- 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
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.shRequirements:
- Python 3.10 or later
- ffmpeg (installed on the system)
Usage
Use the transcription script to process audio files.
Basic Transcription
./scripts/transcribe audio.mp3Advanced 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.,
smallorbase) or use--compute-type int8.