89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
import argparse
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import os
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import sys
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from pathlib import Path
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import yaml
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from faster_whisper import WhisperModel
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from tqdm import tqdm
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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.utils.stdout_wrapper import SAFE_STDOUT
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def transcribe(wav_path: Path, initial_prompt=None, language="ja"):
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segments, _ = model.transcribe(
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str(wav_path), beam_size=5, language=language, initial_prompt=initial_prompt
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)
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texts = [segment.text for segment in segments]
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return "".join(texts)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_name", type=str, required=True)
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parser.add_argument(
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"--initial_prompt",
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type=str,
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default="こんにちは。元気、ですかー？ふふっ、私は……ちゃんと元気だよ！",
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)
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parser.add_argument(
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"--language", type=str, default="ja", choices=["ja", "en", "zh"]
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)
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parser.add_argument("--model", type=str, default="large-v3")
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parser.add_argument("--device", type=str, default="cuda")
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parser.add_argument("--compute_type", type=str, default="bfloat16")
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args = parser.parse_args()
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with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
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path_config: dict[str, str] = yaml.safe_load(f.read())
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dataset_root = Path(path_config["dataset_root"])
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model_name = str(args.model_name)
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input_dir = dataset_root / model_name / "raw"
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output_file = dataset_root / model_name / "esd.list"
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initial_prompt = args.initial_prompt
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language = args.language
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device = args.device
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compute_type = args.compute_type
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output_file.parent.mkdir(parents=True, exist_ok=True)
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logger.info(
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f"Loading Whisper model ({args.model}) with compute_type={compute_type}"
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)
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try:
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model = WhisperModel(args.model, device=device, compute_type=compute_type)
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except ValueError as e:
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logger.warning(f"Failed to load model, so use `auto` compute_type: {e}")
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model = WhisperModel(args.model, device=device)
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wav_files = [f for f in input_dir.rglob("*.wav") if f.is_file()]
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if output_file.exists():
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logger.warning(f"{output_file} exists, backing up to {output_file}.bak")
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backup_path = output_file.with_name(output_file.name + ".bak")
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if backup_path.exists():
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logger.warning(f"{output_file}.bak exists, deleting...")
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backup_path.unlink()
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output_file.rename(backup_path)
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if language == "ja":
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language_id = Languages.JP.value
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elif language == "en":
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language_id = Languages.EN.value
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elif language == "zh":
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language_id = Languages.ZH.value
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else:
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raise ValueError(f"{language} is not supported.")
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wav_files = sorted(wav_files, key=lambda x: x.name)
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for wav_file in tqdm(wav_files, file=SAFE_STDOUT):
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text = transcribe(wav_file, initial_prompt=initial_prompt, language=language)
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with open(output_file, "a", encoding="utf-8") as f:
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f.write(f"{wav_file.name}|{model_name}|{language_id}|{text}\n")
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sys.exit(0)
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