README.md

---
title: Basic Docker SDK Space
emoji: 🐳
colorFrom: purple
colorTo: gray
sdk: docker
app_port: 8000
---

<h1 align="center">WhisperLiveKit</h1>

<p align="center">
  <img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/demo.png" alt="WhisperLiveKit Demo" width="730">
</p>

<p align="center"><b>Real-time, Fully Local Speech-to-Text with Speaker Diarization</b></p>

<p align="center">
  <a href="https://pypi.org/project/whisperlivekit/"><img alt="PyPI Version" src="https://img.shields.io/pypi/v/whisperlivekit?color=g"></a>
  <a href="https://pepy.tech/project/whisperlivekit"><img alt="PyPI Downloads" src="https://static.pepy.tech/personalized-badge/whisperlivekit?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=downloads"></a>
  <a href="https://pypi.org/project/whisperlivekit/"><img alt="Python Versions" src="https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-dark_green"></a>
  <a href="https://8000github.com/QuentinFuxa/WhisperLiveKit/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/QuentinFuxa/WhisperLiveKit?color=blue"></a>
</p>

## 🚀 Overview

This project is based on [Whisper Streaming](https://github.com/ufal/whisper_streaming) and lets you transcribe audio directly from your browser. WhisperLiveKit provides a complete backend solution for real-time speech transcription with an example frontend that you can customize for your own needs. Everything runs locally on your machine ✨

### 🔄 Architecture

WhisperLiveKit consists of two main components:

- **Backend (Server)**: FastAPI WebSocket server that processes audio and provides real-time transcription
- **Frontend Example**: Basic HTML & JavaScript implementation that demonstrates how to capture and stream audio

> **Note**: We recommend installing this library on the server/backend. For the frontend, you can use and adapt the provided HTML template from [whisperlivekit/web/live_transcription.html](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/web/live_transcription.html) for your specific use case.

### ✨ Key Features

- **🎙️ Real-time Transcription** - Convert speech to text instantly as you speak
- **👥 Speaker Diarization** - Identify different speakers in real-time using [Diart](https://github.com/juanmc2005/diart)
- **🔒 Fully Local** - All processing happens on your machine - no data sent to external servers
- **📱 Multi-User Support** - Handle multiple users simultaneously with a single backend/server
 
### ⚙️ Differences from [Whisper Streaming](https://github.com/ufal/whisper_streaming)

- **Multi-User Support** – Handles multiple users simultaneously by decoupling backend and online ASR
- **MLX Whisper Backend** – Optimized for Apple Silicon for faster local processing
- **Buffering Preview** – Displays unvalidated transcription segments
- **Confidence Validation** – Immediately validate high-confidence tokens for faster inference
- **Apple Silicon Optimized** - MLX backend for faster local processing on Mac

## 📖 Quick Start

```bash
# Install the package
pip install whisperlivekit

# Start the transcription server
whisperlivekit-server --model tiny.en

# Open your browser at http://localhost:8000
```

That's it! Start speaking and watch your words appear on screen.

## 🛠️ Installation Options

### Install from PyPI (Recommended)

```bash
pip install whisperlivekit
```

### Install from Source

```bash
git clone https://github.com/QuentinFuxa/WhisperLiveKit
cd WhisperLiveKit
pip install -e .
```

### System Dependencies

FFmpeg is required:

```bash
# Ubuntu/Debian
sudo apt install ffmpeg

# macOS
brew install ffmpeg

# Windows
# Download from https://ffmpeg.org/download.html and add to PATH
```

### Optional Dependencies

```bash
# Voice Activity Controller (prevents hallucinations)
pip install torch

# Sentence-based buffer trimming
pip install mosestokenizer wtpsplit
pip install tokenize_uk  # If you work with Ukrainian text

# Speaker diarization
pip install diart

# Alternative Whisper backends (default is faster-whisper)
pip install whisperlivekit[whisper]              # Original Whisper
pip install whisperlivekit[whisper-timestamped]  # Improved timestamps
pip install whisperlivekit[mlx-whisper]          # Apple Silicon optimization
pip install whisperlivekit[openai]               # OpenAI API
```

### 🎹 Pyannote Models Setup

For diarization, you need access to pyannote.audio models:

1. [Accept user conditions](https://huggingface.co/pyannote/segmentation) for the `pyannote/segmentation` model
2. [Accept user conditions](https://huggingface.co/pyannote/segmentation-3.0) for the `pyannote/segmentation-3.0` model
3. [Accept user conditions](https://huggingface.co/pyannote/embedding) for the `pyannote/embedding` model
4. Login with HuggingFace:
   ```bash
   pip install huggingface_hub
   huggingface-cli login
   ```

## 💻 Usage Examples

### Command-line Interface

Start the transcription server with various options:

```bash
# Basic server with English model
whisperlivekit-server --model tiny.en

# Advanced configuration with diarization
whisperlivekit-server --host 0.0.0.0 --port 8000 --model medium --diarization --language auto
```

### Python API Integration (Backend)

```python
from whisperlivekit import WhisperLiveKit
from whisperlivekit.audio_processor import AudioProcessor
from fastapi import FastAPI, WebSocket
import asyncio
from fastapi.responses import HTMLResponse

# Initialize components
app = FastAPI()
kit = WhisperLiveKit(model="medium", diarization=True)

# Serve the web interface
@app.get("/")
async def get():
    return HTMLResponse(kit.web_interface())  # Use the built-in web interface

# Process WebSocket connections
async def handle_websocket_results(websocket, results_generator):
    async for response in results_generator:
        await websocket.send_json(response)

@app.websocket("/asr")
async def websocket_endpoint(websocket: WebSocket):
    audio_processor = AudioProcessor()
    await websocket.accept()
    results_generator = await audio_processor.create_tasks()
    websocket_task = asyncio.create_task(
        handle_websocket_results(websocket, results_generator)
    )

    try:
        while True:
            message = await websocket.receive_bytes()
            await audio_processor.process_audio(message)
    except Exception as e:
        print(f"WebSocket error: {e}")
        websocket_task.cancel()
```

