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import torch | |
from torch.utils.data import DataLoader | |
from multiprocessing import Pool | |
import commons | |
import utils | |
from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate | |
from tqdm import tqdm | |
import warnings | |
from text import cleaned_text_to_sequence, get_bert | |
config_path = 'configs/config.json' | |
hps = utils.get_hparams_from_file(config_path) | |
def process_line(line): | |
_id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|") | |
phone = phones.split(" ") | |
tone = [int(i) for i in tone.split(" ")] | |
word2ph = [int(i) for i in word2ph.split(" ")] | |
w2pho = [i for i in word2ph] | |
word2ph = [i for i in word2ph] | |
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) | |
if hps.data.add_blank: | |
phone = commons.intersperse(phone, 0) | |
tone = commons.intersperse(tone, 0) | |
language = commons.intersperse(language, 0) | |
for i in range(len(word2ph)): | |
word2ph[i] = word2ph[i] * 2 | |
word2ph[0] += 1 | |
wav_path = f'{_id}' | |
bert_path = wav_path.replace(".wav", ".bert.pt") | |
try: | |
bert = torch.load(bert_path) | |
assert bert.shape[-1] == len(phone) | |
except: | |
bert = get_bert(text, word2ph, language_str) | |
assert bert.shape[-1] == len(phone) | |
torch.save(bert, bert_path) | |
if __name__ == '__main__': | |
lines = [] | |
with open(hps.data.training_files, encoding='utf-8' ) as f: | |
lines.extend(f.readlines()) | |
with open(hps.data.validation_files, encoding='utf-8' ) as f: | |
lines.extend(f.readlines()) | |
with Pool(processes=12) as pool: #A100 40GB suitable config,if coom,please decrease the processess number. | |
for _ in tqdm(pool.imap_unordered(process_line, lines)): | |
pass | |