|
- import torch
- from torch.utils.data import DataLoader
-
- import commons
- import utils
- from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
- from tqdm import tqdm
- from multiprocessing import Pool
-
- 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(" ")]
- # print(text, word2ph,phone, tone, language_str)
- 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)
-
- with open(hps.data.training_files) as f:
- lines = f.readlines()
-
- with Pool(processes=12) as pool: #A100 suitable config,if coom,please decrease the processess number.
- for _ in tqdm(pool.imap_unordered(process_line, lines)):
- pass
|