WhisperLiveKit
Real-time, Fully Local Speech-to-Text with Speaker Diarization
## ๐ Overview
This project is based on [Whisper Streaming](https://github.com/ufal/whisper_streaming) and [SimulStreaming](https://github.com/ufal/SimulStreaming) (**BETA** - ultra-low latency alternative), allowing you to transcribe audio directly from your browser. WhisperLiveKit provides a complete backend solution for real-time speech transcription with a functional, simple and customizable frontend. Everything runs locally on your machine โจ
### ๐ Architecture
WhisperLiveKit consists of three main components:
- **Frontend**: A basic html + JS 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).
- **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.
### โจ Key Features
- **๐๏ธ Real-time Transcription** - Locally (or on-prem) convert speech to text instantly as you speak
- **๐ฅ Speaker Diarization** - Identify different speakers in real-time using [Diart](https://github.com/juanmc2005/diart)
- **๐ Multi-User Support** - Handle multiple users simultaneously with a single backend/server
- **๐ Automatic Silence Chunking** โ Automatically chunks when no audio is detected to limit buffer size
- **โ
Confidence Validation** โ Immediately validate high-confidence tokens for faster inference (WhisperStreaming only)
- **๐๏ธ Buffering Preview** โ Displays unvalidated transcription segments (not compatible with SimulStreaming yet)
- **โ๏ธ Punctuation-Based Speaker Splitting [BETA]** - Align speaker changes with natural sentence boundaries for more readable transcripts
- **โก SimulStreaming Backend [BETA]** - Ultra-low latency transcription using state-of-the-art AlignAtt policy. To use, please copy [simul_whisper](https://github.com/ufal/SimulStreaming/tree/main/simul_whisper) content into `whisperlivekit/simul_whisper` . You must comply with the [Polyform license](https://github.com/ufal/SimulStreaming/blob/main/LICENCE.txt) !!
## ๐ 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 to see the interface.
# Use -ssl-certfile public.crt --ssl-keyfile private.key parameters to use SSL
```
That's it! Start speaking and watch your words appear on screen.
## ๐ ๏ธ 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
pip install whisperlivekit[simulstreaming]
```
### ๐น 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
```
## ๐ป 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
# SimulStreaming backend for ultra-low latency
whisperlivekit-server --backend simulstreaming-whisper --model large-v3 --frame-threshold 20
```
### 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()
```
## โ๏ธ 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` |
| `--punctuation-split` | Use punctuation to improve speaker boundaries | `True` |
| `--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` |
| `--segmentation-model` | Hugging Face model ID for pyannote.audio segmentation model. [Available models](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `pyannote/segmentation-3.0` |
| `--embedding-model` | Hugging Face model ID for pyannote.audio embedding model. [Available models](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `speechbrain/spkrec-ecapa-voxceleb` |
**SimulStreaming-specific Options:**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `--frame-threshold` | AlignAtt frame threshold (lower = faster, higher = more accurate) | `25` |
| `--beams` | Number of beams for beam search (1 = greedy decoding) | `1` |
| `--decoder` | Force decoder type (`beam` or `greedy`) | `auto` |
| `--audio-max-len` | Maximum audio buffer length (seconds) | `30.0` |
| `--audio-min-len` | Minimum audio length to process (seconds) | `0.0` |
| `--cif-ckpt-path` | Path to CIF model for word boundary detection | `None` |
| `--never-fire` | Never truncate incomplete words | `False` |
| `--init-prompt` | Initial prompt for the model | `None` |
| `--static-init-prompt` | Static prompt that doesn't scroll | `None` |
| `--max-context-tokens` | Maximum context tokens | `None` |
| `--model-path` | Direct path to .pt model file. Download it if not found | `./base.pt` |
## ๐ง How It Works
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 'aperรงu')
- Finalized text appears in normal color
- (When enabled) Different speakers are identified and highlighted
## ๐ 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
### ๐ 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
## ๐ฎ 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
## ๐ License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
**โ ๏ธ Important**: When using the SimulStreaming backend, you must also comply with the **PolyForm Noncommercial License 1.0.0** that governs SimulStreaming. For commercial use of the SimulStreaming backend, obtain a commercial license from the [SimulStreaming authors](https://github.com/ufal/SimulStreaming#-licence-and-contributions).
## ๐ค 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
## ๐ Acknowledgments
This project builds upon the foundational work of:
- [Whisper Streaming](https://github.com/ufal/whisper_streaming)
- [SimulStreaming](https://github.com/ufal/SimulStreaming) (BETA backend)
- [Diart](https://github.com/juanmc2005/diart)
- [OpenAI Whisper](https://github.com/openai/whisper)
We extend our gratitude to the original authors for their contributions.
## ๐ Links
- [GitHub Repository](https://github.com/QuentinFuxa/WhisperLiveKit)
- [PyPI Package](https://pypi.org/project/whisperlivekit/)
- [Issue Tracker](https://github.com/QuentinFuxa/WhisperLiveKit/issues)