michael-treadgold/win-dictate
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases 1 Wiki Activity

Initial commit: Win Dictation - AI Voice to Text for Windows with automatic model selection

Browse Source
This commit is contained in:
Treadgold
2025-12-06 10:28:46 +13:00
commit 81b3d0073e
1666 changed files with 388157 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+295
View File
@@ -0,0 +1,295 @@
# Win Dictation - AI Voice to Text for Windows
A high-performance, real-time speech-to-text application for Windows using OpenAI's Whisper model. Convert your voice to text with GPU acceleration support and a modern, user-friendly interface.
![Win Dictation Screenshot](screenshot.png)
## 🚀 Quick Download
**[Download Latest Release (WinDictation.zip)](release/WinDictation.zip)**
Simply extract the ZIP file and run `win-dictation.exe`. The release includes all required DLLs and the Whisper model.
---
## ✨ Features
### Performance
- **Multi-Core CPU Support**: Automatically uses all available CPU cores for maximum performance
- **GPU Acceleration**: Auto-detects and uses CUDA or Vulkan when available
- **Smart Model Selection**: Automatically selects optimal model (tiny.en for CPU-only, base.en for GPU) for best performance
- **Ring Buffer Audio**: Zero audio loss with lock-free ring buffer implementation
- **Optimized Processing**: AVX2/FMA instructions for maximum performance
### User Interface
- **Modern Dark Theme**: Polished, professional interface
- **Real-Time Monitoring**:
- Live VU meter for audio levels
- Buffer status indicator
- GPU/CPU usage display
- **Smooth Animations**: 30 FPS UI updates for responsive experience
- **System Tray Integration**: Minimize to tray with hotkey support
### Audio Processing
- **Voice Activity Detection (VAD)**: Automatically filters silence
- **Continuous Recording**: Maintains context between segments
- **Multiple Microphone Support**: Select from all available input devices
- **16kHz Sample Rate**: Optimized for Whisper model
## 🎯 Usage
### Controls
- **Start/Stop Recording**: Click button or press `Ctrl+Shift+R`
- **Clear Text**: Click "Clear" button
- **Change Microphone**: Select from dropdown (auto-restarts recording)
- **Minimize**: Close window (minimizes to system tray)
- **Exit**: Right-click tray icon → Exit
### Indicators
- **Level**: Real-time audio input level
- **Buffer**: Current audio buffer usage (0-100%)
- **Status**: Shows GPU/CPU mode, recording state, thread count
## 🔨 Building from Source
### Prerequisites
- **Windows 10/11**
- **CMake** (3.5 or newer)
- **C++ Compiler** (MSVC 2019+ or MinGW)
- **PowerShell** (for build script)
- **Optional**: CUDA 12.4+ or Vulkan SDK (for GPU acceleration)
### Build Steps
1. **Clone the repository:**
```powershell
git clone <repository-url>
cd win-dictation
```
2. **Run the build script:**
```powershell
powershell -ExecutionPolicy Bypass -File src/build.ps1
```
The build script will:
- Detect your GPU capabilities (CUDA, Vulkan)
- Download and configure SDL2 automatically
- Build the application with optimal settings
- Download both Whisper models (tiny.en for CPU, base.en for GPU)
- Deploy all required DLLs
3. **Run the application:**
```powershell
build\bin\Release\win-dictation.exe
```
### Manual Build (Alternative)
If you prefer to build manually:
```powershell
# Configure CMake
cmake -B build -DWHISPER_SDL2=ON
# For GPU support (CUDA):
cmake -B build -DWHISPER_SDL2=ON -DGGML_CUDA=ON
# For GPU support (Vulkan):
cmake -B build -DWHISPER_SDL2=ON -DGGML_VULKAN=ON
# Build
cmake --build build --config Release
# The executable will be at: build\bin\Release\win-dictation.exe
```
### SDL2 Setup
The build script automatically downloads SDL2. If building manually, you can:
1. Download SDL2 from: https://github.com/libsdl-org/SDL/releases
2. Extract to `SDL2-mingw/` directory
3. Set `SDL2_DIR` in CMake to point to the SDL2 cmake directory
## ⚙️ Technical Details
### Architecture
#### Ring Buffer Audio Capture
- **Lock-Free Design**: Audio thread never blocks
- **30-Second Buffer**: Handles burst processing without loss
- **Atomic Operations**: Prevents race conditions
#### Processing Pipeline
```
Audio Input → Ring Buffer → VAD → Whisper Inference → Text Output
```
1. **SDL Audio Capture**: 512-sample chunks at 16kHz
2. **Ring Buffer**: Lock-free circular buffer
3. **VAD Processing**: Filters silence before inference
4. **Whisper Inference**: Multi-threaded with context overlap
5. **Text Output**: Appended to UI in real-time
### Performance Optimizations
#### CPU Mode
- All available CPU threads utilized
- AVX2/FMA SIMD instructions
- Optimized memory layout
- Minimal context switching
#### GPU Mode (When Available)
- CUDA 12.4+ or Vulkan SDK required
- Automatic offloading to GPU
- Faster inference times
- Lower CPU usage
### Model
The app automatically selects the optimal model based on your system:
**CPU-Only Systems:**
- Uses `ggml-tiny.en.bin` (75 MB)
- **Parameters**: 39 million
- **Speed**: ~10-15x real-time on CPU
- **Accuracy**: Good for general speech
- Optimized for slower machines
**GPU-Accelerated Systems:**
- Uses `ggml-base.en.bin` (140 MB)
- **Parameters**: 74 million
- **Speed**: >20x real-time on GPU
- **Accuracy**: Excellent for general speech
- Better accuracy with GPU acceleration
Both models are English-only (optimized). The app detects GPU availability at startup and selects the appropriate model automatically. To use a different model, place it in `models/` directory and the app will detect it.
