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# Local Transcription for Streamers
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# Local Transcription
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A local speech-to-text application designed for streamers that provides real-time transcription using Whisper or similar models. Multiple users can run the application locally and sync their transcriptions to a centralized web stream that can be easily captured in OBS or other streaming software.
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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.
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**Version 1.4.0**
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## Features
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- **Standalone Desktop Application**: Use locally with built-in GUI display - no server required
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- **Local Transcription**: Run Whisper (or compatible models) locally on your machine
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- **CPU/GPU Support**: Choose between CPU or GPU processing based on your hardware
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- **Real-time Processing**: Live audio transcription with minimal latency
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- **Real-Time Transcription**: Live speech-to-text using Whisper models with minimal latency
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- **Standalone Desktop App**: PySide6/Qt GUI that works without any server
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- **CPU & GPU Support**: Automatic detection of CUDA (NVIDIA), MPS (Apple Silicon), or CPU fallback
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- **Advanced Voice Detection**: Dual-layer VAD (WebRTC + Silero) for accurate speech detection
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- **OBS Integration**: Built-in web server for browser source capture at `http://localhost:8080`
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- **Multi-User Sync**: Optional Node.js server to sync transcriptions across multiple users
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- **Custom Fonts**: Support for system fonts, web-safe fonts, Google Fonts, and custom font files
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- **Customizable Colors**: User-configurable colors for name, text, and background
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- **Noise Suppression**: Built-in audio preprocessing to reduce background noise
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- **User Configuration**: Set your display name and preferences through the GUI
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- **Optional Multi-user Sync**: Connect to a server to sync transcriptions with other users
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- **OBS Integration**: Web-based output designed for easy browser source capture
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- **Privacy-First**: All processing happens locally; only transcription text is shared
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- **Customizable**: Configure model size, language, and streaming settings
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- **Auto-Updates**: Automatic update checking with release notes display
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- **Cross-Platform**: Builds available for Windows and Linux
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## Quick Start
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@@ -27,468 +30,195 @@ uv sync
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uv run python main.py
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```
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### Building Standalone Executables
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### Using Pre-Built Executables
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To create standalone executables for distribution:
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Download the latest release from the [releases page](https://repo.anhonesthost.net/streamer-tools/local-transcription/releases) and run the executable for your platform.
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### Building from Source
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**Linux:**
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```bash
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./build.sh
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# Output: dist/LocalTranscription/LocalTranscription
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```
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**Windows:**
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```cmd
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build.bat
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# Output: dist\LocalTranscription\LocalTranscription.exe
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```
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For detailed build instructions, see [BUILD.md](BUILD.md).
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## Architecture Overview
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## Usage
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The application can run in two modes:
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### Standalone Mode
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### Standalone Mode (No Server Required):
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1. **Desktop Application**: Captures audio, performs speech-to-text, and displays transcriptions locally in a GUI window
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1. Launch the application
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2. Select your microphone from the audio device dropdown
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3. Choose a Whisper model (smaller = faster, larger = more accurate):
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- `tiny.en` / `tiny` - Fastest, good for quick captions
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- `base.en` / `base` - Balanced speed and accuracy
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- `small.en` / `small` - Better accuracy
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- `medium.en` / `medium` - High accuracy
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- `large-v3` - Best accuracy (requires more resources)
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4. Click **Start** to begin transcription
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5. Transcriptions appear in the main window and at `http://localhost:8080`
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### Multi-user Sync Mode (Optional):
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1. **Local Transcription Client**: Captures audio, performs speech-to-text, and sends results to the web server
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2. **Centralized Web Server**: Aggregates transcriptions from multiple clients and serves a web stream
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3. **Web Stream Interface**: Browser-accessible page displaying synchronized transcriptions (for OBS capture)
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### OBS Browser Source Setup
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## Use Cases
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1. Start the Local Transcription app
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2. In OBS, add a **Browser** source
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3. Set URL to `http://localhost:8080`
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4. Set dimensions (e.g., 1920x300)
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5. Check "Shutdown source when not visible" for performance
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- **Multi-language Streams**: Multiple translators transcribing in different languages
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- **Accessibility**: Provide real-time captions for viewers
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- **Collaborative Podcasts**: Multiple hosts with separate transcriptions
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- **Gaming Commentary**: Track who said what in multiplayer sessions
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### Multi-User Mode (Optional)
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---
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For syncing transcriptions across multiple users (e.g., multi-host streams or translation teams):
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## Implementation Plan
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1. Deploy the Node.js server (see [server/nodejs/README.md](server/nodejs/README.md))
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2. In the app settings, enable **Server Sync**
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3. Enter the server URL (e.g., `http://your-server:3000/api/send`)
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4. Set a room name and passphrase (shared with other users)
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5. In OBS, use the server's display URL with your room name:
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```
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http://your-server:3000/display?room=YOURROOM×tamps=true&maxlines=50
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```
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### Phase 1: Standalone Desktop Application
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## Configuration
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**Objective**: Build a fully functional standalone transcription app with GUI that works without any server
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Settings are stored at `~/.local-transcription/config.yaml` and can be modified through the GUI settings panel.
