58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
import torch
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from torch.utils.data import DataLoader
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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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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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# train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
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# eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
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#
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# collate_fn = TextAudioSpeakerCollate()
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# train_loader = DataLoader(train_dataset, num_workers=12, shuffle=False,
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# batch_size=32, pin_memory=True,
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# drop_last=False, collate_fn=collate_fn)
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# eval_loader = DataLoader(eval_dataset, num_workers=12, shuffle=False,
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# batch_size=32, pin_memory=True,
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# drop_last=False, collate_fn=collate_fn)
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# for _ in tqdm(train_loader):
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# pass
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# for _ in tqdm(eval_loader):
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# pass
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for line in tqdm( open(hps.data.training_files).readlines()):
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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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# print(text, word2ph,phone, tone, language_str)
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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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pold2 = phone
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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'dataset/{spk}/{_id}.wav'
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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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