunk_size - overlap + total_size = latents.shape[2] + batch_size = latents.shape[0] + chunks = [] + i = 0 + for i in range(0, total_size - chunk_size + 1, hop_size): + chunk = latents[:,:,i:i+chunk_size] + chunks.append(chunk) + if i+chunk_size != total_size: + # Final chunk + chunk = latents[:,:,-chunk_size:] + chunks.append(chunk) + chunks = torch.stack(chunks) + num_chunks = chunks.shape[0] + # samples_per_latent is just the downsampling ratio + samples_per_latent = downsampling_ratio + # Create an empty waveform, we will populate it with chunks as decode them + y_size = total_size * samples_per_latent + y_final = torch.zeros((batch_size,io_channels,y_size)).to(latents.device) + for i in range(num_chunks): + x_chunk = chunks[i,:] + # decode the chunk + y_chunk = vae_model.decode_export(x_chunk) + # figure out where to put the audio along the time domain + if i == num_chunks-1: + # final chunk always goes at the end + t_end = y_size + t_start = t_end - y_chunk.shape[2] + else: + t_start = i * hop_size * samples_per_latent + t_end = t_start + chunk_size * samples_per_latent + # remove the edges of the overlaps + ol = (overlap//2) * samples_per_latent + chunk_start = 0 + chunk_end = y_chunk.shape[2] + if i > 0: + # no overlap for the start of the first chunk + t_start += ol + chunk_start += ol + if i < num_chunks-1: + # no overlap for the end of the last chunk + t_end -= ol + chunk_end -= ol + # paste the chunked audio into our y_final output audio + y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end] + return y_final + +def inference(cfm_model, vae_model, cond, text, duration, style_prompt, negative_style_prompt, steps, cfg_strength, sway_sampling_coef, start_time, file_type, vocal_flag, odeint_method): + + with torch.inference_mode(): + generated, _ = cfm_model.sample( + cond=cond, + text=text, + duration=duration, + style_prompt=style_prompt, + negative_style_prompt=negative_style_prompt, + steps=steps, + cfg_strength=cfg_strength, + sway_sampling_coef=sway_sampling_coef, + start_time=start_time, + vocal_flag=vocal_flag, + odeint_method=odeint_method, + ) + + generated = generated.to(torch.float32) + latent = generated.transpose(1, 2) # [b d t] + output = decode_audio(latent, vae_model, chunked=False) + + # Rearrange audio batch to a single sequence + output = rearrange(output, "b d n -> d (b n)") + output_tensor = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).cpu() + output_np = output_tensor.numpy().T.astype(np.float32) + + if file_type == 'wav': + return (44100, output_np) + else: + buffer = io.BytesIO() + output_np = np.int16(output_np * 2**15) + song = pydub.AudioSegment(output_np.tobytes(), frame_rate=44100, sample_width=2, channels=2) + if file_type == 'mp3': + song.export(buffer, format="mp3", bitrate="320k") + else: + song.export(buffer, format="ogg", bitrate="320k") + return buffer.getvalue() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--lrc-path', type=str, default="example/eg.lrc") # lyrics of target song + parser.add_argument('--ref-audio-path', type=str, default="example/eg.mp3") # reference audio as style prompt for target song + parser.add_argument('--audio-length', type=int, default=95) # length of target song + parser.add_argument('--output-dir', type=str, default="example/output") + args = parser.parse_args() + + device = 'cuda' + + audio_length = args.audio_length + if audio_length == 95: + max_frames = 2048 + elif audio_length == 285: + max_frames = 6144 + + cfm, tokenizer, muq, vae = prepare_model(device) + + with open(args.lrc_path, 'r') as f: + lrc = f.read() + lrc_prompt, start_time = get_lrc_token(lrc, tokenizer, device) + + style_prompt = get_audio_style_prompt(muq, args.ref_audio_path) + + negative_style_prompt = get_negative_style_prompt(device) + + latent_prompt = get_reference_latent(device, max_frames) + + s_t = time.time() + generated_song = inference(cfm_model=cfm, + vae_model=vae, + cond=latent_prompt, + text=lrc_prompt, + duration=max_frames, + style_prompt=style_prompt, + negative_style_prompt=negative_style_prompt, + start_time=start_time + ) + e_t = time.time() - s_t + print(f"inference cost {e_t} seconds") + + output_dir = args.output_dir + os.makedirs(output_dir, exist_ok=True) + + output_path = os.path.join(output_dir, "output.wav") + torchaudio.save(output_path, generated_song, sample_rate=44100) + \ No newline at end of file diff --git a/diffrhythm/infer/infer_utils.py b/diffrhythm/infer/infer_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..53e85cd192b41c528331501ed8fd013a33cdbe70 --- /dev/null +++ b/diffrhythm/infer/infer_utils.py @@ -0,0 +1,211 @@ +import torch +import librosa +import random +import json +from muq import MuQMuLan +from mutagen.mp3 import MP3 +import os +import numpy as np +from huggingface_hub import hf_hub_download +from diffrhythm.model import DiT, CFM + + +def prepare_model(device): + # prepare cfm model + dit_ckpt_path = hf_hub_download(repo_id="ASLP-lab/DiffRhythm-base", filename="cfm_model.pt") + dit_full_ckpt_path = hf_hub_download(repo_id="ASLP-lab/DiffRhythm-full", filename="cfm_model.pt") + dit_config_path = "./diffrhythm/config/diffrhythm-1b.json" + with open(dit_config_path) as f: + model_config = json.load(f) + dit_model_cls = DiT + cfm = CFM( + transformer=dit_model_cls(**model_config["model"], use_style_prompt=True, max_pos=2048), + num_channels=model_config["model"]['mel_dim'], + use_style_prompt=True + ) + cfm = cfm.to(device) + cfm = load_checkpoint(cfm, dit_ckpt_path, device=device, use_ema=False) + + cfm_full = CFM( + transformer=dit_model_cls(**model_config["model"], use_style_prompt=True, max_pos=6144), + num_channels=model_config["model"]['mel_dim'], + use_style_prompt=True + ) + cfm_full = cfm_full.to(device) + cfm_full = load_checkpoint(cfm_full, dit_full_ckpt_path, device=device, use_ema=False) + + # prepare tokenizer + tokenizer = CNENTokenizer() + + # prepare muq + muq = MuQMuLan.from_pretrained("OpenMuQ/MuQ-MuLan-large") + muq = muq.to(device).eval() + + # prepare vae + vae_ckpt_path = hf_hub_download(repo_id="ASLP-lab/DiffRhythm-vae", filename="vae_model.pt") + vae = torch.jit.load(vae_ckpt_path, map_location='cpu').to(device) + + return cfm, cfm_full, tokenizer, muq, vae + + +# for song edit, will be added in the future +def get_reference_latent(device, max_frames): + return torch.zeros(1, max_frames, 64).to(device) + +def get_negative_style_prompt(device): + file_path = "./src/negative_prompt.npy" + vocal_stlye = np.load(file_path) + + vocal_stlye = torch.from_numpy(vocal_stlye).to(device) # [1, 512] + vocal_stlye = vocal_stlye.half() + + return vocal_stlye + +def get_audio_style_prompt(model, wav_path): + vocal_flag = False + mulan = model + audio, _ = librosa.load(wav_path, sr=24000) + audio_len = librosa.get_duration(y=audio, sr=24000) + + if audio_len <= 1: + vocal_flag = True + + if audio_len > 10: + start_time = int(audio_len // 2 - 5) + wav = audio[start_time*24000:(start_time+10)*24000] + + else: + wav = audio + wav= torch.tensor(wav, dtype=torch.float32).unsqueeze(0).to(model.device) + + + with torch.no_grad(): + audio_emb = mulan(wavs = wav) # [1, 512] + + audio_emb = audio_emb.half() + + return audio_emb, vocal_flag + +def get_text_style_prompt(model, text_prompt): + mulan = model + + with torch.no_grad(): + text_emb = mulan(texts = text_prompt) # [1, 512] + text_emb = text_emb.half() + + return text_emb + + + +def parse_lyrics(lyrics: str): + lyrics_with_time = [] + lyrics = lyrics.strip() + for