Local Transcription

A real-time speech-to-text desktop application for streamers. Runs locally on your machine with GPU or CPU, displays transcriptions via OBS browser source, and optionally syncs 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
• Cross-Platform: Native desktop app for Windows, macOS, and Linux via Tauri
• Dual Transcription Modes: Local (Whisper) or cloud (Deepgram) with managed billing or BYOK
• 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

Architecture

The application uses a two-process architecture:

1. Tauri Shell (Svelte 5 frontend) — lightweight native window (~50MB) rendering the UI
2. Python Backend (sidecar) — headless process running transcription, audio capture, and the OBS web server

The Tauri frontend communicates with the Python backend via REST API and WebSocket, following the same pattern as voice-to-notes.

Tauri App (user launches this)
  └─ Spawns Python backend as sidecar
       ├─ FastAPI REST API (control endpoints)
       ├─ WebSocket /ws/control (real-time state + transcriptions)
       ├─ OBS web display at http://localhost:8080
       └─ Transcription engine (Whisper or Deepgram)

Legacy GUI: The original PySide6/Qt desktop GUI (main.py) still works alongside the new Tauri frontend during the transition period.

Quick Start

Running from Source

# Install Python dependencies
uv sync

# Run the Tauri app (frontend + backend)
npm install
npm run tauri dev

# Or run just the headless backend (for development)
uv run python -m backend.main_headless

# Or run the legacy PySide6 GUI
uv run python main.py

Using Pre-Built Executables

Download the latest release from the releases page:

• App installer (Tauri shell): .msi (Windows), .dmg (macOS), .deb/.rpm/.AppImage (Linux)
• Sidecar (Python backend): Download the matching sidecar-* zip for your platform (CUDA or CPU)

Building from Source

# Build the Tauri app
npm install
npm run tauri build
# Output: src-tauri/target/release/bundle/

# Build the Python sidecar (headless, no Qt)
uv sync
uv run pyinstaller local-transcription-headless.spec
# Output: dist/local-transcription-backend/

# Build the legacy PySide6 app (Linux)
./build.sh
# Build the legacy PySide6 app (Windows)
build.bat

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

Remote Transcription (Deepgram)

Instead of local Whisper models, you can use cloud-based transcription:

• Managed mode: Sign up via the transcription proxy for metered billing
• BYOK mode: Bring your own Deepgram API key for direct access

Configure in Settings > Remote Transcription.

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 or the REST API.

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
remote.mode	Transcription mode (local/managed/byok)	local
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/
├── src/                             # Svelte 5 frontend (Tauri UI)
│   ├── App.svelte                   # Main app shell
│   ├── lib/components/              # UI components
│   │   ├── Header.svelte
│   │   ├── StatusBar.svelte
│   │   ├── Controls.svelte
│   │   ├── TranscriptionDisplay.svelte
│   │   └── Settings.svelte
│   └── lib/stores/                  # Reactive state management
│       ├── backend.ts               # WebSocket + REST API client
│       ├── config.ts                # App configuration
│       └── transcriptions.ts        # Transcription data
├── src-tauri/                       # Tauri v2 Rust shell
│   ├── src/main.rs
│   └── tauri.conf.json
├── backend/                         # Headless Python backend (sidecar)
│   ├── app_controller.py            # Orchestration logic (engine, sync, config)
│   ├── api_server.py                # FastAPI REST + WebSocket control API
│   └── main_headless.py             # Headless entry point
├── client/                          # Core transcription modules
│   ├── audio_capture.py             # Audio input handling
│   ├── transcription_engine_realtime.py  # RealtimeSTT / Whisper
│   ├── deepgram_transcription.py    # Deepgram cloud transcription
│   ├── noise_suppression.py         # VAD and noise reduction
│   ├── device_utils.py              # CPU/GPU/MPS detection
│   ├── config.py                    # Configuration management
│   ├── server_sync.py               # Multi-user server client
│   └── update_checker.py            # Auto-update functionality
├── gui/                             # Legacy PySide6/Qt GUI
│   ├── main_window_qt.py
│   ├── settings_dialog_qt.py
│   └── transcription_display_qt.py
├── server/                          # Web servers
│   ├── web_display.py               # Local FastAPI server for OBS
│   └── nodejs/                      # Multi-user sync server
├── .gitea/workflows/                # CI/CD
│   ├── release.yml                  # Tauri app builds (all platforms)
│   └── build-sidecar.yml            # Python sidecar builds (CUDA + CPU)
├── config/
│   └── default_config.yaml          # Default settings template
├── main.py                          # Legacy GUI entry point
├── main_cli.py                      # CLI version (for testing)
├── local-transcription.spec         # PyInstaller config (legacy, with PySide6)
├── local-transcription-headless.spec # PyInstaller config (headless sidecar)
├── pyproject.toml                   # Python dependencies
└── package.json                     # Node.js / Tauri dependencies

