| name | use-local-whisper |
| description | Use when the user wants local voice transcription instead of OpenAI Whisper API. Switches to whisper.cpp running on Apple Silicon. WhatsApp only for now. Requires voice-transcription skill to be applied first. |
Use Local Whisper
Switches voice transcription from OpenAI's Whisper API to local whisper.cpp. Runs entirely on-device — no API key, no network, no cost.
Channel support: Currently WhatsApp only. The transcription module (src/transcription.ts) uses Baileys types for audio download. Other channels (Telegram, Discord, etc.) would need their own audio-download logic before this skill can serve them.
Note: The Homebrew package is whisper-cpp, but the CLI binary it installs is whisper-cli.
Prerequisites
voice-transcription skill must be applied first (WhatsApp channel)- macOS with Apple Silicon (M1+) recommended
whisper-cpp installed: brew install whisper-cpp (provides the whisper-cli binary)ffmpeg installed: brew install ffmpeg- A GGML model file downloaded to
data/models/
Phase 1: Pre-flight
Check if already applied
Check if src/transcription.ts already uses whisper-cli:
grep 'whisper-cli' src/transcription.ts && echo "Already applied" || echo "Not applied"
If already applied, skip to Phase 3 (Verify).
Check dependencies are installed
whisper-cli --help >/dev/null 2>&1 && echo "WHISPER_OK" || echo "WHISPER_MISSING"
ffmpeg -version >/dev/null 2>&1 && echo "FFMPEG_OK" || echo "FFMPEG_MISSING"
If missing, install via Homebrew:
brew install whisper-cpp ffmpeg
Check for model file
ls data/models/ggml-*.bin 2>/dev/null || echo "NO_MODEL"
If no model exists, download the base model (148MB, good balance of speed and accuracy):
mkdir -p data/models
curl -L -o data/models/ggml-base.bin "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.bin"
For better accuracy at the cost of speed, use ggml-small.bin (466MB) or ggml-medium.bin (1.5GB).
Phase 2: Apply Code Changes
Ensure WhatsApp fork remote
git remote -v
If whatsapp is missing, add it:
git remote add whatsapp https://github.com/qwibitai/nanoclaw-whatsapp.git
Merge the skill branch
git fetch whatsapp skill/local-whisper
git merge whatsapp/skill/local-whisper || {
git checkout --theirs package-lock.json
git add package-lock.json
git merge --continue
}
This modifies src/transcription.ts to use the whisper-cli binary instead of the OpenAI API.
Validate
npm run build
Phase 3: Verify
Ensure launchd PATH includes Homebrew
The NanoClaw launchd service runs with a restricted PATH. whisper-cli and ffmpeg are in /opt/homebrew/bin/ (Apple Silicon) or /usr/local/bin/ (Intel), which may not be in the plist's PATH.
Check the current PATH:
grep -A1 'PATH' ~/Library/LaunchAgents/com.nanoclaw.plist
If /opt/homebrew/bin is missing, add it to the <string> value inside the PATH key in the plist. Then reload:
launchctl unload ~/Library/LaunchAgents/com.nanoclaw.plist
launchctl load ~/Library/LaunchAgents/com.nanoclaw.plist
Build and restart
npm run build
launchctl kickstart -k gui/$(id -u)/com.nanoclaw
Test
Send a voice note in any registered group. The agent should receive it as [Voice: <transcript>].
Check logs
tail -f logs/nanoclaw.log | grep -i -E "voice|transcri|whisper"
Look for:
Transcribed voice message — successful transcriptionwhisper.cpp transcription failed — check model path, ffmpeg, or PATH
Configuration
Environment variables (optional, set in .env):
| Variable | Default | Description |
|---|
WHISPER_BIN | whisper-cli | Path to whisper.cpp binary |
WHISPER_MODEL | data/models/ggml-base.bin | Path to GGML model file |
Troubleshooting
"whisper.cpp transcription failed": Ensure both whisper-cli and ffmpeg are in PATH. The launchd service uses a restricted PATH — see Phase 3 above. Test manually:
ffmpeg -f lavfi -i anullsrc=r=16000:cl=mono -t 1 -f wav /tmp/test.wav -y
whisper-cli -m data/models/ggml-base.bin -f /tmp/test.wav --no-timestamps -nt
Transcription works in dev but not as service: The launchd plist PATH likely doesn't include /opt/homebrew/bin. See "Ensure launchd PATH includes Homebrew" in Phase 3.
Slow transcription: The base model processes ~30s of audio in <1s on M1+. If slower, check CPU usage — another process may be competing.
Wrong language: whisper.cpp auto-detects language. To force a language, you can set WHISPER_LANG and modify src/transcription.ts to pass -l $WHISPER_LANG.