line in lyrics.split('\n'): + try: + time, lyric = line[1:9], line[10:] + lyric = lyric.strip() + mins, secs = time.split(':') + secs = int(mins) * 60 + float(secs) + lyrics_with_time.append((secs, lyric)) + except: + continue + return lyrics_with_time + +class CNENTokenizer(): + def __init__(self): + with open('./diffrhythm/g2p/g2p/vocab.json', 'r') as file: + self.phone2id:dict = json.load(file)['vocab'] + self.id2phone = {v:k for (k, v) in self.phone2id.items()} + from diffrhythm.g2p.g2p_generation import chn_eng_g2p + self.tokenizer = chn_eng_g2p + def encode(self, text): + phone, token = self.tokenizer(text) + token = [x+1 for x in token] + return token + def decode(self, token): + return "|".join([self.id2phone[x-1] for x in token]) + +def get_lrc_token(max_frames, text, tokenizer, device): + + lyrics_shift = 0 + sampling_rate = 44100 + downsample_rate = 2048 + max_secs = max_frames / (sampling_rate / downsample_rate) + + pad_token_id = 0 + comma_token_id = 1 + period_token_id = 2 + if text == "": + return torch.zeros((max_frames,), dtype=torch.long).unsqueeze(0).to(device), torch.tensor(0.).unsqueeze(0).to(device).half() + + lrc_with_time = parse_lyrics(text) + + modified_lrc_with_time = [] + for i in range(len(lrc_with_time)): + time, line = lrc_with_time[i] + line_token = tokenizer.encode(line) + modified_lrc_with_time.append((time, line_token)) + lrc_with_time = modified_lrc_with_time + + lrc_with_time = [(time_start, line) for (time_start, line) in lrc_with_time if time_start < max_secs] + # lrc_with_time = lrc_with_time[:-1] if len(lrc_with_time) >= 1 else lrc_with_time + + normalized_start_time = 0. + + lrc = torch.zeros((max_frames,), dtype=torch.long) + + tokens_count = 0 + last_end_pos = 0 + for time_start, line in lrc_with_time: + tokens = [token if token != period_token_id else comma_token_id for token in line] + [period_token_id] + tokens = torch.tensor(tokens, dtype=torch.long) + num_tokens = tokens.shape[0] + + gt_frame_start = int(time_start * sampling_rate / downsample_rate) + + frame_shift = random.randint(int(lyrics_shift), int(lyrics_shift)) + + frame_start = max(gt_frame_start - frame_shift, last_end_pos) + frame_len = min(num_tokens, max_frames - frame_start) + + + + lrc[frame_start:frame_start + frame_len] = tokens[:frame_len] + + tokens_count += num_tokens + last_end_pos = frame_start + frame_len + + lrc_emb = lrc.unsqueeze(0).to(device) + + normalized_start_time = torch.tensor(normalized_start_time).unsqueeze(0).to(device) + normalized_start_time = normalized_start_time.half() + + return lrc_emb, normalized_start_time + +def load_checkpoint(model, ckpt_path, device, use_ema=True): + if device == "cuda": + model = model.half() + + ckpt_type = ckpt_path.split(".")[-1] + if ckpt_type == "safetensors": + from safetensors.torch import load_file + + checkpoint = load_file(ckpt_path) + else: + checkpoint = torch.load(ckpt_path, weights_only=True) + + if use_ema: + if ckpt_type == "safetensors": + checkpoint = {"ema_model_state_dict": checkpoint} + checkpoint["model_state_dict"] = { + k.replace("ema_model.", ""): v + for k, v in checkpoint["ema_model_state_dict"].items() + if k not in ["initted", "step"] + } + model.load_state_dict(checkpoint["model_state_dict"], strict=False) + else: + if ckpt_type == "safetensors": + checkpoint = {"model_state_dict": checkpoint} + model.load_state_dict(checkpoint["model_state_dict"], strict=False) + + return model.to(device) \ No newline at end of file diff --git a/diffrhythm/model/__init__.py b/diffrhythm/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..645131cd34aeb6c64c92d6b5125bdd073b235a28 --- /dev/null +++ b/diffrhythm/model/__init__.py @@ -0,0 +1,6 @@ +from diffrhythm.model.cfm import CFM + +from diffrhythm.model.dit import DiT + + +__all__ = ["CFM"] diff --git a/diffrhythm/model/__pycache__/__init__.cpython-310.pyc b/diffrhythm/model/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..48feebd0569f7a2efee244ec45aea497cc816f1e Binary files /dev/null and b/diffrhythm/model/__pycache__/__init__.cpython-310.pyc differ diff --git a/diffrhythm/model/__pycache__/__init__.cpython-311.pyc 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b/diffrhythm/model/cfm.py @@ -0,0 +1,312 @@ +""" +ein notation: +b - batch +n - sequence +nt - text sequence +nw - raw wave length +d - dimension +""" + +from __future__ import annotations +from typing import Callable +from random import random + +import torch +from torch import nn +import torch +import torch.nn.functional as F +from torch.nn.utils.rnn import pad_sequence + +from torchdiffeq import odeint + +from diffrhythm.model.modules import MelSpec +from diffrhythm.model.utils import ( + default, + exists, + list_str_to_idx, + list_str_to_tensor, + lens_to_mask, + mask_from_frac_lengths, +) + +def custom_mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"], device, max_seq_len): # noqa: F722 F821 + max_seq_len = max_seq_len + seq = torch.arange(max_seq_len, device=device).long() + start_mask = seq[None, :] >= start[:, None] + end_mask = seq[None, :] < end[:, None] + return start_mask & end_mask + +class CFM(nn.Module): + def __init__( + self, + transformer: nn.Module, + sigma=0.0, + odeint_kwargs: dict = dict( + method="euler" # 'midpoint' + ), + odeint_options: dict = dict( + min_step=0.05 + ), + audio_drop_prob=0.3, + cond_drop_prob=0.2, + style_drop_prob=0.1, + lrc_drop_prob=0.1, + num_channels=None, + frac_lengths_mask: tuple[float, float] = (0.7, 1.0), + vocab_char_map: dict[str:int] | None = None, + use_style_prompt: bool = False + ): + super().__init__() + + self.frac_lengths_mask = frac_lengths_mask + + self.num_channels = num_channels + + # classifier-free guidance + self.audio_drop_prob = audio_drop_prob + self.cond_drop_prob = cond_drop_prob + self.style_drop_prob = style_drop_prob + self.lrc_drop_prob = lrc_drop_prob + + # transformer + self.transformer = transformer + dim = transformer.dim + self.dim = dim + + # conditional flow related + self.sigma = sigma + + # sampling related + self.odeint_kwargs = odeint_kwargs + + self.odeint_options = odeint_options + + # vocab map for tokenization + self.vocab_char_map = vocab_char_map + + self.use_style_prompt = use_style_prompt + + @property + def device(self): + return next(self.parameters()).device + + @torch.no_grad() + def sample( + self, + cond: float["b n d"] | float["b nw"], # noqa: F722 + text: int["b nt"] | list[str], # noqa: F722 + duration: int | int["b"], # noqa: F821 + *, + style_prompt = None, + style_prompt_lens = None, + negative_style_prompt = None, + lens: int["b"] | None = None, # noqa: F821 + steps=32, + cfg_strength=4.0, + sway_sampling_coef=None, + seed: int | None = None, + max_duration=6144, + vocoder: Callable[[float["b d n"]], float["b nw"]] | None = None, # noqa: F722 + no_ref_audio=False, + duplicate_test=False, + t_inter=0.1, + edit_mask=None, + start_time=None, + latent_pred_start_frame=0, + latent_pred_end_frame=2048, + vocal_flag=False, + odeint_method="euler" + ): + self.eval() + + self.odeint_kwargs = dict(method=odeint_method) + + if next(self.parameters()).dtype == torch.float16: + cond = cond.to(torch.float32) + else: + cond = cond.to(torch.float32) + # raw wave + + if cond.shape[1] > duration: + cond = cond[:, :duration, :] + + if cond.ndim == 2: + cond = self.mel_spec(cond) + cond = cond.permute(0, 2, 1) + assert cond.shape[-1] == self.num_channels + + batch, cond_seq_len, device = *cond.shape[:2], cond.device + if not