Technology Stack

Frontend (Tauri)

• Tauri v2 — Native cross-platform shell (Rust)
• Svelte 5 — Reactive UI framework (TypeScript)
• Vite — Frontend build tool

Backend (Python Sidecar)

• Python 3.9+
• FastAPI + Uvicorn — REST API and WebSocket server
• RealtimeSTT — Real-time speech-to-text with advanced VAD
• faster-whisper — Optimized Whisper model inference (CTranslate2)
• PyTorch — ML framework (CUDA-enabled builds available)
• sounddevice — Cross-platform audio capture
• webrtcvad + silero_vad — Voice activity detection

Multi-User Server (Optional)

• Node.js + Express + WebSocket — Real-time sync server

Build & CI/CD

• PyInstaller — Python sidecar packaging
• Tauri CLI — App bundling (.msi, .dmg, .deb, .rpm, .AppImage)
• Gitea Actions — Automated cross-platform builds
• uv — Fast Python package manager

CI/CD

Two Gitea Actions workflows in .gitea/workflows/:

Workflow	Trigger	Produces
release.yml	Push to main	Tauri app installers for all platforms
build-sidecar.yml	Changes to client/, server/, backend/, or pyproject.toml	Python sidecar zips (CUDA + CPU)

Both workflows require a BUILD_TOKEN secret in the repo settings (Gitea API token with release write access).

Release Artifacts

Platform	App Installer	Sidecar (CUDA)	Sidecar (CPU)
Linux x86_64	.deb, .rpm, .AppImage	sidecar-linux-x86_64-cuda.zip	sidecar-linux-x86_64-cpu.zip
Windows x86_64	.msi, -setup.exe	sidecar-windows-x86_64-cuda.zip	sidecar-windows-x86_64-cpu.zip
macOS ARM64	.dmg	—	sidecar-macos-aarch64-cpu.zip

System Requirements

Minimum

• 4GB RAM
• Any modern CPU

Recommended (for local real-time transcription)

• 8GB+ RAM
• NVIDIA GPU with CUDA support (for GPU acceleration)

For Building

• Tauri app: Node.js 20+, Rust stable, platform SDK (see Tauri prerequisites)
• Python sidecar: Python 3.9+, uv, PyInstaller
• Linux: libgtk-3-dev, libwebkit2gtk-4.1-dev, libappindicator3-dev, librsvg2-dev, patchelf
• Windows: Visual Studio Build Tools, WebView2
• macOS: Xcode Command Line Tools

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 (especially on macOS)
• 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 in CUDA sidecar builds)
• The app automatically falls back to CPU if no GPU is available

Web Server Port Conflicts

• Default port is 8080; the app tries ports 8080-8084 automatically
• Change in settings or edit config file
• Check for conflicts: lsof -i :8080 (Linux/macOS) 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
• Tauri for the cross-platform desktop framework
• Deepgram for cloud transcription API