faster-whisper

A skill for your Moltbot agent that uses faster-whisper to transcribe audio more quickly.

faster-whisper is superior to a OpenAI's Whisper — it's a CTranslate2 reimplementation that's ~4-6x faster with identical accuracy.

Features

• ~4-6x faster than OpenAI's original Whisper (same model weights, CTranslate2 backend)
• ~20x realtime with GPU — transcribe 10 min of audio in ~30 sec
• Distilled models available (~6x faster again with <1% WER loss)
• Word-level timestamps
• Voice activity detection (VAD) — removes silence automatically
• Multilingual support (100+ languages)
• Quantization for CPU efficiency
• GPU acceleration (NVIDIA CUDA)

Installation

Option 1: Install from MoltHub

Via CLI (no installation required):

# Using npx (npm)
npx molthub@latest install faster-whisper

# Using pnpm
pnpm dlx molthub@latest install faster-whisper

# Using bun
bunx molthub@latest install faster-whisper

This downloads and installs the skill into your default skills directory (~/clawd/your-agent/workspace/skills/ or similar).

Via Web UI: Go to https://clawdhub.com/ThePlasmak/faster-whisper and download the zip.

Option 2: Download from GitHub Releases

1. Go to Releases
2. Download the latest faster-whisper-X.X.X.zip
3. Extract it to your agent's skills folder:
   • Default location: ~/clawd/your-agent/workspace/skills/faster-whisper
   • Or wherever your agent's workspace is configured

# Example
cd ~/clawd/your-agent/workspace/skills/
unzip ~/Downloads/faster-whisper-1.0.1.zip -d faster-whisper

If you're lazy: You can also ask your agent to install it by pasting this repo's link (https://github.com/ThePlasmak/faster-whisper) directly in chat.

Note: The release zip excludes repository files (CHANGELOG, LICENSE, README) and only contains the skill itself — this keeps things lightweight.

Setup

Using with your agent

If you're using your agent, it can guide you through the installation automatically.

What your agent does:

• Detects your platform (Windows/Linux/macOS/WSL2)
• Checks for Python, ffmpeg, and GPU drivers
• Runs the appropriate setup script for you

Standalone CLI Setup

If you want to use the transcription scripts directly without your agent:

Windows (Native):

.\setup.ps1   # Auto-installs Python & ffmpeg via winget if needed

Linux / macOS / WSL2:

./setup.sh

What it installs:

• Python 3.10+ (if missing)
• ffmpeg (audio processing)
• faster-whisper + dependencies
• CUDA support (if NVIDIA GPU detected)

How to Use

With your agent

Just ask in natural language:

"Transcribe this audio file" (with file attached)
"Transcribe interview.mp3 with word timestamps"
"Transcribe this in Spanish"
"Transcribe this and save as JSON"

Your agent will:

• Use the GPU if available
• Handle errors and suggest fixes
• Return formatted results

Standalone CLI

Run the transcription script directly:

Linux / macOS / WSL2:

./scripts/transcribe audio.mp3
./scripts/transcribe audio.mp3 --word-timestamps --json

Windows:

.\scripts\transcribe.cmd audio.mp3
.\scripts\transcribe.cmd audio.mp3 --word-timestamps --json

CLI Options

Option	Short	Description
--model	-m	Model name (default: distil-large-v3)
--language	-l	Language code (e.g., en, es, fr) — auto-detects if omitted
--word-timestamps		Include word-level timestamps
--beam-size		Beam search size (default: 5, higher = more accurate but slower)
--vad		Enable voice activity detection (removes silence)
--json	-j	Output as JSON
--output	-o	Save transcript to file
--device		cpu or cuda (auto-detected)
--compute-type		int8, float16, float32 (auto-optimized)
--quiet	-q	Suppress progress messages

CLI Examples

# Basic transcription
./scripts/transcribe interview.mp3

# Specify language (faster than auto-detect)
./scripts/transcribe podcast.mp3 --language en

# High-accuracy with word timestamps
./scripts/transcribe lecture.wav --model large-v3-turbo --word-timestamps

# JSON output saved to file
./scripts/transcribe meeting.m4a --json --output meeting.json

# Fast English-only transcription
./scripts/transcribe audio.mp3 --model distil-medium.en --language en

# Remove silence with VAD
./scripts/transcribe audio.mp3 --vad

Cross-Platform Support

Platform	GPU Accel	Auto-Install
Windows + NVIDIA	✅ CUDA	✅ via winget
Linux + NVIDIA	✅ CUDA	❌ manual
WSL2 + NVIDIA	✅ CUDA	❌ manual
macOS (Apple Silicon)	❌ CPU only	❌ manual
Windows/Linux (no GPU)	❌ CPU only	Windows: ✅ / Linux: ❌

Notes:

• GPU acceleration is ~20-60x faster than CPU
• Apple Silicon Macs run on CPU but are still reasonably fast (~2-3x slower than CUDA)
• On platforms without auto-install, your agent can guide you through manual setup

CUDA Requirements (NVIDIA GPUs)

• Windows: CUDA drivers auto-install with GPU drivers
• Linux/WSL2: Install CUDA Toolkit separately:

  # Ubuntu/Debian
  sudo apt install nvidia-cuda-toolkit
  
  # Check CUDA is available
  nvidia-smi
  

Default Model

distil-large-v3 (756MB download)

• ~6x faster than large-v3
• Within ~1% accuracy of full model
• Best balance of speed and accuracy

See SKILL.md for full model list and recommendations.

