Initial commit: Local Transcription App v1.0
Phase 1 Complete - Standalone Desktop Application Features: - Real-time speech-to-text with Whisper (faster-whisper) - PySide6 desktop GUI with settings dialog - Web server for OBS browser source integration - Audio capture with automatic sample rate detection and resampling - Noise suppression with Voice Activity Detection (VAD) - Configurable display settings (font, timestamps, fade duration) - Settings apply without restart (with automatic model reloading) - Auto-fade for web display transcriptions - CPU/GPU support with automatic device detection - Standalone executable builds (PyInstaller) - CUDA build support (works on systems without CUDA hardware) Components: - Audio capture with sounddevice - Noise reduction with noisereduce + webrtcvad - Transcription with faster-whisper - GUI with PySide6 - Web server with FastAPI + WebSocket - Configuration system with YAML Build System: - Standard builds (CPU-only): build.sh / build.bat - CUDA builds (universal): build-cuda.sh / build-cuda.bat - Comprehensive BUILD.md documentation - Cross-platform support (Linux, Windows) Documentation: - README.md with project overview and quick start - BUILD.md with detailed build instructions - NEXT_STEPS.md with future enhancement roadmap - INSTALL.md with setup instructions 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
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.gitignore
vendored
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vendored
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Virtual environments
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venv/
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env/
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ENV/
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.venv/
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.venv
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# uv
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uv.lock
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.python-version
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# IDE
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# OS
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.DS_Store
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Thumbs.db
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# Application specific
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*.log
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config/*.yaml
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!config/default_config.yaml
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.local-transcription/
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# Model cache
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models/
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.cache/
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# PyInstaller
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*.spec.lock
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259
BUILD.md
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BUILD.md
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# Building Local Transcription
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This guide explains how to build standalone executables for Linux and Windows.
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## Prerequisites
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1. **Python 3.8+** installed on your system
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2. **uv** package manager (install from https://docs.astral.sh/uv/)
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3. All project dependencies installed (`uv sync`)
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## Building for Linux
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### Standard Build (CPU-only):
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```bash
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# Make the build script executable (first time only)
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chmod +x build.sh
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# Run the build script
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./build.sh
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```
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### CUDA Build (GPU Support):
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Build with CUDA support even without NVIDIA hardware:
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```bash
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# Make the build script executable (first time only)
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chmod +x build-cuda.sh
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# Run the CUDA build script
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./build-cuda.sh
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```
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This will:
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- Install PyTorch with CUDA 12.1 support
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- Bundle CUDA runtime libraries (~600MB extra)
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- Create an executable that works on both GPU and CPU systems
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- Automatically fall back to CPU if no CUDA GPU is available
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The executable will be created in `dist/LocalTranscription/LocalTranscription`
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### Manual build:
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```bash
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# Clean previous builds
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rm -rf build dist
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# Build with PyInstaller
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uv run pyinstaller local-transcription.spec
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```
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### Distribution:
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```bash
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cd dist
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tar -czf LocalTranscription-Linux.tar.gz LocalTranscription/
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```
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## Building for Windows
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### Standard Build (CPU-only):
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```cmd
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# Run the build script
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build.bat
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```
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### CUDA Build (GPU Support):
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Build with CUDA support even without NVIDIA hardware:
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```cmd
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# Run the CUDA build script
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build-cuda.bat
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```
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|
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This will:
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- Install PyTorch with CUDA 12.1 support
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- Bundle CUDA runtime libraries (~600MB extra)
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- Create an executable that works on both GPU and CPU systems
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- Automatically fall back to CPU if no CUDA GPU is available
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The executable will be created in `dist\LocalTranscription\LocalTranscription.exe`
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### Manual build:
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```cmd
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# Clean previous builds
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rmdir /s /q build
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rmdir /s /q dist
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# Build with PyInstaller
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uv run pyinstaller local-transcription.spec
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```
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### Distribution:
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- Compress the `dist\LocalTranscription` folder to a ZIP file
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- Or use an installer creator like NSIS or Inno Setup
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## Important Notes
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### Cross-Platform Building
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**You cannot cross-compile!**
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- Linux executables must be built on Linux
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- Windows executables must be built on Windows
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- Mac executables must be built on macOS
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### First Run
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On the first run, the application will:
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1. Create a config directory at `~/.local-transcription/` (Linux) or `%USERPROFILE%\.local-transcription\` (Windows)
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2. Download the Whisper model (if not already present)
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3. The model will be cached in `~/.cache/huggingface/` by default
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### Executable Size
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The built executable will be large (300MB - 2GB+) because it includes:
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- Python runtime
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- PySide6 (Qt framework)
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- PyTorch/faster-whisper
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- NumPy, SciPy, and other dependencies
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### Console Window
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By default, the console window is visible (for debugging). To hide it:
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1. Edit `local-transcription.spec`
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2. Change `console=True` to `console=False` in the `EXE` section
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3. Rebuild
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### GPU Support
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#### Building with CUDA (Recommended for Distribution)
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**Yes, you CAN build with CUDA support on systems without NVIDIA GPUs!**
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PyTorch provides CUDA-enabled builds that bundle the CUDA runtime libraries. This means:
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1. **You don't need NVIDIA hardware** to create CUDA-enabled builds
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2. **The executable will work everywhere** - on systems with or without NVIDIA GPUs
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3. **Automatic fallback** - the app detects available hardware and uses GPU if available, CPU otherwise
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4. **Larger file size** - adds ~600MB-1GB to the executable size
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**How it works:**
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```bash
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# Linux
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./build-cuda.sh
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# Windows
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build-cuda.bat
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```
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The build script will:
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- Install PyTorch with bundled CUDA 12.1 runtime
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- Package all CUDA libraries into the executable
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- Create a universal build that runs on any system
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**When users run the executable:**
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- If they have an NVIDIA GPU with drivers: Uses GPU acceleration
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- If they don't have NVIDIA GPU: Automatically uses CPU
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- No configuration needed - it just works!
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#### Alternative: CPU-Only Builds
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If you only want CPU support (smaller file size):
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```bash
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# Linux
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./build.sh
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# Windows
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build.bat
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```
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#### AMD GPU Support
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- **ROCm**: Requires special PyTorch builds from AMD
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- Not recommended for general distribution
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- Better to use CUDA build (works on all systems) or CPU build
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### Optimizations
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To reduce size:
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1. **Remove unused model sizes**: The app downloads models on-demand, so you don't need to bundle them
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2. **Use UPX compression**: Already enabled in the spec file
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3. **Exclude dev dependencies**: Only build dependencies are needed
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## Testing the Build
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After building, test the executable:
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### Linux:
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```bash
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cd dist/LocalTranscription
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./LocalTranscription
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```
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### Windows:
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```cmd
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cd dist\LocalTranscription
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LocalTranscription.exe
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```
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## Troubleshooting
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### Missing modules error
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If you get "No module named X" errors, add the module to the `hiddenimports` list in `local-transcription.spec`
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### DLL errors (Windows)
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Make sure Visual C++ Redistributable is installed on the target system:
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https://aka.ms/vs/17/release/vc_redist.x64.exe
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### Audio device errors
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The application needs access to audio devices. Ensure:
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- Microphone permissions are granted
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- Audio drivers are installed
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- PulseAudio (Linux) or Windows Audio is running
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### Model download fails
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Ensure internet connection on first run. Models are downloaded from:
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https://huggingface.co/guillaumekln/faster-whisper-base
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## Advanced: Adding an Icon
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1. Create or obtain an `.ico` file (Windows) or `.png` file (Linux)
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2. Edit `local-transcription.spec`
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3. Change `icon=None` to `icon='path/to/your/icon.ico'`
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4. Rebuild
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## Advanced: Creating an Installer
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### Windows (using Inno Setup):
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1. Install Inno Setup: https://jrsoftware.org/isinfo.php
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2. Create an `.iss` script file
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3. Build the installer
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### Linux (using AppImage):
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```bash
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# Install appimagetool
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wget https://github.com/AppImage/AppImageKit/releases/download/continuous/appimagetool-x86_64.AppImage
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chmod +x appimagetool-x86_64.AppImage
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# Create AppDir structure
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mkdir -p LocalTranscription.AppDir/usr/bin
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cp -r dist/LocalTranscription/* LocalTranscription.AppDir/usr/bin/
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# Create desktop file and icon
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# (Create .desktop file and icon as needed)
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# Build AppImage
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./appimagetool-x86_64.AppImage LocalTranscription.AppDir
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```
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## Support
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For build issues, check:
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1. PyInstaller documentation: https://pyinstaller.org/
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2. Project issues: https://github.com/anthropics/claude-code/issues
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194
INSTALL.md
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INSTALL.md
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# Installation Guide
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||||
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## Prerequisites
|
||||
|
||||
- **Python 3.9 or higher**
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- **uv** (Python package installer)
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- **FFmpeg** (required by faster-whisper)
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- **CUDA-capable GPU** (optional, for GPU acceleration)
|
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|
||||
### Installing uv
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|
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If you don't have `uv` installed:
|
||||
|
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```bash
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# On macOS and Linux
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curl -LsSf https://astral.sh/uv/install.sh | sh
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|
||||
# On Windows
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powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
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# Or with pip
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pip install uv
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```
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|
||||
### Installing FFmpeg
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||||
|
||||
#### On Ubuntu/Debian:
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||||
```bash
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sudo apt update
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sudo apt install ffmpeg
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||||
```
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||||
|
||||
#### On macOS (with Homebrew):
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||||
```bash
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||||
brew install ffmpeg
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||||
```
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||||
|
||||
#### On Windows:
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Download from [ffmpeg.org](https://ffmpeg.org/download.html) and add to PATH.
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||||
|
||||
## Installation Steps
|
||||
|
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### 1. Navigate to Project Directory
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||||
|
||||
```bash
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cd /home/jknapp/code/local-transcription
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```
|
||||
|
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### 2. Install Dependencies with uv
|
||||
|
||||
```bash
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||||
# uv will automatically create a virtual environment and install dependencies
|
||||
uv sync
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||||
```
|
||||
|
||||
This single command will:
|
||||
- Create a virtual environment (`.venv/`)
|
||||
- Install all dependencies from `pyproject.toml`
|
||||
- Lock dependencies for reproducibility
|
||||
|
||||
**Note for CUDA users:** If you have an NVIDIA GPU, install PyTorch with CUDA support:
|
||||
|
||||
```bash
|
||||
# For CUDA 11.8
|
||||
uv pip install torch --index-url https://download.pytorch.org/whl/cu118
|
||||
|
||||
# For CUDA 12.1
|
||||
uv pip install torch --index-url https://download.pytorch.org/whl/cu121
|
||||
```
|
||||
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||||
### 3. Run the Application
|
||||
|
||||
```bash
|
||||
# Option 1: Using uv run (automatically uses the venv)
|
||||
uv run python main.py
|
||||
|
||||
# Option 2: Activate venv manually
|
||||
source .venv/bin/activate # On Windows: .venv\Scripts\activate
|
||||
python main.py
|
||||
```
|
||||
|
||||
On first run, the application will:
|
||||
- Download the Whisper model (this may take a few minutes)
|
||||
- Create a configuration file at `~/.local-transcription/config.yaml`
|
||||
|
||||
## Quick Start Commands
|
||||
|
||||
```bash
|
||||
# Install everything
|
||||
uv sync
|
||||
|
||||
# Run the application
|
||||
uv run python main.py
|
||||
|
||||
# Install with server dependencies (for Phase 2+)
|
||||
uv sync --extra server
|
||||
|
||||
# Update dependencies
|
||||
uv sync --upgrade
|
||||
```
|
||||
|
||||
## Configuration
|
||||
|
||||
Settings can be changed through the GUI (Settings button) or by editing:
|
||||
```
|
||||
~/.local-transcription/config.yaml
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Audio Device Issues
|
||||
|
||||
If no audio devices are detected:
|
||||
```bash
|
||||
uv run python -c "import sounddevice as sd; print(sd.query_devices())"
|
||||
```
|
||||
|
||||
### GPU Not Detected
|
||||
|
||||
Check if CUDA is available:
|
||||
```bash
|
||||
uv run python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
|
||||
```
|
||||
|
||||
### Model Download Fails
|
||||
|
||||
Models are downloaded to `~/.cache/huggingface/`. If download fails:
|
||||
- Check internet connection
|
||||
- Ensure sufficient disk space (~1-3 GB depending on model size)
|
||||
|
||||
### uv Command Not Found
|
||||
|
||||
Make sure uv is in your PATH:
|
||||
```bash
|
||||
# Add to ~/.bashrc or ~/.zshrc
|
||||
export PATH="$HOME/.cargo/bin:$PATH"
|
||||
```
|
||||
|
||||
## Performance Tips
|
||||
|
||||
For best real-time performance:
|
||||
|
||||
1. **Use GPU if available** - 5-10x faster than CPU
|
||||
2. **Start with smaller models**:
|
||||
- `tiny`: Fastest, ~39M parameters, 1-2s latency
|
||||
- `base`: Good balance, ~74M parameters, 2-3s latency
|
||||
- `small`: Better accuracy, ~244M parameters, 3-5s latency
|
||||
3. **Enable VAD** (Voice Activity Detection) to skip silent audio
|
||||
4. **Adjust chunk duration**: Smaller = lower latency, larger = better accuracy
|
||||
|
||||
## System Requirements
|
||||
|
||||
### Minimum:
|
||||
- CPU: Dual-core 2GHz+
|
||||
- RAM: 4GB
|
||||
- Model: tiny or base
|
||||
|
||||
### Recommended:
|
||||
- CPU: Quad-core 3GHz+ or GPU (NVIDIA GTX 1060+)
|
||||
- RAM: 8GB
|
||||
- Model: base or small
|
||||
|
||||
### For Best Performance:
|
||||
- GPU: NVIDIA RTX 2060 or better
|
||||
- RAM: 16GB
|
||||
- Model: small or medium
|
||||
|
||||
## Development
|
||||
|
||||
### Install development dependencies:
|
||||
```bash
|
||||
uv sync --extra dev
|
||||
```
|
||||
|
||||
### Run tests:
|
||||
```bash
|
||||
uv run pytest
|
||||
```
|
||||
|
||||
### Format code:
|
||||
```bash
|
||||
uv run black .
|
||||
uv run ruff check .
|
||||
```
|
||||
|
||||
## Why uv?
|
||||
|
||||
`uv` is significantly faster than pip:
|
||||
- **10-100x faster** dependency resolution
|
||||
- **Automatic virtual environment** management
|
||||
- **Reproducible builds** with lockfile
|
||||
- **Drop-in replacement** for pip commands
|
||||
|
||||
Learn more at [astral.sh/uv](https://astral.sh/uv)
|
||||
440
NEXT_STEPS.md
Normal file
440
NEXT_STEPS.md
Normal file
@@ -0,0 +1,440 @@
|
||||
# Next Steps for Local Transcription
|
||||
|
||||
This document outlines potential future enhancements and features for the Local Transcription application.
|
||||
|
||||
## Current Status: Phase 1 Complete ✅
|
||||
|
||||
The application currently has:
|
||||
- ✅ Desktop GUI with PySide6
|
||||
- ✅ Real-time transcription with Whisper (faster-whisper)
|
||||
- ✅ Audio capture with automatic sample rate detection and resampling
|
||||
- ✅ Noise suppression with Voice Activity Detection (VAD)
|
||||
- ✅ Web server for OBS browser source integration
|
||||
- ✅ Configurable display settings (font, timestamps, fade duration)
|
||||
- ✅ Settings apply without restart
|
||||
- ✅ Auto-fade for web display
|
||||
- ✅ Standalone executable builds for Linux and Windows
|
||||
- ✅ CUDA support (with automatic CPU fallback)
|
||||
|
||||
## Phase 2: Multi-User Server Architecture (Optional)
|
||||
|
||||
If you want to enable multiple users to sync their transcriptions to a shared display:
|
||||
|
||||
### Server Components
|
||||
|
||||
1. **WebSocket Server**
|
||||
- Accept connections from multiple clients
|
||||
- Aggregate transcriptions from all connected users
|
||||
- Broadcast to web display clients
|
||||
- Handle user authentication/authorization
|
||||
- Rate limiting and abuse prevention
|
||||
|
||||
2. **Database/Storage** (Optional)
|
||||
- Store transcription history
|
||||
- User management
|
||||
- Session logs for later review
|
||||
- Consider: SQLite, PostgreSQL, or Redis
|
||||
|
||||
3. **Web Admin Interface**
|
||||
- Monitor connected clients
|
||||
- View active sessions
|
||||
- Manage users and permissions
|
||||
- Export transcription logs
|
||||
|
||||
### Client Updates
|
||||
|
||||
1. **Server Sync Toggle**
|
||||
- Enable/disable server sync in Settings
|
||||
- Server URL configuration
|
||||
- API key/authentication setup
|
||||
- Connection status indicator
|
||||
|
||||
2. **Network Handling**
|
||||
- Auto-reconnect on connection loss
|
||||
- Queue transcriptions when offline
|
||||
- Sync when connection restored
|
||||
|
||||
### Implementation Technologies
|
||||
|
||||
- **Server Framework**: FastAPI (already used for web display)
|
||||
- **WebSocket**: Already integrated
|
||||
- **Database**: SQLAlchemy + SQLite/PostgreSQL
|
||||
- **Deployment**: Docker container for easy deployment
|
||||
|
||||
**Estimated Effort**: 2-3 weeks for full implementation
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Enhanced Features
|
||||
|
||||
### Transcription Improvements
|
||||
|
||||
1. **Multi-Language Support**
|
||||
- Automatic language detection
|
||||
- Real-time language switching
|
||||
- Translation between languages
|
||||
- Per-user language settings
|
||||
|
||||
2. **Speaker Diarization**
|
||||
- Detect and label different speakers
|
||||
- Use pyannote.audio or similar
|
||||
- Automatically assign speaker IDs
|
||||
|
||||
3. **Custom Vocabulary**
|
||||
- Add gaming terms, streamer names
|
||||
- Technical jargon support
|
||||
- Proper noun correction
|
||||
|
||||
4. **Punctuation & Formatting**
|
||||
- Automatic punctuation insertion
|
||||
- Sentence capitalization
|
||||
- Better text formatting
|
||||
|
||||
### Display Enhancements
|
||||
|
||||
1. **Theme System**
|
||||
- Light/dark themes
|
||||
- Custom color schemes
|
||||
- User-created themes (JSON/YAML)
|
||||
- Per-element styling
|
||||
|
||||
2. **Animation Options**
|
||||
- Different fade effects
|
||||
- Slide in/out animations
|
||||
- Configurable transition speeds
|
||||
- Particle effects (optional)
|
||||
|
||||
3. **Layout Modes**
|
||||
- Karaoke-style (word highlighting)
|
||||
- Ticker tape (scrolling bottom)
|
||||
- Multi-column for multiple users
|
||||
- Picture-in-picture mode
|
||||
|
||||
4. **Web Display Customization**
|
||||
- CSS customization interface
|
||||
- Live preview in settings
|
||||
- Save/load custom styles
|
||||
- Community theme sharing
|
||||
|
||||
### Audio Processing
|
||||
|
||||
1. **Advanced Noise Reduction**
|
||||
- RNNoise integration
|
||||
- Custom noise profiles
|
||||
- Adaptive filtering
|
||||
- Echo cancellation
|
||||
|
||||
2. **Audio Effects**
|
||||
- Equalization presets
|
||||
- Compression/normalization
|
||||
- Voice enhancement filters
|
||||
|
||||
3. **Multi-Input Support**
|
||||
- Multiple microphones simultaneously
|
||||
- Virtual audio cable integration
|
||||
- Audio routing/mixing
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Integration & Automation
|
||||
|
||||
### OBS Integration
|
||||
|
||||
1. **OBS Plugin** (Advanced)
|
||||
- Native OBS plugin instead of browser source
|
||||
- Lower resource usage
|
||||
- Better performance
|
||||
- Tighter integration
|
||||
|
||||
2. **Scene Integration**
|
||||
- Auto-show/hide based on speech
|
||||
- Integrate with OBS scene switcher
|
||||
- Hotkey support
|
||||
|
||||
### Streaming Platform Integration
|
||||
|
||||
1. **Twitch Integration**
|
||||
- Send captions to Twitch chat
|
||||
- Twitch API integration
|
||||
- Custom Twitch bot
|
||||
|
||||
2. **YouTube Integration**
|
||||
- Live caption upload
|
||||
- YouTube API integration
|
||||
|
||||
3. **Discord Integration**
|
||||
- Send transcriptions to Discord webhook
|
||||
- Discord bot for voice chat transcription
|
||||
|
||||
### Automation
|
||||
|
||||
1. **Hotkey Support**
|
||||
- Global hotkeys for start/stop
|
||||
- Toggle display visibility
|
||||
- Quick settings access
|
||||
|
||||
2. **Voice Commands**
|
||||
- "Hey Transcription, start/stop"
|
||||
- Command detection in audio stream
|
||||
- Configurable wake words
|
||||
|
||||
3. **Auto-Start Options**
|
||||
- Start with OBS
|
||||
- Start on system boot
|
||||
- Auto-detect streaming software
|
||||
|
||||
---
|
||||
|
||||
## Phase 5: Advanced Features
|
||||
|
||||
### AI Enhancements
|
||||
|
||||
1. **Summarization**
|
||||
- Real-time conversation summaries
|
||||
- Key point extraction
|
||||
- Topic detection
|
||||
|
||||
2. **Sentiment Analysis**
|
||||
- Detect tone/emotion
|
||||
- Highlight important moments
|
||||
- Filter profanity (optional)
|
||||
|
||||
3. **Context Awareness**
|
||||
- Remember conversation context
|
||||
- Better transcription accuracy
|
||||
- Adaptive vocabulary
|
||||
|
||||
### Analytics & Insights
|
||||
|
||||
1. **Usage Statistics**
|
||||
- Words per minute
|
||||
- Speaking time per user
|
||||
- Most common words/phrases
|
||||
- Accuracy metrics
|
||||
|
||||
2. **Export Options**
|
||||
- Export to SRT/VTT for video captions
|
||||
- PDF/Word document export
|
||||
- CSV for data analysis
|
||||
- JSON API for custom tools
|
||||
|
||||
3. **Search & Filter**
|
||||
- Search transcription history
|
||||
- Filter by user, date, keyword
|
||||
- Highlight search results
|
||||
|
||||
### Accessibility
|
||||
|
||||
1. **Screen Reader Support**
|
||||
- Full NVDA/JAWS compatibility
|
||||
- Keyboard navigation
|
||||
- Voice feedback
|
||||
|
||||
2. **High Contrast Modes**
|
||||
- Enhanced visibility options
|
||||
- Color blind friendly palettes
|
||||
|
||||
3. **Text-to-Speech**
|
||||
- Read back transcriptions
|
||||
- Multiple voice options
|
||||
- Speed control
|
||||
|
||||
---
|
||||
|
||||
## Performance Optimizations
|
||||
|
||||
### Current Considerations
|
||||
|
||||
1. **Model Optimization**
|
||||
- Quantization (int8, int4)
|
||||
- Smaller model variants
|
||||
- TensorRT optimization (NVIDIA)
|
||||
- ONNX Runtime support
|
||||
|
||||
2. **Caching**
|
||||
- Cache common phrases
|
||||
- Model warm-up on startup
|
||||
- Preload frequently used resources
|
||||
|
||||
3. **Resource Management**
|
||||
- Dynamic batch sizing
|
||||
- Memory pooling
|
||||
- Thread pool optimization
|
||||
|
||||
### Future Optimizations
|
||||
|
||||
1. **Distributed Processing**
|
||||
- Offload to cloud GPU
|
||||
- Share processing across multiple machines
|
||||
- Load balancing
|
||||
|
||||
2. **Edge Computing**
|
||||
- Run on edge devices (Raspberry Pi)
|
||||
- Mobile app support
|
||||
- Embedded systems
|
||||
|
||||
---
|
||||
|
||||
## Community Features
|
||||
|
||||
### Sharing & Collaboration
|
||||
|
||||
1. **Theme Marketplace**
|
||||
- Share custom themes
|
||||
- Download community themes
|
||||
- Rating system
|
||||
|
||||
2. **Plugin System**
|
||||
- Allow community plugins
|
||||
- Custom audio filters
|
||||
- Display widgets
|
||||
- Integration modules
|
||||
|
||||
3. **Documentation**
|
||||
- Video tutorials
|
||||
- Wiki/knowledge base
|
||||
- API documentation
|
||||
- Developer guides
|
||||
|
||||
### User Support
|
||||
|
||||
1. **In-App Help**
|
||||
- Contextual help tooltips
|
||||
- Getting started wizard
|
||||
- Troubleshooting guide
|
||||
|
||||
2. **Community Forum**
|
||||
- GitHub Discussions
|
||||
- Discord server
|
||||
- Reddit community
|
||||
|
||||
---
|
||||
|
||||
## Technical Debt & Maintenance
|
||||
|
||||
### Code Quality
|
||||
|
||||
1. **Testing**
|
||||
- Unit tests for core modules
|
||||
- Integration tests
|
||||
- End-to-end tests
|
||||
- Performance benchmarks
|
||||
|
||||
2. **Documentation**
|
||||
- API documentation
|
||||
- Code comments
|
||||
- Architecture diagrams
|
||||
- Developer setup guide
|
||||
|
||||
3. **CI/CD**
|
||||
- Automated builds
|
||||
- Automated testing
|
||||
- Release automation
|
||||
- Cross-platform testing
|
||||
|
||||
### Security
|
||||
|
||||
1. **Security Audits**
|
||||
- Dependency scanning
|
||||
- Vulnerability assessment
|
||||
- Code security review
|
||||
|
||||
2. **Data Privacy**
|
||||
- Local-first by default
|
||||
- Optional cloud features
|
||||
- GDPR compliance (if applicable)
|
||||
- Clear privacy policy
|
||||
|
||||
---
|
||||
|
||||
## Immediate Quick Wins
|
||||
|
||||
These are small enhancements that could be implemented quickly:
|
||||
|
||||
### Easy (< 1 day)
|
||||
|
||||
- [ ] Add application icon
|
||||
- [ ] Add "About" dialog with version info
|
||||
- [ ] Add keyboard shortcuts (Ctrl+S for settings, etc.)
