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