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#### Components:
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1. **Audio Capture Module**
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- Capture system audio or microphone input
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- Support multiple audio sources (virtual audio cables, physical devices)
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- Real-time audio buffering with configurable chunk sizes
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- **Noise Suppression**: Preprocess audio to reduce background noise
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- Libraries: `pyaudio`, `sounddevice`, `noisereduce`, `webrtcvad`
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### Key Settings
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2. **Noise Suppression Engine**
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- Real-time noise reduction using RNNoise or noisereduce
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- Adjustable noise reduction strength
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- Optional VAD (Voice Activity Detection) to skip silent segments
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- Libraries: `noisereduce`, `rnnoise-python`, `webrtcvad`
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| Setting | Description | Default |
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|---------|-------------|---------|
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| `transcription.model` | Whisper model to use | `base.en` |
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| `transcription.device` | Processing device (auto/cuda/cpu) | `auto` |
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| `transcription.enable_realtime_transcription` | Show preview while speaking | `false` |
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| `transcription.silero_sensitivity` | VAD sensitivity (0-1, lower = more sensitive) | `0.4` |
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| `transcription.post_speech_silence_duration` | Silence before finalizing (seconds) | `0.3` |
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| `transcription.continuous_mode` | Fast speaker mode for quick talkers | `false` |
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| `display.show_timestamps` | Show timestamps with transcriptions | `true` |
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| `display.fade_after_seconds` | Fade out time (0 = never) | `10` |
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| `display.font_source` | Font type (System Font/Web-Safe/Google Font/Custom File) | `System Font` |
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| `web_server.port` | Local web server port | `8080` |
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3. **Transcription Engine**
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- Integrate OpenAI Whisper (or alternatives: faster-whisper, whisper.cpp)
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- Support multiple model sizes (tiny, base, small, medium, large)
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- CPU and GPU inference options
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- Model management and automatic downloading
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- Libraries: `openai-whisper`, `faster-whisper`, `torch`
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4. **Device Selection**
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- Auto-detect available compute devices (CPU, CUDA, MPS for Mac)
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- Allow user to specify preferred device via GUI
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- Graceful fallback if GPU unavailable
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- Display device status and performance metrics
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5. **Desktop GUI Application**
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- Cross-platform GUI using PyQt6, Tkinter, or CustomTkinter
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- Main transcription display window (scrolling text area)
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- Settings panel for configuration
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- User name input field
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- Audio input device selector
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- Model size selector
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- CPU/GPU toggle
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- Start/Stop transcription button
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- Optional: System tray integration
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- Libraries: `PyQt6`, `customtkinter`, or `tkinter`
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6. **Local Display**
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- Real-time transcription display in GUI window
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- Scrolling text with timestamps
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- User name/label shown with transcriptions
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- Copy transcription to clipboard
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- Optional: Save transcription to file (TXT, SRT, VTT)
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#### Tasks:
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- [ ] Set up project structure and dependencies
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- [ ] Implement audio capture with device selection
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- [ ] Add noise suppression and VAD preprocessing
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- [ ] Integrate Whisper model loading and inference
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- [ ] Add CPU/GPU device detection and selection logic
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- [ ] Create real-time audio buffer processing pipeline
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- [ ] Design and implement GUI layout (main window)
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- [ ] Add settings panel with user name configuration
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- [ ] Implement local transcription display area
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- [ ] Add start/stop controls and status indicators
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- [ ] Test transcription accuracy and latency
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- [ ] Test noise suppression effectiveness