|
||||
- [ ] Add system tray icon
|
||||
- [ ] Save window position/size
|
||||
- [ ] Add "Check for Updates" feature
|
||||
- [ ] Export transcriptions to text file
|
||||
|
||||
### Medium (1-3 days)
|
||||
|
||||
- [ ] Add profanity filter (optional)
|
||||
- [ ] Add confidence score display
|
||||
- [ ] Add audio level meter
|
||||
- [ ] Multiple language support in UI
|
||||
- [ ] Dark/light theme toggle
|
||||
- [ ] Backup/restore settings
|
||||
- [ ] Recent transcriptions history
|
||||
|
||||
### Larger (1+ weeks)
|
||||
|
||||
- [ ] Cloud sync for settings
|
||||
- [ ] Mobile companion app
|
||||
- [ ] Browser extension
|
||||
- [ ] API server mode
|
||||
- [ ] Plugin architecture
|
||||
- [ ] Advanced audio visualization
|
||||
|
||||
---
|
||||
|
||||
## Resources & References
|
||||
|
||||
### Documentation
|
||||
- [Faster-Whisper](https://github.com/guillaumekln/faster-whisper)
|
||||
- [PySide6 Documentation](https://doc.qt.io/qtforpython/)
|
||||
- [FastAPI Documentation](https://fastapi.tiangolo.com/)
|
||||
- [PyInstaller Manual](https://pyinstaller.org/en/stable/)
|
||||
|
||||
### Similar Projects
|
||||
- [whisper.cpp](https://github.com/ggerganov/whisper.cpp) - C++ implementation
|
||||
- [Buzz](https://github.com/chidiwilliams/buzz) - Desktop transcription tool
|
||||
- [OpenAI Whisper](https://github.com/openai/whisper) - Original implementation
|
||||
|
||||
### Community
|
||||
- Create GitHub Discussions for feature requests
|
||||
- Set up issue templates
|
||||
- Contributing guidelines
|
||||
- Code of conduct
|
||||
|
||||
---
|
||||
|
||||
## Decision Log
|
||||
|
||||
Track major architectural decisions here:
|
||||
|
||||
### 2025-12-25: PyInstaller for Distribution
|
||||
- **Decision**: Use PyInstaller for creating standalone executables
|
||||
- **Rationale**: Good PySide6 support, active development, cross-platform
|
||||
- **Alternatives Considered**: cx_Freeze, Nuitka, py2exe
|
||||
- **Impact**: Users can run without Python installation
|
||||
|
||||
### 2025-12-25: CUDA Build Strategy
|
||||
- **Decision**: Provide CUDA-enabled builds that bundle CUDA runtime
|
||||
- **Rationale**: Universal builds work everywhere, automatic GPU detection
|
||||
- **Trade-off**: Larger file size (~600MB extra) for better UX
|
||||
- **Impact**: Single build for both GPU and CPU users
|
||||
|
||||
### 2025-12-25: Web Server Always Running
|
||||
- **Decision**: Remove enable/disable toggle, always run web server
|
||||
- **Rationale**: Simplifies UX, no configuration needed for OBS
|
||||
- **Impact**: Uses one local port (8080 by default), minimal overhead
|
||||
|
||||
---
|
||||
|
||||
## Contact & Contribution
|
||||
|
||||
When this project is public:
|
||||
- **Issues**: Report bugs and request features on GitHub Issues
|
||||
- **Pull Requests**: Contributions welcome! See CONTRIBUTING.md
|
||||
- **Discussions**: Join GitHub Discussions for questions and ideas
|
||||
- **License**: [To be determined - consider MIT or Apache 2.0]
|
||||
|
||||
---
|
||||
|
||||
*Last Updated: 2025-12-25*
|
||||
*Version: 1.0.0 (Phase 1 Complete)*
|
||||
494
README.md
Normal file
494
README.md
Normal file
@@ -0,0 +1,494 @@
|
||||
# Local Transcription for Streamers
|
||||
|
||||
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.
|
||||
|
||||
## Features
|
||||
|
||||
- **Standalone Desktop Application**: Use locally with built-in GUI display - no server required
|
||||
- **Local Transcription**: Run Whisper (or compatible models) locally on your machine
|
||||
- **CPU/GPU Support**: Choose between CPU or GPU processing based on your hardware
|
||||
- **Real-time Processing**: Live audio transcription with minimal latency
|
||||
- **Noise Suppression**: Built-in audio preprocessing to reduce background noise
|
||||
- **User Configuration**: Set your display name and preferences through the GUI
|
||||
- **Optional Multi-user Sync**: Connect to a server to sync transcriptions with other users
|
||||
- **OBS Integration**: Web-based output designed for easy browser source capture
|
||||
- **Privacy-First**: All processing happens locally; only transcription text is shared
|
||||
- **Customizable**: Configure model size, language, and streaming settings
|
||||
|
||||
## Quick Start
|
||||
|
||||
### Running from Source
|
||||
|
||||
```bash
|
||||
# Install dependencies
|
||||
uv sync
|
||||
|
||||
# Run the application
|
||||
uv run python main.py
|
||||
```
|
||||
|
||||
### Building Standalone Executables
|
||||
|
||||
To create standalone executables for distribution:
|
||||
|
||||
**Linux:**
|
||||
```bash
|
||||
./build.sh
|
||||
```
|
||||
|
||||
**Windows:**
|
||||
```cmd
|
||||
build.bat
|
||||
```
|
||||
|
||||
For detailed build instructions, see [BUILD.md](BUILD.md).
|
||||
|
||||
## Architecture Overview
|
||||
|
||||
The application can run in two modes:
|
||||
|
||||
### Standalone Mode (No Server Required):
|
||||
1. **Desktop Application**: Captures audio, performs speech-to-text, and displays transcriptions locally in a GUI window
|
||||
|
||||
### Multi-user Sync Mode (Optional):
|
||||
1. **Local Transcription Client**: Captures audio, performs speech-to-text, and sends results to the web server
|
||||
2. **Centralized Web Server**: Aggregates transcriptions from multiple clients and serves a web stream
|
||||
3. **Web Stream Interface**: Browser-accessible page displaying synchronized transcriptions (for OBS capture)
|
||||
|
||||
## Use Cases
|
||||
|
||||
- **Multi-language Streams**: Multiple translators transcribing in different languages
|
||||
- **Accessibility**: Provide real-time captions for viewers
|
||||
- **Collaborative Podcasts**: Multiple hosts with separate transcriptions
|
||||
- **Gaming Commentary**: Track who said what in multiplayer sessions
|
||||
|
||||
---
|
||||
|
||||
## Implementation Plan
|
||||
|
||||
### Phase 1: Standalone Desktop Application
|
||||
|
||||
**Objective**: Build a fully functional standalone transcription app with GUI that works without any server
|
||||
|
||||
#### Components:
|
||||
1. **Audio Capture Module**
|
||||
- Capture system audio or microphone input
|
||||
- Support multiple audio sources (virtual audio cables, physical devices)
|
||||
- Real-time audio buffering with configurable chunk sizes
|
||||
- **Noise Suppression**: Preprocess audio to reduce background noise
|
||||
- Libraries: `pyaudio`, `sounddevice`, `noisereduce`, `webrtcvad`
|
||||
|
||||
2. **Noise Suppression Engine**
|
||||
- Real-time noise reduction using RNNoise or noisereduce
|
||||
- Adjustable noise reduction strength
|
||||
- Optional VAD (Voice Activity Detection) to skip silent segments
|
||||
- Libraries: `noisereduce`, `rnnoise-python`, `webrtcvad`
|
||||
|
||||
3. **Transcription Engine**
|
||||
- Integrate OpenAI Whisper (or alternatives: faster-whisper, whisper.cpp)
|
||||
- Support multiple model sizes (tiny, base, small, medium, large)
|
||||
- CPU and GPU inference options
|
||||
- Model management and automatic downloading
|
||||
- Libraries: `openai-whisper`, `faster-whisper`, `torch`
|
||||
|
||||
4. **Device Selection**
|
||||
- Auto-detect available compute devices (CPU, CUDA, MPS for Mac)
|
||||
- Allow user to specify preferred device via GUI
|
||||
- Graceful fallback if GPU unavailable
|
||||
- Display device status and performance metrics
|
||||
|
||||
5. **Desktop GUI Application**
|
||||
- Cross-platform GUI using PyQt6, Tkinter, or CustomTkinter
|
||||
- Main transcription display window (scrolling text area)
|
||||
- Settings panel for configuration
|
||||
- User name input field
|
||||
- Audio input device selector
|
||||
- Model size selector
|
||||
- CPU/GPU toggle
|
||||
- Start/Stop transcription button
|
||||
- Optional: System tray integration
|
||||
- Libraries: `PyQt6`, `customtkinter`, or `tkinter`
|
||||
|
||||
6. **Local Display**
|
||||
- Real-time transcription display in GUI window
|
||||
- Scrolling text with timestamps
|
||||
- User name/label shown with transcriptions
|
||||
- Copy transcription to clipboard
|
||||
- Optional: Save transcription to file (TXT, SRT, VTT)
|
||||
|
||||
#### Tasks:
|
||||
- [ ] Set up project structure and dependencies
|
||||
- [ ] Implement audio capture with device selection
|
||||
- [ ] Add noise suppression and VAD preprocessing
|
||||
- [ ] Integrate Whisper model loading and inference
|
||||
- [ ] Add CPU/GPU device detection and selection logic
|
||||
- [ ] Create real-time audio buffer processing pipeline
|
||||
- [ ] Design and implement GUI layout (main window)
|
||||
- [ ] Add settings panel with user name configuration
|
||||
- [ ] Implement local transcription display area
|
||||
- [ ] Add start/stop controls and status indicators
|
||||
- [ ] Test transcription accuracy and latency
|
||||
- [ ] Test noise suppression effectiveness
|
||||
|
||||
---
|
||||
|
||||
### Phase 2: Web Server and Sync System
|
||||
|
||||
**Objective**: Create a centralized server to aggregate and serve transcriptions
|
||||
|
||||
#### Components:
|
||||
1. **Web Server**
|
||||
- FastAPI or Flask-based REST API
|
||||
- WebSocket support for real-time updates
|
||||
- User/client registration and management
|
||||
- Libraries: `fastapi`, `uvicorn`, `websockets`
|
||||
|
||||
2. **Transcription Aggregator**
|
||||
- Receive transcription chunks from multiple clients
|
||||
- Associate transcriptions with user IDs/names
|
||||
- Timestamp management and synchronization
|
||||
- Buffer management for smooth streaming
|
||||
|
||||
3. **Database/Storage** (Optional)
|
||||
- Store transcription history (SQLite for simplicity)
|
||||
- Session management
|
||||
- Export functionality (SRT, VTT, TXT formats)
|
||||
|
||||
#### API Endpoints:
|
||||
- `POST /api/register` - Register a new client
|
||||
- `POST /api/transcription` - Submit transcription chunk
|
||||
- `WS /api/stream` - WebSocket for real-time transcription stream
|
||||
- `GET /stream` - Web page for OBS browser source
|
||||
|
||||
#### Tasks:
|
||||
- [ ] Set up FastAPI server with CORS support
|
||||
- [ ] Implement WebSocket handler for real-time streaming
|
||||
- [ ] Create client registration system
|
||||
- [ ] Build transcription aggregation logic
|
||||
- [ ] Add timestamp synchronization
|
||||
- [ ] Create data models for clients and transcriptions
|
||||
|
||||
---
|
||||
|
||||
### Phase 3: Client-Server Communication (Optional Multi-user Mode)
|
||||
|
||||
**Objective**: Add optional server connectivity to enable multi-user transcription sync
|
||||
|
||||
#### Components:
|
||||
1. **HTTP/WebSocket Client**
|
||||
- Register client with server on startup
|
||||
- Send transcription chunks as they're generated
|
||||
- Handle connection drops and reconnection
|
||||
- Libraries: `requests`, `websockets`
|
||||
|
||||
2. **Configuration System**
|
||||
- Config file for server URL, API keys, user settings
|
||||
- Model preferences (size, language)
|
||||
- Audio input settings
|
||||
- Format: YAML or JSON
|
||||
|
||||
3. **Status Monitoring**
|
||||
- Connection status indicator
|
||||
- Transcription queue health
|
||||
- Error handling and logging
|
||||
|
||||
#### Tasks:
|
||||
- [ ] Add "Enable Server Sync" toggle to GUI
|
||||
- [ ] Add server URL configuration field in settings
|
||||
- [ ] Implement WebSocket client for sending transcriptions
|
||||
- [ ] Add configuration file support (YAML/JSON)
|
||||
- [ ] Create connection management with auto-reconnect
|
||||
- [ ] Add local logging and error handling
|
||||
- [ ] Add server connection status indicator to GUI
|
||||
- [ ] Allow app to function normally if server is unavailable
|
||||
|
||||
---
|
||||
|
||||
### Phase 4: Web Stream Interface (OBS Integration)
|
||||
|
||||
**Objective**: Create a web page that displays synchronized transcriptions for OBS
|
||||
|
||||
#### Components:
|
||||
1. **Web Frontend**
|
||||
- HTML/CSS/JavaScript page for displaying transcriptions
|
||||
- Responsive design with customizable styling
|
||||
- Auto-scroll with configurable retention window
|
||||
- Libraries: Vanilla JS or lightweight framework (Alpine.js, htmx)
|
||||
|
||||
2. **Styling Options**
|
||||
- Customizable fonts, colors, sizes
|
||||
- Background transparency for OBS chroma key
|
||||
- User name/ID display options
|
||||
- Timestamp display (optional)
|
||||
|
||||
3. **Display Modes**
|
||||
- Scrolling captions (like live TV captions)
|
||||
- Multi-user panel view (separate sections per user)
|
||||
- Overlay mode (minimal UI for transparency)
|
||||
|
||||
#### Tasks:
|
||||
- [ ] Create HTML template for transcription display
|
||||
- [ ] Implement WebSocket client in JavaScript
|
||||
- [ ] Add CSS styling with OBS-friendly transparency
|
||||
- [ ] Create customization controls (URL parameters or UI)
|
||||
- [ ] Test with OBS browser source
|
||||
- [ ] Add configurable retention/scroll behavior
|
||||
|
||||
---
|
||||
|
||||
### Phase 5: Advanced Features
|
||||
|
||||
**Objective**: Enhance functionality and user experience
|
||||
|
||||
#### Features:
|
||||
1. **Language Detection**
|
||||
- Auto-detect spoken language
|
||||
- Multi-language support in single stream
|
||||
- Language selector in GUI
|
||||
|
||||
2. **Speaker Diarization** (Optional)
|
||||
- Identify different speakers
|
||||
- Label transcriptions by speaker
|
||||
- Useful for multi-host streams
|
||||
|
||||
3. **Profanity Filtering**
|
||||
- Optional word filtering/replacement
|
||||
- Customizable filter lists
|
||||
- Toggle in GUI settings
|
||||
|
||||
4. **Advanced Noise Profiles**
|
||||
- Save and load custom noise profiles
|
||||
- Adaptive noise suppression
|
||||
- Different profiles for different environments
|
||||
|
||||
5. **Export Functionality**
|
||||
- Save transcriptions in multiple formats (TXT, SRT, VTT, JSON)
|
||||
- Export button in GUI
|
||||
- Automatic session saving
|
||||
|
||||
6. **Hotkey Support**
|
||||
- Global hotkeys to start/stop transcription
|
||||
- Mute/unmute hotkey
|
||||
- Quick save hotkey
|
||||
|
||||
7. **Docker Support**
|
||||
- Containerized server deployment
|
||||
- Docker Compose for easy multi-component setup
|
||||
- Pre-built images for easy deployment
|
||||
|
||||
8. **Themes and Customization**
|
||||
- Dark/light theme toggle
|
||||
- Customizable font sizes and colors for display
|
||||
- OBS-friendly transparent overlay mode
|
||||
|
||||
#### Tasks:
|
||||
- [ ] Add language detection and multi-language support
|
||||
- [ ] Implement speaker diarization
|
||||
- [ ] Create optional profanity filter
|
||||
- [ ] Add export functionality (SRT, VTT, plain text, JSON)
|
||||
- [ ] Implement global hotkey support
|
||||
- [ ] Create Docker containers for server component
|
||||
- [ ] Add theme customization options
|
||||
- [ ] Create advanced noise profile management
|
||||
|
||||
---
|
||||
|
||||
## Technology Stack
|
||||
|
||||
### Local Client:
|
||||
- **Python 3.9+**
|
||||
- **GUI**: PyQt6 / CustomTkinter / tkinter
|
||||
- **Audio**: PyAudio / sounddevice
|
||||
- **Noise Suppression**: noisereduce / rnnoise-python
|
||||
- **VAD**: webrtcvad
|
||||
- **ML Framework**: PyTorch (for Whisper)
|
||||
- **Transcription**: openai-whisper / faster-whisper
|
||||
- **Networking**: websockets, requests (optional for server sync)
|
||||
- **Config**: PyYAML / json
|
||||
|
||||
### Server:
|
||||
- **Backend**: FastAPI / Flask
|
||||
- **WebSocket**: python-websockets / FastAPI WebSockets
|
||||
- **Server**: Uvicorn / Gunicorn
|
||||
- **Database** (optional): SQLite / PostgreSQL
|
||||
- **CORS**: fastapi-cors
|
||||
|
||||
### Web Interface:
|
||||
- **Frontend**: HTML5, CSS3, JavaScript (ES6+)
|
||||
- **Real-time**: WebSocket API
|
||||
- **Styling**: CSS Grid/Flexbox for layout
|
||||
|
||||
---
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
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)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Installation (Planned)
|
||||
|
||||
### Prerequisites:
|
||||
- Python 3.9 or higher
|
||||
- CUDA-capable GPU (optional, for GPU acceleration)
|
||||
- FFmpeg (required by Whisper)
|
||||
|
||||
### Steps:
|
||||
|
||||
1. **Clone the repository**
|
||||
```bash
|
||||
git clone <repository-url>
|
||||
cd local-transcription
|
||||
```
|
||||
|
||||
2. **Install dependencies**
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
3. **Download Whisper models**
|
||||
```bash
|
||||
# Models will be auto-downloaded on first run
|
||||
# Or manually download:
|
||||
python -c "import whisper; whisper.load_model('base')"
|
||||
```
|
||||
|
||||
4. **Configure client**
|
||||
```bash
|
||||
cp config/client_config.example.yaml config/client_config.yaml
|
||||
# Edit config/client_config.yaml with your settings
|
||||
```
|
||||
|
||||
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.)
|
||||
|
||||
---
|
||||
|
||||
## Contributing
|
||||
|
||||
Contributions are welcome! Please feel free to submit issues or pull requests.
