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import torch |
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from torch.utils.data import DataLoader |
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from multiprocessing import Pool |
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import commons |
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import utils |
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from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate |
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from tqdm import tqdm |
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import warnings |
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from text import cleaned_text_to_sequence, get_bert |
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config_path = 'configs/config.json' |
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hps = utils.get_hparams_from_file(config_path) |
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def process_line(line): |
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_id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|") |
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phone = phones.split(" ") |
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tone = [int(i) for i in tone.split(" ")] |
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word2ph = [int(i) for i in word2ph.split(" ")] |
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w2pho = [i for i in word2ph] |
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word2ph = [i for i in word2ph] |
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) |
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if hps.data.add_blank: |
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phone = commons.intersperse(phone, 0) |
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tone = commons.intersperse(tone, 0) |
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language = commons.intersperse(language, 0) |
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for i in range(len(word2ph)): |
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word2ph[i] = word2ph[i] * 2 |
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word2ph[0] += 1 |
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wav_path = f'{_id}' |
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bert_path = wav_path.replace(".wav", ".bert.pt") |
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try: |
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bert = torch.load(bert_path) |
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assert bert.shape[-1] == len(phone) |
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except: |
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bert = get_bert(text, word2ph, language_str) |
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assert bert.shape[-1] == len(phone) |
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torch.save(bert, bert_path) |
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if __name__ == '__main__': |
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lines = [] |
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with open(hps.data.training_files, encoding='utf-8' ) as f: |
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lines.extend(f.readlines()) |
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with open(hps.data.validation_files, encoding='utf-8' ) as f: |
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lines.extend(f.readlines()) |
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with Pool(processes=6) as pool: |
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for _ in tqdm(pool.imap_unordered(process_line, lines)): |
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pass |
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