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--3.13-dark_green"></a>
  <a href="https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-MIT-dark_green"></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 a functional and simple frontend that you can customize for your own needs. Everything runs locally on your machine ✨

### 🔄 Architecture

WhisperLiveKit consists of three main components:

- **Frontend**: A basic HTML & JavaScript interface that captures microphone audio and streams it to the backend via WebSockets. You can use and adapt the provided template at [whisperlivekit/web/live_transcription.html](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/web/live_transcription.html) for your specific use case.
- **Backend (Web Server)**: A FastAPI-based WebSocket server that receives streamed audio data, processes it in real time, and returns transcriptions to the frontend. This is where the WebSocket logic and routing live.
- **Core Backend (Library Logic)**: A server-agnostic core that handles audio processing, ASR, and diarization. It exposes reusable components that take in audio bytes and return transcriptions. This makes it easy to plug into any WebSocket or audio stream pipeline.


### ✨ 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
 
### ⚙️ Core differences from [Whisper Streaming](https://github.com/ufal/whisper_streaming)

- **Automatic Silence Chunking** – Automatically chunks when no audio is detected to limit buffer size
- **Multi-User Support** – Handles multiple users simultaneously by decoupling backend and online ASR
- **Confidence Validation** – Immediately validate high-confidence tokens for faster inference
- **MLX Whisper Backend** – Optimized for Apple Silicon for faster local processing
- **Buffering Preview** – Displays unvalidated transcription segments

## 📖 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
```

### Quick Start with SSL
```bash
# You must provide a certificate and key
whisperlivekit-server -ssl-certfile public.crt --ssl-keyfile private.key

# Open your browser at https://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)
Check [basic_server.py](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/basic_server.py) for a complete example.

```python
from whisperlivekit import TranscriptionEngine, AudioProcessor, get_web_interface_html, parse_args
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.responses import HTMLResponse
from contextlib import asynccontextmanager
import asyncio

# Global variable for the transcription engine
transcription_engine = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    global transcription_engine
    # Example: Initialize with specific parameters directly
    # You can also load from command-line arguments using parse_args()
    # args = parse_args()
    # transcription_engine = TranscriptionEngine(**vars(args))
    transcription_engine = TranscriptionEngine(model="medium", diarization=True, lan="en")
    yield

app = FastAPI(lifespan=lifespan)

# Serve the web interface
@app.get("/")
async def get():
    return HTMLResponse(get_web_interface_html())

# Process WebSocket connections
async def handle_websocket_results(websocket: WebSocket, results_generator):
    try:
        async for response in results_generator:
            await websocket.send_json(response)
        await websocket.send_json({"type": "ready_to_stop"})
    except WebSocketDisconnect:
        print("WebSocket disconnected during results handling.")

@app.websocket("/asr")
async def websocket_endpoint(websocket: WebSocket):
    global transcription_engine

    # Create a new AudioProcessor for each connection, passing the shared engine
    audio_processor = AudioProcessor(transcription_engine=transcription_engine)    
    results_generator = await audio_processor.create_tasks()
    send_results_to_client = handle_websocket_results(websocket, results_generator)
    results_task = asyncio.create_task(send_results_to_client)
    await websocket.accept()
    try:
        while True:
            message = await websocket.receive_bytes()
            await audio_processor.process_audio(message)        
    except WebSocketDisconnect:
        print(f"Client disconnected: {websocket.client}")
    except Exception as e:
        await websocket.close(code=1011, reason=f"Server error: {e}")
    finally:
        results_task.cancel()
        try:
            await results_task
        except asyncio.CancelledError:
            logger.info("Results task successfully cancelled.")
```

### Frontend Implementation

The package includes a simple HTML/JavaScript implementation that you can adapt for your project. You can find it in `whisperlivekit/web/live_transcription.html`, or load its content using the `get_web_interface_html()` function from `whisperlivekit`:

```python
from whisperlivekit import get_web_interface_html

# ... later in your code where you need the HTML string ...
html_content = get_web_interface_html()
```

## ⚙️ 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` |
| `--ssl-certfile` | Path to the SSL certificate file (for HTTPS support) | `None` |
| `--ssl-keyfile` | Path to the SSL private key file (for HTTPS support) | `None` |

## 🔧 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)