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
[Choose appropriate license - MIT, Apache 2.0, etc.]
|
||||
|
||||
---
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
- OpenAI Whisper for the excellent speech recognition model
|
||||
- The streaming community for inspiration and use cases
|
||||
56
build-cuda.bat
Normal file
56
build-cuda.bat
Normal file
@@ -0,0 +1,56 @@
|
||||
@echo off
|
||||
REM Build script for Windows with CUDA support
|
||||
|
||||
echo Building Local Transcription with CUDA support...
|
||||
echo ==================================================
|
||||
echo.
|
||||
echo This will create a build that supports both CPU and CUDA GPUs.
|
||||
echo The executable will be larger (~2-3GB) but will work on any system.
|
||||
echo.
|
||||
|
||||
set /p INSTALL_CUDA="Install PyTorch with CUDA support? (y/n) "
|
||||
if /i "%INSTALL_CUDA%"=="y" (
|
||||
echo Installing PyTorch with CUDA 12.1 support...
|
||||
|
||||
REM Uninstall CPU-only version if present
|
||||
uv pip uninstall -y torch
|
||||
|
||||
REM Install CUDA-enabled PyTorch
|
||||
REM This installs PyTorch with bundled CUDA runtime
|
||||
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
|
||||
echo CUDA-enabled PyTorch installed
|
||||
echo.
|
||||
)
|
||||
|
||||
REM Clean previous builds
|
||||
echo Cleaning previous builds...
|
||||
if exist build rmdir /s /q build
|
||||
if exist dist rmdir /s /q dist
|
||||
|
||||
REM Build with PyInstaller
|
||||
echo Running PyInstaller...
|
||||
uv run pyinstaller local-transcription.spec
|
||||
|
||||
REM Check if build succeeded
|
||||
if exist "dist\LocalTranscription" (
|
||||
echo.
|
||||
echo Build successful!
|
||||
echo Executable location: dist\LocalTranscription\LocalTranscription.exe
|
||||
echo.
|
||||
echo CUDA Support: YES (falls back to CPU if CUDA not available^)
|
||||
echo.
|
||||
echo To run the application:
|
||||
echo cd dist\LocalTranscription
|
||||
echo LocalTranscription.exe
|
||||
echo.
|
||||
echo To create a distributable package:
|
||||
echo - Compress the dist\LocalTranscription folder to a ZIP file
|
||||
echo - Name it: LocalTranscription-Windows-CUDA.zip
|
||||
echo.
|
||||
echo Note: This build will work on systems with or without NVIDIA GPUs.
|
||||
) else (
|
||||
echo.
|
||||
echo Build failed!
|
||||
exit /b 1
|
||||
)
|
||||
57
build-cuda.sh
Executable file
57
build-cuda.sh
Executable file
@@ -0,0 +1,57 @@
|
||||
#!/bin/bash
|
||||
# Build script for Linux with CUDA support
|
||||
|
||||
echo "Building Local Transcription with CUDA support..."
|
||||
echo "=================================================="
|
||||
echo ""
|
||||
echo "This will create a build that supports both CPU and CUDA GPUs."
|
||||
echo "The executable will be larger (~2-3GB) but will work on any system."
|
||||
echo ""
|
||||
|
||||
# Check if we should install CUDA-enabled PyTorch
|
||||
read -p "Install PyTorch with CUDA support? (y/n) " -n 1 -r
|
||||
echo
|
||||
if [[ $REPLY =~ ^[Yy]$ ]]
|
||||
then
|
||||
echo "Installing PyTorch with CUDA 12.1 support..."
|
||||
# Uninstall CPU-only version if present
|
||||
uv pip uninstall -y torch
|
||||
|
||||
# Install CUDA-enabled PyTorch
|
||||
# This installs PyTorch with bundled CUDA runtime
|
||||
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
|
||||
echo "✓ CUDA-enabled PyTorch installed"
|
||||
echo ""
|
||||
fi
|
||||
|
||||
# Clean previous builds
|
||||
echo "Cleaning previous builds..."
|
||||
rm -rf build dist
|
||||
|
||||
# Build with PyInstaller
|
||||
echo "Running PyInstaller..."
|
||||
uv run pyinstaller local-transcription.spec
|
||||
|
||||
# Check if build succeeded
|
||||
if [ -d "dist/LocalTranscription" ]; then
|
||||
echo ""
|
||||
echo "✓ Build successful!"
|
||||
echo "Executable location: dist/LocalTranscription/LocalTranscription"
|
||||
echo ""
|
||||
echo "CUDA Support: YES (falls back to CPU if CUDA not available)"
|
||||
echo ""
|
||||
echo "To run the application:"
|
||||
echo " cd dist/LocalTranscription"
|
||||
echo " ./LocalTranscription"
|
||||
echo ""
|
||||
echo "To create a distributable package:"
|
||||
echo " cd dist"
|
||||
echo " tar -czf LocalTranscription-Linux-CUDA.tar.gz LocalTranscription/"
|
||||
echo ""
|
||||
echo "Note: This build will work on systems with or without NVIDIA GPUs."
|
||||
else
|
||||
echo ""
|
||||
echo "✗ Build failed!"
|
||||
exit 1
|
||||
fi
|
||||
34
build.bat
Normal file
34
build.bat
Normal file
@@ -0,0 +1,34 @@
|
||||
@echo off
|
||||
REM Build script for Windows
|
||||
|
||||
echo Building Local Transcription for Windows...
|
||||
echo ==========================================
|
||||
echo.
|
||||
|
||||
REM Clean previous builds
|
||||
echo Cleaning previous builds...
|
||||
if exist build rmdir /s /q build
|
||||
if exist dist rmdir /s /q dist
|
||||
|
||||
REM Build with PyInstaller
|
||||
echo Running PyInstaller...
|
||||
uv run pyinstaller local-transcription.spec
|
||||
|
||||
REM Check if build succeeded
|
||||
if exist "dist\LocalTranscription" (
|
||||
echo.
|
||||
echo Build successful!
|
||||
echo Executable location: dist\LocalTranscription\LocalTranscription.exe
|
||||
echo.
|
||||
echo To run the application:
|
||||
echo cd dist\LocalTranscription
|
||||
echo LocalTranscription.exe
|
||||
echo.
|
||||
echo To create a distributable package:
|
||||
echo - Install 7-Zip or WinRAR
|
||||
echo - Compress the dist\LocalTranscription folder to a ZIP file
|
||||
) else (
|
||||
echo.
|
||||
echo Build failed!
|
||||
exit /b 1
|
||||
)
|
||||
32
build.sh
Executable file
32
build.sh
Executable file
@@ -0,0 +1,32 @@
|
||||
#!/bin/bash
|
||||
# Build script for Linux
|
||||
|
||||
echo "Building Local Transcription for Linux..."
|
||||
echo "========================================="
|
||||
|
||||
# Clean previous builds
|
||||
echo "Cleaning previous builds..."
|
||||
rm -rf build dist
|
||||
|
||||
# Build with PyInstaller
|
||||
echo "Running PyInstaller..."
|
||||
uv run pyinstaller local-transcription.spec
|
||||
|
||||
# Check if build succeeded
|
||||
if [ -d "dist/LocalTranscription" ]; then
|
||||
echo ""
|
||||
echo "✓ Build successful!"
|
||||
echo "Executable location: dist/LocalTranscription/LocalTranscription"
|
||||
echo ""
|
||||
echo "To run the application:"
|
||||
echo " cd dist/LocalTranscription"
|
||||
echo " ./LocalTranscription"
|
||||
echo ""
|
||||
echo "To create a distributable package:"
|
||||
echo " cd dist"
|
||||
echo " tar -czf LocalTranscription-Linux.tar.gz LocalTranscription/"
|
||||
else
|
||||
echo ""
|
||||
echo "✗ Build failed!"
|
||||
exit 1
|
||||
fi
|
||||
0
client/__init__.py
Normal file
0
client/__init__.py
Normal file
246
client/audio_capture.py
Normal file
246
client/audio_capture.py
Normal file
@@ -0,0 +1,246 @@
|
||||
"""Audio capture module for recording microphone or system audio."""
|
||||
|
||||
import numpy as np
|
||||
import sounddevice as sd
|
||||
from scipy import signal
|
||||
from typing import Callable, Optional, List, Tuple
|
||||
from threading import Thread, Event
|
||||
import queue
|
||||
|
||||
|
||||
class AudioCapture:
|
||||
"""Captures audio from input devices and provides chunks for processing."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sample_rate: int = 16000,
|
||||
chunk_duration: float = 3.0,
|
||||
device: Optional[int] = None
|
||||
):
|
||||
"""
|
||||
Initialize audio capture.
|
||||
|
||||
Args:
|
||||
sample_rate: Target audio sample rate in Hz (16000 for Whisper)
|
||||
chunk_duration: Duration of each audio chunk in seconds
|
||||
device: Input device index, or None for default
|
||||
"""
|
||||
self.target_sample_rate = sample_rate
|
||||
self.chunk_duration = chunk_duration
|
||||
self.device = device
|
||||
self.chunk_size = int(sample_rate * chunk_duration)
|
||||
|
||||
# Hardware sample rate (will be auto-detected)
|
||||
self.hardware_sample_rate = None
|
||||
|
||||
self.audio_queue = queue.Queue()
|
||||
self.is_recording = False
|
||||
self.stop_event = Event()
|
||||
self.recording_thread: Optional[Thread] = None
|
||||
|
||||
def _detect_sample_rate(self) -> int:
|
||||
"""
|
||||
Detect a supported sample rate for the audio device.
|
||||
|
||||
Returns:
|
||||
Supported sample rate
|
||||
"""
|
||||
# Try common sample rates in order of preference
|
||||
common_rates = [self.target_sample_rate, 48000, 44100, 22050, 32000, 8000]
|
||||
|
||||
for rate in common_rates:
|
||||
try:
|
||||
# Try to create a test stream
|
||||
with sd.InputStream(
|
||||
device=self.device,
|
||||
channels=1,
|
||||
samplerate=rate,
|
||||
blocksize=1024
|
||||
):
|
||||
print(f"Using hardware sample rate: {rate} Hz")
|
||||
return rate
|
||||
except sd.PortAudioError:
|
||||
continue
|
||||
|
||||
# If nothing works, default to 48000
|
||||
print(f"Warning: Could not detect sample rate, defaulting to 48000 Hz")
|
||||
return 48000
|
||||
|
||||
def _resample(self, audio: np.ndarray, from_rate: int, to_rate: int) -> np.ndarray:
|
||||
"""
|
||||
Resample audio from one sample rate to another.
|
||||
|
||||
Args:
|
||||
audio: Input audio data
|
||||
from_rate: Source sample rate
|
||||
to_rate: Target sample rate
|
||||
|
||||
Returns:
|
||||
Resampled audio
|
||||
"""
|
||||
if from_rate == to_rate:
|
||||
return audio
|
||||
|
||||
# Calculate resampling ratio
|
||||
num_samples = int(len(audio) * to_rate / from_rate)
|
||||
|
||||
# Use scipy's resample for high-quality resampling
|
||||
resampled = signal.resample(audio, num_samples)
|
||||
|
||||
return resampled.astype(np.float32)
|
||||
|
||||
@staticmethod
|
||||
def get_input_devices() -> List[Tuple[int, str]]:
|
||||
"""
|
||||
Get list of available input audio devices.
|
||||
|
||||
Returns:
|
||||
List of (device_index, device_name) tuples
|
||||
"""
|
||||
devices = []
|
||||
device_list = sd.query_devices()
|
||||
|
||||
for i, device in enumerate(device_list):
|
||||
# Only include devices with input channels
|
||||
if device['max_input_channels'] > 0:
|
||||
devices.append((i, device['name']))
|
||||
|
||||
return devices
|
||||
|
||||
@staticmethod
|
||||
def get_default_device() -> Optional[Tuple[int, str]]:
|
||||
"""
|
||||
Get the default input device.
|
||||
|
||||
Returns:
|
||||
(device_index, device_name) tuple or None
|
||||
"""
|
||||
try:
|
||||
default_device = sd.query_devices(kind='input')
|
||||
device_list = sd.query_devices()
|
||||
|
||||
for i, device in enumerate(device_list):
|
||||
if device['name'] == default_device['name']:
|
||||
return (i, device['name'])
|
||||
except:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
def _audio_callback(self, indata, frames, time_info, status):
|
||||
"""Callback function for sounddevice stream."""
|
||||
if status:
|
||||
print(f"Audio status: {status}")
|
||||
|
||||
# Copy audio data to queue
|
||||
audio_data = indata.copy().flatten()
|
||||
self.audio_queue.put(audio_data)
|
||||
|
||||
def start_recording(self, callback: Optional[Callable[[np.ndarray], None]] = None):
|
||||
"""
|
||||
Start recording audio.
|
||||
|
||||
Args:
|
||||
callback: Optional callback function to receive audio chunks
|
||||
"""
|
||||
if self.is_recording:
|
||||
return
|
||||
|
||||
# Detect supported sample rate
|
||||
self.hardware_sample_rate = self._detect_sample_rate()
|
||||
|
||||
self.is_recording = True
|
||||
self.stop_event.clear()
|
||||
|
||||
def record_loop():
|
||||
"""Recording loop that runs in a separate thread."""
|
||||
buffer = np.array([], dtype=np.float32)
|
||||
|
||||
# Calculate hardware chunk size
|
||||
hardware_chunk_size = int(self.hardware_sample_rate * self.chunk_duration)
|
||||
|
||||
try:
|
||||
with sd.InputStream(
|
||||
device=self.device,
|
||||
channels=1,
|
||||
samplerate=self.hardware_sample_rate,
|
||||
callback=self._audio_callback,
|
||||
blocksize=int(self.hardware_sample_rate * 0.1) # 100ms blocks
|
||||
):
|
||||
while not self.stop_event.is_set():
|
||||
try:
|
||||
# Get audio data from queue (with timeout)
|
||||
audio_chunk = self.audio_queue.get(timeout=0.1)
|
||||
buffer = np.concatenate([buffer, audio_chunk])
|
||||
|
||||
# If we have enough data for a full chunk
|
||||
if len(buffer) >= hardware_chunk_size:
|
||||
# Extract chunk
|
||||
chunk = buffer[:hardware_chunk_size]
|
||||
buffer = buffer[hardware_chunk_size:]
|
||||
|
||||
# Resample to target rate if needed
|
||||
if self.hardware_sample_rate != self.target_sample_rate:
|
||||
chunk = self._resample(
|
||||
chunk,
|
||||
self.hardware_sample_rate,
|
||||
self.target_sample_rate
|
||||
)
|
||||
|
||||
# Send to callback if provided
|
||||
if callback:
|
||||
callback(chunk)
|
||||
|
||||
except queue.Empty:
|
||||
continue
|
||||
except Exception as e:
|
||||
print(f"Error in recording loop: {e}")
|
||||
except Exception as e:
|
||||
print(f"Error opening audio stream: {e}")
|
||||
self.is_recording = False
|
||||
|
||||
self.recording_thread = Thread(target=record_loop, daemon=True)
|
||||
self.recording_thread.start()
|
||||
|
||||
def stop_recording(self):
|
||||
"""Stop recording audio."""
|
||||
if not self.is_recording:
|
||||
return
|
||||
|
||||
self.is_recording = False
|
||||
self.stop_event.set()
|
||||
|
||||
if self.recording_thread:
|
||||
self.recording_thread.join(timeout=2.0)
|
||||
self.recording_thread = None
|
||||
|
||||
def get_audio_chunk(self, timeout: float = 1.0) -> Optional[np.ndarray]:
|
||||
"""
|
||||
Get the next audio chunk from the queue.
|
||||
|
||||
Args:
|
||||
timeout: Maximum time to wait for a chunk
|
||||
|
||||
Returns:
|
||||
Audio chunk as numpy array or None if timeout
|
||||
"""
|
||||
try:
|
||||
return self.audio_queue.get(timeout=timeout)
|
||||
except queue.Empty:
|
||||
return None
|
||||
|
||||
def is_recording_active(self) -> bool:
|
||||
"""Check if recording is currently active."""
|
||||
return self.is_recording
|
||||
|
||||
def clear_queue(self):
|
||||
"""Clear any pending audio chunks from the queue."""
|
||||
while not self.audio_queue.empty():
|
||||
try:
|
||||
self.audio_queue.get_nowait()
|
||||
except queue.Empty:
|
||||
break
|
||||
|
||||
def __del__(self):
|
||||
"""Cleanup when object is destroyed."""
|
||||
self.stop_recording()
|
||||
141
client/config.py
Normal file
141
client/config.py
Normal file
@@ -0,0 +1,141 @@
|
||||
"""Configuration management for the local transcription application."""
|
||||
|
||||
import os
|
||||
import yaml
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
|
||||
class Config:
|
||||
"""Manages application configuration with YAML file storage."""
|
||||
|
||||
def __init__(self, config_path: Optional[str] = None):
|
||||
"""
|
||||
Initialize configuration.
|
||||
|
||||
Args:
|
||||
config_path: Path to configuration file. If None, uses default location.
|
||||
"""
|
||||
self.app_dir = Path.home() / ".local-transcription"
|
||||
self.app_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if config_path is None:
|
||||
self.config_path = self.app_dir / "config.yaml"
|
||||
else:
|
||||
self.config_path = Path(config_path)
|
||||
|
||||
self.config: Dict[str, Any] = {}
|
||||
self.load()
|
||||
|
||||
def load(self) -> None:
|
||||
"""Load configuration from file or create default if not exists."""
|
||||
if self.config_path.exists():
|
||||
with open(self.config_path, 'r') as f:
|
||||
self.config = yaml.safe_load(f) or {}
|
||||
else:
|
||||
# Load default configuration
|
||||
default_config_path = Path(__file__).parent.parent / "config" / "default_config.yaml"
|
||||
if default_config_path.exists():
|
||||
with open(default_config_path, 'r') as f:
|
||||
self.config = yaml.safe_load(f) or {}
|
||||
else:
|
||||
self.config = self._get_default_config()
|
||||
|
||||
# Save the default configuration
|
||||
self.save()
|
||||
|
||||
def save(self) -> None:
|
||||
"""Save current configuration to file."""
|
||||
with open(self.config_path, 'w') as f:
|
||||
yaml.dump(self.config, f, default_flow_style=False, indent=2)
|
||||
|
||||
def get(self, key_path: str, default: Any = None) -> Any:
|
||||
"""
|
||||
Get configuration value using dot notation.
|
||||
|
||||
Args:
|
||||
key_path: Dot-separated path to config value (e.g., "audio.sample_rate")
|
||||
default: Default value if key not found
|
||||
|
||||
Returns:
|
||||
Configuration value or default
|
||||
"""
|
||||
keys = key_path.split('.')
|
||||
value = self.config
|
||||
|
||||
for key in keys:
|
||||
if isinstance(value, dict) and key in value:
|
||||
value = value[key]
|
||||
else:
|
||||
return default
|
||||
|
||||
return value
|
||||
|
||||
def set(self, key_path: str, value: Any) -> None:
|
||||
"""
|
||||
Set configuration value using dot notation.
|
||||
|
||||
Args:
|
||||
key_path: Dot-separated path to config value (e.g., "audio.sample_rate")
|
||||
value: Value to set
|
||||
"""
|
||||
keys = key_path.split('.')
|
||||
config = self.config
|
||||
|
||||
# Navigate to the parent dict
|
||||
for key in keys[:-1]:
|
||||
if key not in config:
|
||||
config[key] = {}
|
||||
config = config[key]
|
||||
|
||||
# Set the value
|
||||
config[keys[-1]] = value
|
||||
self.save()
|
||||
|
||||
def _get_default_config(self) -> Dict[str, Any]:
|
||||
"""Get hardcoded default configuration."""
|
||||
return {
|
||||
'user': {
|
||||
'name': 'User',
|
||||
'id': ''
|
||||
},
|
||||
'audio': {
|
||||
'input_device': 'default',
|
||||
'sample_rate': 16000,
|
||||
'chunk_duration': 3.0
|
||||
},
|
||||
'noise_suppression': {
|
||||
'enabled': True,
|
||||
'strength': 0.7,
|
||||
'method': 'noisereduce'
|
||||
},
|
||||
'transcription': {
|
||||
'model': 'base',
|
||||
'device': 'auto',
|
||||
'language': 'en',
|
||||
'task': 'transcribe'
|
||||
},
|
||||
'processing': {
|
||||
'use_vad': True,
|
||||
'min_confidence': 0.5
|
||||
},
|
||||
'server_sync': {
|
||||
'enabled': False,
|
||||
'url': 'ws://localhost:8000',
|
||||
'api_key': ''
|
||||
},
|
||||
'display': {
|
||||
'show_timestamps': True,
|
||||
'max_lines': 100,
|
||||
'font_size': 12,
|
||||
'theme': 'dark'
|
||||
}
|
||||
}
|
||||
|
||||
def reset_to_default(self) -> None:
|
||||
"""Reset configuration to default values."""
|
||||
self.config = self._get_default_config()
|
||||
self.save()
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"Config(path={self.config_path})"
|
||||
128
client/device_utils.py
Normal file
128
client/device_utils.py
Normal file
@@ -0,0 +1,128 @@
|
||||
"""Utilities for detecting and managing compute devices (CPU/GPU)."""
|
||||
|
||||
import torch
|
||||
from typing import List, Tuple
|
||||
|
||||
|
||||
class DeviceManager:
|
||||
"""Manages device detection and selection for transcription."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize device manager and detect available devices."""
|
||||
self.available_devices = self._detect_devices()
|
||||
self.current_device = self.available_devices[0] if self.available_devices else "cpu"
|
||||
|
||||
def _detect_devices(self) -> List[str]:
|
||||
"""
|
||||
Detect available compute devices.
|
||||
|
||||
Returns:
|
||||
List of available device names
|
||||
"""
|
||||
devices = ["cpu"]
|
||||
|
||||
# Check for CUDA (NVIDIA GPU)
|
||||
if torch.cuda.is_available():
|
||||
devices.append("cuda")
|
||||
|
||||
# Check for MPS (Apple Silicon GPU)
|
||||
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
|
||||
devices.append("mps")
|
||||
|
||||
return devices
|
||||
|
||||
def get_device_info(self) -> List[Tuple[str, str]]:
|
||||
"""
|
||||
Get detailed information about available devices.
|
||||
|
||||
Returns:
|
||||
List of (device_name, device_description) tuples
|
||||
"""
|
||||
info = []
|
||||
|
||||
for device in self.available_devices:
|
||||
if device == "cpu":
|
||||
info.append(("cpu", "CPU"))
|
||||
elif device == "cuda":
|
||||
try:
|
||||
gpu_name = torch.cuda.get_device_name(0)
|
||||
info.append(("cuda", f"CUDA GPU: {gpu_name}"))
|
||||
except:
|
||||
info.append(("cuda", "CUDA GPU"))
|
||||
elif device == "mps":
|
||||
info.append(("mps", "Apple Silicon GPU (MPS)"))
|
||||
|
||||
return info
|
||||
|
||||
def set_device(self, device: str) -> bool:
|
||||
"""
|
||||
Set the current device for transcription.
|
||||
|
||||
Args:
|
||||
device: Device name ('cpu', 'cuda', 'mps', or 'auto')
|
||||
|
||||
Returns:
|
||||
True if device was set successfully, False otherwise
|
||||
"""
|
||||
if device == "auto":
|
||||
# Auto-select best available device
|
||||
if "cuda" in self.available_devices:
|
||||
self.current_device = "cuda"
|
||||
elif "mps" in self.available_devices:
|
||||
self.current_device = "mps"
|
||||
else:
|
||||
self.current_device = "cpu"
|
||||
return True
|
||||
|
||||
if device in self.available_devices:
|
||||
self.current_device = device
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def get_device(self) -> str:
|
||||
"""
|
||||
Get the currently selected device.
|
||||
|
||||
Returns:
|
||||
Current device name
|
||||
"""
|
||||
return self.current_device
|
||||
|
||||
def is_gpu_available(self) -> bool:
|
||||
"""
|
||||
Check if any GPU is available.
