50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
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from core.utils import *
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from core.asr_backend.demucs_vl import demucs_audio
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from core.asr_backend.audio_preprocess import process_transcription, convert_video_to_audio, split_audio, save_results, normalize_audio_volume
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from core._1_ytdlp import find_video_files
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from core.utils.models import *
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@check_file_exists(_2_CLEANED_CHUNKS)
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def transcribe():
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# 1. video to audio
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video_file = find_video_files()
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convert_video_to_audio(video_file)
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# 2. Demucs vocal separation:
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if load_key("demucs"):
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demucs_audio()
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vocal_audio = normalize_audio_volume(_VOCAL_AUDIO_FILE, _VOCAL_AUDIO_FILE, format="mp3")
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else:
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vocal_audio = _RAW_AUDIO_FILE
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# 3. Extract audio
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segments = split_audio(_RAW_AUDIO_FILE)
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# 4. Transcribe audio by clips
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all_results = []
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runtime = load_key("whisper.runtime")
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if runtime == "local":
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from core.asr_backend.whisperX_local import transcribe_audio as ts
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rprint("[cyan]🎤 Transcribing audio with local model...[/cyan]")
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elif runtime == "cloud":
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from core.asr_backend.whisperX_302 import transcribe_audio_302 as ts
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rprint("[cyan]🎤 Transcribing audio with 302 API...[/cyan]")
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elif runtime == "elevenlabs":
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from core.asr_backend.elevenlabs_asr import transcribe_audio_elevenlabs as ts
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rprint("[cyan]🎤 Transcribing audio with ElevenLabs API...[/cyan]")
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for start, end in segments:
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result = ts(_RAW_AUDIO_FILE, vocal_audio, start, end)
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all_results.append(result)
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# 5. Combine results
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combined_result = {'segments': []}
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for result in all_results:
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combined_result['segments'].extend(result['segments'])
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# 6. Process df
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df = process_transcription(combined_result)
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save_results(df)
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if __name__ == "__main__":
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transcribe()
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