import os
from webbrowser import get
os.environ["OMP_NUM_THREADS"] = "1"
import yaml
from utils.multiprocess_utils import chunked_multiprocess_run
import random
import json
# from resemblyzer import VoiceEncoder
from tqdm import tqdm
from preprocessing.data_gen_utils import get_mel2ph, get_pitch_parselmouth, build_phone_encoder,get_pitch_crepe
from utils.hparams import set_hparams, hparams
import numpy as np
from utils.indexed_datasets import IndexedDatasetBuilder
class BinarizationError(Exception):
pass
BASE_ITEM_ATTRIBUTES = ['txt', 'ph', 'wav_fn', 'tg_fn', 'spk_id']
class BaseBinarizer:
'''
Base class for data processing.
1. *process* and *process_data_split*:
process entire data, generate the train-test split (support parallel processing);
2. *process_item*:
process singe piece of data;
3. *get_pitch*:
infer the pitch using some algorithm;
4. *get_align*:
get the alignment using 'mel2ph' format (see https://arxiv.org/abs/1905.09263).
5. phoneme encoder, voice encoder, etc.
Subclasses should define:
1. *load_metadata*:
how to read multiple datasets from files;
2. *train_item_names*, *valid_item_names*, *test_item_names*:
how to split the dataset;
3. load_ph_set:
the phoneme set.
'''
def __init__(self, item_attributes=BASE_ITEM_ATTRIBUTES):
self.binarization_args = hparams['binarization_args']
#self.pre_align_args = hparams['pre_align_args']
self.items = {}
# every item in self.items has some attributes
self.item_attributes = item_attributes
self.load_meta_data()
# check program correctness 检查itemdict的key只能在给定的列表中取值
assert all([attr in self.item_attributes for attr in list(self.items.values())[0].keys()])
self.item_names = sorted(list(self.items.keys()))
if self.binarization_args['shuffle']:
random.seed(1234)
random.shuffle(self.item_names)
# set default get_pitch algorithm
if hparams['use_crepe']:
self.get_pitch_algorithm = get_pitch_crepe
else:
self.get_pitch_algorithm = get_pitch_parselmouth
def load_meta_data(self):
raise NotImplementedError
@property
def train_item_names(self):
raise NotImplementedError
@property
def valid_item_names(self):
raise NotImplementedError
@property
def test_item_names(self):
raise NotImplementedError
def build_spk_map(self):
spk_map = set()
for item_name in self.item_names:
spk_name = self.items[item_name]['spk_id']
spk_map.add(spk_name)
spk_map = {x: i for i, x in enumerate(sorted(list(spk_map)))}
assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map)
return spk_map
def item_name2spk_id(self, item_name):
return self.spk_map[self.items[item_name]['spk_id']]
def _phone_encoder(self):
'''
use hubert encoder
'''
raise NotImplementedError
'''
create 'phone_set.json' file if it doesn't exist
'''
ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json"
ph_set = []
if hparams['reset_phone_dict'] or not os.path.exists(ph_set_fn):
self.load_ph_set(ph_set)
ph_set = sorted(set(ph_set))
json.dump(ph_set, open(ph_set_fn, 'w', encoding='utf-8'))
print("| Build phone set: ", ph_set)
else:
ph_set = json.load(open(ph_set_fn, 'r', encoding='utf-8'))
print("| Load phone set: ", ph_set)
return build_phone_encoder(hparams['binary_data_dir'])
def load_ph_set(self, ph_set):
raise NotImplementedError
def meta_data_iterator(self, prefix):
if prefix == 'valid':
item_names = self.valid_item_names
elif prefix == 'test':
item_names = self.test_item_names
else:
item_names = self.train_item_names
for item_name in item_names:
meta_data = self.items[item_name]
yield item_name, meta_data
def process(self):
os.makedirs(hparams['binary_data_dir'], exist_ok=True)
self.spk_map = self.build_spk_map()
