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#

1. Auto-detects audio length via ffprobe
2. Splits long audio (>10min) into 10-min chunks automatically
3. Uses base model by default (~1.5GB RAM — safe on any 16GB machine)
4. Merges transcripts seamlessly — no gaps, no duplicates
5. Cleans up temp files automatically

Usage#

bash

# 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#

ModelRAMSpeedAccuracyRecommended Fortiny~1GB⚡⚡⚡★★Quick previews, low-RAM systemsbase~1.5GB⚡⚡★★★Default — best balance ✅small~2.5GB⚡★★★★When accuracy matters moremedium~5GB🐢★★★★★32GB+ RAM onlyturbo~6GB🐢🐢★★★★★Dedicated transcription machines

OpenClaw Integration#

Add to your agent's BOOTSTRAP.md:

markdown

## 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#

VariableDefaultDescriptionWHISPER_MODEL``baseWhisper model sizeWHISPER_LANG``enAudio 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#

bash

# 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 LengthModelRAM PeakTimeResult2 min voice memobase1.4GB~15s✅ Perfect12 min podcast clipbase1.5GB (chunked)~90s✅ 2 chunks, seamless45 min interviewbase1.5GB (chunked)6min✅ 5 chunks, seamless2 min voice memotiny0.9GB8s✅ 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