---

name: mlx-whisper description: > Set up mlx-whisper as the local audio transcription engine for OpenClaw on Apple Silicon Macs (M1/M2/M3/M4). Automatically transcribes voice notes sent via Telegram or WhatsApp before the agent processes them. Use when the user wants to enable voice message transcription locally without any API key, or asks to set up Whisper, mlx-whisper, or local speech-to-text in OpenClaw. Apple Silicon only (macOS darwin). Note: Requires internet for initial model download (~465MB), but runs inference locally. metadata: { "openclaw": { "emoji": "🎙️", "os": ["darwin"], "requires": { "bins": ["python3", "pip3"] }, "install": [ { "id": "pip-mlx-whisper", "kind": "exec", "command": "pip3", "args": ["install", "mlx-whisper"], "label": "Install mlx-whisper (Apple Silicon)", }, ], }, }

mlx-whisper — Local Voice Transcription for Apple Silicon

Enables automatic transcription of voice notes in OpenClaw using Apple's MLX framework. No API key required. Works fully offline. ~60× faster than standard Whisper on M1/M2/M3/M4.

How it works

1. User sends a voice note (Telegram .ogg / WhatsApp .opus)
2. OpenClaw downloads the audio file
3. Passes it to mlx-whisper-transcribe.sh via {{MediaPath}}
4. Transcript is injected as the message body
5. Agent replies to the text content

Setup

Step 1 — Install mlx-whisper

pip3 install mlx-whisper

Verify:

python3 -c "import mlx_whisper; print('OK')"

Step 2 — Install the wrapper script

Find the Python bin path:

python3 -m site --user-base
# e.g. /Users/<you>/Library/Python/3.9

Copy bin/mlx-whisper-transcribe.sh from this skill to <user-base>/bin/mlx-whisper-transcribe.sh, then make it executable:

PYBIN=$(python3 -m site --user-base)/bin
cp {baseDir}/bin/mlx-whisper-transcribe.sh "$PYBIN/mlx-whisper-transcribe.sh"
chmod +x "$PYBIN/mlx-whisper-transcribe.sh"

Test it:

"$PYBIN/mlx-whisper-transcribe.sh" /path/to/audio.ogg
# First run downloads the model (~465MB). Subsequent runs are instant.

Step 3 — Configure OpenClaw

Add to ~/.openclaw/openclaw.json under tools.media.audio:

{
  "tools": {
    "media": {
      "audio": {
        "enabled": true,
        "models": [
          {
            "type": "cli",
            "command": "<user-base>/bin/mlx-whisper-transcribe.sh",
            "args": ["{{MediaPath}}"],
            "timeoutSeconds": 60
          }
        ]
      }
    }
  }
}

Replace <user-base> with the output of python3 -m site --user-base.

Step 4 — Restart OpenClaw

openclaw gateway restart

Or restart the OpenClaw app from the menu bar.

Models

The wrapper uses whisper-small-mlx by default (465MB, good balance of speed and accuracy). To change, edit bin/mlx-whisper-transcribe.sh and update path_or_hf_repo:

Model	Size	Use case
mlx-community/whisper-tiny-mlx	75MB	Fastest, basic accuracy
mlx-community/whisper-small-mlx	465MB	Recommended
mlx-community/whisper-medium-mlx	1.5GB	Higher accuracy
mlx-community/whisper-large-v3-mlx	3GB	Best accuracy

Language hint (optional)

Pass a language code as the second argument to skip auto-detection (faster):

mlx-whisper-transcribe.sh audio.ogg zh   # Chinese
mlx-whisper-transcribe.sh audio.ogg en   # English

In openclaw.json, add the language to args:

"args": ["{{MediaPath}}", "zh"]

Performance (M3 MacBook Pro, 8GB)

Audio length	Transcription time
10 sec	~1 sec
1 min	~7 sec
30 min	~3.5 min

Troubleshooting

• mlx_whisper not found: Run pip3 install mlx-whisper again
• Empty transcript: Audio may be silent or music-only (Whisper transcribes speech only)
• Timeout: Increase timeoutSeconds for long audio files
• Wrong language: Add "language": "zh" or the target language code to args
• Model download fails: Check internet connection; models are cached after first run in ~/.cache/huggingface