Skill v1.0.0

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rushindrasinha/ares-skills/whatsapp-voice-transcriber

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PublishedOctober 21, 2026 at 05:55 PM

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version: "1.0.0" name: whatsapp-voice-transcriber description: Use when a user sends a voice note and wants it transcribed, or says "transcribe this voice note", "what did I say?", "convert voice to text", or "transcribe this audio". Handles OGG, MP3, WAV, and other audio formats. Outputs transcription with content classification (Idea/Task, Draft, or Note). Uses mlx_whisper large-v3-turbo for Apple Silicon — fully local, no API cost. metadata: emoji: 🎙️ category: automation platform: macOS (Apple Silicon)

WhatsApp Voice Transcriber

Transcribes voice notes locally using mlx_whisper (Apple Silicon) and classifies by content type. No API cost, runs fully offline.

Requirements

mlx_whisper (recommended): pip install mlx-whisper — uses large-v3-turbo model on Apple Silicon
Fallback: openai-whisper (pip install openai-whisper) — base model
Python 3.10+
OpenClaw CLI (optional, for send-back delivery)

Usage

bash

python3 scripts/transcribe.py <audio_file_path>

Example:

bash

python3 scripts/transcribe.py /tmp/voice_note.ogg

Configuration (env vars)

Variable	Default	Description
`MLX_WHISPER_PATH`	`/opt/homebrew/bin/mlx_whisper`	Path to mlx_whisper binary
`WHISPER_MODEL`	`mlx-community/whisper-large-v3-turbo`	Whisper model to use
`NOTIFY_TARGET`	_(empty)_	Phone/JID to send result to via openclaw
`NOTIFY_CHANNEL`	`whatsapp`	Channel for notification
`OPENCLAW_PATH`	`/opt/homebrew/bin/openclaw`	Path to openclaw binary

Set NOTIFY_TARGET to auto-send transcription after processing:

bash

NOTIFY_TARGET="+1234567890" python3 scripts/transcribe.py /tmp/voice.ogg

Classification

Heuristic classifier labels each transcription:

💡 Idea / Task — keywords: remind, task, build, idea, need to
✉️ Draft — keywords: tell, message, send, write, reply
📝 Note — everything else

OpenClaw Integration

Wire into your agent skill dispatcher to auto-transcribe incoming voice notes:

python

# In your OpenClaw agent logic:
import subprocess, tempfile, os
def handle_voice_note(attachment_path):
    result = subprocess.run(
        ["python3", "~/clawd/.agents/skills/whatsapp-voice-transcriber/scripts/transcribe.py",
         attachment_path],
        capture_output=True, text=True
    )
    return result.stdout.strip()

Model Notes

mlx_whisper large-v3-turbo — best for Apple Silicon (M1/M2/M3). Highly accurate, fast enough for real-time. ~1GB model download on first use.
whisper base — fallback only. Much lower accuracy. Use if no Apple Silicon.
Model is downloaded automatically on first run.

Scripts

scripts/transcribe.py — Main script. Transcribes audio, classifies, prints, and optionally sends.

All versions