|
||||
|
||||
Returns:
|
||||
True if CUDA or MPS is available
|
||||
"""
|
||||
return "cuda" in self.available_devices or "mps" in self.available_devices
|
||||
|
||||
def get_device_for_whisper(self) -> str:
|
||||
"""
|
||||
Get device string formatted for faster-whisper.
|
||||
|
||||
Returns:
|
||||
Device string for faster-whisper ('cpu', 'cuda', etc.)
|
||||
"""
|
||||
if self.current_device == "mps":
|
||||
# faster-whisper doesn't support MPS, fall back to CPU
|
||||
return "cpu"
|
||||
return self.current_device
|
||||
|
||||
def get_compute_type(self) -> str:
|
||||
"""
|
||||
Get the appropriate compute type for the current device.
|
||||
|
||||
Returns:
|
||||
Compute type string for faster-whisper
|
||||
"""
|
||||
if self.current_device == "cuda":
|
||||
# Use float16 for GPU for better performance
|
||||
return "float16"
|
||||
else:
|
||||
# Use int8 for CPU for better performance
|
||||
return "int8"
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"DeviceManager(current={self.current_device}, available={self.available_devices})"
|
||||
164
client/noise_suppression.py
Normal file
164
client/noise_suppression.py
Normal file
@@ -0,0 +1,164 @@
|
||||
"""Noise suppression module for reducing background noise in audio."""
|
||||
|
||||
import warnings
|
||||
# Suppress pkg_resources deprecation warning from webrtcvad
|
||||
warnings.filterwarnings("ignore", message=".*pkg_resources.*", category=UserWarning)
|
||||
|
||||
import numpy as np
|
||||
import noisereduce as nr
|
||||
import webrtcvad
|
||||
from typing import Optional
|
||||
|
||||
|
||||
class NoiseSuppressor:
|
||||
"""Handles noise reduction and voice activity detection."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sample_rate: int = 16000,
|
||||
method: str = "noisereduce",
|
||||
strength: float = 0.7,
|
||||
use_vad: bool = True
|
||||
):
|
||||
"""
|
||||
Initialize noise suppressor.
|
||||
|
||||
Args:
|
||||
sample_rate: Audio sample rate in Hz
|
||||
method: Noise reduction method ('noisereduce' or 'none')
|
||||
strength: Noise reduction strength (0.0 to 1.0)
|
||||
use_vad: Whether to use Voice Activity Detection
|
||||
"""
|
||||
self.sample_rate = sample_rate
|
||||
self.method = method
|
||||
self.strength = max(0.0, min(1.0, strength)) # Clamp to [0, 1]
|
||||
self.use_vad = use_vad
|
||||
|
||||
# Initialize VAD if requested
|
||||
self.vad = None
|
||||
if use_vad:
|
||||
try:
|
||||
# WebRTC VAD supports 16kHz, 32kHz, and 48kHz
|
||||
if sample_rate in [8000, 16000, 32000, 48000]:
|
||||
self.vad = webrtcvad.Vad(2) # Aggressiveness: 0-3 (2 is balanced)
|
||||
else:
|
||||
print(f"Warning: VAD not supported for sample rate {sample_rate}Hz")
|
||||
self.use_vad = False
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to initialize VAD: {e}")
|
||||
self.use_vad = False
|
||||
|
||||
# Store noise profile for adaptive reduction
|
||||
self.noise_profile: Optional[np.ndarray] = None
|
||||
|
||||
def reduce_noise(self, audio: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Apply noise reduction to audio.
|
||||
|
||||
Args:
|
||||
audio: Audio data as numpy array (float32, range [-1, 1])
|
||||
|
||||
Returns:
|
||||
Noise-reduced audio
|
||||
"""
|
||||
if self.method == "none" or self.strength == 0.0:
|
||||
return audio
|
||||
|
||||
try:
|
||||
# Ensure audio is float32
|
||||
audio = audio.astype(np.float32)
|
||||
|
||||
if self.method == "noisereduce":
|
||||
# Apply noisereduce noise reduction
|
||||
reduced = nr.reduce_noise(
|
||||
y=audio,
|
||||
sr=self.sample_rate,
|
||||
prop_decrease=self.strength,
|
||||
stationary=True
|
||||
)
|
||||
return reduced.astype(np.float32)
|
||||
else:
|
||||
return audio
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in noise reduction: {e}")
|
||||
return audio
|
||||
|
||||
def is_speech(self, audio: np.ndarray) -> bool:
|
||||
"""
|
||||
Detect if audio contains speech using VAD.
|
||||
|
||||
Args:
|
||||
audio: Audio data as numpy array (float32, range [-1, 1])
|
||||
|
||||
Returns:
|
||||
True if speech is detected, False otherwise
|
||||
"""
|
||||
if not self.use_vad or self.vad is None:
|
||||
return True # Assume speech if VAD not available
|
||||
|
||||
try:
|
||||
# Convert float32 audio to int16 for VAD
|
||||
audio_int16 = (audio * 32767).astype(np.int16)
|
||||
|
||||
# VAD requires specific frame sizes (10, 20, or 30 ms)
|
||||
frame_duration_ms = 30
|
||||
frame_size = int(self.sample_rate * frame_duration_ms / 1000)
|
||||
|
||||
# Process audio in frames
|
||||
num_frames = len(audio_int16) // frame_size
|
||||
speech_frames = 0
|
||||
|
||||
for i in range(num_frames):
|
||||
frame = audio_int16[i * frame_size:(i + 1) * frame_size]
|
||||
if self.vad.is_speech(frame.tobytes(), self.sample_rate):
|
||||
speech_frames += 1
|
||||
|
||||
# Consider it speech if more than 30% of frames contain speech
|
||||
return speech_frames > (num_frames * 0.3)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in VAD: {e}")
|
||||
return True # Assume speech on error
|
||||
|
||||
def process(self, audio: np.ndarray, skip_silent: bool = True) -> Optional[np.ndarray]:
|
||||
"""
|
||||
Process audio with noise reduction and optional VAD filtering.
|
||||
|
||||
Args:
|
||||
audio: Audio data as numpy array
|
||||
skip_silent: If True, return None for non-speech audio
|
||||
|
||||
Returns:
|
||||
Processed audio or None if silent (when skip_silent=True)
|
||||
"""
|
||||
# Check for speech first (before noise reduction)
|
||||
if skip_silent and self.use_vad:
|
||||
if not self.is_speech(audio):
|
||||
return None
|
||||
|
||||
# Apply noise reduction
|
||||
processed_audio = self.reduce_noise(audio)
|
||||
|
||||
return processed_audio
|
||||
|
||||
def set_strength(self, strength: float):
|
||||
"""
|
||||
Update noise reduction strength.
|
||||
|
||||
Args:
|
||||
strength: New strength value (0.0 to 1.0)
|
||||
"""
|
||||
self.strength = max(0.0, min(1.0, strength))
|
||||
|
||||
def set_vad_enabled(self, enabled: bool):
|
||||
"""
|
||||
Enable or disable Voice Activity Detection.
|
||||
|
||||
Args:
|
||||
enabled: True to enable VAD, False to disable
|
||||
"""
|
||||
self.use_vad = enabled and self.vad is not None
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"NoiseSuppressor(method={self.method}, strength={self.strength}, vad={self.use_vad})"
|
||||
232
client/transcription_engine.py
Normal file
232
client/transcription_engine.py
Normal file
@@ -0,0 +1,232 @@
|
||||
"""Transcription engine using faster-whisper for speech-to-text."""
|
||||
|
||||
import numpy as np
|
||||
from faster_whisper import WhisperModel
|
||||
from typing import Optional, List, Tuple
|
||||
from datetime import datetime
|
||||
import threading
|
||||
|
||||
|
||||
class TranscriptionResult:
|
||||
"""Represents a transcription result."""
|
||||
|
||||
def __init__(self, text: str, confidence: float, timestamp: datetime, user_name: str = ""):
|
||||
"""
|
||||
Initialize transcription result.
|
||||
|
||||
Args:
|
||||
text: Transcribed text
|
||||
confidence: Confidence score (0.0 to 1.0)
|
||||
timestamp: Timestamp of transcription
|
||||
user_name: Name of the user/speaker
|
||||
"""
|
||||
self.text = text.strip()
|
||||
self.confidence = confidence
|
||||
self.timestamp = timestamp
|
||||
self.user_name = user_name
|
||||
|
||||
def __repr__(self) -> str:
|
||||
time_str = self.timestamp.strftime("%H:%M:%S")
|
||||
if self.user_name:
|
||||
return f"[{time_str}] {self.user_name}: {self.text}"
|
||||
return f"[{time_str}] {self.text}"
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""Convert to dictionary."""
|
||||
return {
|
||||
'text': self.text,
|
||||
'confidence': self.confidence,
|
||||
'timestamp': self.timestamp.isoformat(),
|
||||
'user_name': self.user_name
|
||||
}
|
||||
|
||||
|
||||
class TranscriptionEngine:
|
||||
"""Handles speech-to-text transcription using faster-whisper."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_size: str = "base",
|
||||
device: str = "cpu",
|
||||
compute_type: str = "int8",
|
||||
language: str = "en",
|
||||
min_confidence: float = 0.5
|
||||
):
|
||||
"""
|
||||
Initialize transcription engine.
|
||||
|
||||
Args:
|
||||
model_size: Whisper model size ('tiny', 'base', 'small', 'medium', 'large')
|
||||
device: Device to use ('cpu', 'cuda', 'auto')
|
||||
compute_type: Compute type ('int8', 'float16', 'float32')
|
||||
language: Language code for transcription
|
||||
min_confidence: Minimum confidence threshold for transcriptions
|
||||
"""
|
||||
self.model_size = model_size
|
||||
self.device = device
|
||||
self.compute_type = compute_type
|
||||
self.language = language
|
||||
self.min_confidence = min_confidence
|
||||
self.model: Optional[WhisperModel] = None
|
||||
self.model_lock = threading.Lock()
|
||||
self.is_loaded = False
|
||||
|
||||
def load_model(self) -> bool:
|
||||
"""
|
||||
Load the Whisper model.
|
||||
|
||||
Returns:
|
||||
True if model loaded successfully, False otherwise
|
||||
"""
|
||||
try:
|
||||
print(f"Loading Whisper {self.model_size} model on {self.device}...")
|
||||
|
||||
with self.model_lock:
|
||||
self.model = WhisperModel(
|
||||
self.model_size,
|
||||
device=self.device,
|
||||
compute_type=self.compute_type
|
||||
)
|
||||
self.is_loaded = True
|
||||
|
||||
print(f"Model loaded successfully!")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error loading model: {e}")
|
||||
self.is_loaded = False
|
||||
return False
|
||||
|
||||
def transcribe(
|
||||
self,
|
||||
audio: np.ndarray,
|
||||
sample_rate: int = 16000,
|
||||
user_name: str = ""
|
||||
) -> Optional[TranscriptionResult]:
|
||||
"""
|
||||
Transcribe audio to text.
|
||||
|
||||
Args:
|
||||
audio: Audio data as numpy array (float32)
|
||||
sample_rate: Audio sample rate in Hz
|
||||
user_name: Name of the user/speaker
|
||||
|
||||
Returns:
|
||||
TranscriptionResult or None if transcription failed or confidence too low
|
||||
"""
|
||||
if not self.is_loaded or self.model is None:
|
||||
print("Model not loaded")
|
||||
return None
|
||||
|
||||
try:
|
||||
# Ensure audio is float32
|
||||
audio = audio.astype(np.float32)
|
||||
|
||||
# Transcribe using faster-whisper
|
||||
with self.model_lock:
|
||||
segments, info = self.model.transcribe(
|
||||
audio,
|
||||
language=self.language if self.language != "auto" else None,
|
||||
vad_filter=True, # Use built-in VAD
|
||||
vad_parameters=dict(
|
||||
min_silence_duration_ms=500
|
||||
)
|
||||
)
|
||||
|
||||
# Collect all segments
|
||||
full_text = ""
|
||||
total_confidence = 0.0
|
||||
segment_count = 0
|
||||
|
||||
for segment in segments:
|
||||
full_text += segment.text + " "
|
||||
total_confidence += segment.avg_logprob
|
||||
segment_count += 1
|
||||
|
||||
# Calculate average confidence
|
||||
if segment_count == 0:
|
||||
return None
|
||||
|
||||
# Convert log probability to approximate confidence (0-1 range)
|
||||
# avg_logprob is typically in range [-1, 0], so we transform it
|
||||
avg_confidence = np.exp(total_confidence / segment_count)
|
||||
|
||||
# Filter by minimum confidence
|
||||
if avg_confidence < self.min_confidence:
|
||||
return None
|
||||
|
||||
# Clean up text
|
||||
text = full_text.strip()
|
||||
|
||||
if not text:
|
||||
return None
|
||||
|
||||
# Create result
|
||||
result = TranscriptionResult(
|
||||
text=text,
|
||||
confidence=avg_confidence,
|
||||
timestamp=datetime.now(),
|
||||
user_name=user_name
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during transcription: {e}")
|
||||
return None
|
||||
|
||||
def change_model(self, model_size: str) -> bool:
|
||||
"""
|
||||
Change to a different model size.
|
||||
|
||||
Args:
|
||||
model_size: New model size
|
||||
|
||||
Returns:
|
||||
True if model changed successfully
|
||||
"""
|
||||
self.model_size = model_size
|
||||
self.is_loaded = False
|
||||
self.model = None
|
||||
return self.load_model()
|
||||
|
||||
def change_device(self, device: str, compute_type: Optional[str] = None) -> bool:
|
||||
"""
|
||||
Change compute device.
|
||||
|
||||
Args:
|
||||
device: New device ('cpu', 'cuda', etc.)
|
||||
compute_type: Optional new compute type
|
||||
|
||||
Returns:
|
||||
True if device changed successfully
|
||||
"""
|
||||
self.device = device
|
||||
if compute_type:
|
||||
self.compute_type = compute_type
|
||||
|
||||
self.is_loaded = False
|
||||
self.model = None
|
||||
return self.load_model()
|
||||
|
||||
def change_language(self, language: str):
|
||||
"""
|
||||
Change transcription language.
|
||||
|
||||
Args:
|
||||
language: Language code or 'auto'
|
||||
"""
|
||||
self.language = language
|
||||
|
||||
def unload_model(self):
|
||||
"""Unload the model from memory."""
|
||||
with self.model_lock:
|
||||
self.model = None
|
||||
self.is_loaded = False
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"TranscriptionEngine(model={self.model_size}, device={self.device}, loaded={self.is_loaded})"
|
||||
|
||||
def __del__(self):
|
||||
"""Cleanup when object is destroyed."""
|
||||
self.unload_model()
|
||||
40
config/default_config.yaml
Normal file
40
config/default_config.yaml
Normal file
@@ -0,0 +1,40 @@
|
||||
user:
|
||||
name: "User"
|
||||
id: ""
|
||||
|
||||
audio:
|
||||
input_device: "default"
|
||||
sample_rate: 16000
|
||||
chunk_duration: 3.0
|
||||
|
||||
noise_suppression:
|
||||
enabled: true
|
||||
strength: 0.7
|
||||
method: "noisereduce"
|
||||
|
||||
transcription:
|
||||
model: "base"
|
||||
device: "auto"
|
||||
language: "en"
|
||||
task: "transcribe"
|
||||
|
||||
processing:
|
||||
use_vad: true
|
||||
min_confidence: 0.5
|
||||
|
||||
server_sync:
|
||||
enabled: false
|
||||
url: "ws://localhost:8000"
|
||||
api_key: ""
|
||||
|
||||
display:
|
||||
show_timestamps: true
|
||||
max_lines: 100
|
||||
font_family: "Courier"
|
||||
font_size: 12
|
||||
theme: "dark"
|
||||
fade_after_seconds: 10 # Time before transcriptions fade out (0 = never fade)
|
||||
|
||||
web_server:
|
||||
port: 8080
|
||||
host: "127.0.0.1"
|
||||
0
gui/__init__.py
Normal file
0
gui/__init__.py
Normal file
364
gui/main_window.py
Normal file
364
gui/main_window.py
Normal file
@@ -0,0 +1,364 @@
|
||||
"""Main application window for the local transcription app."""
|
||||
|
||||
import customtkinter as ctk
|
||||
from tkinter import filedialog, messagebox
|
||||
import threading
|
||||
from pathlib import Path
|
||||
import sys
|
||||
|
||||
# Add parent directory to path for imports
|
||||
sys.path.append(str(Path(__file__).parent.parent))
|
||||
|
||||
from client.config import Config
|
||||
from client.device_utils import DeviceManager
|
||||
from client.audio_capture import AudioCapture
|
||||
from client.noise_suppression import NoiseSuppressor
|
||||
from client.transcription_engine import TranscriptionEngine
|
||||
from gui.transcription_display import TranscriptionDisplay
|
||||
from gui.settings_dialog import SettingsDialog
|
||||
|
||||
|
||||
class MainWindow(ctk.CTk):
|
||||
"""Main application window."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the main window."""
|
||||
super().__init__()
|
||||
|
||||
# Application state
|
||||
self.is_transcribing = False
|
||||
self.config = Config()
|
||||
self.device_manager = DeviceManager()
|
||||
|
||||
# Components (initialized later)
|
||||
self.audio_capture: AudioCapture = None
|
||||
self.noise_suppressor: NoiseSuppressor = None
|
||||
self.transcription_engine: TranscriptionEngine = None
|
||||
|
||||
# Configure window
|
||||
self.title("Local Transcription")
|
||||
self.geometry("900x700")
|
||||
|
||||
# Set theme
|
||||
ctk.set_appearance_mode(self.config.get('display.theme', 'dark'))
|
||||
ctk.set_default_color_theme("blue")
|
||||
|
||||
# Create UI
|
||||
self._create_widgets()
|
||||
|
||||
# Handle window close
|
||||
self.protocol("WM_DELETE_WINDOW", self._on_closing)
|
||||
|
||||
# Initialize components after GUI is ready (delay to avoid XCB threading issues)
|
||||
self.after(100, self._initialize_components)
|
||||
|
||||
def _create_widgets(self):
|
||||
"""Create all UI widgets."""
|
||||
# Header frame
|
||||
header_frame = ctk.CTkFrame(self, height=80)
|
||||
header_frame.pack(fill="x", padx=10, pady=(10, 0))
|
||||
header_frame.pack_propagate(False)
|
||||
|
||||
# Title
|
||||
title_label = ctk.CTkLabel(
|
||||
header_frame,
|
||||
text="Local Transcription",
|
||||
font=("", 24, "bold")
|
||||
)
|
||||
title_label.pack(side="left", padx=20, pady=20)
|
||||
|
||||
# Settings button
|
||||
self.settings_button = ctk.CTkButton(
|
||||
header_frame,
|
||||
text="⚙ Settings",
|
||||
command=self._open_settings,
|
||||
width=120
|
||||
)
|
||||
self.settings_button.pack(side="right", padx=20, pady=20)
|
||||
|
||||
# Status frame
|
||||
status_frame = ctk.CTkFrame(self, height=60)
|
||||
status_frame.pack(fill="x", padx=10, pady=(10, 0))
|
||||
status_frame.pack_propagate(False)
|
||||
|
||||
# Status label
|
||||
self.status_label = ctk.CTkLabel(
|
||||
status_frame,
|
||||
text="⚫ Ready",
|
||||
font=("", 14)
|
||||
)
|
||||
self.status_label.pack(side="left", padx=20)
|
||||
|
||||
# Device info
|
||||
device_info = self.device_manager.get_device_info()
|
||||
device_text = device_info[0][1] if device_info else "No device"
|
||||
self.device_label = ctk.CTkLabel(
|
||||
status_frame,
|
||||
text=f"Device: {device_text}",
|
||||
font=("", 12)
|
||||
)
|
||||
self.device_label.pack(side="left", padx=20)
|
||||
|
||||
# User name display
|
||||
user_name = self.config.get('user.name', 'User')
|
||||
self.user_label = ctk.CTkLabel(
|
||||
status_frame,
|
||||
text=f"User: {user_name}",
|
||||
font=("", 12)
|
||||
)
|
||||
self.user_label.pack(side="left", padx=20)
|
||||
|
||||
# Transcription display frame
|
||||
display_frame = ctk.CTkFrame(self)
|
||||
display_frame.pack(fill="both", expand=True, padx=10, pady=10)
|
||||
|
||||
# Transcription display
|
||||
self.transcription_display = TranscriptionDisplay(
|
||||
display_frame,
|
||||
max_lines=self.config.get('display.max_lines', 100),
|
||||
show_timestamps=self.config.get('display.show_timestamps', True),
|
||||
font=("Courier", self.config.get('display.font_size', 12))
|
||||
)
|
||||
self.transcription_display.pack(fill="both", expand=True, padx=10, pady=10)
|
||||
|
||||
# Control frame
|
||||
control_frame = ctk.CTkFrame(self, height=80)
|
||||
control_frame.pack(fill="x", padx=10, pady=(0, 10))
|
||||
control_frame.pack_propagate(False)
|
||||
|
||||
# Start/Stop button
|
||||
self.start_button = ctk.CTkButton(
|
||||
control_frame,
|
||||
text="▶ Start Transcription",
|
||||
command=self._toggle_transcription,
|
||||
width=200,
|
||||
height=50,
|
||||
font=("", 16, "bold"),
|
||||
fg_color="green"
|
||||
)
|
||||
self.start_button.pack(side="left", padx=20, pady=15)
|
||||
|
||||
# Clear button
|
||||
self.clear_button = ctk.CTkButton(
|
||||
control_frame,
|
||||
text="Clear",
|
||||
command=self._clear_transcriptions,
|
||||
width=120,
|
||||
height=50
|
||||
)
|
||||
self.clear_button.pack(side="left", padx=10, pady=15)
|
||||
|
||||
# Save button
|
||||
self.save_button = ctk.CTkButton(
|
||||
control_frame,
|
||||
text="💾 Save",
|
||||
command=self._save_transcriptions,
|
||||
width=120,
|
||||
height=50
|
||||
)
|
||||
self.save_button.pack(side="left", padx=10, pady=15)
|
||||
|
||||
def _initialize_components(self):
|
||||
"""Initialize audio, noise suppression, and transcription components."""
|
||||
# Update status
|
||||
self.status_label.configure(text="⚙ Initializing...")
|
||||
self.update()
|
||||
|
||||
try:
|
||||
# Set device based on config
|
||||
device_config = self.config.get('transcription.device', 'auto')
|
||||
self.device_manager.set_device(device_config)
|
||||
|
||||
# Initialize transcription engine
|
||||
model_size = self.config.get('transcription.model', 'base')
|
||||
language = self.config.get('transcription.language', 'en')
|
||||
device = self.device_manager.get_device_for_whisper()
|
||||
compute_type = self.device_manager.get_compute_type()
|
||||
|
||||
self.transcription_engine = TranscriptionEngine(
|
||||
model_size=model_size,
|
||||
device=device,
|
||||
compute_type=compute_type,
|
||||
language=language,
|
||||
min_confidence=self.config.get('processing.min_confidence', 0.5)
|
||||
)
|
||||
|
||||
# Load model (synchronously to avoid X11 threading issues)
|
||||
success = self.transcription_engine.load_model()
|
||||
|
||||
if success:
|
||||
self.status_label.configure(text="✓ Ready")
|
||||
else:
|
||||
self.status_label.configure(text="❌ Model loading failed")
|
||||
messagebox.showerror("Error", "Failed to load transcription model")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error initializing components: {e}")
|
||||
self.status_label.configure(text="❌ Initialization failed")
|
||||
messagebox.showerror("Error", f"Failed to initialize:\n{e}")
|
||||
|
||||
def _update_status(self, status: str):
|
||||
"""Update status label (thread-safe)."""
