fac4f9a8ab
"text_processing" is clearer, but the import statement is longer. "nlp" is shorter and makes it clear that it is natural language processing.
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
import argparse
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from concurrent.futures import ThreadPoolExecutor
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import torch
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import torch.multiprocessing as mp
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from tqdm import tqdm
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import utils
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from config import config
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from style_bert_vits2.logging import logger
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from style_bert_vits2.models import commons
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from style_bert_vits2.nlp import cleaned_text_to_sequence, extract_bert_feature
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from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk
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from style_bert_vits2.utils.stdout_wrapper import SAFE_STDOUT
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pyopenjtalk.initialize()
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def process_line(x):
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line, add_blank = x
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device = config.bert_gen_config.device
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if config.bert_gen_config.use_multi_device:
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rank = mp.current_process()._identity
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rank = rank[0] if len(rank) > 0 else 0
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if torch.cuda.is_available():
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gpu_id = rank % torch.cuda.device_count()
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device = torch.device(f"cuda:{gpu_id}")
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else:
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device = torch.device("cpu")
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wav_path, _, 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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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 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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bert_path = wav_path.replace(".WAV", ".wav").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 Exception:
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bert = extract_bert_feature(text, word2ph, language_str, device)
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assert bert.shape[-1] == len(phone)
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torch.save(bert, bert_path)
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preprocess_text_config = config.preprocess_text_config
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-c", "--config", type=str, default=config.bert_gen_config.config_path
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)
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parser.add_argument(
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"--num_processes", type=int, default=config.bert_gen_config.num_processes
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)
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args, _ = parser.parse_known_args()
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config_path = args.config
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hps = utils.get_hparams_from_file(config_path)
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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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add_blank = [hps.data.add_blank] * len(lines)
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if len(lines) != 0:
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num_processes = args.num_processes
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with ThreadPoolExecutor(max_workers=num_processes) as executor:
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_ = list(
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tqdm(
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executor.map(process_line, zip(lines, add_blank)),
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total=len(lines),
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file=SAFE_STDOUT,
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)
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)
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logger.info(f"bert.pt is generated! total: {len(lines)} bert.pt files.")
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