exists(lens): + lens = torch.full((batch,), cond_seq_len, device=device, dtype=torch.long) + + # text + + if isinstance(text, list): + if exists(self.vocab_char_map): + text = list_str_to_idx(text, self.vocab_char_map).to(device) + else: + text = list_str_to_tensor(text).to(device) + assert text.shape[0] == batch + + if exists(text): + text_lens = (text != -1).sum(dim=-1) + + + # duration + cond_mask = lens_to_mask(lens) + if edit_mask is not None: + cond_mask = cond_mask & edit_mask + + latent_pred_start_frame = torch.tensor([latent_pred_start_frame]).to(cond.device) + latent_pred_end_frame = duration + latent_pred_end_frame = torch.tensor([latent_pred_end_frame]).to(cond.device) + fixed_span_mask = custom_mask_from_start_end_indices(cond_seq_len, latent_pred_start_frame, latent_pred_end_frame, device=cond.device, max_seq_len=duration) + + fixed_span_mask = fixed_span_mask.unsqueeze(-1) + step_cond = torch.where(fixed_span_mask, torch.zeros_like(cond), cond) + + if isinstance(duration, int): + duration = torch.full((batch,), duration, device=device, dtype=torch.long) + + + duration = duration.clamp(max=max_duration) + max_duration = duration.amax() + + # duplicate test corner for inner time step oberservation + if duplicate_test: + test_cond = F.pad(cond, (0, 0, cond_seq_len, max_duration - 2 * cond_seq_len), value=0.0) + + + if batch > 1: + mask = lens_to_mask(duration) + else: # save memory and speed up, as single inference need no mask currently + mask = None + + # test for no ref audio + if no_ref_audio: + cond = torch.zeros_like(cond) + + start_time_embed, positive_text_embed, positive_text_residuals = self.transformer.forward_timestep_invariant(text, step_cond.shape[1], drop_text=False, start_time=start_time) + _, negative_text_embed, negative_text_residuals = self.transformer.forward_timestep_invariant(text, step_cond.shape[1], drop_text=True, start_time=start_time) + + if vocal_flag: + style_prompt = negative_style_prompt + negative_style_prompt = torch.zeros_like(style_prompt) + + text_embed = torch.cat([positive_text_embed, negative_text_embed], 0) + text_residuals = [torch.cat([a, b], 0) for a, b in zip(positive_text_residuals, negative_text_residuals)] + step_cond = torch.cat([step_cond, step_cond], 0) + style_prompt = torch.cat([style_prompt, negative_style_prompt], 0) + start_time_embed = torch.cat([start_time_embed, start_time_embed], 0) + + + def fn(t, x): + x = torch.cat([x, x], 0) + pred = self.transformer( + x=x, text_embed=text_embed, text_residuals=text_residuals, cond=step_cond, time=t, + drop_audio_cond=True, drop_prompt=False, style_prompt=style_prompt, start_time=start_time_embed + ) + + positive_pred, negative_pred = pred.chunk(2, 0) + cfg_pred = positive_pred + (positive_pred - negative_pred) * cfg_strength + + return cfg_pred + + # noise input + # to make sure batch inference result is same with different batch size, and for sure single inference + # still some difference maybe due to convolutional layers + y0 = [] + for dur in duration: + if exists(seed): + torch.manual_seed(seed) + y0.append(torch.randn(dur, self.num_channels, device=self.device, dtype=step_cond.dtype)) + y0 = pad_sequence(y0, padding_value=0, batch_first=True) + + t_start = 0 + + # duplicate test corner for inner time step oberservation + if duplicate_test: + t_start = t_inter + y0 = (1 - t_start) * y0 + t_start * test_cond + steps = int(steps * (1 - t_start)) + + t = torch.linspace(t_start, 1, steps, device=self.device, dtype=step_cond.dtype) + if sway_sampling_coef is not None: + t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t) + + trajectory = odeint(fn, y0, t, **self.odeint_kwargs) + + sampled = trajectory[-1] + out = sampled + out = torch.where(fixed_span_mask, out, cond) + + if exists(vocoder): + out = out.permute(0, 2, 1) + out = vocoder(out) + + return out, trajectory + + def forward( + self, + inp: float["b n d"] | float["b nw"], # mel or raw wave # noqa: F722 + text: int["b nt"] | list[str], # noqa: F722 + style_prompt = None, + style_prompt_lens = None, + lens: int["b"] | None = None, # noqa: F821 + noise_scheduler: str | None = None, + grad_ckpt = False, + start_time = None, + ): + + batch, seq_len, dtype, device, _σ1 = *inp.shape[:2], inp.dtype, self.device, self.sigma + + # lens and mask + if not exists(lens): + lens = torch.full((batch,), seq_len, device=device) + + mask = lens_to_mask(lens, length=seq_len) # useless here, as collate_fn will pad to max length in batch + + # get a random span to mask out for training conditionally + frac_lengths = torch.zeros((batch,), device=self.device).float().uniform_(*self.frac_lengths_mask) + rand_span_mask = mask_from_frac_lengths(lens, frac_lengths) + + if exists(mask): + rand_span_mask = mask + # rand_span_mask &= mask + + # mel is x1 + x1 = inp + + # x0 is gaussian noise + x0 = torch.randn_like(x1) + + # time step + time = torch.normal(mean=0, std=1, size=(batch,), device=self.device) + time = torch.nn.functional.sigmoid(time) + # TODO. noise_scheduler + + # sample xt (φ_t(x) in the paper) + t = time.unsqueeze(-1).unsqueeze(-1) + φ = (1 - t) * x0 + t * x1 + flow = x1 - x0 + + # only predict what is within the random mask span for infilling + cond = torch.where(rand_span_mask[..., None], torch.zeros_like(x1), x1) + + # transformer and cfg training with a drop rate + drop_audio_cond = random() < self.audio_drop_prob # p_drop in voicebox paper + drop_text = random() < self.lrc_drop_prob + drop_prompt = random() < self.style_drop_prob + + # if want rigourously mask out padding, record in collate_fn in dataset.py, and pass in here + # adding mask will use more memory, thus also need to adjust batchsampler with scaled down threshold for long sequences + pred = self.transformer( + x=φ, cond=cond, text=text, time=time, drop_audio_cond=drop_audio_cond, drop_text=drop_text, drop_prompt=drop_prompt, + style_prompt=style_prompt, style_prompt_lens=style_prompt_lens, grad_ckpt=grad_ckpt, start_time=start_time + ) + + # flow matching loss + loss = F.mse_loss(pred, flow, reduction="none") + loss = loss[rand_span_mask] + + return loss.mean(), cond, pred diff --git a/diffrhythm/model/dit.py b/diffrhythm/model/dit.py new file mode 100644 index 0000000000000000000000000000000000000000..c417902f2180826fae0baba47004eed356dcc016 --- /dev/null +++ b/diffrhythm/model/dit.py @@ -0,0 +1,202 @@ +""" +ein notation: +b - batch +n - sequence +nt - text sequence +nw - raw wave length +d - dimension +""" + +from __future__ import annotations + +import torch +from torch import nn +import torch +import torch.nn.functional as F +from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaRotaryEmbedding +from transformers.models.llama import LlamaConfig +from torch.utils.checkpoint import checkpoint + +from diffrhythm.model.modules import ( + TimestepEmbedding, + ConvNeXtV2Block, + ConvPositionEmbedding, + DiTBlock, + AdaLayerNormZero_Final, + precompute_freqs_cis, + get_pos_embed_indices, +) +# from liger_kernel.transformers import apply_liger_kernel_to_llama +# apply_liger_kernel_to_llama() + +# Text embedding +class TextEmbedding(nn.Module): + def __init__(self, text_num_embeds, text_dim, max_pos, conv_layers=0, conv_mult=2): + super().