|
||||
self.after(0, lambda: self.status_label.configure(text=status))
|
||||
|
||||
def _toggle_transcription(self):
|
||||
"""Start or stop transcription."""
|
||||
if not self.is_transcribing:
|
||||
self._start_transcription()
|
||||
else:
|
||||
self._stop_transcription()
|
||||
|
||||
def _start_transcription(self):
|
||||
"""Start transcription."""
|
||||
try:
|
||||
# Check if engine is ready
|
||||
if not self.transcription_engine or not self.transcription_engine.is_loaded:
|
||||
messagebox.showerror("Error", "Transcription engine not ready")
|
||||
return
|
||||
|
||||
# Get audio device
|
||||
audio_device_str = self.config.get('audio.input_device', 'default')
|
||||
audio_device = None if audio_device_str == 'default' else int(audio_device_str)
|
||||
|
||||
# Initialize audio capture
|
||||
self.audio_capture = AudioCapture(
|
||||
sample_rate=self.config.get('audio.sample_rate', 16000),
|
||||
chunk_duration=self.config.get('audio.chunk_duration', 3.0),
|
||||
device=audio_device
|
||||
)
|
||||
|
||||
# Initialize noise suppressor
|
||||
self.noise_suppressor = NoiseSuppressor(
|
||||
sample_rate=self.config.get('audio.sample_rate', 16000),
|
||||
method="noisereduce" if self.config.get('noise_suppression.enabled', True) else "none",
|
||||
strength=self.config.get('noise_suppression.strength', 0.7),
|
||||
use_vad=self.config.get('processing.use_vad', True)
|
||||
)
|
||||
|
||||
# Start recording
|
||||
self.audio_capture.start_recording(callback=self._process_audio_chunk)
|
||||
|
||||
# Update UI
|
||||
self.is_transcribing = True
|
||||
self.start_button.configure(text="⏸ Stop Transcription", fg_color="red")
|
||||
self.status_label.configure(text="🔴 Recording...")
|
||||
|
||||
except Exception as e:
|
||||
messagebox.showerror("Error", f"Failed to start transcription:\n{e}")
|
||||
print(f"Error starting transcription: {e}")
|
||||
|
||||
def _stop_transcription(self):
|
||||
"""Stop transcription."""
|
||||
try:
|
||||
# Stop recording
|
||||
if self.audio_capture:
|
||||
self.audio_capture.stop_recording()
|
||||
|
||||
# Update UI
|
||||
self.is_transcribing = False
|
||||
self.start_button.configure(text="▶ Start Transcription", fg_color="green")
|
||||
self.status_label.configure(text="✓ Ready")
|
||||
|
||||
except Exception as e:
|
||||
messagebox.showerror("Error", f"Failed to stop transcription:\n{e}")
|
||||
print(f"Error stopping transcription: {e}")
|
||||
|
||||
def _process_audio_chunk(self, audio_chunk):
|
||||
"""Process an audio chunk (noise suppression + transcription)."""
|
||||
def process():
|
||||
try:
|
||||
# Apply noise suppression
|
||||
processed_audio = self.noise_suppressor.process(audio_chunk, skip_silent=True)
|
||||
|
||||
# Skip if silent (VAD filtered it out)
|
||||
if processed_audio is None:
|
||||
return
|
||||
|
||||
# Transcribe
|
||||
user_name = self.config.get('user.name', 'User')
|
||||
result = self.transcription_engine.transcribe(
|
||||
processed_audio,
|
||||
sample_rate=self.config.get('audio.sample_rate', 16000),
|
||||
user_name=user_name
|
||||
)
|
||||
|
||||
# Display result
|
||||
if result:
|
||||
self.after(0, lambda: self.transcription_display.add_transcription(
|
||||
text=result.text,
|
||||
user_name=result.user_name,
|
||||
timestamp=result.timestamp
|
||||
))
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing audio: {e}")
|
||||
|
||||
# Run in background thread
|
||||
threading.Thread(target=process, daemon=True).start()
|
||||
|
||||
def _clear_transcriptions(self):
|
||||
"""Clear all transcriptions."""
|
||||
if messagebox.askyesno("Clear Transcriptions", "Are you sure you want to clear all transcriptions?"):
|
||||
self.transcription_display.clear()
|
||||
|
||||
def _save_transcriptions(self):
|
||||
"""Save transcriptions to file."""
|
||||
filepath = filedialog.asksaveasfilename(
|
||||
defaultextension=".txt",
|
||||
filetypes=[("Text files", "*.txt"), ("All files", "*.*")]
|
||||
)
|
||||
|
||||
if filepath:
|
||||
if self.transcription_display.save_to_file(filepath):
|
||||
messagebox.showinfo("Saved", f"Transcriptions saved to:\n{filepath}")
|
||||
else:
|
||||
messagebox.showerror("Error", "Failed to save transcriptions")
|
||||
|
||||
def _open_settings(self):
|
||||
"""Open settings dialog."""
|
||||
# Get audio devices
|
||||
audio_devices = AudioCapture.get_input_devices()
|
||||
if not audio_devices:
|
||||
audio_devices = [(0, "Default")]
|
||||
|
||||
# Get compute devices
|
||||
compute_devices = self.device_manager.get_device_info()
|
||||
compute_devices.insert(0, ("auto", "Auto-detect"))
|
||||
|
||||
# Open settings dialog
|
||||
SettingsDialog(
|
||||
self,
|
||||
self.config,
|
||||
audio_devices,
|
||||
compute_devices,
|
||||
on_save=self._on_settings_saved
|
||||
)
|
||||
|
||||
def _on_settings_saved(self):
|
||||
"""Handle settings being saved."""
|
||||
# Update user label
|
||||
user_name = self.config.get('user.name', 'User')
|
||||
self.user_label.configure(text=f"User: {user_name}")
|
||||
|
||||
# Update display settings
|
||||
self.transcription_display.set_max_lines(self.config.get('display.max_lines', 100))
|
||||
self.transcription_display.set_show_timestamps(self.config.get('display.show_timestamps', True))
|
||||
|
||||
# Note: Model/device changes require restart
|
||||
messagebox.showinfo(
|
||||
"Settings Saved",
|
||||
"Some settings (model size, device) require restarting the application to take effect."
|
||||
)
|
||||
|
||||
def _on_closing(self):
|
||||
"""Handle window closing."""
|
||||
# Stop transcription if running
|
||||
if self.is_transcribing:
|
||||
self._stop_transcription()
|
||||
|
||||
# Unload model
|
||||
if self.transcription_engine:
|
||||
self.transcription_engine.unload_model()
|
||||
|
||||
# Close window
|
||||
self.destroy()
|
||||
524
gui/main_window_qt.py
Normal file
524
gui/main_window_qt.py
Normal file
@@ -0,0 +1,524 @@
|
||||
"""PySide6 main application window for the local transcription app."""
|
||||
|
||||
from PySide6.QtWidgets import (
|
||||
QMainWindow, QWidget, QVBoxLayout, QHBoxLayout,
|
||||
QPushButton, QLabel, QFileDialog, QMessageBox
|
||||
)
|
||||
from PySide6.QtCore import Qt, QThread, Signal
|
||||
from PySide6.QtGui import QFont
|
||||
from pathlib import Path
|
||||
import sys
|
||||
|
||||
# Add parent directory to path for imports
|
||||
sys.path.append(str(Path(__file__).parent.parent))
|
||||
|
||||
from client.config import Config
|
||||
from client.device_utils import DeviceManager
|
||||
from client.audio_capture import AudioCapture
|
||||
from client.noise_suppression import NoiseSuppressor
|
||||
from client.transcription_engine import TranscriptionEngine
|
||||
from gui.transcription_display_qt import TranscriptionDisplay
|
||||
from gui.settings_dialog_qt import SettingsDialog
|
||||
from server.web_display import TranscriptionWebServer
|
||||
import asyncio
|
||||
from threading import Thread
|
||||
|
||||
|
||||
class WebServerThread(Thread):
|
||||
"""Thread for running the web server."""
|
||||
|
||||
def __init__(self, web_server):
|
||||
super().__init__(daemon=True)
|
||||
self.web_server = web_server
|
||||
self.loop = None
|
||||
|
||||
def run(self):
|
||||
"""Run the web server in async event loop."""
|
||||
self.loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(self.loop)
|
||||
self.loop.run_until_complete(self.web_server.start())
|
||||
|
||||
|
||||
class ModelLoaderThread(QThread):
|
||||
"""Thread for loading the Whisper model without blocking the GUI."""
|
||||
|
||||
finished = Signal(bool, str) # success, message
|
||||
|
||||
def __init__(self, transcription_engine):
|
||||
super().__init__()
|
||||
self.transcription_engine = transcription_engine
|
||||
|
||||
def run(self):
|
||||
"""Load the model in background thread."""
|
||||
try:
|
||||
success = self.transcription_engine.load_model()
|
||||
if success:
|
||||
self.finished.emit(True, "Model loaded successfully")
|
||||
else:
|
||||
self.finished.emit(False, "Failed to load model")
|
||||
except Exception as e:
|
||||
self.finished.emit(False, f"Error loading model: {e}")
|
||||
|
||||
|
||||
class MainWindow(QMainWindow):
|
||||
"""Main application window using PySide6."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the main window."""
|
||||
super().__init__()
|
||||
|
||||
# Application state
|
||||
self.is_transcribing = False
|
||||
self.config = Config()
|
||||
self.device_manager = DeviceManager()
|
||||
|
||||
# Components (initialized later)
|
||||
self.audio_capture: AudioCapture = None
|
||||
self.noise_suppressor: NoiseSuppressor = None
|
||||
self.transcription_engine: TranscriptionEngine = None
|
||||
self.model_loader_thread: ModelLoaderThread = None
|
||||
|
||||
# Track current model settings
|
||||
self.current_model_size: str = None
|
||||
self.current_device_config: str = None
|
||||
|
||||
# Web server components
|
||||
self.web_server: TranscriptionWebServer = None
|
||||
self.web_server_thread: WebServerThread = None
|
||||
|
||||
# Configure window
|
||||
self.setWindowTitle("Local Transcription")
|
||||
self.resize(900, 700)
|
||||
|
||||
# Create UI
|
||||
self._create_widgets()
|
||||
|
||||
# Initialize components (in background)
|
||||
self._initialize_components()
|
||||
|
||||
# Start web server if enabled
|
||||
self._start_web_server_if_enabled()
|
||||
|
||||
def _create_widgets(self):
|
||||
"""Create all UI widgets."""
|
||||
# Central widget
|
||||
central_widget = QWidget()
|
||||
self.setCentralWidget(central_widget)
|
||||
|
||||
main_layout = QVBoxLayout()
|
||||
central_widget.setLayout(main_layout)
|
||||
|
||||
# Header
|
||||
header_widget = QWidget()
|
||||
header_widget.setFixedHeight(80)
|
||||
header_layout = QHBoxLayout()
|
||||
header_widget.setLayout(header_layout)
|
||||
|
||||
title_label = QLabel("Local Transcription")
|
||||
title_font = QFont()
|
||||
title_font.setPointSize(24)
|
||||
title_font.setBold(True)
|
||||
title_label.setFont(title_font)
|
||||
header_layout.addWidget(title_label)
|
||||
|
||||
header_layout.addStretch()
|
||||
|
||||
self.settings_button = QPushButton("⚙ Settings")
|
||||
self.settings_button.setFixedSize(120, 40)
|
||||
self.settings_button.clicked.connect(self._open_settings)
|
||||
header_layout.addWidget(self.settings_button)
|
||||
|
||||
main_layout.addWidget(header_widget)
|
||||
|
||||
# Status bar
|
||||
status_widget = QWidget()
|
||||
status_widget.setFixedHeight(60)
|
||||
status_layout = QHBoxLayout()
|
||||
status_widget.setLayout(status_layout)
|
||||
|
||||
self.status_label = QLabel("⚫ Initializing...")
|
||||
status_font = QFont()
|
||||
status_font.setPointSize(14)
|
||||
self.status_label.setFont(status_font)
|
||||
status_layout.addWidget(self.status_label)
|
||||
|
||||
device_info = self.device_manager.get_device_info()
|
||||
device_text = device_info[0][1] if device_info else "No device"
|
||||
self.device_label = QLabel(f"Device: {device_text}")
|
||||
status_layout.addWidget(self.device_label)
|
||||
|
||||
user_name = self.config.get('user.name', 'User')
|
||||
self.user_label = QLabel(f"User: {user_name}")
|
||||
status_layout.addWidget(self.user_label)
|
||||
|
||||
status_layout.addStretch()
|
||||
|
||||
main_layout.addWidget(status_widget)
|
||||
|
||||
# Transcription display
|
||||
self.transcription_display = TranscriptionDisplay(
|
||||
max_lines=self.config.get('display.max_lines', 100),
|
||||
show_timestamps=self.config.get('display.show_timestamps', True),
|
||||
font_family=self.config.get('display.font_family', 'Courier'),
|
||||
font_size=self.config.get('display.font_size', 12)
|
||||
)
|
||||
main_layout.addWidget(self.transcription_display)
|
||||
|
||||
# Control buttons
|
||||
control_widget = QWidget()
|
||||
control_widget.setFixedHeight(80)
|
||||
control_layout = QHBoxLayout()
|
||||
control_widget.setLayout(control_layout)
|
||||
|
||||
self.start_button = QPushButton("▶ Start Transcription")
|
||||
self.start_button.setFixedSize(240, 50)
|
||||
button_font = QFont()
|
||||
button_font.setPointSize(14)
|
||||
button_font.setBold(True)
|
||||
self.start_button.setFont(button_font)
|
||||
self.start_button.clicked.connect(self._toggle_transcription)
|
||||
self.start_button.setStyleSheet("background-color: #2ecc71; color: white;")
|
||||
control_layout.addWidget(self.start_button)
|
||||
|
||||
self.clear_button = QPushButton("Clear")
|
||||
self.clear_button.setFixedSize(120, 50)
|
||||
self.clear_button.clicked.connect(self._clear_transcriptions)
|
||||
control_layout.addWidget(self.clear_button)
|
||||
|
||||
self.save_button = QPushButton("💾 Save")
|
||||
self.save_button.setFixedSize(120, 50)
|
||||
self.save_button.clicked.connect(self._save_transcriptions)
|
||||
control_layout.addWidget(self.save_button)
|
||||
|
||||
control_layout.addStretch()
|
||||
|
||||
main_layout.addWidget(control_widget)
|
||||
|
||||
def _initialize_components(self):
|
||||
"""Initialize audio, noise suppression, and transcription components."""
|
||||
# Update status
|
||||
self.status_label.setText("⚙ Initializing...")
|
||||
|
||||
# Set device based on config
|
||||
device_config = self.config.get('transcription.device', 'auto')
|
||||
self.device_manager.set_device(device_config)
|
||||
|
||||
# Initialize transcription engine
|
||||
model_size = self.config.get('transcription.model', 'base')
|
||||
language = self.config.get('transcription.language', 'en')
|
||||
device = self.device_manager.get_device_for_whisper()
|
||||
compute_type = self.device_manager.get_compute_type()
|
||||
|
||||
# Track current settings
|
||||
self.current_model_size = model_size
|
||||
self.current_device_config = device_config
|
||||
|
||||
self.transcription_engine = TranscriptionEngine(
|
||||
model_size=model_size,
|
||||
device=device,
|
||||
compute_type=compute_type,
|
||||
language=language,
|
||||
min_confidence=self.config.get('processing.min_confidence', 0.5)
|
||||
)
|
||||
|
||||
# Load model in background thread
|
||||
self.model_loader_thread = ModelLoaderThread(self.transcription_engine)
|
||||
self.model_loader_thread.finished.connect(self._on_model_loaded)
|
||||
self.model_loader_thread.start()
|
||||
|
||||
def _on_model_loaded(self, success: bool, message: str):
|
||||
"""Handle model loading completion."""
|
||||
if success:
|
||||
host = self.config.get('web_server.host', '127.0.0.1')
|
||||
port = self.config.get('web_server.port', 8080)
|
||||
self.status_label.setText(f"✓ Ready | Web: http://{host}:{port}")
|
||||
self.start_button.setEnabled(True)
|
||||
else:
|
||||
self.status_label.setText("❌ Model loading failed")
|
||||
QMessageBox.critical(self, "Error", message)
|
||||
self.start_button.setEnabled(False)
|
||||
|
||||
def _start_web_server_if_enabled(self):
|
||||
"""Start web server."""
|
||||
host = self.config.get('web_server.host', '127.0.0.1')
|
||||
port = self.config.get('web_server.port', 8080)
|
||||
show_timestamps = self.config.get('display.show_timestamps', True)
|
||||
fade_after_seconds = self.config.get('display.fade_after_seconds', 10)
|
||||
|
||||
print(f"Starting web server at http://{host}:{port}")
|
||||
self.web_server = TranscriptionWebServer(
|
||||
host=host,
|
||||
port=port,
|
||||
show_timestamps=show_timestamps,
|
||||
fade_after_seconds=fade_after_seconds
|
||||
)
|
||||
self.web_server_thread = WebServerThread(self.web_server)
|
||||
self.web_server_thread.start()
|
||||
|
||||
def _toggle_transcription(self):
|
||||
"""Start or stop transcription."""
|
||||
if not self.is_transcribing:
|
||||
self._start_transcription()
|
||||
else:
|
||||
self._stop_transcription()
|
||||
|
||||
def _start_transcription(self):
|
||||
"""Start transcription."""
|
||||
try:
|
||||
# Check if engine is ready
|
||||
if not self.transcription_engine or not self.transcription_engine.is_loaded:
|
||||
QMessageBox.critical(self, "Error", "Transcription engine not ready")
|
||||
return
|
||||
|
||||
# Get audio device
|
||||
audio_device_str = self.config.get('audio.input_device', 'default')
|
||||
audio_device = None if audio_device_str == 'default' else int(audio_device_str)
|
||||
|
||||
# Initialize audio capture
|
||||
self.audio_capture = AudioCapture(
|
||||
sample_rate=self.config.get('audio.sample_rate', 16000),
|
||||
chunk_duration=self.config.get('audio.chunk_duration', 3.0),
|
||||
device=audio_device
|
||||
)
|
||||
|
||||
# Initialize noise suppressor
|
||||
self.noise_suppressor = NoiseSuppressor(
|
||||
sample_rate=self.config.get('audio.sample_rate', 16000),
|
||||
method="noisereduce" if self.config.get('noise_suppression.enabled', True) else "none",
|
||||
strength=self.config.get('noise_suppression.strength', 0.7),
|
||||
use_vad=self.config.get('processing.use_vad', True)
|
||||
)
|
||||
|
||||
# Start recording
|
||||
self.audio_capture.start_recording(callback=self._process_audio_chunk)
|
||||
|
||||
# Update UI
|
||||
self.is_transcribing = True
|
||||
self.start_button.setText("⏸ Stop Transcription")
|
||||
self.start_button.setStyleSheet("background-color: #e74c3c; color: white;")
|
||||
self.status_label.setText("🔴 Recording...")
|
||||
|
||||
except Exception as e:
|
||||
QMessageBox.critical(self, "Error", f"Failed to start transcription:\n{e}")
|
||||
print(f"Error starting transcription: {e}")
|
||||
|
||||
def _stop_transcription(self):
|
||||
"""Stop transcription."""
|
||||
try:
|
||||
# Stop recording
|
||||
if self.audio_capture:
|
||||
self.audio_capture.stop_recording()
|
||||
|
||||
# Update UI
|
||||
self.is_transcribing = False
|
||||
self.start_button.setText("▶ Start Transcription")
|
||||
self.start_button.setStyleSheet("background-color: #2ecc71; color: white;")
|
||||
self.status_label.setText("✓ Ready")
|
||||
|
||||
except Exception as e:
|
||||
QMessageBox.critical(self, "Error", f"Failed to stop transcription:\n{e}")
|
||||
print(f"Error stopping transcription: {e}")
|
||||
|
||||
def _process_audio_chunk(self, audio_chunk):
|
||||
"""Process an audio chunk (noise suppression + transcription)."""
|
||||
def process():
|
||||
try:
|
||||
# Apply noise suppression
|
||||
processed_audio = self.noise_suppressor.process(audio_chunk, skip_silent=True)
|
||||
|
||||
# Skip if silent (VAD filtered it out)
|
||||
if processed_audio is None:
|
||||
return
|
||||
|
||||
# Transcribe
|
||||
user_name = self.config.get('user.name', 'User')
|
||||
result = self.transcription_engine.transcribe(
|
||||
processed_audio,
|
||||
sample_rate=self.config.get('audio.sample_rate', 16000),
|
||||
user_name=user_name
|
||||
)
|
||||
|
||||
# Display result (use Qt signal for thread safety)
|
||||
if result:
|
||||
# We need to update UI from main thread
|
||||
# Note: We don't pass timestamp - let the display widget create it
|
||||
from PySide6.QtCore import QMetaObject, Q_ARG
|
||||
QMetaObject.invokeMethod(
|
||||
self.transcription_display,
|
||||
"add_transcription",
|
||||
Qt.QueuedConnection,
|
||||
Q_ARG(str, result.text),
|
||||
Q_ARG(str, result.user_name)
|
||||
)
|
||||
|
||||
# Broadcast to web server if enabled
|
||||
if self.web_server and self.web_server_thread:
|
||||
asyncio.run_coroutine_threadsafe(
|
||||
self.web_server.broadcast_transcription(
|
||||
result.text,
|
||||
result.user_name,
|
||||
result.timestamp
|
||||
),
|
||||
self.web_server_thread.loop
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing audio: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
# Run in background thread
|
||||
from threading import Thread
|
||||
Thread(target=process, daemon=True).start()
|
||||
|
||||
def _clear_transcriptions(self):
|
||||
"""Clear all transcriptions."""
|
||||
reply = QMessageBox.question(
|
||||
self,
|
||||
"Clear Transcriptions",
|
||||
"Are you sure you want to clear all transcriptions?",
|
||||
QMessageBox.Yes | QMessageBox.No
|
||||
)
|
||||
|
||||
if reply == QMessageBox.Yes:
|
||||
self.transcription_display.clear_all()
|
||||
|
||||
def _save_transcriptions(self):
|
||||
"""Save transcriptions to file."""
