# Faster Whisper — 4x Faster Speech-to-Text

> Faster Whisper is a reimplementation of OpenAI Whisper using CTranslate2, up to 4x faster with less memory. 21.8K+ GitHub stars. GPU/CPU, 8-bit quantization, word timestamps, VAD. MIT licensed.

## Install

Save as a script file and run:

## Quick Use

```bash
# Install
pip install faster-whisper

# Transcribe audio
python -c "
from faster_whisper import WhisperModel
model = WhisperModel('large-v3', device='cuda', compute_type='float16')
segments, info = model.transcribe('audio.mp3')
print(f'Language: {info.language} (prob {info.language_probability:.2f})')
for segment in segments:
    print(f'[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}')
"
```

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## Intro

Faster Whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, achieving up to 4x faster transcription with comparable accuracy and less memory usage. With 21,800+ GitHub stars and MIT license, it supports GPU and CPU execution, 8-bit quantization for efficiency, batched transcription, word-level timestamps, and Voice Activity Detection (VAD) filtering. On a 13-minute audio sample with the Large-v2 model, Faster Whisper completes in 1m03s vs OpenAI Whisper's 2m23s.

**Best for**: Developers needing fast, accurate speech-to-text transcription for audio/video processing
**Works with**: Claude Code, OpenAI Codex, Cursor, Gemini CLI, Windsurf
**Models**: All Whisper models + Distil-Whisper variants

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## Key Features

- **4x faster**: CTranslate2 engine dramatically reduces transcription time
- **Less memory**: Lower VRAM usage than original Whisper implementation
- **8-bit quantization**: Further reduce memory with minimal accuracy loss
- **Word-level timestamps**: Precise timing for each word in the transcript
- **VAD filtering**: Skip silent sections using Silero VAD
- **Batched transcription**: Process multiple audio segments in parallel
- **Distil-Whisper support**: Compatible with smaller, faster distilled models

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### FAQ

**Q: What is Faster Whisper?**
A: Faster Whisper is a CTranslate2 reimplementation of OpenAI Whisper with 21.8K+ stars. It runs up to 4x faster with less memory, supports 8-bit quantization, word timestamps, and VAD filtering. MIT licensed.

**Q: How do I install Faster Whisper?**
A: Run `pip install faster-whisper`. Then use `WhisperModel('large-v3')` in Python to transcribe audio files with GPU or CPU.

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## Source & Thanks

> Created by [SYSTRAN](https://github.com/SYSTRAN). Licensed under MIT.
> [SYSTRAN/faster-whisper](https://github.com/SYSTRAN/faster-whisper) — 21,800+ GitHub stars

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Source: https://tokrepo.com/en/workflows/24576b2c-a9d1-4f7a-9696-b1e5c50a17f3
Author: Script Depot