Faster Whisper Local

---
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.

```bash
./setup.sh
```

Requirements:
- Python 3.10 or later
- ffmpeg (installed on the system)

## Usage

Use the transcription script to process audio files.

### Basic Transcription
```bash
./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`.