107 lines
3.6 KiB
Python
107 lines
3.6 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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from config import config
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from style_bert_vits2.constants import Languages
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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.models.hyper_parameters import HyperParameters
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from style_bert_vits2.nlp import (
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bert_models,
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cleaned_text_to_sequence,
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extract_bert_feature,
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)
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from style_bert_vits2.nlp.japanese import pyopenjtalk_worker
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from style_bert_vits2.nlp.japanese.user_dict import update_dict
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from style_bert_vits2.utils.stdout_wrapper import SAFE_STDOUT
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bert_models.load_model(Languages.JP)
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bert_models.load_tokenizer(Languages.JP)
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bert_models.load_model(Languages.EN)
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bert_models.load_tokenizer(Languages.EN)
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bert_models.load_model(Languages.ZH)
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bert_models.load_tokenizer(Languages.ZH)
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# このプロセスからはワーカーを起動して辞書を使いたいので、ここで初期化
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pyopenjtalk_worker.initialize_worker()
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# dict_data/ 以下の辞書データを pyopenjtalk に適用
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update_dict()
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def process_line(x: tuple[str, bool]):
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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 = f"cuda:{gpu_id}"
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else:
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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(
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phone, tone, Languages[language_str]
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)
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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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args, _ = parser.parse_known_args()
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config_path = args.config
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hps = HyperParameters.load_from_json(config_path)
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lines: list[str] = []
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with open(hps.data.training_files, "r", encoding="utf-8") as f:
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lines.extend(f.readlines())
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with open(hps.data.validation_files, "r", 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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# pyopenjtalkの別ワーカー化により、並列処理でエラーがでる模様なので、一旦シングルスレッド強制にする
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num_processes = 1
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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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