79 lines
2.8 KiB
Python
79 lines
2.8 KiB
Python
import argparse
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import os
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from faster_whisper import WhisperModel
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from tqdm import tqdm
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from common.constants import Languages
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from common.log import logger
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from common.stdout_wrapper import SAFE_STDOUT
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def transcribe(wav_path, initial_prompt=None, language="ja"):
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segments, _ = model.transcribe(
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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("--input_dir", type=str, default="raw")
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parser.add_argument("--output_file", type=str, default="esd.list")
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parser.add_argument(
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"--initial_prompt", type=str, default="こんにちは。元気、ですかー？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("--speaker_name", type=str, required=True)
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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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speaker_name = args.speaker_name
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input_dir = args.input_dir
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output_file = args.output_file
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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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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: {e}")
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model = WhisperModel(args.model, device=device)
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wav_files = [
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os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith(".wav")
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]
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if os.path.exists(output_file):
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logger.warning(f"{output_file} exists, backing up to {output_file}.bak")
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if os.path.exists(output_file + ".bak"):
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logger.warning(f"{output_file}.bak exists, deleting...")
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os.remove(output_file + ".bak")
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os.rename(output_file, output_file + ".bak")
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if language == "ja":
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language = Languages.JP
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elif language == "en":
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language = Languages.EN
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elif language == "zh":
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language = Languages.ZH
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else:
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raise ValueError(f"{language} is not supported.")
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with open(output_file, "w", encoding="utf-8") as f:
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for wav_file in tqdm(wav_files, file=SAFE_STDOUT):
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file_name = os.path.basename(wav_file)
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text = transcribe(wav_file, initial_prompt=initial_prompt)
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f.write(f"{file_name}|{speaker_name}|{language}|{text}\n")
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f.flush()
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