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name: faster-whisper description: Local speech-to-text using faster-whisper. High-performance transcription with GPU acceleration support. Includes word-level timestamps and distilled models. Use when asked to "transcribe audio", "whisper", or "speech to text". metadata: { "openclaw": { "requires": { "bins": ["ffmpeg", "python3"], "pip": ["faster-whisper", "torch"], }, }, }

Faster-Whisper

High-performance local speech-to-text using faster-whisper.

Setup

1. Run Setup Script

Execute the setup script to create a virtual environment and install dependencies. It will automatically detect NVIDIA GPUs for CUDA acceleration.

./setup.sh

Requirements:

• Python 3.10 or later
• ffmpeg (installed on the system)

Usage

Use the transcription script to process audio files.

Basic Transcription

./scripts/transcribe audio.mp3

Advanced Options

• Specific Model: ./scripts/transcribe audio.mp3 --model large-v3-turbo
• Word Timestamps: ./scripts/transcribe audio.mp3 --word-timestamps
• JSON Output: ./scripts/transcribe audio.mp3 --json
• VAD (Silence Removal): ./scripts/transcribe audio.mp3 --vad

Available Models

• distil-large-v3 (default): Best balance of speed and accuracy.
• large-v3-turbo: Recommended for multilingual or highest accuracy tasks.
• medium.en, small.en: Faster, English-only versions.

Troubleshooting

• No GPU detected: Ensure NVIDIA drivers and CUDA are correctly installed. CPU transcription is significantly slower.
• OOM Error: Use a smaller model (e.g., small or base) or use --compute-type int8.