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@@ -22,48 +22,59 @@ to transcribe the second speaker.
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Note: Be aware of your audio sample rate, which defaults to 44.1kHz.
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"""
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+import re
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from pathlib import Path
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import click
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-import librosa
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import soundfile as sf
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-import whisper
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+from faster_whisper import WhisperModel
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from loguru import logger
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-from merge_asr_files import merge_and_delete_files
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+from pydub import AudioSegment
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from tqdm import tqdm
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from fish_speech.utils.file import AUDIO_EXTENSIONS, list_files
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@click.command()
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-@click.option("--model-size", default="large", help="Size of the Whisper model")
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+@click.option("--model-size", default="large-v3", help="Size of the Whisper model")
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+@click.option(
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+ "--compute-type",
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+ default="float16",
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+ help="Computation Precision of the Whisper model [float16 / int8_float16 / int8]",
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+)
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@click.option("--audio-dir", required=True, help="Directory containing audio files")
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@click.option(
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"--save-dir", required=True, help="Directory to save processed audio files"
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)
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@click.option(
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"--sample-rate",
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- default=None,
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+ default=44100,
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type=int,
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help="Output sample rate, default to input sample rate",
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)
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-@click.option("--device", default="cuda", help="Device to use")
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-@click.option("--language", default="ZH", help="Language of the transcription")
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-def main(model_size, audio_dir, save_dir, sample_rate, device, language):
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- logger.info("Loading / Downloading OpenAI Whisper model...")
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- model = whisper.load_model(
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- name=model_size,
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+@click.option("--device", default="cuda", help="Device to use [cuda / cpu]")
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+@click.option("--language", default="auto", help="Language of the transcription")
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+def main(model_size, compute_type, audio_dir, save_dir, sample_rate, device, language):
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+ logger.info("Loading / Downloading Faster Whisper model...")
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+
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+ model = WhisperModel(
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+ model_size,
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device=device,
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- download_root=str(Path(".cache/whisper").resolve()),
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+ compute_type=compute_type,
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+ download_root="faster_whisper",
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)
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+
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logger.info("Model loaded.")
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save_path = Path(save_dir)
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save_path.mkdir(parents=True, exist_ok=True)
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- original_files = []
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+
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audio_files = list_files(
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path=audio_dir, extensions=AUDIO_EXTENSIONS, recursive=True
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)
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+
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+ numbered_suffix_pattern = re.compile(r"-\d{3}$")
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+
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for file_path in tqdm(audio_files, desc="Processing audio file"):
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file_stem = file_path.stem
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file_suffix = file_path.suffix
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@@ -71,44 +82,97 @@ def main(model_size, audio_dir, save_dir, sample_rate, device, language):
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rel_path = Path(file_path).relative_to(audio_dir)
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(save_path / rel_path.parent).mkdir(parents=True, exist_ok=True)
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- if (save_path / rel_path.parent / f"{rel_path.stem}.wav").exists() and (
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- save_path / rel_path.parent / f"{rel_path.stem}.lab"
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- ).exists():
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+ # Skip files that already have a .lab file or a -{3-digit number} suffix
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+ numbered_suffix = numbered_suffix_pattern.search(file_stem)
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+ lab_file = file_path.with_suffix(".lab")
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+
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+ if numbered_suffix and lab_file.exists():
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+ continue
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+
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+ if not numbered_suffix and lab_file.with_stem(lab_file.stem + "-001").exists():
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+ if file_path.exists():
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+ file_path.unlink()
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continue
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- audio, sr = librosa.load(file_path, sr=sample_rate, mono=False)
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- transcription = model.transcribe(str(file_path), language=language)
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+ audio = AudioSegment.from_file(file_path)
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- for segment in transcription.get("segments", []):
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- id, text, start, end = (
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- segment["id"],
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- segment["text"],
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- segment["start"],
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- segment["end"],
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- )
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+ segments, info = model.transcribe(
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+ file_path, beam_size=5, language=None if language == "auto" else language
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+ )
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- extract = audio[..., int(start * sr) : int(end * sr)]
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- audio_save_path = (
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- save_path / rel_path.parent / f"{file_stem}-{id}{file_suffix}"
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+ print(
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+ "Detected language '%s' with probability %f"
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+ % (info.language, info.language_probability)
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+ )
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+ print("Total len(ms): ", len(audio))
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+
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+ for segment in segments:
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+ id, start, end, text = (
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+ segment.id,
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+ segment.start,
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+ segment.end,
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+ segment.text,
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)
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- sf.write(
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- audio_save_path,
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- extract,
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- samplerate=sr,
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+ print("Segment %03d [%.2fs -> %.2fs] %s" % (id, start, end, text))
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+ start_ms = int(start * 1000)
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+ end_ms = int(end * 1000) + 200 # add 0.2s avoid truncating
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+ segment_audio = audio[start_ms:end_ms]
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+ audio_save_path = (
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+ save_path / rel_path.parent / f"{file_stem}-{id:03d}{file_suffix}"
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)
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- original_files.append(audio_save_path)
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+ segment_audio.export(audio_save_path, format=file_suffix[1:])
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+ print(f"Exported {audio_save_path}")
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- transcript_save_path = save_path / rel_path.parent / f"{file_stem}-{id}.lab"
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+ transcript_save_path = (
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+ save_path / rel_path.parent / f"{file_stem}-{id:03d}.lab"
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+ )
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with open(
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transcript_save_path,
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"w",
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encoding="utf-8",
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) as f:
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- f.write(text)
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- original_files.append(transcript_save_path)
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+ f.write(segment.text)
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- merge_and_delete_files(save_dir, original_files)
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+ file_path.unlink()
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if __name__ == "__main__":
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main()
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+ exit(0)
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+
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+ audio = AudioSegment.from_wav(
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+ r"D:\PythonProject\原神语音中文\胡桃\vo_hutao_draw_appear.wav"
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+ )
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+
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+ model_size = "large-v3"
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+
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+ model = WhisperModel(
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+ model_size,
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+ device="cuda",
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+ compute_type="float16",
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+ download_root="faster_whisper",
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+ )
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+
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+ segments, info = model.transcribe(
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+ r"D:\PythonProject\原神语音中文\胡桃\vo_hutao_draw_appear.wav",
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+ beam_size=5,
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+ )
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+
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+ print(
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+ "Detected language '%s' with probability %f"
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+ % (info.language, info.language_probability)
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+ )
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+ print("Total len(ms): ", len(audio))
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+
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+ for i, segment in enumerate(segments):
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+ print(
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+ "Segment %03d [%.2fs -> %.2fs] %s"
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+ % (i, segment.start, segment.end, segment.text)
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+ )
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+ start_ms = int(segment.start * 1000)
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+ end_ms = int(segment.end * 1000)
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+ segment_audio = audio[start_ms:end_ms]
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+ segment_audio.export(f"segment_{i:03d}.wav", format="wav")
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+ print(f"Exported segment_{i:03d}.wav")
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+
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+ print("All segments have been exported.")
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spicysama