Add audio transcription workflow (faster-whisper) and gitignore for raw audio

scripts/transcribe.py

@@ -0,0 +1,66 @@
+"""
+Transcribe course book listening-exercise audio to German text using
+faster-whisper (GPU-accelerated via CTranslate2).
+
+Setup (one time):
+    pip install faster-whisper
+
+Usage:
+    python transcribe.py <path-to-audio-folder> <path-to-output-folder>
+
+Example:
+    python transcribe.py "D:/MenschenA1.1/audio/Lektion01" "../A1.1/course-book/transcripts/lektion-01"
+
+Each audio file gets a matching .txt file with the same basename.
+Review the output — ASR on textbook dialogue audio is generally good but can
+mangle names, numbers, and fast/overlapping speech. Correct those before
+committing.
+"""
+
+import sys
+from pathlib import Path
+
+from faster_whisper import WhisperModel
+
+# "large-v3" is most accurate; drop to "medium" if VRAM is limited (~5GB vs ~10GB).
+MODEL_SIZE = "large-v3"
+DEVICE = "cuda"
+COMPUTE_TYPE = "float16"  # use "int8_float16" if you run out of VRAM
+
+AUDIO_EXTENSIONS = {".mp3", ".wav", ".m4a", ".ogg", ".flac"}
+
+
+def main() -> None:
+    if len(sys.argv) != 3:
+        print(__doc__)
+        sys.exit(1)
+
+    audio_dir = Path(sys.argv[1])
+    out_dir = Path(sys.argv[2])
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    model = WhisperModel(MODEL_SIZE, device=DEVICE, compute_type=COMPUTE_TYPE)
+
+    audio_files = sorted(
+        p for p in audio_dir.iterdir() if p.suffix.lower() in AUDIO_EXTENSIONS
+    )
+    if not audio_files:
+        print(f"No audio files found in {audio_dir}")
+        return
+
+    for audio_path in audio_files:
+        print(f"Transcribing {audio_path.name} ...")
+        segments, info = model.transcribe(str(audio_path), language="de", beam_size=5)
+
+        out_path = out_dir / (audio_path.stem + ".txt")
+        with out_path.open("w", encoding="utf-8") as f:
+            for segment in segments:
+                f.write(f"[{segment.start:6.1f}s] {segment.text.strip()}\n")
+
+        print(f"  -> {out_path}")
+
+    print("\nDone. Review transcripts for ASR errors before committing.")
+
+
+if __name__ == "__main__":
+    main()