__init__() + self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim) # use 0 as filler token + + if conv_layers > 0: + self.extra_modeling = True + self.precompute_max_pos = max_pos # ~44s of 24khz audio + self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False) + self.text_blocks = nn.Sequential( + *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)] + ) + else: + self.extra_modeling = False + + def forward(self, text: int["b nt"], seq_len, drop_text=False): # noqa: F722 + batch, text_len = text.shape[0], text.shape[1] + + if drop_text: # cfg for text + text = torch.zeros_like(text) + + text = self.text_embed(text) # b n -> b n d + + # possible extra modeling + if self.extra_modeling: + # sinus pos emb + batch_start = torch.zeros((batch,), dtype=torch.long) + pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos) + text_pos_embed = self.freqs_cis[pos_idx] + text = text + text_pos_embed + + # convnextv2 blocks + text = self.text_blocks(text) + + return text + + +# noised input audio and context mixing embedding +class InputEmbedding(nn.Module): + def __init__(self, mel_dim, text_dim, out_dim, cond_dim): + super().__init__() + self.proj = nn.Linear(mel_dim * 2 + text_dim + cond_dim * 2, out_dim) + self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim) + + def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], style_emb, time_emb, drop_audio_cond=False): # noqa: F722 + if drop_audio_cond: # cfg for cond audio + cond = torch.zeros_like(cond) + + style_emb = style_emb.unsqueeze(1).repeat(1, x.shape[1], 1) + time_emb = time_emb.unsqueeze(1).repeat(1, x.shape[1], 1) + x = self.proj(torch.cat((x, cond, text_embed, style_emb, time_emb), dim=-1)) + x = self.conv_pos_embed(x) + x + return x + + +# Transformer backbone using DiT blocks + + +class DiT(nn.Module): + def __init__( + self, + *, + dim, + depth=8, + heads=8, + dim_head=64, + dropout=0.1, + ff_mult=4, + mel_dim=100, + text_num_embeds=256, + text_dim=None, + conv_layers=0, + long_skip_connection=False, + use_style_prompt=False, + max_pos=2048, + ): + super().__init__() + + cond_dim = 512 + self.time_embed = TimestepEmbedding(cond_dim) + self.start_time_embed = TimestepEmbedding(cond_dim) + if text_dim is None: + text_dim = mel_dim + self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers, max_pos=max_pos) + self.input_embed = InputEmbedding(mel_dim, text_dim, dim, cond_dim=cond_dim) + + + self.dim = dim + self.depth = depth + + llama_config = LlamaConfig(hidden_size=dim, intermediate_size=dim * ff_mult, hidden_act='silu', max_position_embeddings=max_pos) + llama_config._attn_implementation = 'sdpa' + + self.transformer_blocks = nn.ModuleList( + [LlamaDecoderLayer(llama_config, layer_idx=i) for i in range(depth)] + ) + self.rotary_emb = LlamaRotaryEmbedding(config=llama_config) + self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None + + self.text_fusion_linears = nn.ModuleList( + [ + nn.Sequential( + nn.Linear(cond_dim, dim), + nn.SiLU() + ) for i in range(depth // 2) + ] + ) + for layer in self.text_fusion_linears: + for p in layer.parameters(): + p.detach().zero_() + + self.norm_out = AdaLayerNormZero_Final(dim, cond_dim) # final modulation + self.proj_out = nn.Linear(dim, mel_dim) + + + def forward_timestep_invariant(self, text, seq_len, drop_text, start_time): + s_t = self.start_time_embed(start_time) + text_embed = self.text_embed(text, seq_len, drop_text=drop_text) + text_residuals = [] + for layer in self.text_fusion_linears: + text_residual = layer(text_embed) + text_residuals.append(text_residual) + return s_t, text_embed, text_residuals + + + def forward( + self, + x: float["b n d"], # nosied input audio # noqa: F722 + text_embed: int["b nt"], # text # noqa: F722 + text_residuals, + cond: float["b n d"], # masked cond audio # noqa: F722 + time: float["b"] | float[""], # time step # noqa: F821 F722 + drop_audio_cond, # cfg for cond audio + drop_prompt=False, + style_prompt=None, # [b d t] + start_time=None, + ): + batch, seq_len = x.shape[0], x.shape[1] + if time.ndim == 0: + time = time.repeat(batch) + + t = self.time_embed(time) + c = t + start_time + + if drop_prompt: + style_prompt = torch.zeros_like(style_prompt) + + style_embed = style_prompt # [b, 512] + + x = self.input_embed(x, cond, text_embed, style_embed, c, drop_audio_cond=drop_audio_cond) + + if self.long_skip_connection is not None: + residual = x + + pos_ids = torch.arange(x.shape[1], device=x.device) + pos_ids = pos_ids.unsqueeze(0).repeat(x.shape[0], 1) + rotary_embed = self.rotary_emb(x, pos_ids) + + for i, block in enumerate(self.transformer_blocks): + x, *_ = block(x, position_embeddings=rotary_embed) + if i < self.depth // 2: + x = x + text_residuals[i] + + if self.long_skip_connection is not None: + x = self.long_skip_connection(torch.cat((x, residual), dim=-1)) + + x = self.norm_out(x, c) + output = self.proj_out(x) + + return output diff --git a/diffrhythm/model/modules.py b/diffrhythm/model/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..c217332715bc1b665f9378ec6f88fd619b9329cd --- /dev/null +++ b/diffrhythm/model/modules.py @@ -0,0 +1,612 @@ +""" +ein notation: +b - batch +n - sequence +nt - text sequence +nw - raw wave length +d - dimension +""" + +from __future__ import annotations +from typing import Optional +import math + +import torch +from torch import nn +import torch +import torch.nn.functional as F +import torchaudio + +from x_transformers.x_transformers import apply_rotary_pos_emb + + + +class FiLMLayer(nn.Module): + """ + Feature-wise Linear Modulation (FiLM) layer + Reference: https://arxiv.org/abs/1709.07871 + """ + def __init__(self, in_channels, cond_channels): + + super(FiLMLayer, self).__init__() + self.in_channels = in_channels + self.film = nn.Conv1d(cond_channels, in_channels * 2, 1) + + def forward(self, x, c): + gamma, beta = torch.chunk(self.film(c.unsqueeze(2)), chunks=2, dim=1) + gamma = gamma.transpose(1, 2) + beta = beta.transpose(1, 2) + # print(gamma.shape, beta.shape) + return gamma * x + beta + +# raw wav to mel spec + + +class MelSpec(nn.Module): + def __init__( + self, + filter_length=1024, + hop_length=256, + win_length=1024, + n_mel_channels=100, + target_sample_rate=24_000, + normalize=False, + power=1, + norm=None, + center=True, + ): + super().__init__() + self.n_mel_channels = n_mel_channels + + self.mel_stft = torchaudio.transforms.MelSpectrogram( + sample_rate=target_sample_rate, + n_fft=filter_length, + win_length=win_length, + hop_length=hop_length, + n_mels=n_mel_channels, + power=power, + center=center, + normalized=normalize, + norm=norm, + ) + + self.register_buffer("dummy", torch.tensor(0), persistent=False) + + def forward(self, inp): + if len(inp.shape) == 3: + inp = inp.squeeze(1) # 'b 1 nw -> b nw' + + assert len(inp.shape) == 2 + + if self.dummy.device != inp.device: + self.to(inp.device) + + mel = self.mel_stft(inp) + mel = mel.clamp(min=1e-5).log() + return mel + + +# sinusoidal position embedding + + +class SinusPositionEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x, scale=1000): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) + emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +# convolutional position embedding + + +class ConvPositionEmbedding(nn.Module): + def __init__(self, dim, kernel_size=31, groups=16): + super().