print("| spk_map: ", self.spk_map)
spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json"
json.dump(self.spk_map, open(spk_map_fn, 'w', encoding='utf-8'))
self.phone_encoder =self._phone_encoder()
self.process_data_split('valid')
self.process_data_split('test')
self.process_data_split('train')
def process_data_split(self, prefix):
data_dir = hparams['binary_data_dir']
args = []
builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
lengths = []
f0s = []
total_sec = 0
# if self.binarization_args['with_spk_embed']:
# voice_encoder = VoiceEncoder().cuda()
for item_name, meta_data in self.meta_data_iterator(prefix):
args.append([item_name, meta_data, self.binarization_args])
spec_min=[]
spec_max=[]
# code for single cpu processing
for i in tqdm(reversed(range(len(args))), total=len(args)):
a = args[i]
item = self.process_item(*a)
if item is None:
continue
spec_min.append(item['spec_min'])
spec_max.append(item['spec_max'])
# item['spk_embe'] = voice_encoder.embed_utterance(item['wav']) \
# if self.binardization_args['with_spk_embed'] else None
if not self.binarization_args['with_wav'] and 'wav' in item:
if hparams['debug']:
print("del wav")
del item['wav']
if(hparams['debug']):
print(item)
builder.add_item(item)
lengths.append(item['len'])
total_sec += item['sec']
# if item.get('f0') is not None:
# f0s.append(item['f0'])
if prefix=='train':
spec_max=np.max(spec_max,0)
spec_min=np.min(spec_min,0)
print(spec_max.shape)
with open(hparams['config_path'], encoding='utf-8') as f:
_hparams=yaml.safe_load(f)
_hparams['spec_max']=spec_max.tolist()
_hparams['spec_min']=spec_min.tolist()
with open(hparams['config_path'], 'w', encoding='utf-8') as f:
yaml.safe_dump(_hparams,f)
builder.finalize()
np.save(f'{data_dir}/{prefix}_lengths.npy', lengths)
if len(f0s) > 0:
f0s = np.concatenate(f0s, 0)
f0s = f0s[f0s != 0]
np.save(f'{data_dir}/{prefix}_f0s_mean_std.npy', [np.mean(f0s).item(), np.std(f0s).item()])
print(f"| {prefix} total duration: {total_sec:.3f}s")
def process_item(self, item_name, meta_data, binarization_args):
from preprocessing.process_pipeline import File2Batch
return File2Batch.temporary_dict2processed_input(item_name, meta_data, self.phone_encoder, binarization_args)
def get_align(self, meta_data, mel, phone_encoded, res):
raise NotImplementedError
def get_align_from_textgrid(self, meta_data, mel, phone_encoded, res):
'''
NOTE: this part of script is *isolated* from other scripts, which means
it may not be compatible with the current version.
'''
return
tg_fn, ph = meta_data['tg_fn'], meta_data['ph']
if tg_fn is not None and os.path.exists(tg_fn):
mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams)
else:
raise BinarizationError(f"Align not found")
if mel2ph.max() - 1 >= len(phone_encoded):
raise BinarizationError(
f"Align does not match: mel2ph.max() - 1: {mel2ph.max() - 1}, len(phone_encoded): {len(phone_encoded)}")
res['mel2ph'] = mel2ph
res['dur'] = dur
def get_f0cwt(self, f0, res):
'''
NOTE: this part of script is *isolated* from other scripts, which means
it may not be compatible with the current version.
'''
return
from utils.cwt import get_cont_lf0, get_lf0_cwt
uv, cont_lf0_lpf = get_cont_lf0(f0)
logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf)
cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org
Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm)
if np.any(np.isnan(Wavelet_lf0)):
raise BinarizationError("NaN CWT")
res['cwt_spec'] = Wavelet_lf0
res['cwt_scales'] = scales
res['f0_mean'] = logf0s_mean_org
res['f0_std'] = logf0s_std_org
if __name__ == "__main__":
set_hparams()
BaseBinarizer().process()