b239f04eb6
Pipeline audio-only (fără video pe disc) prin ffmpeg direct din URL CloudFront, transcriere cu faster-whisper (CPU), sumarizări structurate cu diagrame SVG la scară reală (STYLE.md documentează toate cerințele de format).
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
"""
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Transcribe audio/<module>/*.mp3 -> transcripts/<module>/*.txt using
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faster-whisper (CPU, CTranslate2). Deletes the source audio after a
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successful transcript (nobody wants the audio kept — text only).
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Resumable: skips lectures with an existing transcript.
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"""
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import argparse
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import logging
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import sys
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from pathlib import Path
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from faster_whisper import WhisperModel
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
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log = logging.getLogger(__name__)
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def transcribe_file(model: WhisperModel, audio_path: Path, txt_path: Path) -> None:
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segments, info = model.transcribe(str(audio_path), language="ro", beam_size=5, vad_filter=True)
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txt_path.parent.mkdir(parents=True, exist_ok=True)
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with open(txt_path, "w", encoding="utf-8") as f:
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for seg in segments:
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f.write(seg.text.strip() + "\n")
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log.info(f" Transcribed: {txt_path.name} (lang={info.language}, duration={info.duration:.0f}s)")
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def main():
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p = argparse.ArgumentParser(description="Transcribe ATM course audio")
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p.add_argument("--audio-dir", default="audio")
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p.add_argument("--out", default="transcripts")
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p.add_argument("--model", default="medium", help="faster-whisper model size")
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p.add_argument("--only", default=None, help="Limit to one module dir name (for piloting)")
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p.add_argument("--keep-audio", action="store_true", help="Don't delete source mp3 after transcribing")
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args = p.parse_args()
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audio_dir = Path(args.audio_dir)
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out_dir = Path(args.out)
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mp3_files = sorted(audio_dir.glob("*/*.mp3"))
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if args.only:
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mp3_files = [f for f in mp3_files if f.parent.name == args.only]
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if not mp3_files:
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log.error(f"No audio files found under {audio_dir}")
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sys.exit(1)
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log.info(f"Loading faster-whisper model '{args.model}' (CPU, int8)...")
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model = WhisperModel(args.model, device="cpu", compute_type="int8")
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done = skipped = 0
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for audio_path in mp3_files:
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txt_path = out_dir / audio_path.parent.name / (audio_path.stem + ".txt")
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if txt_path.exists() and txt_path.stat().st_size > 50:
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log.info(f" Skipping (exists): {txt_path.name}")
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skipped += 1
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continue
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log.info(f"Transcribing: {audio_path}")
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transcribe_file(model, audio_path, txt_path)
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if not args.keep_audio:
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audio_path.unlink()
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done += 1
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log.info("=" * 60)
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log.info(f"Transcribed {done}, skipped {skipped}.")
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if __name__ == "__main__":
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main()
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