__init__() + assert kernel_size % 2 != 0 + self.conv1d = nn.Sequential( + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2), + nn.Mish(), + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2), + nn.Mish(), + ) + + def forward(self, x: float["b n d"], mask: bool["b n"] | None = None): # noqa: F722 + if mask is not None: + mask = mask[..., None] + x = x.masked_fill(~mask, 0.0) + + x = x.permute(0, 2, 1) + x = self.conv1d(x) + out = x.permute(0, 2, 1) + + if mask is not None: + out = out.masked_fill(~mask, 0.0) + + return out + + +# rotary positional embedding related + + +def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0): + # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning + # has some connection to NTK literature + # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ + # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py + theta *= theta_rescale_factor ** (dim / (dim - 2)) + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) + t = torch.arange(end, device=freqs.device) # type: ignore + freqs = torch.outer(t, freqs).float() # type: ignore + freqs_cos = torch.cos(freqs) # real part + freqs_sin = torch.sin(freqs) # imaginary part + return torch.cat([freqs_cos, freqs_sin], dim=-1) + + +def get_pos_embed_indices(start, length, max_pos, scale=1.0): + # length = length if isinstance(length, int) else length.max() + scale = scale * torch.ones_like(start, dtype=torch.float32) # in case scale is a scalar + pos = ( + start.unsqueeze(1) + + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long() + ) + # avoid extra long error. + pos = torch.where(pos < max_pos, pos, max_pos - 1) + return pos + + +# Global Response Normalization layer (Instance Normalization ?) + + +class GRN(nn.Module): + def __init__(self, dim): + super().__init__() + self.gamma = nn.Parameter(torch.zeros(1, 1, dim)) + self.beta = nn.Parameter(torch.zeros(1, 1, dim)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=1, keepdim=True) + Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) + return self.gamma * (x * Nx) + self.beta + x + + +# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py +# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108 + + +class ConvNeXtV2Block(nn.Module): + def __init__( + self, + dim: int, + intermediate_dim: int, + dilation: int = 1, + ): + super().__init__() + padding = (dilation * (7 - 1)) // 2 + self.dwconv = nn.Conv1d( + dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation + ) # depthwise conv + self.norm = nn.LayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.grn = GRN(intermediate_dim) + self.pwconv2 = nn.Linear(intermediate_dim, dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + residual = x + x = x.transpose(1, 2) # b n d -> b d n + x = self.dwconv(x) + x = x.transpose(1, 2) # b d n -> b n d + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.grn(x) + x = self.pwconv2(x) + return residual + x + + +# AdaLayerNormZero +# return with modulated x for attn input, and params for later mlp modulation + + +class AdaLayerNormZero(nn.Module): + def __init__(self, dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(dim, dim * 6) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, x, emb=None): + emb = self.linear(self.silu(emb)) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1) + + x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return x, gate_msa, shift_mlp, scale_mlp, gate_mlp + + +# AdaLayerNormZero for final layer +# return only with modulated x for attn input, cuz no more mlp modulation + + +class AdaLayerNormZero_Final(nn.Module): + def __init__(self, dim, cond_dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(cond_dim, dim * 2) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, x, emb): + emb = self.linear(self.silu(emb)) + scale, shift = torch.chunk(emb, 2, dim=1) + + x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :] + return x + + +# FeedForward + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + + activation = nn.GELU(approximate=approximate) + #activation = nn.SiLU() + project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation) + self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)) + + def forward(self, x): + return self.ff(x) + + +# Attention with possible joint part +# modified from diffusers/src/diffusers/models/attention_processor.py + + +class Attention(nn.Module): + def __init__( + self, + processor: JointAttnProcessor | AttnProcessor, + dim: int, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + context_dim: Optional[int] = None, # if not None -> joint attention + context_pre_only=None, + ): + super().__init__() + + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") + + self.processor = processor + + self.dim = dim + self.heads = heads + self.inner_dim = dim_head * heads + self.dropout = dropout + + self.context_dim = context_dim + self.context_pre_only = context_pre_only + + self.to_q = nn.Linear(dim, self.inner_dim) + self.to_k = nn.Linear(dim, self.inner_dim) + self.to_v = nn.Linear(dim, self.inner_dim) + + if self.context_dim is not None: + self.to_k_c = nn.Linear(context_dim, self.inner_dim) + self.to_v_c = nn.Linear(context_dim, self.inner_dim) + if self.context_pre_only is not None: + self.to_q_c = nn.Linear(context_dim, self.inner_dim) + + self.to_out = nn.ModuleList([]) + self.to_out.append(nn.Linear(self.inner_dim, dim)) + self.to_out.append(nn.Dropout(dropout)) + + if self.context_pre_only is not None and not self.context_pre_only: + self.to_out_c = nn.Linear(self.inner_dim, dim) + + def forward( + self, + x: float["b n d"], # noised input x # noqa: F722 + c: float["b n d"] = None, # context c # noqa: F722 + mask: bool["b n"] | None = None, # noqa: F722 + rope=None, # rotary position embedding for x + c_rope=None, # rotary position embedding for c + ) -> torch.Tensor: + if c is not None: + return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope) + else: + return self.processor(self, x, mask=mask, rope=rope) + + +# Attention processor + + +class AttnProcessor: + def __init__(self): + pass + + def __call__( + self, + attn: Attention, + x: float["b n d"], # noised input x # noqa: F722 + mask: bool["b n"] | None = None, # noqa: F722 + rope=None, # rotary position embedding + ) -> torch.FloatTensor: + batch_size = x.shape[0] + + # `sample` projections. + query = attn.to_q(x) + key = attn.to_k(x) + value = attn.to_v(x) + + # apply rotary position embedding + if rope is not None: + freqs, xpos_scale = rope + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + + query = apply_rotary_pos_emb(query, freqs, q_xpos_scale) + key = apply_rotary_pos_emb(key, freqs, k_xpos_scale) + + # attention + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # mask. e.g. inference got a batch with different target durations, mask out the padding + if mask is not None: + attn_mask = mask + attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n' + attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2]) + else: + attn_mask = None + + x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False) + x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + x = x.to(query.dtype) + + # linear proj + x = attn.to_out[0](x) + # dropout + x = attn.to_out[1](x) + + if mask is not None: + mask = mask.unsqueeze(-1) + x = x.masked_fill(~mask, 0.0) + + return x + + +# Joint Attention processor for MM-DiT +# modified from diffusers/src/diffusers/models/attention_processor.py + + +class JointAttnProcessor: + def __init__(self): + pass + + def __call__( + self, + attn: Attention, + x: float["b n d"], # noised input x # noqa: F722 + c: float["b nt d"] = None, # context c, here text # noqa: F722 + mask: bool["b n"] | None = None, # noqa: F722 + rope=None, # rotary position embedding for x + c_rope=None, # rotary position embedding for c + ) -> torch.FloatTensor: + residual = x + + batch_size = c.shape[0] + + # `sample` projections. + query = attn.to_q(x) + key = attn.to_k(x) + value = attn.to_v(x) + + # `context` projections. + c_query = attn.to_q_c(c) + c_key = attn.to_k_c(c) + c_value = attn.to_v_c(c) + + # apply rope for context and noised input independently + if rope is not None: + freqs, xpos_scale = rope + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + query = apply_rotary_pos_emb(query, freqs, q_xpos_scale) + key = apply_rotary_pos_emb(key, freqs, k_xpos_scale) + if c_rope is not None: + freqs, xpos_scale = c_rope + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale) + c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale) + + # attention + query = torch.cat([query, c_query], dim=1) + key = torch.cat([key, c_key], dim=1) + value = torch.cat([value, c_value], dim=1) + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # mask. e.g. inference got a batch with different target durations, mask out the padding + if mask is not None: + attn_mask = F.pad(mask, (0, c.shape[1]), value=True) # no mask for c (text) + attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n' + attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2]) + else: + attn_mask = None + + x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False) + x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + x = x.to(query.dtype) + + # Split the attention outputs. + x, c = ( + x[:, : residual.shape[1]], + x[:, residual.shape[1] :], + ) + + # linear proj + x = attn.to_out[0](x) + # dropout + x = attn.to_out[1](x) + if not attn.context_pre_only: + c = attn.to_out_c(c) + + if mask is not None: + mask = mask.unsqueeze(-1) + x = x.masked_fill(~mask, 0.0) + # c = c.masked_fill(~mask, 0.) # no mask for c (text) + + return x, c + + +# DiT Block + + +class DiTBlock(nn.Module): + def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, use_style_prompt=False): + super().__init__() + + self.attn_norm = AdaLayerNormZero(dim) + self.attn = Attention( + processor=AttnProcessor(), + dim=dim, + heads=heads, + dim_head=dim_head, + dropout=dropout, + ) + + self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + + self.use_style_prompt = use_style_prompt + if use_style_prompt: + #self.film = FiLMLayer(dim, dim) + self.prompt_norm = AdaLayerNormZero_Final(dim) + + def forward(self, x, t, c=None, mask=None, rope=None): # x: noised input, t: time embedding + if c is not None and self.use_style_prompt: + #x = self.film(x, c) + x = self.prompt_norm(x, c) + + # pre-norm & modulation for attention input + norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t) + + # attention + attn_output = self.attn(x=norm, mask=mask, rope=rope) + + # process attention output for input x + x = x + gate_msa.unsqueeze(1) * attn_output + + norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ff_output = self.ff(norm) + x = x + gate_mlp.unsqueeze(1) * ff_output + + return x + + +# MMDiT Block https://arxiv.org/abs/2403.03206 + + +class MMDiTBlock(nn.Module): + r""" + modified from diffusers/src/diffusers/models/attention.py + + notes. + _c: context related. text, cond, etc. (left part in sd3 fig2.b) + _x: noised input related. (right part) + context_pre_only: last layer only do prenorm + modulation cuz no more ffn + """ + + def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_pre_only=False): + super().__init__() + + self.context_pre_only = context_pre_only + + self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim) + self.attn_norm_x = AdaLayerNormZero(dim) + self.attn = Attention( + processor=JointAttnProcessor(), + dim=dim, + heads=heads, + dim_head=dim_head, + dropout=dropout, + context_dim=dim, + context_pre_only=context_pre_only, + ) + + if not context_pre_only: + self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_c = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + else: + self.ff_norm_c = None + self.ff_c = None + self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + + def forward(self, x, c, t, mask=None, rope=None, c_rope=None): # x: noised input, c: context, t: time embedding + # pre-norm & modulation for attention input + if self.context_pre_only: + norm_c = self.attn_norm_c(c, t) + else: + norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t) + norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t) + + # attention + x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope) + + # process attention output for context c + if self.context_pre_only: + c = None + else: # if not last layer + c = c + c_gate_msa.unsqueeze(1) * c_attn_output + + norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None] + c_ff_output = self.ff_c(norm_c) + c = c + c_gate_mlp.unsqueeze(1) * c_ff_output + + # process attention output for input x + x = x + x_gate_msa.unsqueeze(1) * x_attn_output + + norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None] + x_ff_output = self.ff_x(norm_x) + x = x + x_gate_mlp.unsqueeze(1) * x_ff_output + + return c, x + + +# time step conditioning embedding + + +class TimestepEmbedding(nn.Module): + def __init__(self, dim, freq_embed_dim=256): + super().__init__() + self.time_embed = SinusPositionEmbedding(freq_embed_dim) + self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) + + def forward(self, timestep: float["b"]): # noqa: F821 + time_hidden = self.time_embed(timestep) + time_hidden = time_hidden.to(timestep.dtype) + time = self.time_mlp(time_hidden.to(torch.float32)) # Ensures dtype match + # b d + return time diff --git a/diffrhythm/model/trainer.py b/diffrhythm/model/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..a1777475b579097fec75b8e24590f58a9c7af8f8 --- /dev/null +++ b/diffrhythm/model/trainer.py @@ -0,0 +1,350 @@ +from __future__ import annotations + +import os +import gc +from tqdm import tqdm +import wandb + +import torch +from torch.optim import AdamW +from torch.optim.lr_scheduler import LinearLR, SequentialLR, ConstantLR + +from accelerate import Accelerator +from accelerate.utils import DistributedDataParallelKwargs +from diffrhythm.dataset.custom_dataset_align2f5 import LanceDiffusionDataset + +from torch.utils.data import DataLoader, DistributedSampler + +from ema_pytorch import EMA + +from diffrhythm.model import CFM +from diffrhythm.model.utils import exists, default + +import time + +# from apex.optimizers.fused_adam import FusedAdam + +# trainer + + +class Trainer: + def __init__( + self, + model: CFM, + args, + epochs, + learning_rate, + #dataloader, + num_warmup_updates=20000, + save_per_updates=1000, + checkpoint_path=None, + batch_size=32, + batch_size_type: str = "sample", + max_samples=32, + grad_accumulation_steps=1, + max_grad_norm=1.0, + noise_scheduler: str | None = None, + duration_predictor: torch.nn.Module | None = None, + wandb_project="test_e2-tts", + wandb_run_name="test_run", + wandb_resume_id: str = None, + last_per_steps=None, + accelerate_kwargs: dict = dict(), + ema_kwargs: dict = dict(), + bnb_optimizer: bool = False, + reset_lr: bool = False, + use_style_prompt: bool = False, + grad_ckpt: bool = False + ): + self.args = args + + ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=False, ) + + logger = "wandb" if wandb.api.api_key else None + #logger = None + print(f"Using logger: {logger}") + # print("-----------1-------------") + import tbe.common + # print("-----------2-------------") + self.accelerator = Accelerator( + log_with=logger, + kwargs_handlers=[ddp_kwargs], + gradient_accumulation_steps=grad_accumulation_steps, + **accelerate_kwargs, + ) + # print("-----------3-------------") + + if logger == "wandb": + if