## 📊 Performance Benchmarks
### CPU-Only (24 threads, tiny.en model)
- **Latency**: ~1-2 seconds
- **Throughput**: ~10-15x real-time
- **CPU Usage**: 40-60% during speech
- **Memory**: ~200 MB
- **Model**: Automatically selected for CPU-only systems
### CPU-Only (24 threads, base.en model - if manually selected)
- **Latency**: ~2-3 seconds
- **Throughput**: ~5x real-time
- **CPU Usage**: 60-80% during speech
- **Memory**: ~500 MB
### GPU-Accelerated (RTX 3090, base.en model)
- **Latency**: <1 second
- **Throughput**: >20x real-time
- **GPU Usage**: 20-30%
- **CPU Usage**: <10%
- **Memory**: ~1 GB (VRAM)
- **Model**: Automatically selected for GPU systems
## 🔧 Troubleshooting
### GPU Not Detected
- **CUDA**: Install CUDA Toolkit 12.4 or newer (CUDA 13.0 recommended)
- See `src/CUDA-SETUP.md` for detailed installation guide
- **Vulkan**: Install Vulkan SDK
- CPU-only mode still provides excellent performance with all cores
### Audio Not Working
- Check microphone permissions in Windows Settings
- Verify correct device selected in dropdown
- Test microphone in Windows Sound settings
### Poor Transcription Quality
- Ensure microphone is close (6-12 inches)
- Reduce background noise
- Check VU meter shows green when speaking
- Try a larger model (medium.en or large-v3-turbo)
### High CPU Usage
- Normal during active transcription
- Reduces during silence (VAD filtering)
- Consider enabling GPU acceleration
## 📁 Project Structure
```
win-dictation/
├── src/ # Main application source code
│ ├── main.cpp # UI and Windows message handling
│ ├── transcriber.* # Core transcription logic
│ └── build.ps1 # Automated build script
├── whisper/ # Whisper.cpp library
├── ggml/ # GGML tensor library
├── common/ # Shared utilities
├── models/ # Whisper model files
├── release/ # Pre-built release package
│ └── WinDictation.zip
└── CMakeLists.txt # Main build configuration
```
## 🆕 Recent Improvements
### v2.0 (Current)
- ✅ **Ring buffer** implementation - no more dropped audio
- ✅ **Multi-core CPU** support - uses all available threads
- ✅ **GPU auto-detection** - CUDA/Vulkan support
- ✅ **Modern UI** - dark theme, smooth animations
- ✅ **VAD integration** - skip silence for efficiency
- ✅ **Better error handling** - graceful fallbacks
- ✅ **Status indicators** - real-time monitoring
- ✅ **Build script** - automated setup and deployment
## 🔮 Future Enhancements
- [ ] Push-to-talk mode
- [ ] Multiple language support
- [ ] Punctuation model integration
- [ ] Export to file (TXT, SRT)
- [ ] Custom hotkey configuration
- [ ] Noise reduction filter
- [ ] Model switching in UI
- [ ] Real-time word highlighting
## 📝 License
This project uses the MIT license, following the same license as [whisper.cpp](https://github.com/ggerganov/whisper.cpp).
## 🤝 Contributing
Improvements welcome! The code is designed to be:
- **Readable**: Clear structure and comments
- **Maintainable**: Modular design
- **Extensible**: Easy to add features
- **Performant**: Optimized critical paths
## 📚 Resources
- [Whisper.cpp](https://github.com/ggerganov/whisper.cpp) - Core library
- [Whisper Paper](https://arxiv.org/abs/2212.04356) - Research paper
- [Model Download](https://huggingface.co/ggerganov/whisper.cpp) - Additional models
- [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) - GPU acceleration
- [Vulkan SDK](https://vulkan.lunarg.com/) - Alternative GPU backend
## 💡 Tips
### For Best Results
1. Use a quality microphone
2. Position mic 6-12 inches from mouth
3. Speak clearly and naturally
4. Minimize background noise
5. Keep buffer below 50% (adjust step_ms if needed)
### For Development
- See `src/transcriber.h/cpp` for core logic
- See `src/main.cpp` for UI implementation
- Adjust parameters in `WhisperConfig` struct
- Enable logging in `whisper_full_params`
---
**Built with ❤️ using whisper.cpp**