|
||||
filepath, _ = QFileDialog.getSaveFileName(
|
||||
self,
|
||||
"Save Transcriptions",
|
||||
"",
|
||||
"Text files (*.txt);;All files (*.*)"
|
||||
)
|
||||
|
||||
if filepath:
|
||||
if self.transcription_display.save_to_file(filepath):
|
||||
QMessageBox.information(self, "Saved", f"Transcriptions saved to:\n{filepath}")
|
||||
else:
|
||||
QMessageBox.critical(self, "Error", "Failed to save transcriptions")
|
||||
|
||||
def _open_settings(self):
|
||||
"""Open settings dialog."""
|
||||
# Get audio devices
|
||||
audio_devices = AudioCapture.get_input_devices()
|
||||
if not audio_devices:
|
||||
audio_devices = [(0, "Default")]
|
||||
|
||||
# Get compute devices
|
||||
compute_devices = self.device_manager.get_device_info()
|
||||
compute_devices.insert(0, ("auto", "Auto-detect"))
|
||||
|
||||
# Open settings dialog
|
||||
dialog = SettingsDialog(
|
||||
self,
|
||||
self.config,
|
||||
audio_devices,
|
||||
compute_devices,
|
||||
on_save=self._on_settings_saved
|
||||
)
|
||||
dialog.exec()
|
||||
|
||||
def _on_settings_saved(self):
|
||||
"""Handle settings being saved."""
|
||||
# Update user label
|
||||
user_name = self.config.get('user.name', 'User')
|
||||
self.user_label.setText(f"User: {user_name}")
|
||||
|
||||
# Update display settings
|
||||
show_timestamps = self.config.get('display.show_timestamps', True)
|
||||
self.transcription_display.set_max_lines(self.config.get('display.max_lines', 100))
|
||||
self.transcription_display.set_show_timestamps(show_timestamps)
|
||||
self.transcription_display.set_font(
|
||||
self.config.get('display.font_family', 'Courier'),
|
||||
self.config.get('display.font_size', 12)
|
||||
)
|
||||
|
||||
# Update web server settings
|
||||
if self.web_server:
|
||||
self.web_server.show_timestamps = show_timestamps
|
||||
self.web_server.fade_after_seconds = self.config.get('display.fade_after_seconds', 10)
|
||||
|
||||
# Check if model/device settings changed - reload model if needed
|
||||
new_model = self.config.get('transcription.model', 'base')
|
||||
new_device_config = self.config.get('transcription.device', 'auto')
|
||||
|
||||
# Only reload if model size or device changed
|
||||
if self.current_model_size != new_model or self.current_device_config != new_device_config:
|
||||
self._reload_model()
|
||||
else:
|
||||
QMessageBox.information(self, "Settings Saved", "Settings have been applied successfully!")
|
||||
|
||||
def _reload_model(self):
|
||||
"""Reload the transcription model with new settings."""
|
||||
# Stop transcription if running
|
||||
was_transcribing = self.is_transcribing
|
||||
if was_transcribing:
|
||||
self._stop_transcription()
|
||||
|
||||
# Update status
|
||||
self.status_label.setText("⚙ Reloading model...")
|
||||
self.start_button.setEnabled(False)
|
||||
|
||||
# Unload current model
|
||||
if self.transcription_engine:
|
||||
self.transcription_engine.unload_model()
|
||||
|
||||
# Set device based on config
|
||||
device_config = self.config.get('transcription.device', 'auto')
|
||||
self.device_manager.set_device(device_config)
|
||||
|
||||
# Re-initialize transcription engine
|
||||
model_size = self.config.get('transcription.model', 'base')
|
||||
language = self.config.get('transcription.language', 'en')
|
||||
device = self.device_manager.get_device_for_whisper()
|
||||
compute_type = self.device_manager.get_compute_type()
|
||||
|
||||
# Update tracked settings
|
||||
self.current_model_size = model_size
|
||||
self.current_device_config = device_config
|
||||
|
||||
self.transcription_engine = TranscriptionEngine(
|
||||
model_size=model_size,
|
||||
device=device,
|
||||
compute_type=compute_type,
|
||||
language=language,
|
||||
min_confidence=self.config.get('processing.min_confidence', 0.5)
|
||||
)
|
||||
|
||||
# Load model in background thread
|
||||
if self.model_loader_thread and self.model_loader_thread.isRunning():
|
||||
self.model_loader_thread.wait()
|
||||
|
||||
self.model_loader_thread = ModelLoaderThread(self.transcription_engine)
|
||||
self.model_loader_thread.finished.connect(self._on_model_reloaded)
|
||||
self.model_loader_thread.start()
|
||||
|
||||
def _on_model_reloaded(self, success: bool, message: str):
|
||||
"""Handle model reloading completion."""
|
||||
if success:
|
||||
host = self.config.get('web_server.host', '127.0.0.1')
|
||||
port = self.config.get('web_server.port', 8080)
|
||||
self.status_label.setText(f"✓ Ready | Web: http://{host}:{port}")
|
||||
self.start_button.setEnabled(True)
|
||||
QMessageBox.information(self, "Settings Saved", "Model reloaded successfully with new settings!")
|
||||
else:
|
||||
self.status_label.setText("❌ Model loading failed")
|
||||
QMessageBox.critical(self, "Error", f"Failed to reload model:\n{message}")
|
||||
self.start_button.setEnabled(False)
|
||||
|
||||
def closeEvent(self, event):
|
||||
"""Handle window closing."""
|
||||
# Stop transcription if running
|
||||
if self.is_transcribing:
|
||||
self._stop_transcription()
|
||||
|
||||
# Unload model
|
||||
if self.transcription_engine:
|
||||
self.transcription_engine.unload_model()
|
||||
|
||||
# Wait for model loader thread
|
||||
if self.model_loader_thread and self.model_loader_thread.isRunning():
|
||||
self.model_loader_thread.wait()
|
||||
|
||||
event.accept()
|
||||
310
gui/settings_dialog.py
Normal file
310
gui/settings_dialog.py
Normal file
@@ -0,0 +1,310 @@
|
||||
"""Settings dialog for configuring the application."""
|
||||
|
||||
import customtkinter as ctk
|
||||
from tkinter import messagebox
|
||||
from typing import Callable, List, Tuple
|
||||
|
||||
|
||||
class SettingsDialog(ctk.CTkToplevel):
|
||||
"""Dialog window for application settings."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
config,
|
||||
audio_devices: List[Tuple[int, str]],
|
||||
compute_devices: List[Tuple[str, str]],
|
||||
on_save: Callable = None
|
||||
):
|
||||
"""
|
||||
Initialize settings dialog.
|
||||
|
||||
Args:
|
||||
parent: Parent window
|
||||
config: Configuration object
|
||||
audio_devices: List of (device_index, device_name) tuples
|
||||
compute_devices: List of (device_id, device_description) tuples
|
||||
on_save: Callback function when settings are saved
|
||||
"""
|
||||
super().__init__(parent)
|
||||
|
||||
self.config = config
|
||||
self.audio_devices = audio_devices
|
||||
self.compute_devices = compute_devices
|
||||
self.on_save = on_save
|
||||
|
||||
# Window configuration
|
||||
self.title("Settings")
|
||||
self.geometry("600x700")
|
||||
self.resizable(False, False)
|
||||
|
||||
# Make dialog modal
|
||||
self.transient(parent)
|
||||
self.grab_set()
|
||||
|
||||
self._create_widgets()
|
||||
self._load_current_settings()
|
||||
|
||||
def _create_widgets(self):
|
||||
"""Create all settings widgets."""
|
||||
# Main container with padding
|
||||
main_frame = ctk.CTkFrame(self)
|
||||
main_frame.pack(fill="both", expand=True, padx=20, pady=20)
|
||||
|
||||
# User Settings Section
|
||||
user_frame = ctk.CTkFrame(main_frame)
|
||||
user_frame.pack(fill="x", pady=(0, 15))
|
||||
|
||||
ctk.CTkLabel(user_frame, text="User Settings", font=("", 16, "bold")).pack(
|
||||
anchor="w", padx=10, pady=(10, 5)
|
||||
)
|
||||
|
||||
# User name
|
||||
name_frame = ctk.CTkFrame(user_frame)
|
||||
name_frame.pack(fill="x", padx=10, pady=5)
|
||||
ctk.CTkLabel(name_frame, text="Display Name:", width=150).pack(side="left", padx=5)
|
||||
self.name_entry = ctk.CTkEntry(name_frame, width=300)
|
||||
self.name_entry.pack(side="left", padx=5)
|
||||
|
||||
# Audio Settings Section
|
||||
audio_frame = ctk.CTkFrame(main_frame)
|
||||
audio_frame.pack(fill="x", pady=(0, 15))
|
||||
|
||||
ctk.CTkLabel(audio_frame, text="Audio Settings", font=("", 16, "bold")).pack(
|
||||
anchor="w", padx=10, pady=(10, 5)
|
||||
)
|
||||
|
||||
# Audio device
|
||||
device_frame = ctk.CTkFrame(audio_frame)
|
||||
device_frame.pack(fill="x", padx=10, pady=5)
|
||||
ctk.CTkLabel(device_frame, text="Input Device:", width=150).pack(side="left", padx=5)
|
||||
device_names = [name for _, name in self.audio_devices]
|
||||
self.audio_device_menu = ctk.CTkOptionMenu(device_frame, values=device_names, width=300)
|
||||
self.audio_device_menu.pack(side="left", padx=5)
|
||||
|
||||
# Chunk duration
|
||||
chunk_frame = ctk.CTkFrame(audio_frame)
|
||||
chunk_frame.pack(fill="x", padx=10, pady=5)
|
||||
ctk.CTkLabel(chunk_frame, text="Chunk Duration (s):", width=150).pack(side="left", padx=5)
|
||||
self.chunk_entry = ctk.CTkEntry(chunk_frame, width=100)
|
||||
self.chunk_entry.pack(side="left", padx=5)
|
||||
|
||||
# Transcription Settings Section
|
||||
transcription_frame = ctk.CTkFrame(main_frame)
|
||||
transcription_frame.pack(fill="x", pady=(0, 15))
|
||||
|
||||
ctk.CTkLabel(transcription_frame, text="Transcription Settings", font=("", 16, "bold")).pack(
|
||||
anchor="w", padx=10, pady=(10, 5)
|
||||
)
|
||||
|
||||
# Model size
|
||||
model_frame = ctk.CTkFrame(transcription_frame)
|
||||
model_frame.pack(fill="x", padx=10, pady=5)
|
||||
ctk.CTkLabel(model_frame, text="Model Size:", width=150).pack(side="left", padx=5)
|
||||
self.model_menu = ctk.CTkOptionMenu(
|
||||
model_frame,
|
||||
values=["tiny", "base", "small", "medium", "large"],
|
||||
width=200
|
||||
)
|
||||
self.model_menu.pack(side="left", padx=5)
|
||||
|
||||
# Compute device
|
||||
compute_frame = ctk.CTkFrame(transcription_frame)
|
||||
compute_frame.pack(fill="x", padx=10, pady=5)
|
||||
ctk.CTkLabel(compute_frame, text="Compute Device:", width=150).pack(side="left", padx=5)
|
||||
device_descs = [desc for _, desc in self.compute_devices]
|
||||
self.compute_device_menu = ctk.CTkOptionMenu(compute_frame, values=device_descs, width=300)
|
||||
self.compute_device_menu.pack(side="left", padx=5)
|
||||
|
||||
# Language
|
||||
lang_frame = ctk.CTkFrame(transcription_frame)
|
||||
lang_frame.pack(fill="x", padx=10, pady=5)
|
||||
ctk.CTkLabel(lang_frame, text="Language:", width=150).pack(side="left", padx=5)
|
||||
self.lang_menu = ctk.CTkOptionMenu(
|
||||
lang_frame,
|
||||
values=["auto", "en", "es", "fr", "de", "it", "pt", "ru", "zh", "ja", "ko"],
|
||||
width=200
|
||||
)
|
||||
self.lang_menu.pack(side="left", padx=5)
|
||||
|
||||
# Noise Suppression Section
|
||||
noise_frame = ctk.CTkFrame(main_frame)
|
||||
noise_frame.pack(fill="x", pady=(0, 15))
|
||||
|
||||
ctk.CTkLabel(noise_frame, text="Noise Suppression", font=("", 16, "bold")).pack(
|
||||
anchor="w", padx=10, pady=(10, 5)
|
||||
)
|
||||
|
||||
# Enable noise suppression
|
||||
ns_enable_frame = ctk.CTkFrame(noise_frame)
|
||||
ns_enable_frame.pack(fill="x", padx=10, pady=5)
|
||||
self.noise_enabled_var = ctk.BooleanVar()
|
||||
self.noise_enabled_check = ctk.CTkCheckBox(
|
||||
ns_enable_frame,
|
||||
text="Enable Noise Suppression",
|
||||
variable=self.noise_enabled_var
|
||||
)
|
||||
self.noise_enabled_check.pack(side="left", padx=5)
|
||||
|
||||
# Noise suppression strength
|
||||
strength_frame = ctk.CTkFrame(noise_frame)
|
||||
strength_frame.pack(fill="x", padx=10, pady=5)
|
||||
ctk.CTkLabel(strength_frame, text="Strength:", width=150).pack(side="left", padx=5)
|
||||
self.noise_strength_slider = ctk.CTkSlider(
|
||||
strength_frame,
|
||||
from_=0.0,
|
||||
to=1.0,
|
||||
number_of_steps=20,
|
||||
width=300
|
||||
)
|
||||
self.noise_strength_slider.pack(side="left", padx=5)
|
||||
self.noise_strength_label = ctk.CTkLabel(strength_frame, text="0.7", width=40)
|
||||
self.noise_strength_label.pack(side="left", padx=5)
|
||||
self.noise_strength_slider.configure(command=self._update_strength_label)
|
||||
|
||||
# VAD
|
||||
vad_frame = ctk.CTkFrame(noise_frame)
|
||||
vad_frame.pack(fill="x", padx=10, pady=5)
|
||||
self.vad_enabled_var = ctk.BooleanVar()
|
||||
self.vad_enabled_check = ctk.CTkCheckBox(
|
||||
vad_frame,
|
||||
text="Enable Voice Activity Detection",
|
||||
variable=self.vad_enabled_var
|
||||
)
|
||||
self.vad_enabled_check.pack(side="left", padx=5)
|
||||
|
||||
# Display Settings Section
|
||||
display_frame = ctk.CTkFrame(main_frame)
|
||||
display_frame.pack(fill="x", pady=(0, 15))
|
||||
|
||||
ctk.CTkLabel(display_frame, text="Display Settings", font=("", 16, "bold")).pack(
|
||||
anchor="w", padx=10, pady=(10, 5)
|
||||
)
|
||||
|
||||
# Show timestamps
|
||||
ts_frame = ctk.CTkFrame(display_frame)
|
||||
ts_frame.pack(fill="x", padx=10, pady=5)
|
||||
self.timestamps_var = ctk.BooleanVar()
|
||||
self.timestamps_check = ctk.CTkCheckBox(
|
||||
ts_frame,
|
||||
text="Show Timestamps",
|
||||
variable=self.timestamps_var
|
||||
)
|
||||
self.timestamps_check.pack(side="left", padx=5)
|
||||
|
||||
# Max lines
|
||||
maxlines_frame = ctk.CTkFrame(display_frame)
|
||||
maxlines_frame.pack(fill="x", padx=10, pady=5)
|
||||
ctk.CTkLabel(maxlines_frame, text="Max Lines:", width=150).pack(side="left", padx=5)
|
||||
self.maxlines_entry = ctk.CTkEntry(maxlines_frame, width=100)
|
||||
self.maxlines_entry.pack(side="left", padx=5)
|
||||
|
||||
# Buttons
|
||||
button_frame = ctk.CTkFrame(main_frame)
|
||||
button_frame.pack(fill="x", pady=(10, 0))
|
||||
|
||||
self.save_button = ctk.CTkButton(
|
||||
button_frame,
|
||||
text="Save",
|
||||
command=self._save_settings,
|
||||
width=120
|
||||
)
|
||||
self.save_button.pack(side="right", padx=5)
|
||||
|
||||
self.cancel_button = ctk.CTkButton(
|
||||
button_frame,
|
||||
text="Cancel",
|
||||
command=self.destroy,
|
||||
width=120,
|
||||
fg_color="gray"
|
||||
)
|
||||
self.cancel_button.pack(side="right", padx=5)
|
||||
|
||||
def _update_strength_label(self, value):
|
||||
"""Update the noise strength label."""
|
||||
self.noise_strength_label.configure(text=f"{value:.1f}")
|
||||
|
||||
def _load_current_settings(self):
|
||||
"""Load current settings from config."""
|
||||
# User settings
|
||||
self.name_entry.insert(0, self.config.get('user.name', 'User'))
|
||||
|
||||
# Audio settings
|
||||
current_device = self.config.get('audio.input_device', 'default')
|
||||
for idx, (dev_idx, dev_name) in enumerate(self.audio_devices):
|
||||
if str(dev_idx) == current_device or current_device == 'default' and idx == 0:
|
||||
self.audio_device_menu.set(dev_name)
|
||||
break
|
||||
|
||||
self.chunk_entry.insert(0, str(self.config.get('audio.chunk_duration', 3.0)))
|
||||
|
||||
# Transcription settings
|
||||
self.model_menu.set(self.config.get('transcription.model', 'base'))
|
||||
|
||||
current_compute = self.config.get('transcription.device', 'auto')
|
||||
for dev_id, dev_desc in self.compute_devices:
|
||||
if dev_id == current_compute or (current_compute == 'auto' and dev_id == self.compute_devices[0][0]):
|
||||
self.compute_device_menu.set(dev_desc)
|
||||
break
|
||||
|
||||
self.lang_menu.set(self.config.get('transcription.language', 'en'))
|
||||
|
||||
# Noise suppression
|
||||
self.noise_enabled_var.set(self.config.get('noise_suppression.enabled', True))
|
||||
strength = self.config.get('noise_suppression.strength', 0.7)
|
||||
self.noise_strength_slider.set(strength)
|
||||
self._update_strength_label(strength)
|
||||
self.vad_enabled_var.set(self.config.get('processing.use_vad', True))
|
||||
|
||||
# Display settings
|
||||
self.timestamps_var.set(self.config.get('display.show_timestamps', True))
|
||||
self.maxlines_entry.insert(0, str(self.config.get('display.max_lines', 100)))
|
||||
|
||||
def _save_settings(self):
|
||||
"""Save settings to config."""
|
||||
try:
|
||||
# User settings
|
||||
self.config.set('user.name', self.name_entry.get())
|
||||
|
||||
# Audio settings
|
||||
selected_audio = self.audio_device_menu.get()
|
||||
for dev_idx, dev_name in self.audio_devices:
|
||||
if dev_name == selected_audio:
|
||||
self.config.set('audio.input_device', str(dev_idx))
|
||||
break
|
||||
|
||||
chunk_duration = float(self.chunk_entry.get())
|
||||
self.config.set('audio.chunk_duration', chunk_duration)
|
||||
|
||||
# Transcription settings
|
||||
self.config.set('transcription.model', self.model_menu.get())
|
||||
|
||||
selected_compute = self.compute_device_menu.get()
|
||||
for dev_id, dev_desc in self.compute_devices:
|
||||
if dev_desc == selected_compute:
|
||||
self.config.set('transcription.device', dev_id)
|
||||
break
|
||||
|
||||
self.config.set('transcription.language', self.lang_menu.get())
|
||||
|
||||
# Noise suppression
|
||||
self.config.set('noise_suppression.enabled', self.noise_enabled_var.get())
|
||||
self.config.set('noise_suppression.strength', self.noise_strength_slider.get())
|
||||
self.config.set('processing.use_vad', self.vad_enabled_var.get())
|
||||
|
||||
# Display settings
|
||||
self.config.set('display.show_timestamps', self.timestamps_var.get())
|
||||
max_lines = int(self.maxlines_entry.get())
|
||||
self.config.set('display.max_lines', max_lines)
|
||||
|
||||
# Call save callback
|
||||
if self.on_save:
|
||||
self.on_save()
|
||||
|
||||
messagebox.showinfo("Settings Saved", "Settings have been saved successfully!")
|
||||
self.destroy()
|
||||
|
||||
except ValueError as e:
|
||||
messagebox.showerror("Invalid Input", f"Please check your input values:\n{e}")
|
||||
except Exception as e:
|
||||
messagebox.showerror("Error", f"Failed to save settings:\n{e}")
|
||||
261
gui/settings_dialog_qt.py
Normal file
261
gui/settings_dialog_qt.py
Normal file
@@ -0,0 +1,261 @@
|
||||
"""PySide6 settings dialog for configuring the application."""
|
||||
|
||||
from PySide6.QtWidgets import (
|
||||
QDialog, QVBoxLayout, QHBoxLayout, QFormLayout,
|
||||
QLabel, QLineEdit, QComboBox, QCheckBox, QSlider,
|
||||
QPushButton, QMessageBox, QGroupBox
|
||||
)
|
||||
from PySide6.QtCore import Qt
|
||||
from typing import Callable, List, Tuple
|
||||
|
||||
|
||||
class SettingsDialog(QDialog):
|
||||
"""Dialog window for application settings using PySide6."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
config,
|
||||
audio_devices: List[Tuple[int, str]],
|
||||
compute_devices: List[Tuple[str, str]],
|
||||
on_save: Callable = None
|
||||
):
|
||||
"""
|
||||
Initialize settings dialog.
|
||||
|
||||
Args:
|
||||
parent: Parent window
|
||||
config: Configuration object
|
||||
audio_devices: List of (device_index, device_name) tuples
|
||||
compute_devices: List of (device_id, device_description) tuples
|
||||
on_save: Callback function when settings are saved
|
||||
"""
|
||||
super().__init__(parent)
|
||||
|
||||
self.config = config
|
||||
self.audio_devices = audio_devices
|
||||
self.compute_devices = compute_devices
|
||||
self.on_save = on_save
|
||||
|
||||
# Window configuration
|
||||
self.setWindowTitle("Settings")
|
||||
self.setMinimumSize(600, 700)
|
||||
self.setModal(True)
|
||||
|
||||
self._create_widgets()
|
||||
self._load_current_settings()
|
||||
|
||||
def _create_widgets(self):
|
||||
"""Create all settings widgets."""
