# Local Transcription

A real-time speech-to-text desktop application for streamers. Run locally on your machine with GPU or CPU, display transcriptions via OBS browser source, and optionally sync with other users through a multi-user server.

**Version 1.4.0**

## Features

- **Real-Time Transcription**: Live speech-to-text using Whisper models with minimal latency
- **Standalone Desktop App**: PySide6/Qt GUI that works without any server
- **CPU & GPU Support**: Automatic detection of CUDA (NVIDIA), MPS (Apple Silicon), or CPU fallback
- **Advanced Voice Detection**: Dual-layer VAD (WebRTC + Silero) for accurate speech detection
- **OBS Integration**: Built-in web server for browser source capture at `http://localhost:8080`
- **Multi-User Sync**: Optional Node.js server to sync transcriptions across multiple users
- **Custom Fonts**: Support for system fonts, web-safe fonts, Google Fonts, and custom font files
- **Customizable Colors**: User-configurable colors for name, text, and background
- **Noise Suppression**: Built-in audio preprocessing to reduce background noise
- **Auto-Updates**: Automatic update checking with release notes display
- **Cross-Platform**: Builds available for Windows and Linux

## Quick Start

### Running from Source

```bash
# Install dependencies
uv sync

# Run the application
uv run python main.py
```

### Using Pre-Built Executables

Download the latest release from the [releases page](https://repo.anhonesthost.net/streamer-tools/local-transcription/releases) and run the executable for your platform.

### Building from Source

**Linux:**
```bash
./build.sh
# Output: dist/LocalTranscription/LocalTranscription
```

**Windows:**
```cmd
build.bat
# Output: dist\LocalTranscription\LocalTranscription.exe
```

For detailed build instructions, see [BUILD.md](BUILD.md).

## Usage

### Standalone Mode

1. Launch the application
2. Select your microphone from the audio device dropdown
3. Choose a Whisper model (smaller = faster, larger = more accurate):
   - `tiny.en` / `tiny` - Fastest, good for quick captions
   - `base.en` / `base` - Balanced speed and accuracy
   - `small.en` / `small` - Better accuracy
   - `medium.en` / `medium` - High accuracy
   - `large-v3` - Best accuracy (requires more resources)
4. Click **Start** to begin transcription
5. Transcriptions appear in the main window and at `http://localhost:8080`

### OBS Browser Source Setup

1. Start the Local Transcription app
2. In OBS, add a **Browser** source
3. Set URL to `http://localhost:8080`
4. Set dimensions (e.g., 1920x300)
5. Check "Shutdown source when not visible" for performance

### Multi-User Mode (Optional)

For syncing transcriptions across multiple users (e.g., multi-host streams or translation teams):

1. Deploy the Node.js server (see [server/nodejs/README.md](server/nodejs/README.md))
2. In the app settings, enable **Server Sync**
3. Enter the server URL (e.g., `http://your-server:3000/api/send`)
4. Set a room name and passphrase (shared with other users)
5. In OBS, use the server's display URL with your room name:
   ```
   http://your-server:3000/display?room=YOURROOM&timestamps=true&maxlines=50
   ```

## Configuration

Settings are stored at `~/.local-transcription/config.yaml` and can be modified through the GUI settings panel.

### Key Settings

| Setting | Description | Default |
|---------|-------------|---------|
| `transcription.model` | Whisper model to use | `base.en` |
| `transcription.device` | Processing device (auto/cuda/cpu) | `auto` |
| `transcription.enable_realtime_transcription` | Show preview while speaking | `false` |
| `transcription.silero_sensitivity` | VAD sensitivity (0-1, lower = more sensitive) | `0.4` |
| `transcription.post_speech_silence_duration` | Silence before finalizing (seconds) | `0.3` |
| `transcription.continuous_mode` | Fast speaker mode for quick talkers | `false` |
| `display.show_timestamps` | Show timestamps with transcriptions | `true` |
| `display.fade_after_seconds` | Fade out time (0 = never) | `10` |
| `display.font_source` | Font type (System Font/Web-Safe/Google Font/Custom File) | `System Font` |
| `web_server.port` | Local web server port | `8080` |

See [config/default_config.yaml](config/default_config.yaml) for all available options.

