File size: 1,768 Bytes
8a1292d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
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=2) as pool: #A100 40GB suitable config,if coom,please decrease the processess number.
for _ in tqdm(pool.imap_unordered(process_line, lines)):
pass
|