exists(wandb_resume_id): + init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name, "id": wandb_resume_id}} + else: + init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name}} + self.accelerator.init_trackers( + project_name=wandb_project, + init_kwargs=init_kwargs, + config={ + "epochs": epochs, + "learning_rate": learning_rate, + "num_warmup_updates": num_warmup_updates, + "batch_size": batch_size, + "batch_size_type": batch_size_type, + "max_samples": max_samples, + "grad_accumulation_steps": grad_accumulation_steps, + "max_grad_norm": max_grad_norm, + "gpus": self.accelerator.num_processes, + "noise_scheduler": noise_scheduler, + }, + ) + + self.precision = self.accelerator.state.mixed_precision + self.precision = self.precision.replace("no", "fp32") + print("!!!!!!!!!!!!!!!!!", self.precision) + + self.model = model + #self.model = torch.compile(model) + + #self.dataloader = dataloader + + if self.is_main: + self.ema_model = EMA(model, include_online_model=False, **ema_kwargs) + + self.ema_model.to(self.accelerator.device) + if self.accelerator.state.distributed_type in ["DEEPSPEED", "FSDP"]: + self.ema_model.half() + + self.epochs = epochs + self.num_warmup_updates = num_warmup_updates + self.save_per_updates = save_per_updates + self.last_per_steps = default(last_per_steps, save_per_updates * grad_accumulation_steps) + self.checkpoint_path = default(checkpoint_path, "ckpts/test_e2-tts") + + self.max_samples = max_samples + self.grad_accumulation_steps = grad_accumulation_steps + self.max_grad_norm = max_grad_norm + + self.noise_scheduler = noise_scheduler + + self.duration_predictor = duration_predictor + + self.reset_lr = reset_lr + + self.use_style_prompt = use_style_prompt + + self.grad_ckpt = grad_ckpt + + if bnb_optimizer: + import bitsandbytes as bnb + + self.optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=learning_rate) + else: + self.optimizer = AdamW(model.parameters(), lr=learning_rate) + #self.optimizer = FusedAdam(model.parameters(), lr=learning_rate) + + #self.model = torch.compile(self.model) + if self.accelerator.state.distributed_type == "DEEPSPEED": + self.accelerator.state.deepspeed_plugin.deepspeed_config['train_micro_batch_size_per_gpu'] = batch_size + + self.get_dataloader() + self.get_scheduler() + # self.get_constant_scheduler() + + self.model, self.optimizer, self.scheduler, self.train_dataloader = self.accelerator.prepare(self.model, self.optimizer, self.scheduler, self.train_dataloader) + + def get_scheduler(self): + warmup_steps = ( + self.num_warmup_updates * self.accelerator.num_processes + ) # consider a fixed warmup steps while using accelerate multi-gpu ddp + total_steps = len(self.train_dataloader) * self.epochs / self.grad_accumulation_steps + decay_steps = total_steps - warmup_steps + warmup_scheduler = LinearLR(self.optimizer, start_factor=1e-8, end_factor=1.0, total_iters=warmup_steps) + decay_scheduler = LinearLR(self.optimizer, start_factor=1.0, end_factor=1e-8, total_iters=decay_steps) + # constant_scheduler = ConstantLR(self.optimizer, factor=1, total_iters=decay_steps) + self.scheduler = SequentialLR( + self.optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[warmup_steps] + ) + + def get_constant_scheduler(self): + total_steps = len(self.train_dataloader) * self.epochs / self.grad_accumulation_steps + self.scheduler = ConstantLR(self.optimizer, factor=1, total_iters=total_steps) + + def get_dataloader(self): + prompt_path = self.args.prompt_path.split('|') + lrc_path = self.args.lrc_path.split('|') + latent_path = self.args.latent_path.split('|') + ldd = LanceDiffusionDataset(*LanceDiffusionDataset.init_data(self.args.dataset_path), \ + max_frames=self.args.max_frames, min_frames=self.args.min_frames, \ + align_lyrics=self.args.align_lyrics, lyrics_slice=self.args.lyrics_slice, \ + use_style_prompt=self.args.use_style_prompt, parse_lyrics=self.args.parse_lyrics, + lyrics_shift=self.args.lyrics_shift, downsample_rate=self.args.downsample_rate, \ + skip_empty_lyrics=self.args.skip_empty_lyrics, tokenizer_type=self.args.tokenizer_type, precision=self.precision, \ + start_time=time.time(), pure_prob=self.args.pure_prob) + + # start_time = time.time() + self.train_dataloader = DataLoader( + dataset=ldd, + batch_size=self.args.batch_size, # 每个批次的样本数 + shuffle=True, # 是否随机打乱数据 + num_workers=4, # 用于加载数据的子进程数 + pin_memory=True, # 加速GPU训练 + collate_fn=ldd.custom_collate_fn, + persistent_workers=True + ) + + + @property + def is_main(self): + return self.accelerator.is_main_process + + def save_checkpoint(self, step, last=False): + self.accelerator.wait_for_everyone() + if self.is_main: + checkpoint = dict( + model_state_dict=self.accelerator.unwrap_model(self.model).state_dict(), + optimizer_state_dict=self.accelerator.unwrap_model(self.optimizer).state_dict(), + ema_model_state_dict=self.ema_model.state_dict(), + scheduler_state_dict=self.scheduler.state_dict(), + step=step, + ) + if not os.path.exists(self.checkpoint_path): + os.makedirs(self.checkpoint_path) + if last: + self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_last.pt") + print(f"Saved last checkpoint at step {step}") + else: + self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_{step}.pt") + + def load_checkpoint(self): + if ( + not exists(self.checkpoint_path) + or not os.path.exists(self.checkpoint_path) + or not os.listdir(self.checkpoint_path) + ): + return 0 + + self.accelerator.wait_for_everyone() + if "model_last.pt" in os.listdir(self.checkpoint_path): + latest_checkpoint = "model_last.pt" + else: + latest_checkpoint = sorted( + [f for f in os.listdir(self.checkpoint_path) if f.endswith(".pt")], + key=lambda x: int("".join(filter(str.isdigit, x))), + )[-1] + + checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location="cpu") + + ### **1. 过滤 `ema_model` 的不匹配参数** + if self.is_main: + ema_dict = self.ema_model.state_dict() + ema_checkpoint_dict = checkpoint["ema_model_state_dict"] + + filtered_ema_dict = { + k: v for k, v in ema_checkpoint_dict.items() + if k in ema_dict and ema_dict[k].shape == v.shape # 仅加载 shape 匹配的参数 + } + + print(f"Loading {len(filtered_ema_dict)} / {len(ema_checkpoint_dict)} ema_model params") + self.ema_model.load_state_dict(filtered_ema_dict, strict=False) + + ### **2. 过滤 `model` 的不匹配参数** + model_dict = self.accelerator.unwrap_model(self.model).state_dict() + checkpoint_model_dict = checkpoint["model_state_dict"] + + filtered_model_dict = { + k: v for k, v in checkpoint_model_dict.items() + if k in model_dict and model_dict[k].shape == v.shape # 仅加载 shape 匹配的参数 + } + + print(f"Loading {len(filtered_model_dict)} / {len(checkpoint_model_dict)} model params") + self.accelerator.unwrap_model(self.model).load_state_dict(filtered_model_dict, strict=False) + + ### **3. 