## Project Structure

```
local-transcription/
├── client/                      # Core transcription modules
│   ├── audio_capture.py         # Audio input handling
│   ├── transcription_engine_realtime.py  # RealtimeSTT integration
│   ├── noise_suppression.py     # VAD and noise reduction
│   ├── device_utils.py          # CPU/GPU detection
│   ├── config.py                # Configuration management
│   ├── server_sync.py           # Multi-user server client
│   └── update_checker.py        # Auto-update functionality
├── gui/                         # Desktop application UI
│   ├── main_window_qt.py        # Main application window
│   ├── settings_dialog_qt.py    # Settings dialog
│   └── transcription_display_qt.py  # Display widget
├── server/                      # Web servers
│   ├── web_display.py           # Local FastAPI server for OBS
│   └── nodejs/                  # Multi-user sync server
│       ├── server.js            # Express + WebSocket server
│       └── README.md            # Deployment instructions
├── config/
│   └── default_config.yaml      # Default settings template
├── main.py                      # GUI entry point
├── main_cli.py                  # CLI version (for testing)
├── build.sh                     # Linux build script
├── build.bat                    # Windows build script
└── local-transcription.spec     # PyInstaller configuration
```

## Technology Stack

### Desktop Application
- **Python 3.9+**
- **PySide6** - Qt6 GUI framework
- **RealtimeSTT** - Real-time speech-to-text with advanced VAD
- **faster-whisper** - Optimized Whisper model inference
- **PyTorch** - ML framework (CUDA-enabled)
- **sounddevice** - Cross-platform audio capture
- **webrtcvad + silero_vad** - Voice activity detection
- **noisereduce** - Noise suppression

### Web Servers
- **FastAPI + Uvicorn** - Local web display server
- **Node.js + Express + WebSocket** - Multi-user sync server

### Build Tools
- **PyInstaller** - Executable packaging
- **uv** - Fast Python package manager

## System Requirements

### Minimum
- Python 3.9+
- 4GB RAM
- Any modern CPU

### Recommended (for real-time performance)
- 8GB+ RAM
- NVIDIA GPU with CUDA support (for GPU acceleration)
- FFmpeg (installed automatically with dependencies)

### For Building
- **Linux**: gcc, Python dev headers
- **Windows**: Visual Studio Build Tools, Python dev headers

## Troubleshooting

### Model Loading Issues
- Models download automatically on first use to `~/.cache/huggingface/`
- First run requires internet connection
- Check disk space (models range from 75MB to 3GB)

### Audio Device Issues
```bash
# List available audio devices
uv run python main_cli.py --list-devices
```
- Ensure microphone permissions are granted
- Try different device indices in settings

### GPU Not Detected
```bash
# Check CUDA availability
uv run python -c "import torch; print(torch.cuda.is_available())"
```
- Install NVIDIA drivers (CUDA toolkit is bundled)
- The app automatically falls back to CPU if no GPU is available

### Web Server Port Conflicts
- Default port is 8080
- Change in settings or edit config file
- Check for conflicts: `lsof -i :8080` (Linux) or `netstat -ano | findstr :8080` (Windows)

## Use Cases

- **Live Streaming Captions**: Add real-time captions to your Twitch/YouTube streams
- **Multi-Language Translation**: Multiple translators transcribing in different languages
- **Accessibility**: Provide captions for hearing-impaired viewers
- **Podcast Recording**: Real-time transcription for multi-host shows
- **Gaming Commentary**: Track who said what in multiplayer sessions

## Contributing

Contributions are welcome! Please feel free to submit issues or pull requests at the [repository](https://repo.anhonesthost.net/streamer-tools/local-transcription).

## License

MIT License

## Acknowledgments

- [OpenAI Whisper](https://github.com/openai/whisper) for the speech recognition model
- [RealtimeSTT](https://github.com/KoljaB/RealtimeSTT) for real-time transcription capabilities
- [faster-whisper](https://github.com/guillaumekln/faster-whisper) for optimized inference