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---
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### Phase 2: Web Server and Sync System
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**Objective**: Create a centralized server to aggregate and serve transcriptions
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#### Components:
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1. **Web Server**
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- FastAPI or Flask-based REST API
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- WebSocket support for real-time updates
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- User/client registration and management
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- Libraries: `fastapi`, `uvicorn`, `websockets`
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2. **Transcription Aggregator**
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- Receive transcription chunks from multiple clients
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- Associate transcriptions with user IDs/names
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- Timestamp management and synchronization
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- Buffer management for smooth streaming
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3. **Database/Storage** (Optional)
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- Store transcription history (SQLite for simplicity)
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- Session management
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- Export functionality (SRT, VTT, TXT formats)
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#### API Endpoints:
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- `POST /api/register` - Register a new client
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- `POST /api/transcription` - Submit transcription chunk
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- `WS /api/stream` - WebSocket for real-time transcription stream
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- `GET /stream` - Web page for OBS browser source
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#### Tasks:
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- [ ] Set up FastAPI server with CORS support
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- [ ] Implement WebSocket handler for real-time streaming
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- [ ] Create client registration system
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- [ ] Build transcription aggregation logic
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- [ ] Add timestamp synchronization
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- [ ] Create data models for clients and transcriptions
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---
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### Phase 3: Client-Server Communication (Optional Multi-user Mode)
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**Objective**: Add optional server connectivity to enable multi-user transcription sync
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#### Components:
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1. **HTTP/WebSocket Client**
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- Register client with server on startup
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- Send transcription chunks as they're generated
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- Handle connection drops and reconnection
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- Libraries: `requests`, `websockets`
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2. **Configuration System**
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- Config file for server URL, API keys, user settings
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- Model preferences (size, language)
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- Audio input settings
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- Format: YAML or JSON
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3. **Status Monitoring**
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- Connection status indicator
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- Transcription queue health
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- Error handling and logging
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#### Tasks:
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- [ ] Add "Enable Server Sync" toggle to GUI
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- [ ] Add server URL configuration field in settings
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- [ ] Implement WebSocket client for sending transcriptions
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- [ ] Add configuration file support (YAML/JSON)
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- [ ] Create connection management with auto-reconnect
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- [ ] Add local logging and error handling
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- [ ] Add server connection status indicator to GUI
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- [ ] Allow app to function normally if server is unavailable
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---
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### Phase 4: Web Stream Interface (OBS Integration)
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**Objective**: Create a web page that displays synchronized transcriptions for OBS
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#### Components:
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1. **Web Frontend**
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- HTML/CSS/JavaScript page for displaying transcriptions
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- Responsive design with customizable styling
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- Auto-scroll with configurable retention window
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- Libraries: Vanilla JS or lightweight framework (Alpine.js, htmx)
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2. **Styling Options**
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- Customizable fonts, colors, sizes
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- Background transparency for OBS chroma key
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- User name/ID display options
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- Timestamp display (optional)
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3. **Display Modes**
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- Scrolling captions (like live TV captions)
