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import torch
from multiprocessing import Pool
import commons
import utils
from tqdm import tqdm
from text import check_bert_models, cleaned_text_to_sequence, get_bert
import argparse
import torch.multiprocessing as mp
from config import config
def process_line(x):
line, add_blank = x
device = config.bert_gen_config.device
if config.bert_gen_config.use_multi_device:
rank = mp.current_process()._identity
rank = rank[0] if len(rank) > 0 else 0
if torch.cuda.is_available():
gpu_id = rank % torch.cuda.device_count()
device = torch.device(f"cuda:{gpu_id}")
else:
device = torch.device("cpu")
wav_path, _, 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(" ")]
word2ph = [i for i in word2ph]
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
if 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
bert_path = wav_path.replace(".WAV", ".wav").replace(".wav", ".bert.pt")
try:
bert = torch.load(bert_path)
assert bert.shape[-1] == len(phone)
except Exception:
bert = get_bert(text, word2ph, language_str, device)
assert bert.shape[-1] == len(phone)
torch.save(bert, bert_path)
preprocess_text_config = config.preprocess_text_config
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-c", "--config", type=str, default=config.bert_gen_config.config_path
)
parser.add_argument(
"--num_processes", type=int, default=config.bert_gen_config.num_processes
)
args, _ = parser.parse_known_args()
config_path = args.config
hps = utils.get_hparams_from_file(config_path)
check_bert_models()
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())
add_blank = [hps.data.add_blank] * len(lines)
if len(lines) != 0:
num_processes = args.num_processes
with Pool(processes=num_processes) as pool:
for _ in tqdm(
pool.imap_unordered(process_line, zip(lines, add_blank)),
total=len(lines),
):
# 这里是缩进的代码块，表示循环体
pass # 使用pass语句作为占位符
print(f"bert生成完毕!, 共有{len(lines)}个bert.pt生成!")
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