|
||||
main_layout = QVBoxLayout()
|
||||
self.setLayout(main_layout)
|
||||
|
||||
# User Settings Group
|
||||
user_group = QGroupBox("User Settings")
|
||||
user_layout = QFormLayout()
|
||||
|
||||
self.name_input = QLineEdit()
|
||||
user_layout.addRow("Display Name:", self.name_input)
|
||||
|
||||
user_group.setLayout(user_layout)
|
||||
main_layout.addWidget(user_group)
|
||||
|
||||
# Audio Settings Group
|
||||
audio_group = QGroupBox("Audio Settings")
|
||||
audio_layout = QFormLayout()
|
||||
|
||||
self.audio_device_combo = QComboBox()
|
||||
device_names = [name for _, name in self.audio_devices]
|
||||
self.audio_device_combo.addItems(device_names)
|
||||
audio_layout.addRow("Input Device:", self.audio_device_combo)
|
||||
|
||||
self.chunk_input = QLineEdit()
|
||||
audio_layout.addRow("Chunk Duration (s):", self.chunk_input)
|
||||
|
||||
audio_group.setLayout(audio_layout)
|
||||
main_layout.addWidget(audio_group)
|
||||
|
||||
# Transcription Settings Group
|
||||
transcription_group = QGroupBox("Transcription Settings")
|
||||
transcription_layout = QFormLayout()
|
||||
|
||||
self.model_combo = QComboBox()
|
||||
self.model_combo.addItems(["tiny", "base", "small", "medium", "large"])
|
||||
transcription_layout.addRow("Model Size:", self.model_combo)
|
||||
|
||||
self.compute_device_combo = QComboBox()
|
||||
device_descs = [desc for _, desc in self.compute_devices]
|
||||
self.compute_device_combo.addItems(device_descs)
|
||||
transcription_layout.addRow("Compute Device:", self.compute_device_combo)
|
||||
|
||||
self.lang_combo = QComboBox()
|
||||
self.lang_combo.addItems(["auto", "en", "es", "fr", "de", "it", "pt", "ru", "zh", "ja", "ko"])
|
||||
transcription_layout.addRow("Language:", self.lang_combo)
|
||||
|
||||
transcription_group.setLayout(transcription_layout)
|
||||
main_layout.addWidget(transcription_group)
|
||||
|
||||
# Noise Suppression Group
|
||||
noise_group = QGroupBox("Noise Suppression")
|
||||
noise_layout = QVBoxLayout()
|
||||
|
||||
self.noise_enabled_check = QCheckBox("Enable Noise Suppression")
|
||||
noise_layout.addWidget(self.noise_enabled_check)
|
||||
|
||||
# Strength slider
|
||||
strength_layout = QHBoxLayout()
|
||||
strength_layout.addWidget(QLabel("Strength:"))
|
||||
|
||||
self.noise_strength_slider = QSlider(Qt.Horizontal)
|
||||
self.noise_strength_slider.setMinimum(0)
|
||||
self.noise_strength_slider.setMaximum(100)
|
||||
self.noise_strength_slider.setValue(70)
|
||||
self.noise_strength_slider.valueChanged.connect(self._update_strength_label)
|
||||
strength_layout.addWidget(self.noise_strength_slider)
|
||||
|
||||
self.noise_strength_label = QLabel("0.7")
|
||||
strength_layout.addWidget(self.noise_strength_label)
|
||||
|
||||
noise_layout.addLayout(strength_layout)
|
||||
|
||||
self.vad_enabled_check = QCheckBox("Enable Voice Activity Detection")
|
||||
noise_layout.addWidget(self.vad_enabled_check)
|
||||
|
||||
noise_group.setLayout(noise_layout)
|
||||
main_layout.addWidget(noise_group)
|
||||
|
||||
# Display Settings Group
|
||||
display_group = QGroupBox("Display Settings")
|
||||
display_layout = QFormLayout()
|
||||
|
||||
self.timestamps_check = QCheckBox()
|
||||
display_layout.addRow("Show Timestamps:", self.timestamps_check)
|
||||
|
||||
self.maxlines_input = QLineEdit()
|
||||
display_layout.addRow("Max Lines:", self.maxlines_input)
|
||||
|
||||
self.font_family_combo = QComboBox()
|
||||
self.font_family_combo.addItems(["Courier", "Arial", "Times New Roman", "Consolas", "Monaco", "Monospace"])
|
||||
display_layout.addRow("Font Family:", self.font_family_combo)
|
||||
|
||||
self.font_size_input = QLineEdit()
|
||||
display_layout.addRow("Font Size:", self.font_size_input)
|
||||
|
||||
self.fade_seconds_input = QLineEdit()
|
||||
display_layout.addRow("Fade After (seconds):", self.fade_seconds_input)
|
||||
|
||||
display_group.setLayout(display_layout)
|
||||
main_layout.addWidget(display_group)
|
||||
|
||||
# Buttons
|
||||
button_layout = QHBoxLayout()
|
||||
button_layout.addStretch()
|
||||
|
||||
self.cancel_button = QPushButton("Cancel")
|
||||
self.cancel_button.clicked.connect(self.reject)
|
||||
button_layout.addWidget(self.cancel_button)
|
||||
|
||||
self.save_button = QPushButton("Save")
|
||||
self.save_button.clicked.connect(self._save_settings)
|
||||
self.save_button.setDefault(True)
|
||||
button_layout.addWidget(self.save_button)
|
||||
|
||||
main_layout.addLayout(button_layout)
|
||||
|
||||
def _update_strength_label(self, value):
|
||||
"""Update the noise strength label."""
|
||||
self.noise_strength_label.setText(f"{value / 100:.1f}")
|
||||
|
||||
def _load_current_settings(self):
|
||||
"""Load current settings from config."""
|
||||
# User settings
|
||||
self.name_input.setText(self.config.get('user.name', 'User'))
|
||||
|
||||
# Audio settings
|
||||
current_device = self.config.get('audio.input_device', 'default')
|
||||
for idx, (dev_idx, dev_name) in enumerate(self.audio_devices):
|
||||
if str(dev_idx) == current_device or (current_device == 'default' and idx == 0):
|
||||
self.audio_device_combo.setCurrentIndex(idx)
|
||||
break
|
||||
|
||||
self.chunk_input.setText(str(self.config.get('audio.chunk_duration', 3.0)))
|
||||
|
||||
# Transcription settings
|
||||
model = self.config.get('transcription.model', 'base')
|
||||
self.model_combo.setCurrentText(model)
|
||||
|
||||
current_compute = self.config.get('transcription.device', 'auto')
|
||||
for idx, (dev_id, dev_desc) in enumerate(self.compute_devices):
|
||||
if dev_id == current_compute or (current_compute == 'auto' and idx == 0):
|
||||
self.compute_device_combo.setCurrentIndex(idx)
|
||||
break
|
||||
|
||||
lang = self.config.get('transcription.language', 'en')
|
||||
self.lang_combo.setCurrentText(lang)
|
||||
|
||||
# Noise suppression
|
||||
self.noise_enabled_check.setChecked(self.config.get('noise_suppression.enabled', True))
|
||||
strength = self.config.get('noise_suppression.strength', 0.7)
|
||||
self.noise_strength_slider.setValue(int(strength * 100))
|
||||
self._update_strength_label(int(strength * 100))
|
||||
self.vad_enabled_check.setChecked(self.config.get('processing.use_vad', True))
|
||||
|
||||
# Display settings
|
||||
self.timestamps_check.setChecked(self.config.get('display.show_timestamps', True))
|
||||
self.maxlines_input.setText(str(self.config.get('display.max_lines', 100)))
|
||||
|
||||
font_family = self.config.get('display.font_family', 'Courier')
|
||||
self.font_family_combo.setCurrentText(font_family)
|
||||
|
||||
self.font_size_input.setText(str(self.config.get('display.font_size', 12)))
|
||||
self.fade_seconds_input.setText(str(self.config.get('display.fade_after_seconds', 10)))
|
||||
|
||||
def _save_settings(self):
|
||||
"""Save settings to config."""
|
||||
try:
|
||||
# User settings
|
||||
self.config.set('user.name', self.name_input.text())
|
||||
|
||||
# Audio settings
|
||||
selected_audio_idx = self.audio_device_combo.currentIndex()
|
||||
dev_idx, _ = self.audio_devices[selected_audio_idx]
|
||||
self.config.set('audio.input_device', str(dev_idx))
|
||||
|
||||
chunk_duration = float(self.chunk_input.text())
|
||||
self.config.set('audio.chunk_duration', chunk_duration)
|
||||
|
||||
# Transcription settings
|
||||
self.config.set('transcription.model', self.model_combo.currentText())
|
||||
|
||||
selected_compute_idx = self.compute_device_combo.currentIndex()
|
||||
dev_id, _ = self.compute_devices[selected_compute_idx]
|
||||
self.config.set('transcription.device', dev_id)
|
||||
|
||||
self.config.set('transcription.language', self.lang_combo.currentText())
|
||||
|
||||
# Noise suppression
|
||||
self.config.set('noise_suppression.enabled', self.noise_enabled_check.isChecked())
|
||||
self.config.set('noise_suppression.strength', self.noise_strength_slider.value() / 100.0)
|
||||
self.config.set('processing.use_vad', self.vad_enabled_check.isChecked())
|
||||
|
||||
# Display settings
|
||||
self.config.set('display.show_timestamps', self.timestamps_check.isChecked())
|
||||
max_lines = int(self.maxlines_input.text())
|
||||
self.config.set('display.max_lines', max_lines)
|
||||
self.config.set('display.font_family', self.font_family_combo.currentText())
|
||||
font_size = int(self.font_size_input.text())
|
||||
self.config.set('display.font_size', font_size)
|
||||
fade_seconds = int(self.fade_seconds_input.text())
|
||||
self.config.set('display.fade_after_seconds', fade_seconds)
|
||||
|
||||
# Call save callback
|
||||
if self.on_save:
|
||||
self.on_save()
|
||||
|
||||
QMessageBox.information(self, "Settings Saved", "Settings have been saved successfully!")
|
||||
self.accept()
|
||||
|
||||
except ValueError as e:
|
||||
QMessageBox.critical(self, "Invalid Input", f"Please check your input values:\n{e}")
|
||||
except Exception as e:
|
||||
QMessageBox.critical(self, "Error", f"Failed to save settings:\n{e}")
|
||||
127
gui/transcription_display.py
Normal file
127
gui/transcription_display.py
Normal file
@@ -0,0 +1,127 @@
|
||||
"""Transcription display widget for showing real-time transcriptions."""
|
||||
|
||||
import customtkinter as ctk
|
||||
from typing import List
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class TranscriptionDisplay(ctk.CTkTextbox):
|
||||
"""Custom text widget for displaying transcriptions."""
|
||||
|
||||
def __init__(self, master, max_lines: int = 100, show_timestamps: bool = True, **kwargs):
|
||||
"""
|
||||
Initialize transcription display.
|
||||
|
||||
Args:
|
||||
master: Parent widget
|
||||
max_lines: Maximum number of lines to keep in display
|
||||
show_timestamps: Whether to show timestamps
|
||||
**kwargs: Additional arguments for CTkTextbox
|
||||
"""
|
||||
super().__init__(master, **kwargs)
|
||||
|
||||
self.max_lines = max_lines
|
||||
self.show_timestamps = show_timestamps
|
||||
self.line_count = 0
|
||||
|
||||
# Configure text widget
|
||||
self.configure(state="disabled") # Read-only by default
|
||||
|
||||
def add_transcription(self, text: str, user_name: str = "", timestamp: datetime = None):
|
||||
"""
|
||||
Add a new transcription to the display.
|
||||
|
||||
Args:
|
||||
text: Transcription text
|
||||
user_name: User/speaker name
|
||||
timestamp: Timestamp of transcription
|
||||
"""
|
||||
if timestamp is None:
|
||||
timestamp = datetime.now()
|
||||
|
||||
# Build the display line
|
||||
line_parts = []
|
||||
|
||||
if self.show_timestamps:
|
||||
time_str = timestamp.strftime("%H:%M:%S")
|
||||
line_parts.append(f"[{time_str}]")
|
||||
|
||||
if user_name:
|
||||
line_parts.append(f"{user_name}:")
|
||||
|
||||
line_parts.append(text)
|
||||
|
||||
line = " ".join(line_parts) + "\n"
|
||||
|
||||
# Add to display
|
||||
self.configure(state="normal")
|
||||
self.insert("end", line)
|
||||
self.configure(state="disabled")
|
||||
|
||||
# Auto-scroll to bottom
|
||||
self.see("end")
|
||||
|
||||
# Track line count
|
||||
self.line_count += 1
|
||||
|
||||
# Remove old lines if exceeding max
|
||||
if self.line_count > self.max_lines:
|
||||
self._remove_oldest_lines(self.line_count - self.max_lines)
|
||||
|
||||
def _remove_oldest_lines(self, num_lines: int):
|
||||
"""
|
||||
Remove oldest lines from the display.
|
||||
|
||||
Args:
|
||||
num_lines: Number of lines to remove
|
||||
"""
|
||||
self.configure(state="normal")
|
||||
self.delete("1.0", f"{num_lines + 1}.0")
|
||||
self.configure(state="disabled")
|
||||
self.line_count -= num_lines
|
||||
|
||||
def clear(self):
|
||||
"""Clear all transcriptions."""
|
||||
self.configure(state="normal")
|
||||
self.delete("1.0", "end")
|
||||
self.configure(state="disabled")
|
||||
self.line_count = 0
|
||||
|
||||
def get_all_text(self) -> str:
|
||||
"""
|
||||
Get all transcription text.
|
||||
|
||||
Returns:
|
||||
All text in the display
|
||||
"""
|
||||
return self.get("1.0", "end")
|
||||
|
||||
def set_max_lines(self, max_lines: int):
|
||||
"""Update maximum number of lines to keep."""
|
||||
self.max_lines = max_lines
|
||||
|
||||
# Trim if necessary
|
||||
if self.line_count > self.max_lines:
|
||||
self._remove_oldest_lines(self.line_count - self.max_lines)
|
||||
|
||||
def set_show_timestamps(self, show: bool):
|
||||
"""Update whether to show timestamps."""
|
||||
self.show_timestamps = show
|
||||
|
||||
def save_to_file(self, filepath: str) -> bool:
|
||||
"""
|
||||
Save transcriptions to a file.
|
||||
|
||||
Args:
|
||||
filepath: Path to save file
|
||||
|
||||
Returns:
|
||||
True if saved successfully
|
||||
"""
|
||||
try:
|
||||
with open(filepath, 'w') as f:
|
||||
f.write(self.get_all_text())
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Error saving transcriptions: {e}")
|
||||
return False
|
||||
159
gui/transcription_display_qt.py
Normal file
159
gui/transcription_display_qt.py
Normal file
@@ -0,0 +1,159 @@
|
||||
"""PySide6 transcription display widget for showing real-time transcriptions."""
|
||||
|
||||
from PySide6.QtWidgets import QTextEdit
|
||||
from PySide6.QtGui import QFont, QTextCursor
|
||||
from PySide6.QtCore import Qt, Slot
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class TranscriptionDisplay(QTextEdit):
|
||||
"""Custom text widget for displaying transcriptions using PySide6."""
|
||||
|
||||
def __init__(self, parent=None, max_lines=100, show_timestamps=True, font_family="Courier", font_size=12):
|
||||
"""
|
||||
Initialize transcription display.
|
||||
|
||||
Args:
|
||||
parent: Parent widget
|
||||
max_lines: Maximum number of lines to keep in display
|
||||
show_timestamps: Whether to show timestamps
|
||||
font_family: Font family name
|
||||
font_size: Font size in points
|
||||
"""
|
||||
super().__init__(parent)
|
||||
|
||||
self.max_lines = max_lines
|
||||
self.show_timestamps = show_timestamps
|
||||
self.line_count = 0
|
||||
self.font_family = font_family
|
||||
self.font_size = font_size
|
||||
|
||||
# Configure text widget
|
||||
self.setReadOnly(True)
|
||||
self.setFont(QFont(font_family, font_size))
|
||||
|
||||
# Set dark theme styling
|
||||
self.setStyleSheet("""
|
||||
QTextEdit {
|
||||
background-color: #2b2b2b;
|
||||
color: #ffffff;
|
||||
border: 1px solid #3d3d3d;
|
||||
border-radius: 5px;
|
||||
padding: 10px;
|
||||
}
|
||||
""")
|
||||
|
||||
@Slot(str, str)
|
||||
def add_transcription(self, text: str, user_name: str = "", timestamp: datetime = None):
|
||||
"""
|
||||
Add a new transcription to the display.
|
||||
|
||||
Args:
|
||||
text: Transcription text
|
||||
user_name: User/speaker name
|
||||
timestamp: Timestamp of transcription
|
||||
"""
|
||||
if timestamp is None:
|
||||
timestamp = datetime.now()
|
||||
|
||||
# Build the display line
|
||||
line_parts = []
|
||||
|
||||
if self.show_timestamps:
|
||||
time_str = timestamp.strftime("%H:%M:%S")
|
||||
line_parts.append(f"[{time_str}]")
|
||||
|
||||
if user_name:
|
||||
line_parts.append(f"{user_name}:")
|
||||
|
||||
line_parts.append(text)
|
||||
|
||||
line = " ".join(line_parts)
|
||||
|
||||
# Add to display
|
||||
self.append(line)
|
||||
|
||||
# Auto-scroll to bottom
|
||||
cursor = self.textCursor()
|
||||
cursor.movePosition(QTextCursor.End)
|
||||
self.setTextCursor(cursor)
|
||||
|
||||
# Track line count
|
||||
self.line_count += 1
|
||||
|
||||
# Remove old lines if exceeding max
|
||||
if self.line_count > self.max_lines:
|
||||
self._remove_oldest_lines(self.line_count - self.max_lines)
|
||||
|
||||
def _remove_oldest_lines(self, num_lines: int):
|
||||
"""
|
||||
Remove oldest lines from the display.
|
||||
|
||||
Args:
|
||||
num_lines: Number of lines to remove
|
||||
"""
|
||||
cursor = self.textCursor()
|
||||
cursor.movePosition(QTextCursor.Start)
|
||||
|
||||
for _ in range(num_lines):
|
||||
cursor.select(QTextCursor.BlockUnderCursor)
|
||||
cursor.removeSelectedText()
|
||||
cursor.deleteChar() # Remove the newline
|
||||
|
||||
self.line_count -= num_lines
|
||||
|
||||
def clear_all(self):
|
||||
"""Clear all transcriptions."""
|
||||
self.clear()
|
||||
self.line_count = 0
|
||||
|
||||
def get_all_text(self) -> str:
|
||||
"""
|
||||
Get all transcription text.
|
||||
|
||||
Returns:
|
||||
All text in the display
|
||||
"""
|
||||
return self.toPlainText()
|
||||
|
||||
def set_max_lines(self, max_lines: int):
|
||||
"""Update maximum number of lines to keep."""
|
||||
self.max_lines = max_lines
|
||||
|
||||
# Trim if necessary
|
||||
if self.line_count > self.max_lines:
|
||||
self._remove_oldest_lines(self.line_count - self.max_lines)
|
||||
|
||||
def set_show_timestamps(self, show: bool):
|
||||
"""Update whether to show timestamps."""
|
||||
self.show_timestamps = show
|
||||
|
||||
def set_font(self, font_family: str, font_size: int):
|
||||
"""
|
||||
Update font settings.
|
||||
|
||||
Args:
|
||||
font_family: Font family name
|
||||
font_size: Font size in points
|
||||
"""
|
||||
self.font_family = font_family
|
||||
self.font_size = font_size
|
||||
super().setFont(QFont(font_family, font_size))
|
||||
|
||||
def save_to_file(self, filepath: str) -> bool:
|
||||
"""
|
||||
Save transcriptions to a file.
|
||||
|
||||
Args:
|
||||
filepath: Path to save file
|
||||
|
||||
Returns:
|
||||
True if saved successfully
|
||||
"""
|
||||
try:
|
||||
with open(filepath, 'w') as f:
|
||||
f.write(self.toPlainText())
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Error saving transcriptions: {e}")
|
||||
return False
|
||||
86
local-transcription.spec
Normal file
86
local-transcription.spec
Normal file
@@ -0,0 +1,86 @@
|
||||
# -*- mode: python ; coding: utf-8 -*-
|
||||
"""PyInstaller spec file for Local Transcription app."""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
block_cipher = None
|
||||
|
||||
# Determine if we're on Windows
|
||||
is_windows = sys.platform == 'win32'
|
||||
|
||||
a = Analysis(
|
||||
['main.py'],
|
||||
pathex=[],
|
||||
binaries=[],
|
||||
datas=[
|
||||
('config/default_config.yaml', 'config'),
|
||||
],
|
||||
hiddenimports=[
|
||||
'PySide6.QtCore',
|
||||
'PySide6.QtWidgets',
|
||||
'PySide6.QtGui',
|
||||
'faster_whisper',
|
||||
'faster_whisper.transcribe',
|
||||
'faster_whisper.vad',
|
||||
'ctranslate2',
|
||||
'sounddevice',
|
||||
'noisereduce',
|
||||
'webrtcvad',
|
||||
'scipy',
|
||||
'scipy.signal',
|
||||
'numpy',
|
||||
'fastapi',
|
||||
'uvicorn',
|
||||
'uvicorn.logging',
|
||||
'uvicorn.loops',
|
||||
'uvicorn.loops.auto',
|
||||
'uvicorn.protocols',
|
||||
'uvicorn.protocols.http',
|
||||
'uvicorn.protocols.http.auto',
|
||||
'uvicorn.protocols.websockets',
|
||||
'uvicorn.protocols.websockets.auto',
|
||||
'uvicorn.lifespan',
|
||||
'uvicorn.lifespan.on',
|
||||
],
|
||||
hookspath=[],
|
||||
hooksconfig={},
|
||||
runtime_hooks=[],
|
||||
excludes=[],
|
||||
win_no_prefer_redirects=False,
|
||||
win_private_assemblies=False,
|
||||
cipher=block_cipher,
|
||||
noarchive=False,
|
||||
)
|
||||
|
||||
pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
|
||||
|
||||
exe = EXE(
|
||||
pyz,
|
||||
a.scripts,
|
||||
[],
|
||||
exclude_binaries=True,
|
||||
name='LocalTranscription',
|
||||
debug=False,
|
||||
bootloader_ignore_signals=False,
|
||||
strip=False,
|
||||
upx=True,
|
||||
console=True, # Set to False to hide console window
|
||||
disable_windowed_traceback=False,
|
||||
argv_emulation=False,
|
||||
target_arch=None,
|
||||
codesign_identity=None,
|
||||
entitlements_file=None,
|
||||
icon=None, # Add icon file path here if you have one
|
||||
)
|
||||
|
||||
coll = COLLECT(
|
||||
exe,
|
||||
a.binaries,
|
||||
a.zipfiles,
|
||||
a.datas,
|
||||
strip=False,
|
||||
upx=True,
|
||||
upx_exclude=[],
|
||||
name='LocalTranscription',
|
||||
)
|
||||
52
main.py
Normal file
52
main.py
Normal file
@@ -0,0 +1,52 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Local Transcription Application
|
||||
|
||||
A standalone desktop application for real-time speech-to-text transcription
|
||||
using Whisper models. Supports CPU/GPU processing, noise suppression, and
|
||||
optional multi-user server synchronization.