Troubleshooting

CUDA Not Detected

Symptom: Script says "CUDA not available — using CPU (this will be slow!)"

Solutions:

1. Check NVIDIA drivers are installed:

   # Windows/Linux/WSL2
   nvidia-smi
   

   If this fails, install/update your NVIDIA drivers

2. WSL2 users: Install CUDA drivers on Windows, not inside WSL2

   • Follow: NVIDIA CUDA on WSL2 Guide

3. Reinstall PyTorch with CUDA:

   # Linux/macOS/WSL2
   .venv/bin/pip install torch --index-url https://download.pytorch.org/whl/cu121
   
   # Windows
   .venv\Scripts\pip install torch --index-url https://download.pytorch.org/whl/cu121
   

4. Verify CUDA is working:

   # Linux/macOS/WSL2
   .venv/bin/python -c "import torch; print(torch.cuda.is_available())"
   
   # Windows
   .venv\Scripts\python -c "import torch; print(torch.cuda.is_available())"
   

Out of Memory Errors

Symptom: RuntimeError: CUDA out of memory or OutOfMemoryError

Solutions:

1. Use a smaller model:

   # Try distil-medium instead of distil-large-v3
   ./scripts/transcribe audio.mp3 --model distil-medium.en
   

2. Use int8 quantization (reduces VRAM by ~4x):

   ./scripts/transcribe audio.mp3 --compute-type int8
   

3. Fall back to CPU for large files:

   ./scripts/transcribe audio.mp3 --device cpu
   

4. Split long audio files into smaller chunks (5-10 min segments)

VRAM Requirements:

Model	float16	int8
distil-large-v3	~2GB	~1GB
large-v3	~5GB	~2GB
medium	~3GB	~1.5GB
small	~2GB	~1GB

ffmpeg Not Found

Symptom: FileNotFoundError: ffmpeg not found

Solutions:

1. Windows: Re-run setup script (auto-installs via winget)

   .\setup.ps1
   

2. Linux:

   # Ubuntu/Debian
   sudo apt install ffmpeg
   
   # Fedora/RHEL
   sudo dnf install ffmpeg
   
   # Arch
   sudo pacman -S ffmpeg
   

3. macOS:

   brew install ffmpeg
   

4. Verify installation:

   ffmpeg -version
   

Model Download Fails

Symptom: HTTPError, ConnectionError, or timeout during first run

Solutions:

1. Check internet connection

2. Retry with increased timeout:

   • Models download automatically on first use
   • Download sizes: 75MB (tiny) to 3GB (large-v3)

3. Use a VPN if Hugging Face is blocked in your region

4. Manual download:

   from faster_whisper import WhisperModel
   model = WhisperModel("distil-large-v3", device="cpu")
   

Very Slow Transcription

Symptom: Transcription takes longer than the audio duration

Expected speeds:

• GPU (CUDA): ~20-30x realtime (30 min audio → ~1-2 min)
• Apple Silicon (CPU): ~2-5x realtime (30 min audio → ~6-15 min)
• Intel CPU: ~0.5-1x realtime (30 min audio → 30-60 min)

Solutions:

1. Ensure GPU is being used:

   # Look for "Loading model: ... (cuda, float16) on NVIDIA ..."
   ./scripts/transcribe audio.mp3
   

2. Use a smaller/distilled model:

   ./scripts/transcribe audio.mp3 --model distil-small.en
   

3. Specify language (skips auto-detection):

   ./scripts/transcribe audio.mp3 --language en
   

4. Reduce beam size:

   ./scripts/transcribe audio.mp3 --beam-size 1
   

Audio Format Issues

Symptom: Error: Unsupported format or transcription produces garbage

Solutions:

1. Supported formats: MP3, WAV, M4A, FLAC, OGG, WebM

   • Most common formats work via ffmpeg

2. Convert problematic formats:

   ffmpeg -i input.xyz -ar 16000 output.mp3
   

3. Check audio isn't corrupted:

   ffmpeg -i audio.mp3 -f null -
   

Python Version Issues

Symptom: SyntaxError or ImportError during setup

Solutions:

1. Requires Python 3.10 or newer:

   python --version  # or python3 --version
   

2. Windows: Setup script auto-installs Python 3.12 via winget

3. Linux/macOS: Install Python 3.10+:

   # Ubuntu
   sudo apt install python3.12 python3.12-venv
   
   # macOS
   brew install python@3.12
   

Still Having Issues?

1. Check the logs: Run without --quiet to see detailed error messages
2. Ask your agent: Paste the error — it can usually diagnose faster-whisper or installation issues
3. Open an issue: GitHub Issues
4. Include:
   • Platform (Windows/Linux/macOS/WSL2)
   • GPU model (if any)
   • Python version
   • Full error message

References

• faster-whisper GitHub
• Distil-Whisper Paper
• OpenAI Whisper Models