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- Multi-user panel view (separate sections per user)
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- Overlay mode (minimal UI for transparency)
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#### Tasks:
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- [ ] Create HTML template for transcription display
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- [ ] Implement WebSocket client in JavaScript
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- [ ] Add CSS styling with OBS-friendly transparency
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- [ ] Create customization controls (URL parameters or UI)
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- [ ] Test with OBS browser source
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- [ ] Add configurable retention/scroll behavior
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---
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### Phase 5: Advanced Features
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**Objective**: Enhance functionality and user experience
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#### Features:
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1. **Language Detection**
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- Auto-detect spoken language
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- Multi-language support in single stream
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- Language selector in GUI
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2. **Speaker Diarization** (Optional)
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- Identify different speakers
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- Label transcriptions by speaker
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- Useful for multi-host streams
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3. **Profanity Filtering**
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- Optional word filtering/replacement
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- Customizable filter lists
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- Toggle in GUI settings
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4. **Advanced Noise Profiles**
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- Save and load custom noise profiles
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- Adaptive noise suppression
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- Different profiles for different environments
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5. **Export Functionality**
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- Save transcriptions in multiple formats (TXT, SRT, VTT, JSON)
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- Export button in GUI
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- Automatic session saving
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6. **Hotkey Support**
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- Global hotkeys to start/stop transcription
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- Mute/unmute hotkey
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- Quick save hotkey
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7. **Docker Support**
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- Containerized server deployment
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- Docker Compose for easy multi-component setup
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- Pre-built images for easy deployment
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8. **Themes and Customization**
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- Dark/light theme toggle
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- Customizable font sizes and colors for display
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- OBS-friendly transparent overlay mode
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|
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#### Tasks:
|
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- [ ] Add language detection and multi-language support
|
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- [ ] Implement speaker diarization
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- [ ] Create optional profanity filter
|
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- [ ] Add export functionality (SRT, VTT, plain text, JSON)
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- [ ] Implement global hotkey support
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- [ ] Create Docker containers for server component
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- [ ] Add theme customization options
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- [ ] Create advanced noise profile management
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|
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---
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## Technology Stack
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### Local Client:
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- **Python 3.9+**
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- **GUI**: PyQt6 / CustomTkinter / tkinter
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- **Audio**: PyAudio / sounddevice
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- **Noise Suppression**: noisereduce / rnnoise-python
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- **VAD**: webrtcvad
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- **ML Framework**: PyTorch (for Whisper)
|
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- **Transcription**: openai-whisper / faster-whisper
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- **Networking**: websockets, requests (optional for server sync)
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- **Config**: PyYAML / json
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|
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### Server:
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- **Backend**: FastAPI / Flask
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- **WebSocket**: python-websockets / FastAPI WebSockets
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- **Server**: Uvicorn / Gunicorn
|
||||
- **Database** (optional): SQLite / PostgreSQL
|
||||
- **CORS**: fastapi-cors
|
||||
|
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### Web Interface:
|
||||
- **Frontend**: HTML5, CSS3, JavaScript (ES6+)
|
||||
- **Real-time**: WebSocket API
|
||||
- **Styling**: CSS Grid/Flexbox for layout
|
||||
|
||||
---
|
||||
See [config/default_config.yaml](config/default_config.yaml) for all available options.