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add project root to Python path
|
||||
project_root = Path(__file__).parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from PySide6.QtWidgets import QApplication
|
||||
from gui.main_window_qt import MainWindow
|
||||
|
||||
|
||||
def main():
|
||||
"""Main application entry point."""
|
||||
try:
|
||||
print("Starting Local Transcription Application...")
|
||||
print("=" * 50)
|
||||
|
||||
# Create Qt application
|
||||
app = QApplication(sys.argv)
|
||||
|
||||
# Set application info
|
||||
app.setApplicationName("Local Transcription")
|
||||
app.setOrganizationName("LocalTranscription")
|
||||
|
||||
# Create and show main window
|
||||
window = MainWindow()
|
||||
window.show()
|
||||
|
||||
# Run application
|
||||
sys.exit(app.exec())
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\nApplication interrupted by user")
|
||||
sys.exit(0)
|
||||
except Exception as e:
|
||||
print(f"Fatal error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
221
main_cli.py
Executable file
221
main_cli.py
Executable file
@@ -0,0 +1,221 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Local Transcription CLI
|
||||
|
||||
Command-line version of the transcription application.
|
||||
Works without GUI - perfect for testing and headless operation.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
from pathlib import Path
|
||||
import signal
|
||||
import argparse
|
||||
|
||||
# Add project root to Python path
|
||||
project_root = Path(__file__).parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from client.config import Config
|
||||
from client.device_utils import DeviceManager
|
||||
from client.audio_capture import AudioCapture
|
||||
from client.noise_suppression import NoiseSuppressor
|
||||
from client.transcription_engine import TranscriptionEngine
|
||||
|
||||
|
||||
class TranscriptionCLI:
|
||||
"""CLI transcription application."""
|
||||
|
||||
def __init__(self, args):
|
||||
"""Initialize the CLI application."""
|
||||
self.args = args
|
||||
self.config = Config()
|
||||
self.device_manager = DeviceManager()
|
||||
self.is_running = False
|
||||
|
||||
# Override config with command-line arguments
|
||||
if args.model:
|
||||
self.config.set('transcription.model', args.model)
|
||||
if args.device:
|
||||
self.config.set('transcription.device', args.device)
|
||||
if args.language:
|
||||
self.config.set('transcription.language', args.language)
|
||||
if args.user:
|
||||
self.config.set('user.name', args.user)
|
||||
|
||||
# Components
|
||||
self.audio_capture = None
|
||||
self.noise_suppressor = None
|
||||
self.transcription_engine = None
|
||||
|
||||
def initialize(self):
|
||||
"""Initialize all components."""
|
||||
print("=" * 60)
|
||||
print("Local Transcription CLI")
|
||||
print("=" * 60)
|
||||
|
||||
# Device setup
|
||||
device_config = self.config.get('transcription.device', 'auto')
|
||||
self.device_manager.set_device(device_config)
|
||||
|
||||
print(f"\nUser: {self.config.get('user.name', 'User')}")
|
||||
print(f"Model: {self.config.get('transcription.model', 'base')}")
|
||||
print(f"Language: {self.config.get('transcription.language', 'en')}")
|
||||
print(f"Device: {self.device_manager.current_device}")
|
||||
|
||||
# Initialize transcription engine
|
||||
print(f"\nLoading Whisper model...")
|
||||
model_size = self.config.get('transcription.model', 'base')
|
||||
language = self.config.get('transcription.language', 'en')
|
||||
device = self.device_manager.get_device_for_whisper()
|
||||
compute_type = self.device_manager.get_compute_type()
|
||||
|
||||
self.transcription_engine = TranscriptionEngine(
|
||||
model_size=model_size,
|
||||
device=device,
|
||||
compute_type=compute_type,
|
||||
language=language,
|
||||
min_confidence=self.config.get('processing.min_confidence', 0.5)
|
||||
)
|
||||
|
||||
success = self.transcription_engine.load_model()
|
||||
if not success:
|
||||
print("❌ Failed to load model!")
|
||||
return False
|
||||
|
||||
print("✓ Model loaded successfully!")
|
||||
|
||||
# Initialize audio capture
|
||||
audio_device_str = self.config.get('audio.input_device', 'default')
|
||||
audio_device = None if audio_device_str == 'default' else int(audio_device_str)
|
||||
|
||||
self.audio_capture = AudioCapture(
|
||||
sample_rate=self.config.get('audio.sample_rate', 16000),
|
||||
chunk_duration=self.config.get('audio.chunk_duration', 3.0),
|
||||
device=audio_device
|
||||
)
|
||||
|
||||
# Initialize noise suppressor
|
||||
self.noise_suppressor = NoiseSuppressor(
|
||||
sample_rate=self.config.get('audio.sample_rate', 16000),
|
||||
method="noisereduce" if self.config.get('noise_suppression.enabled', True) else "none",
|
||||
strength=self.config.get('noise_suppression.strength', 0.7),
|
||||
use_vad=self.config.get('processing.use_vad', True)
|
||||
)
|
||||
|
||||
print("\n✓ All components initialized!")
|
||||
return True
|
||||
|
||||
def process_audio_chunk(self, audio_chunk):
|
||||
"""Process an audio chunk."""
|
||||
try:
|
||||
# Apply noise suppression
|
||||
processed_audio = self.noise_suppressor.process(audio_chunk, skip_silent=True)
|
||||
|
||||
# Skip if silent
|
||||
if processed_audio is None:
|
||||
return
|
||||
|
||||
# Transcribe
|
||||
user_name = self.config.get('user.name', 'User')
|
||||
result = self.transcription_engine.transcribe(
|
||||
processed_audio,
|
||||
sample_rate=self.config.get('audio.sample_rate', 16000),
|
||||
user_name=user_name
|
||||
)
|
||||
|
||||
# Display result
|
||||
if result:
|
||||
print(f"{result}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing audio: {e}")
|
||||
|
||||
def run(self):
|
||||
"""Run the transcription loop."""
|
||||
if not self.initialize():
|
||||
return 1
|
||||
|
||||
# Setup signal handler for graceful shutdown
|
||||
def signal_handler(sig, frame):
|
||||
print("\n\nStopping transcription...")
|
||||
self.is_running = False
|
||||
|
||||
signal.signal(signal.SIGINT, signal_handler)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("🎤 Recording... (Press Ctrl+C to stop)")
|
||||
print("=" * 60)
|
||||
print()
|
||||
|
||||
# Start recording
|
||||
self.is_running = True
|
||||
self.audio_capture.start_recording(callback=self.process_audio_chunk)
|
||||
|
||||
# Keep running until interrupted
|
||||
try:
|
||||
while self.is_running:
|
||||
signal.pause()
|
||||
except AttributeError:
|
||||
# signal.pause() not available on Windows
|
||||
import time
|
||||
while self.is_running:
|
||||
time.sleep(0.1)
|
||||
|
||||
# Cleanup
|
||||
self.audio_capture.stop_recording()
|
||||
self.transcription_engine.unload_model()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("✓ Transcription stopped")
|
||||
print("=" * 60)
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Local Transcription CLI - Real-time speech-to-text'
|
||||
)
|
||||
parser.add_argument(
|
||||
'-m', '--model',
|
||||
choices=['tiny', 'base', 'small', 'medium', 'large'],
|
||||
help='Whisper model size'
|
||||
)
|
||||
parser.add_argument(
|
||||
'-d', '--device',
|
||||
choices=['cpu', 'cuda', 'auto'],
|
||||
help='Compute device'
|
||||
)
|
||||
parser.add_argument(
|
||||
'-l', '--language',
|
||||
help='Language code (e.g., en, es, fr) or "auto"'
|
||||
)
|
||||
parser.add_argument(
|
||||
'-u', '--user',
|
||||
help='User/speaker name'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--list-devices',
|
||||
action='store_true',
|
||||
help='List available audio input devices'
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# List devices if requested
|
||||
if args.list_devices:
|
||||
print("Available audio input devices:")
|
||||
devices = AudioCapture.get_input_devices()
|
||||
for idx, name in devices:
|
||||
print(f" [{idx}] {name}")
|
||||
return 0
|
||||
|
||||
# Run application
|
||||
app = TranscriptionCLI(args)
|
||||
return app.run()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
59
pyproject.toml
Normal file
59
pyproject.toml
Normal file
@@ -0,0 +1,59 @@
|
||||
[project]
|
||||
name = "local-transcription"
|
||||
version = "0.1.0"
|
||||
description = "A standalone desktop application for real-time speech-to-text transcription using Whisper models"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.9"
|
||||
license = {text = "MIT"}
|
||||
authors = [
|
||||
{name = "Your Name", email = "your.email@example.com"}
|
||||
]
|
||||
keywords = ["transcription", "speech-to-text", "whisper", "streaming", "obs"]
|
||||
|
||||
dependencies = [
|
||||
"numpy>=1.24.0",
|
||||
"pyyaml>=6.0",
|
||||
"sounddevice>=0.4.6",
|
||||
"scipy>=1.10.0",
|
||||
"noisereduce>=3.0.0",
|
||||
"webrtcvad>=2.0.10",
|
||||
"faster-whisper>=0.10.0",
|
||||
"torch>=2.0.0",
|
||||
"PySide6>=6.6.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
server = [
|
||||
"fastapi>=0.104.0",
|
||||
"uvicorn>=0.24.0",
|
||||
"websockets>=12.0",
|
||||
"requests>=2.31.0",
|
||||
]
|
||||
dev = [
|
||||
"pytest>=7.4.0",
|
||||
"black>=23.0.0",
|
||||
"ruff>=0.1.0",
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
local-transcription = "main:main"
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[tool.hatch.build.targets.wheel]
|
||||
packages = ["client", "gui"]
|
||||
|
||||
[tool.uv]
|
||||
dev-dependencies = [
|
||||
"pyinstaller>=6.17.0",
|
||||
]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
target-version = "py39"
|
||||
|
||||
[tool.black]
|
||||
line-length = 100
|
||||
target-version = ["py39"]
|
||||
23
requirements.txt
Normal file
23
requirements.txt
Normal file
@@ -0,0 +1,23 @@
|
||||
# Core Dependencies
|
||||
numpy>=1.24.0
|
||||
pyyaml>=6.0
|
||||
|
||||
# Audio Processing
|
||||
sounddevice>=0.4.6
|
||||
scipy>=1.10.0
|
||||
|
||||
# Noise Suppression
|
||||
noisereduce>=3.0.0
|
||||
webrtcvad>=2.0.10
|
||||
|
||||
# Transcription - Using faster-whisper for better real-time performance
|
||||
faster-whisper>=0.10.0
|
||||
torch>=2.0.0
|
||||
|
||||
# GUI - Using CustomTkinter for modern look
|
||||
customtkinter>=5.2.0
|
||||
pillow>=10.0.0
|
||||
|
||||
# Optional: Server sync dependencies (will move to requirements-server.txt later)
|
||||
# websockets>=12.0
|
||||
# requests>=2.31.0
|
||||
0
server/__init__.py
Normal file
0
server/__init__.py
Normal file
233
server/web_display.py
Normal file
233
server/web_display.py
Normal file
@@ -0,0 +1,233 @@
|
||||
"""Web server for displaying transcriptions in a browser (for OBS browser source)."""
|
||||
|
||||
import asyncio
|
||||
from fastapi import FastAPI, WebSocket
|
||||
from fastapi.responses import HTMLResponse
|
||||
from typing import List, Optional
|
||||
import json
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class TranscriptionWebServer:
|
||||
"""Web server for displaying transcriptions."""
|
||||
|
||||
def __init__(self, host: str = "127.0.0.1", port: int = 8080, show_timestamps: bool = True, fade_after_seconds: int = 10):
|
||||
"""
|
||||
Initialize web server.
|
||||
|
||||
Args:
|
||||
host: Server host address
|
||||
port: Server port
|
||||
show_timestamps: Whether to show timestamps in transcriptions
|
||||
fade_after_seconds: Time in seconds before transcriptions fade out (0 = never fade)
|
||||
"""
|
||||
self.host = host
|
||||
self.port = port
|
||||
self.show_timestamps = show_timestamps
|
||||
self.fade_after_seconds = fade_after_seconds
|
||||
self.app = FastAPI()
|
||||
self.active_connections: List[WebSocket] = []
|
||||
self.transcriptions = [] # Store recent transcriptions
|
||||
|
||||
# Setup routes
|
||||
self._setup_routes()
|
||||
|
||||
def _setup_routes(self):
|
||||
"""Setup FastAPI routes."""
|
||||
|
||||
@self.app.get("/", response_class=HTMLResponse)
|
||||
async def get_display():
|
||||
"""Serve the transcription display page."""
|
||||
return self._get_html()
|
||||
|
||||
@self.app.websocket("/ws")
|
||||
async def websocket_endpoint(websocket: WebSocket):
|
||||
"""WebSocket endpoint for real-time updates."""
|
||||
await websocket.accept()
|
||||
self.active_connections.append(websocket)
|
||||
|
||||
try:
|
||||
# Send recent transcriptions
|
||||
for trans in self.transcriptions[-20:]: # Last 20
|
||||
await websocket.send_json(trans)
|
||||
|
||||
# Keep connection alive
|
||||
while True:
|
||||
# Wait for ping/pong to keep connection alive
|
||||
await websocket.receive_text()
|
||||
except:
|
||||
self.active_connections.remove(websocket)
|
||||
|
||||
def _get_html(self) -> str:
|
||||
"""Generate HTML for transcription display."""
|
||||
return f"""
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>Transcription Display</title>
|
||||
<style>
|
||||
body {{
|
||||
margin: 0;
|
||||
padding: 20px;
|
||||
background: transparent;
|
||||
font-family: Arial, sans-serif;
|
||||
color: white;
|
||||
}}
|
||||
#transcriptions {{
|
||||
max-height: 100vh;
|
||||
overflow-y: auto;
|
||||
}}
|
||||
.transcription {{
|
||||
margin: 10px 0;
|
||||
padding: 10px;
|
||||
background: rgba(0, 0, 0, 0.7);
|
||||
border-radius: 5px;
|
||||
animation: slideIn 0.3s ease-out;
|
||||
transition: opacity 1s ease-out;
|
||||
}}
|
||||
.transcription.fading {{
|
||||
opacity: 0;
|
||||
}}
|
||||
.timestamp {{
|
||||
color: #888;
|
||||
font-size: 0.9em;
|
||||
margin-right: 10px;
|
||||
}}
|
||||
.user {{
|
||||
color: #4CAF50;
|
||||
font-weight: bold;
|
||||
margin-right: 10px;
|
||||
}}
|
||||
.text {{
|
||||
color: white;
|
||||
}}
|
||||
@keyframes slideIn {{
|
||||
from {{
|
||||
opacity: 0;
|
||||
transform: translateY(-10px);
|
||||
}}
|
||||
to {{
|
||||
opacity: 1;
|
||||
transform: translateY(0);
|
||||
}}
|
||||
}}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div id="transcriptions"></div>
|
||||
|
||||
<script>
|
||||
const container = document.getElementById('transcriptions');
|
||||
const ws = new WebSocket(`ws://${{window.location.host}}/ws`);
|
||||
const fadeAfterSeconds = {self.fade_after_seconds};
|
||||
|
||||
ws.onmessage = (event) => {{
|
||||
const data = JSON.parse(event.data);
|
||||
addTranscription(data);
|
||||
}};
|
||||
|
||||
ws.onclose = () => {{
|
||||
console.log('WebSocket closed. Attempting to reconnect...');
|
||||
setTimeout(() => location.reload(), 3000);
|
||||
}};
|
||||
|
||||
// Send keepalive pings
|
||||
setInterval(() => {{
|
||||
if (ws.readyState === WebSocket.OPEN) {{
|
||||
ws.send('ping');
|
||||
}}
|
||||
}}, 30000);
|
||||
|
||||
function addTranscription(data) {{
|
||||
const div = document.createElement('div');
|
||||
div.className = 'transcription';
|
||||
|
||||
let html = '';
|
||||
if (data.timestamp) {{
|
||||
html += `<span class="timestamp">[${{data.timestamp}}]</span>`;
|
||||
}}
|
||||
if (data.user_name) {{
|
||||
html += `<span class="user">${{data.user_name}}:</span>`;
|
||||
}}
|
||||
html += `<span class="text">${{data.text}}</span>`;
|
||||
|
||||
div.innerHTML = html;
|
||||
container.appendChild(div);
|
||||
|
||||
// Auto-scroll to bottom
|
||||
container.scrollTop = container.scrollHeight;
|
||||
|
||||
// Set up fade-out if enabled
|
||||
if (fadeAfterSeconds > 0) {{
|
||||
setTimeout(() => {{
|
||||
// Start fade animation
|
||||
div.classList.add('fading');
|
||||
|
||||
// Remove element after fade completes
|
||||
setTimeout(() => {{
|
||||
if (div.parentNode === container) {{
|
||||
container.removeChild(div);
|
||||
}}
|
||||
}}, 1000); // Match the CSS transition duration
|
||||
}}, fadeAfterSeconds * 1000);
|
||||
}}
|
||||
|
||||
// Limit to 50 transcriptions (fallback)
|
||||
while (container.children.length > 50) {{
|
||||
container.removeChild(container.firstChild);
|
||||
}}
|
||||
}}
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
async def broadcast_transcription(self, text: str, user_name: str = "", timestamp: Optional[datetime] = None):
|
||||
"""
|
||||
Broadcast a transcription to all connected clients.
|
||||
|
||||
Args:
|
||||
text: Transcription text
|
||||
user_name: User/speaker name
|
||||
timestamp: Timestamp of transcription
|
||||
"""
|
||||
if timestamp is None:
|
||||
timestamp = datetime.now()
|
||||
|
||||
trans_data = {
|
||||
"text": text,
|
||||
"user_name": user_name,
|
||||
}
|
||||
|
||||
# Only include timestamp if enabled
|
||||
if self.show_timestamps:
|
||||
trans_data["timestamp"] = timestamp.strftime("%H:%M:%S")
|
||||
|
||||
# Store transcription
|
||||
self.transcriptions.append(trans_data)
|
||||
if len(self.transcriptions) > 100:
|
||||
self.transcriptions.pop(0)
|
||||
|
||||
# Broadcast to all connected clients
|
||||
disconnected = []
|
||||
for connection in self.active_connections:
|
||||
try:
|
||||
await connection.send_json(trans_data)
|
||||
except:
|
||||
disconnected.append(connection)
|
||||
|
||||
# Remove disconnected clients
|
||||
for conn in disconnected:
|
||||
self.active_connections.remove(conn)
|
||||
|
||||
async def start(self):
|
||||
"""Start the web server."""
|
||||
import uvicorn
|
||||
config = uvicorn.Config(
|
||||
self.app,
|
||||
host=self.host,
|
||||
port=self.port,
|
||||
log_level="warning"
|
||||
)
|
||||
server = uvicorn.Server(config)
|
||||
await server.serve()
|
||||
124
test_components.py
Normal file
124
test_components.py
Normal file
@@ -0,0 +1,124 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script to verify all components work without GUI.
|
||||
This can run in headless environments.
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add project root to Python path
|
||||
project_root = Path(__file__).parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
print("=" * 60)
|
||||
print("Testing Local Transcription Components (No GUI)")
|
||||
print("=" * 60)
|
||||
|
||||
# Test 1: Configuration
|
||||
print("\n1. Testing Configuration System...")
|
||||
try:
|
||||
from client.config import Config
|
||||
config = Config()
|
||||
print(f" ✓ Config loaded: {config.config_path}")
|
||||
print(f" ✓ User name: {config.get('user.name')}")
|
||||
print(f" ✓ Model: {config.get('transcription.model')}")
|
||||
except Exception as e:
|
||||
print(f" ✗ Config failed: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Test 2: Device Detection
|
||||
print("\n2. Testing Device Detection...")
|
||||
try:
|
||||
from client.device_utils import DeviceManager
|
||||
device_mgr = DeviceManager()
|
||||
print(f" ✓ Available devices: {device_mgr.available_devices}")
|
||||
print(f" ✓ Current device: {device_mgr.current_device}")
|
||||
print(f" ✓ GPU available: {device_mgr.is_gpu_available()}")
|
||||
|
||||
device_info = device_mgr.get_device_info()
|
||||
for dev_id, dev_desc in device_info:
|
||||
print(f" - {dev_id}: {dev_desc}")
|
||||
except Exception as e:
|
||||
print(f" ✗ Device detection failed: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Test 3: Audio Devices
|
||||
print("\n3. Testing Audio Capture...")
|
||||
try:
|
||||
from client.audio_capture import AudioCapture
|
||||
devices = AudioCapture.get_input_devices()
|
||||
print(f" ✓ Found {len(devices)} audio input device(s)")
|
||||
for idx, name in devices[:5]: # Show first 5
|
||||
print(f" - [{idx}] {name}")
|
||||
if len(devices) > 5:
|
||||
print(f" ... and {len(devices) - 5} more")
|
||||
except Exception as e:
|
||||
print(f" ✗ Audio capture failed: {e}")
|
||||
|
||||
# Test 4: Noise Suppression
|
||||
print("\n4. Testing Noise Suppression...")
|
||||
try:
|
||||
from client.noise_suppression import NoiseSuppressor
|
||||
import numpy as np
|
||||
|
||||
suppressor = NoiseSuppressor(sample_rate=16000, method="noisereduce", strength=0.7)
|
||||
print(f" ✓ Noise suppressor created: {suppressor}")
|
||||
|
||||
# Test with dummy audio
|
||||
test_audio = np.random.randn(16000).astype(np.float32) * 0.1
|
||||
processed = suppressor.process(test_audio, skip_silent=False)
|
||||
print(f" ✓ Processed audio shape: {processed.shape}")
|
||||
except Exception as e:
|
||||
print(f" ✗ Noise suppression failed: {e}")
|
||||
|
||||
# Test 5: Transcription Engine
|
||||
print("\n5. Testing Transcription Engine (Loading Model)...")
|
||||
try:
|
||||
from client.transcription_engine import TranscriptionEngine
|
||||
|
||||
device = device_mgr.get_device_for_whisper()
|
||||
compute_type = device_mgr.get_compute_type()
|
||||
|
||||
print(f" → Using device: {device} with compute type: {compute_type}")
|
||||
print(f" → Loading model (this may take 1-2 minutes on first run)...")
|
||||
|
||||
engine = TranscriptionEngine(
|
||||
model_size="tiny", # Use tiny for faster testing
|
||||
device=device,
|
||||
compute_type=compute_type,
|
||||
language="en"
|
||||
)
|
||||
|
||||
success = engine.load_model()
|
||||
if success:
|
||||
print(f" ✓ Model loaded successfully!")
|
||||
print(f" ✓ Engine: {engine}")
|
||||
|
||||
# Test transcription with dummy audio
|
||||
print(f"\n Testing transcription with silent audio...")
|
||||
test_audio = np.zeros(48000, dtype=np.float32) # 3 seconds of silence
|
||||
result = engine.transcribe(test_audio, sample_rate=16000, user_name="Test")
|
||||
|
||||
if result:
|
||||
print(f" ✓ Transcription result: '{result.text}'")
|
||||
else:
|
||||
print(f" ℹ No transcription (expected for silent audio)")
|
||||
|
||||
engine.unload_model()
|
||||
else:
|
||||
print(f" ✗ Model loading failed")
|
||||
sys.exit(1)
|
||||
|
||||
except Exception as e:
|
||||
print(f" ✗ Transcription engine failed: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("✓ All Components Tested Successfully!")
|
||||
print("=" * 60)
|
||||
print("\nThe application is ready to use!")
|
||||
print("Run 'uv run python main.py' on a system with a display.")
|
||||
print("=" * 60)
|
||||
Reference in New Issue
Block a user