rushindrasinha/ares-skills/whatsapp-voice-transcriber

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

python3 scripts/transcribe.py <audio_file_path>

Example:

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

Configuration (env vars)

Set NOTIFY_TARGET to auto-send transcription after processing:

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:

# 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.

<!-- A·R·E·S 🛡️ | Mumbai | 2026-04-05 | github.com/rushindrasinha -->