modbender Auto-Whisper Safe — RAM-Friendly Voice Transcription
Transcribe voice messages and long audio files using OpenAI Whisper without crashing your machine. Designed for 16GB RAM systems running other processes (like OpenClaw agents).
The Problem
Whisper's turbo and large models use 6-10GB RAM. On a 16GB machine running OpenClaw + Ollama + other services, this causes OOM crashes. Existing Whisper skills don't handle this.
The Solution
- Auto-detects audio length via ffprobe
- Splits long audio (>10min) into 10-min chunks automatically
- Uses
basemodel by default (~1.5GB RAM — safe on any 16GB machine) - Merges transcripts seamlessly — no gaps, no duplicates
- Cleans up temp files automatically
Usage
# Basic usage
./transcribe.sh /path/to/audio.ogg
# Custom model (if you have more RAM)
WHISPER_MODEL=small ./transcribe.sh /path/to/audio.ogg
# Custom language
WHISPER_LANG=en ./transcribe.sh /path/to/audio.ogg
# Custom output directory
./transcribe.sh /path/to/audio.ogg /path/to/output/
RAM Usage by Model
| Model | RAM | Speed | Accuracy | Recommended For |
|---|---|---|---|---|
tiny |
~1GB | ⚡⚡⚡ | ★★ | Quick previews, low-RAM systems |
base |
~1.5GB | ⚡⚡ | ★★★ | Default — best balance ✅ |
small |
~2.5GB | ⚡ | ★★★★ | When accuracy matters more |
medium |
~5GB | 🐢 | ★★★★★ | 32GB+ RAM only |
turbo |
~6GB | 🐢🐢 | ★★★★★ | Dedicated transcription machines |
OpenClaw Integration
Add to your agent's BOOTSTRAP.md:
## Voice Message Handling
When you receive `<media:audio>`, ALWAYS transcribe first:
1. Run: `./skills/auto-whisper-safe/transcribe.sh <audio-path>`
2. Read the output transcript file
3. Respond based on the transcribed content
Do this automatically — voice messages are meant to be transcribed.
Environment Variables
| Variable | Default | Description |
|---|---|---|
WHISPER_MODEL |
base |
Whisper model size |
WHISPER_LANG |
en |
Audio language (ISO code) |
How Chunking Works
- Audio ≤10min → transcribed directly (no splitting)
- Audio >10min → split into 10-min segments via ffmpeg
- Each segment transcribed independently
- Transcripts concatenated in order
- Temp files cleaned up on exit (even on errors)
Installation
# macOS
brew install openai-whisper ffmpeg
# Ubuntu/Debian
pip install openai-whisper
apt install ffmpeg
# Verify
whisper --help && ffmpeg -version
Why This Over Other Whisper Skills
- ✅ RAM-safe: Won't crash your 16GB machine
- ✅ Auto-chunking: Handles 1-hour podcasts without issues
- ✅ Cleanup: No temp files left behind
- ✅ Progress: Shows chunk-by-chunk progress
- ✅ Configurable: Model + language via env vars
- ✅ OpenClaw-native: Drop-in for any agent's BOOTSTRAP.md
Real-World Performance
Tested on Ubuntu 22.04, 16GB RAM, running OpenClaw (10 agents) + Ollama simultaneously:
| Audio Length | Model | RAM Peak | Time | Result |
|---|---|---|---|---|
| 2 min voice memo | base | 1.4GB | ~15s | ✅ Perfect |
| 12 min podcast clip | base | 1.5GB (chunked) | ~90s | ✅ 2 chunks, seamless |
| 45 min interview | base | 1.5GB (chunked) | ~6min | ✅ 5 chunks, seamless |
| 2 min voice memo | tiny | 0.9GB | ~8s | ✅ Good enough for quick reads |
Supported Audio Formats
ffmpeg handles the conversion, so virtually any format works:
- ✅
.ogg(Telegram voice messages) - ✅
.mp3,.m4a,.wav,.flac - ✅
.webm(browser recordings) - ✅
.opus(WhatsApp voice messages)
Changelog
v1.0.0
- Initial release
- Auto-chunking for long audio (>10min)
- RAM-safe defaults (base model, 1.5GB)
- Progress tracking per chunk
- Automatic temp file cleanup
- Configurable model and language