### Frontend Implementation

The package includes a simple HTML/JavaScript implementation that you can adapt for your project. You can get in in [whisperlivekit/web/live_transcription.html](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/web/live_transcription.html), or using :

```python
kit.web_interface()
```

## ⚙️ Configuration Reference

WhisperLiveKit offers extensive configuration options:

| Parameter | Description | Default |
|-----------|-------------|---------|
| `--host` | Server host address | `localhost` |
| `--port` | Server port | `8000` |
| `--model` | Whisper model size | `tiny` |
| `--language` | Source language code or `auto` | `en` |
| `--task` | `transcribe` or `translate` | `transcribe` |
| `--backend` | Processing backend | `faster-whisper` |
| `--diarization` | Enable speaker identification | `False` |
| `--confidence-validation` | Use confidence scores for faster validation | `False` |
| `--min-chunk-size` | Minimum audio chunk size (seconds) | `1.0` |
| `--vac` | Use Voice Activity Controller | `False` |
| `--no-vad` | Disable Voice Activity Detection | `False` |
| `--buffer_trimming` | Buffer trimming strategy (`sentence` or `segment`) | `segment` |
| `--warmup-file` | Audio file path for model warmup | `jfk.wav` |

## 🔧 How It Works

<p align="center">
  <img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/demo.png" alt="WhisperLiveKit in Action" width="500">
</p>

1. **Audio Capture**: Browser's MediaRecorder API captures audio in webm/opus format
2. **Streaming**: Audio chunks are sent to the server via WebSocket
3. **Processing**: Server decodes audio with FFmpeg and streams into Whisper for transcription
4. **Real-time Output**: 
   - Partial transcriptions appear immediately in light gray (the 'aperçu')
   - Finalized text appears in normal color
   - (When enabled) Different speakers are identified and highlighted

## 🚀 Deployment Guide

To deploy WhisperLiveKit in production:

1. **Server Setup** (Backend):
   ```bash
   # Install production ASGI server
   pip install uvicorn gunicorn

   # Launch with multiple workers
   gunicorn -k uvicorn.workers.UvicornWorker -w 4 your_app:app
   ```

2. **Frontend Integration**:
   - Host your customized version of the example HTML/JS in your web application
   - Ensure WebSocket connection points to your server's address

3. **Nginx Configuration** (recommended for production):
   ```nginx
   server {
       listen 80;
       server_name your-domain.com;

       location / {
           proxy_pass http://localhost:8000;
           proxy_set_header Upgrade $http_upgrade;
           proxy_set_header Connection "upgrade";
           proxy_set_header Host $host;
       }
   }
   ```

4. **HTTPS Support**: For secure deployments, use "wss://" instead of "ws://" in WebSocket URL

### 🐋 Docker

A basic Dockerfile is provided which allows re-use of Python package installation options. See below usage examples:

**NOTE:** For **larger** models, ensure that your **docker runtime** has enough **memory** available.

#### All defaults
- Create a reusable image with only the basics and then run as a named container:
```bash
docker build -t whisperlivekit-defaults .
docker create --gpus all --name whisperlivekit -p 8000:8000 whisperlivekit-defaults
docker start -i whisperlivekit
```

> **Note**: If you're running on a system without NVIDIA GPU support (such as Mac with Apple Silicon or any system without CUDA capabilities), you need to **remove the `--gpus all` flag** from the `docker create` command. Without GPU acceleration, transcription will use CPU only, which may be significantly slower. Consider using small models for better performance on CPU-only systems.

#### Customization
- Customize the container options:
```bash
docker build -t whisperlivekit-defaults .
docker create --gpus all --name whisperlivekit-base -p 8000:8000 whisperlivekit-defaults --model base
docker start -i whisperlivekit-base
```

- `--build-arg` Options:
  - `EXTRAS="whisper-timestamped"` - Add extras to the image's installation (no spaces). Remember to set necessary container options!
  - `HF_PRECACHE_DIR="./.cache/"` - Pre-load a model cache for faster first-time start
  - `HF_TOKEN="./token"` - Add your Hugging Face Hub access token to download gated models

## 🔮 Use Cases

- **Meeting Transcription**: Capture discussions in real-time
- **Accessibility Tools**: Help hearing-impaired users follow conversations
- **Content Creation**: Transcribe podcasts or videos automatically
- **Customer Service**: Transcribe support calls with speaker identification

## 🤝 Contributing

Contributions are welcome! Here's how to get started:

1. Fork the repository
2. Create a feature branch: `git checkout -b feature/amazing-feature`
3. Commit your changes: `git commit -m 'Add amazing feature'`
4. Push to your branch: `git push origin feature/amazing-feature`
5. Open a Pull Request

## 🙏 Acknowledgments

This project builds upon the foundational work of:
- [Whisper Streaming](https://github.com/ufal/whisper_streaming)
- [Diart](https://github.com/juanmc2005/diart)
- [OpenAI Whisper](https://github.com/openai/whisper)

We extend our gratitude to the original authors for their contributions.

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🔗 Links

- [GitHub Repository](https://github.com/QuentinFuxa/WhisperLiveKit)
- [PyPI Package](https://pypi.org/project/whisperlivekit/)
- [Issue Tracker](https://github.com/QuentinFuxa/WhisperLiveKit/issues)