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Transcribe audio files locally using faster-whisper (100% offline, free, high quality)
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
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| name | whisper-local-transcription |
| description | Transcribe audio files locally using faster-whisper (100% offline, free, high quality) |
Transcribe audio files (Telegram voice messages, etc.) using faster-whisper 100% locally and free.
Model: medium (already downloaded and cached)
Python: 3.12 (system Python at /usr/bin/python3)
Location: /home/alvarobiano/transcribe_audio.py
# Install in user space (Python 3.12)
pip3 install --break-system-packages faster-whisper
# Verify (use system Python, NOT the one at ~/.local/bin)
export PYTHONPATH=""
/usr/bin/python3 -c "from faster_whisper import WhisperModel; print('OK')"
The server has two Python versions:
/home/alvarobiano/.local/bin/python3 → Python 3.14 (BROKEN for faster-whisper)/usr/bin/python3 → Python 3.12 (WORKS)The script auto-switches to the correct Python version. Just run:
/home/alvarobiano/transcribe_audio.py <audio_file>
When user sends audio via Telegram, the audio is saved to a temp file. Run:
/usr/bin/python3 /home/alvarobiano/transcribe_audio.py <path_to_audio>
from faster_whisper import WhisperModel
model = WhisperModel('medium', device='cpu', compute_type='int8')
segments, info = model.transcribe("audio.ogg", language="pt")
/home/alvarobiano/transcribe_audio.py~/.cache/huggingface/hub/