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

# Install dependencies
uv sync

# Run the application
uv run python main.py

Using Pre-Built Executables

Download the latest release from the releases page and run the executable for your platform.

Building from Source

Linux:

./build.sh
# Output: dist/LocalTranscription/LocalTranscription

Windows:

build.bat
# Output: dist\LocalTranscription\LocalTranscription.exe

For detailed build instructions, see 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)
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 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

# 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

# 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.

License

MIT License

Acknowledgments

• OpenAI Whisper for the speech recognition model
• RealtimeSTT for real-time transcription capabilities
• faster-whisper for optimized inference