|
||||
|
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## Project Structure
|
||||
|
||||
```
|
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local-transcription/
|
||||
client/ # Local transcription client
|
||||
__init__.py
|
||||
audio_capture.py # Audio input handling
|
||||
transcription_engine.py # Whisper integration
|
||||
network_client.py # Server communication
|
||||
config.py # Configuration management
|
||||
main.py # Client entry point
|
||||
server/ # Centralized web server
|
||||
__init__.py
|
||||
api.py # FastAPI routes
|
||||
websocket_handler.py # WebSocket management
|
||||
models.py # Data models
|
||||
database.py # Optional DB layer
|
||||
main.py # Server entry point
|
||||
web/ # Web stream interface
|
||||
index.html # OBS browser source page
|
||||
styles.css # Customizable styling
|
||||
app.js # WebSocket client & UI logic
|
||||
config/
|
||||
client_config.example.yaml
|
||||
server_config.example.yaml
|
||||
tests/
|
||||
test_audio.py
|
||||
test_transcription.py
|
||||
test_server.py
|
||||
requirements.txt # Python dependencies
|
||||
README.md
|
||||
main.py # Combined launcher (optional)
|
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├── client/ # Core transcription modules
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│ ├── audio_capture.py # Audio input handling
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||||
│ ├── 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
|
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│ ├── web_display.py # Local FastAPI server for OBS
|
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│ └── nodejs/ # Multi-user sync server
|
||||
│ ├── server.js # Express + WebSocket server
|
||||
│ └── README.md # Deployment instructions
|
||||
├── config/
|
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│ └── 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
|
||||
|
||||
## Installation (Planned)
|
||||
### 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
|
||||
|
||||
### Prerequisites:
|
||||
- Python 3.9 or higher
|
||||
- CUDA-capable GPU (optional, for GPU acceleration)
|
||||
- FFmpeg (required by Whisper)
|
||||
### Web Servers
|
||||
- **FastAPI + Uvicorn** - Local web display server
|
||||
- **Node.js + Express + WebSocket** - Multi-user sync server
|
||||
|
||||
### Steps:
|
||||
### Build Tools
|
||||
- **PyInstaller** - Executable packaging
|
||||
- **uv** - Fast Python package manager
|
||||
|
||||
1. **Clone the repository**
|
||||
## 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
|
||||
git clone <repository-url>
|
||||
cd local-transcription
|
||||
# List available audio devices
|
||||
uv run python main_cli.py --list-devices
|
||||
```
|
||||
- Ensure microphone permissions are granted
|
||||
- Try different device indices in settings
|
||||
|
||||
2. **Install dependencies**
|
||||
### GPU Not Detected
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
# 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
|
||||
|
||||
3. **Download Whisper models**
|
||||
```bash
|
||||
# Models will be auto-downloaded on first run
|
||||
# Or manually download:
|
||||
python -c "import whisper; whisper.load_model('base')"
|
||||
```
|
||||
### 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)
|
||||
|
||||
4. **Configure client**
|
||||
```bash
|
||||
cp config/client_config.example.yaml config/client_config.yaml
|
||||
# Edit config/client_config.yaml with your settings
|
||||
```
|
||||
## Use Cases
|
||||
|
||||
5. **Run the server** (one instance)
|
||||
```bash
|
||||
python server/main.py
|
||||
```
|
||||
|
||||
6. **Run the client** (on each user's machine)
|
||||
```bash
|
||||
python client/main.py
|
||||
```
|
||||
|
||||
7. **Add to OBS**
|
||||
- Add a Browser Source
|
||||
- URL: `http://<server-ip>:8000/stream`
|
||||
- Set width/height as needed
|
||||
- Check "Shutdown source when not visible" for performance
|
||||
|
||||
---
|
||||
|
||||
## Configuration (Planned)
|
||||
|
||||
### Client Configuration:
|
||||
```yaml
|
||||
user:
|
||||
name: "Streamer1" # Display name for transcriptions
|
||||
id: "unique-user-id" # Optional unique identifier
|
||||
|
||||
audio:
|
||||
input_device: "default" # or specific device index
|
||||
sample_rate: 16000
|
||||
chunk_duration: 2.0 # seconds
|
||||
|
||||
noise_suppression:
|
||||
enabled: true # Enable/disable noise reduction
|
||||
strength: 0.7 # 0.0 to 1.0 - reduction strength
|
||||
method: "noisereduce" # "noisereduce" or "rnnoise"
|
||||
|
||||
transcription:
|
||||
model: "base" # tiny, base, small, medium, large
|
||||
device: "cuda" # cpu, cuda, mps
|
||||
language: "en" # or "auto" for detection
|
||||
task: "transcribe" # or "translate"
|
||||
|
||||
processing:
|
||||
use_vad: true # Voice Activity Detection
|
||||
min_confidence: 0.5 # Minimum transcription confidence
|
||||
|
||||
server_sync:
|
||||
enabled: false # Enable multi-user server sync
|
||||
url: "ws://localhost:8000" # Server URL (when enabled)
|
||||
api_key: "" # Optional API key
|
||||
|
||||
display:
|
||||
show_timestamps: true # Show timestamps in local display
|
||||
max_lines: 100 # Maximum lines to keep in display
|
||||
font_size: 12 # GUI font size
|
||||
```
|
||||
|
||||
### Server Configuration:
|
||||
```yaml
|
||||
server:
|
||||
host: "0.0.0.0"
|
||||
port: 8000
|
||||
api_key_required: false
|
||||
|
||||
stream:
|
||||
max_clients: 10
|
||||
buffer_size: 100 # messages to buffer
|
||||
retention_time: 300 # seconds
|
||||
|
||||
database:
|
||||
enabled: false
|
||||
path: "transcriptions.db"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Roadmap
|
||||
|
||||
- [x] Project planning and architecture design
|
||||
- [ ] Phase 1: Standalone desktop application with GUI
|
||||
- [ ] Phase 2: Web server and sync system (optional multi-user mode)
|
||||
- [ ] Phase 3: Client-server communication (optional)
|
||||
- [ ] Phase 4: Web stream interface for OBS (optional)
|
||||
- [ ] Phase 5: Advanced features (hotkeys, themes, Docker, etc.)
|
||||
|
||||
---
|
||||
- **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.
|
||||
|
||||
---
|
||||
Contributions are welcome! Please feel free to submit issues or pull requests at the [repository](https://repo.anhonesthost.net/streamer-tools/local-transcription).
|
||||
|
||||
## License
|
||||
|
||||
[Choose appropriate license - MIT, Apache 2.0, etc.]
|
||||
|
||||
---
|
||||
MIT License
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
- OpenAI Whisper for the excellent speech recognition model
|
||||
- The streaming community for inspiration and use cases
|
||||
- [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
|
||||
|
||||
Reference in New Issue
Block a user