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

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2026-08-28 21:29:46 +03:30
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commit e65626cbc9
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# Raw audio files are not tracked — only their transcripts (text) are.
**/audio/
*.mp3
*.wav
*.m4a
*.ogg
*.flac
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@@ -22,6 +22,16 @@ Source: [ ] Course book [ ] Workbook
- e.g. fill-in-the-blank conjugation, matching, listening comprehension - e.g. fill-in-the-blank conjugation, matching, listening comprehension
## Audio / Listening
Transcripts are ASR-generated (faster-whisper) unless noted otherwise —
review for errors, especially names/numbers.
### Track [X.X] — [short description, e.g. "Anna and Ben introduce themselves"]
```
[transcript text or path to transcripts/lektion-XX/trackname.txt]
```
## Notes / gotchas ## Notes / gotchas
- Anything tricky, false friends, exceptions worth flagging in future practice - Anything tricky, false friends, exceptions worth flagging in future practice
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@@ -32,6 +32,25 @@ A1.2/ ← added once I move on to the next book
4. Update `INDEX.md` with a one-line entry for the lesson. 4. Update `INDEX.md` with a one-line entry for the lesson.
5. Commit. 5. Commit.
## Audio (listening exercises)
Course book audio has no printed transcript (the workbook's back section is
the *Lösungsschlüssel* — answer key — not a transcript, so that doesn't help
here). Raw audio files are **not** stored in this repo (see `.gitignore`);
only their transcripts are, since that's what gives Claude context.
Workflow:
1. Get the audio files locally (publisher CD/app/download), any folder outside git.
2. Run `scripts/transcribe.py` (uses `faster-whisper` on GPU) to transcribe them:
```bash
pip install faster-whisper
python scripts/transcribe.py "<path-to-lesson-audio>" "A1.1/course-book/transcripts/lektion-XX"
```
3. Skim the `.txt` output for ASR mistakes (names, numbers, fast speech) and fix them.
4. Reference/paste the transcript into the lesson's `.md` file under "Audio / Listening",
or just point at the `transcripts/lektion-XX/*.txt` path — either works, transcripts
are small text files so committing them is fine.
## Adding a new book (e.g. A1.2) ## Adding a new book (e.g. A1.2)
Copy the `A1.1/` folder structure (minus content) into a new `A1.2/` folder Copy the `A1.1/` folder structure (minus content) into a new `A1.2/` folder
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"""
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()