加载优化器、调度器和步数** + if "step" in checkpoint: + if self.scheduler and not self.reset_lr: + self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"]) + step = checkpoint["step"] + else: + step = 0 + + del checkpoint + gc.collect() + print("Checkpoint loaded at step", step) + return step + + def train(self, resumable_with_seed: int = None): + train_dataloader = self.train_dataloader + + start_step = self.load_checkpoint() + global_step = start_step + + if resumable_with_seed > 0: + orig_epoch_step = len(train_dataloader) + skipped_epoch = int(start_step // orig_epoch_step) + skipped_batch = start_step % orig_epoch_step + skipped_dataloader = self.accelerator.skip_first_batches(train_dataloader, num_batches=skipped_batch) + else: + skipped_epoch = 0 + + for epoch in range(skipped_epoch, self.epochs): + self.model.train() + if resumable_with_seed > 0 and epoch == skipped_epoch: + progress_bar = tqdm( + skipped_dataloader, + desc=f"Epoch {epoch+1}/{self.epochs}", + unit="step", + disable=not self.accelerator.is_local_main_process, + initial=skipped_batch, + total=orig_epoch_step, + smoothing=0.15 + ) + else: + progress_bar = tqdm( + train_dataloader, + desc=f"Epoch {epoch+1}/{self.epochs}", + unit="step", + disable=not self.accelerator.is_local_main_process, + smoothing=0.15 + ) + + for batch in progress_bar: + with self.accelerator.accumulate(self.model): + text_inputs = batch["lrc"] + mel_spec = batch["latent"].permute(0, 2, 1) + mel_lengths = batch["latent_lengths"] + style_prompt = batch["prompt"] + style_prompt_lens = batch["prompt_lengths"] + start_time = batch["start_time"] + + loss, cond, pred = self.model( + mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler, + style_prompt=style_prompt if self.use_style_prompt else None, + style_prompt_lens=style_prompt_lens if self.use_style_prompt else None, + grad_ckpt=self.grad_ckpt, start_time=start_time + ) + self.accelerator.backward(loss) + + if self.max_grad_norm > 0 and self.accelerator.sync_gradients: + self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm) + + self.optimizer.step() + self.scheduler.step() + self.optimizer.zero_grad() + + if self.is_main: + self.ema_model.update() + + global_step += 1 + + if self.accelerator.is_local_main_process: + self.accelerator.log({"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_step) + + progress_bar.set_postfix(step=str(global_step), loss=loss.item()) + + if global_step % (self.save_per_updates * self.grad_accumulation_steps) == 0: + self.save_checkpoint(global_step) + + if global_step % self.last_per_steps == 0: + self.save_checkpoint(global_step, last=True) + + self.save_checkpoint(global_step, last=True) + + self.accelerator.end_training() diff --git a/diffrhythm/model/utils.py b/diffrhythm/model/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f34ea04a54a9fd4070cb72ee73ffb880ecbfc340 --- /dev/null +++ b/diffrhythm/model/utils.py @@ -0,0 +1,182 @@ +from __future__ import annotations + +import os +import random +from collections import defaultdict +from importlib.resources import files + +import torch +from torch.nn.utils.rnn import pad_sequence + + +# seed everything + + +def seed_everything(seed=0): + random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +# helpers + + +def exists(v): + return v is not None + + +def default(v, d): + return v if exists(v) else d + + +# tensor helpers + + +def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]: # noqa: F722 F821 + if not exists(length): + length = t.amax() + + seq = torch.arange(length, device=t.device) + return seq[None, :] < t[:, None] + + +def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]): # noqa: F722 F821 + max_seq_len = 2048 + seq = torch.arange(max_seq_len, device=start.device).long() + start_mask = seq[None, :] >= start[:, None] + end_mask = seq[None, :] < end[:, None] + return start_mask & end_mask + + +def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]): # noqa: F722 F821 + lengths = (frac_lengths * seq_len).long() + max_start = seq_len - lengths + + rand = torch.rand_like(frac_lengths) + start = (max_start * rand).long().clamp(min=0) + end = start + lengths + + return mask_from_start_end_indices(seq_len, start, end) + + +def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]: # noqa: F722 + if not exists(mask): + return t.mean(dim=1) + + t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device)) + num = t.sum(dim=1) + den = mask.float().sum(dim=1) + + return num / den.clamp(min=1.0) + + +# simple utf-8 tokenizer, since paper went character based +def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]: # noqa: F722 + list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text] # ByT5 style + text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True) + return text + + +# char tokenizer, based on custom dataset's extracted .txt file +def list_str_to_idx( + text: list[str] | list[list[str]], + vocab_char_map: dict[str, int], # {char: idx} + padding_value=-1, +) -> int["b nt"]: # noqa: F722 + list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text] # pinyin or char style + text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True) + return text + + +# Get tokenizer + + +def get_tokenizer(dataset_name, tokenizer: str = "pinyin"): + """ + tokenizer - "pinyin" do g2p for only chinese characters, need .txt vocab_file + - "char" for char-wise tokenizer, need .txt vocab_file + - "byte" for utf-8 tokenizer + - "custom" if you're directly passing in a path to the vocab.txt you want to use + vocab_size - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols + - if use "char", derived from unfiltered character & symbol counts of custom dataset + - if use "byte", set to 256 (unicode byte range) + """ + if tokenizer in ["pinyin", "char"]: + tokenizer_path = os.path.join(files("diffrhythm").joinpath("../../data"), f"{dataset_name}_{tokenizer}/vocab.txt") + with open(tokenizer_path, "r", encoding="utf-8") as f: + vocab_char_map = {} + for i, char in enumerate(f): + vocab_char_map[char[:-1]] = i + vocab_size = len(vocab_char_map) + assert vocab_char_map[" "] == 0, "make sure space is of idx 0 in vocab.txt, cuz 0 is used for unknown char" + + elif tokenizer == "byte": + vocab_char_map = None + vocab_size = 256 + + elif tokenizer == "custom": + with open(dataset_name, "r", encoding="utf-8") as f: + vocab_char_map = {} + for i, char in enumerate(f): + vocab_char_map[char[:-1]] = i + vocab_size = len(vocab_char_map) + + return vocab_char_map, vocab_size + + +# convert char to pinyin + + +def convert_char_to_pinyin(text_list, polyphone=True): + final_text_list = [] + god_knows_why_en_testset_contains_zh_quote = str.maketrans( + {"“": '"', "”": '"', "‘": "'", "’": "'"} + ) # in case librispeech (orig no-pc) test-clean + custom_trans = str.maketrans({";": ","}) # add custom trans here, to address oov + for text in text_list: + char_list = [] + text = text.translate(god_knows_why_en_testset_contains_zh_quote) + text = text.translate(custom_trans) + for seg in jieba.cut(text): + seg_byte_len = len(bytes(seg, "UTF-8")) + if seg_byte_len == len(seg): # if pure alphabets and symbols + if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"": + char_list.append(" ") + char_list.extend(seg) + elif polyphone and seg_byte_len == 3 * len(seg): # if pure chinese characters + seg = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True) + for c in seg: + if c not in "。，、；：？！《》【】—…": + char_list.append(" ") + char_list.append(c) + else: # if mixed chinese characters, alphabets and symbols + for c in seg: + if ord(c) < 256: + char_list.extend(c) + else: + if c not in "。，、；：？！《》【】—…": + char_list.append(" ") + char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True)) + else: # if is zh punc + char_list.append(c) + final_text_list.append(char_list) + + return final_text_list + + +# filter func for dirty data with many repetitions + + +def repetition_found(text, length=2, tolerance=10): + pattern_count = defaultdict(int) + for i in range(len(text) - length + 1): + pattern = text[i : i + length] + pattern_count[pattern] += 1 + for pattern, count in pattern_count.items(): + if count > tolerance: + return True + return False