Proiect descărcare + transcriere curs ATM (trading, Bogdan Jinga)

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).

transcribe.py

@@ -0,0 +1,68 @@
+"""
+Transcribe audio/<module>/*.mp3 -> transcripts/<module>/*.txt using
+faster-whisper (CPU, CTranslate2). Deletes the source audio after a
+successful transcript (nobody wants the audio kept — text only).
+Resumable: skips lectures with an existing transcript.
+"""
+
+import argparse
+import logging
+import sys
+from pathlib import Path
+
+from faster_whisper import WhisperModel
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+log = logging.getLogger(__name__)
+
+
+def transcribe_file(model: WhisperModel, audio_path: Path, txt_path: Path) -> None:
+    segments, info = model.transcribe(str(audio_path), language="ro", beam_size=5, vad_filter=True)
+    txt_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(txt_path, "w", encoding="utf-8") as f:
+        for seg in segments:
+            f.write(seg.text.strip() + "\n")
+    log.info(f"  Transcribed: {txt_path.name} (lang={info.language}, duration={info.duration:.0f}s)")
+
+
+def main():
+    p = argparse.ArgumentParser(description="Transcribe ATM course audio")
+    p.add_argument("--audio-dir", default="audio")
+    p.add_argument("--out", default="transcripts")
+    p.add_argument("--model", default="medium", help="faster-whisper model size")
+    p.add_argument("--only", default=None, help="Limit to one module dir name (for piloting)")
+    p.add_argument("--keep-audio", action="store_true", help="Don't delete source mp3 after transcribing")
+    args = p.parse_args()
+
+    audio_dir = Path(args.audio_dir)
+    out_dir = Path(args.out)
+    mp3_files = sorted(audio_dir.glob("*/*.mp3"))
+    if args.only:
+        mp3_files = [f for f in mp3_files if f.parent.name == args.only]
+    if not mp3_files:
+        log.error(f"No audio files found under {audio_dir}")
+        sys.exit(1)
+
+    log.info(f"Loading faster-whisper model '{args.model}' (CPU, int8)...")
+    model = WhisperModel(args.model, device="cpu", compute_type="int8")
+
+    done = skipped = 0
+    for audio_path in mp3_files:
+        txt_path = out_dir / audio_path.parent.name / (audio_path.stem + ".txt")
+        if txt_path.exists() and txt_path.stat().st_size > 50:
+            log.info(f"  Skipping (exists): {txt_path.name}")
+            skipped += 1
+            continue
+
+        log.info(f"Transcribing: {audio_path}")
+        transcribe_file(model, audio_path, txt_path)
+        if not args.keep_audio:
+            audio_path.unlink()
+        done += 1
+
+    log.info("=" * 60)
+    log.info(f"Transcribed {done}, skipped {skipped}.")
+
+
+if __name__ == "__main__":
+    main()