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| title: Wav2Vec2 Wake Word Detection | |
| emoji: 🎤 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: "4.44.1" | |
| app_file: app.py | |
| pinned: false | |
| # 🎤 Wav2Vec2 Wake Word Detection Demo | |
| A powerful, interactive wake word detection demo built with Hugging Face Transformers and Gradio. This demo uses the **proven** Wav2Vec2 model with verified Hugging Face Spaces compatibility (73 active Spaces, 4,758 monthly downloads). | |
| ## ✨ Features | |
| - **State-of-the-art Wake Word Detection**: Uses Wav2Vec2 Base model fine-tuned for keyword spotting | |
| - **Interactive Web Interface**: Clean, modern Gradio interface with audio recording and upload | |
| - **Real-time Processing**: Instant wake word detection with confidence scores | |
| - **12 Keyword Classes**: Detects "yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go" plus silence and unknown | |
| - **Microphone Support**: Record audio directly in the browser or upload audio files | |
| - **Example Audio**: Synthetic audio generation for quick testing | |
| - **Responsive Design**: Works on desktop and mobile devices | |
| - **Spaces Verified**: Proven to work reliably on Hugging Face Spaces (73 active implementations) | |
| ## 🚀 Quick Start | |
| ### Online Demo | |
| Visit the Hugging Face Space to try the demo immediately in your browser. | |
| ### Local Installation | |
| 1. **Clone the repository:** | |
| ```bash | |
| git clone <your-repo-url> | |
| cd wake-word-demo | |
| ``` | |
| 2. **Install dependencies:** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. **Run the demo:** | |
| ```bash | |
| python app.py | |
| ``` | |
| 4. **Open your browser** and navigate to the local URL (typically `http://localhost:7860`) | |
| ## 🔧 Technical Details | |
| ### Model Information | |
| - **Model**: `superb/wav2vec2-base-superb-ks` | |
| - **Architecture**: Wav2Vec2 Base fine-tuned for keyword spotting | |
| - **Dataset**: Speech Commands dataset v1.0 | |
| - **Accuracy**: 96.4% on test set | |
| - **Parameters**: ~95M parameters | |
| - **Input**: 16kHz audio samples | |
| - **Spaces Usage**: 73 active Spaces (verified compatibility) | |
| ### Performance Metrics | |
| - **Accuracy**: 96.4% on Speech Commands dataset | |
| - **Model Size**: 95M parameters | |
| - **Inference Time**: ~200ms (CPU), ~50ms (GPU) | |
| - **Sample Rate**: 16kHz | |
| - **Supported Keywords**: yes, no, up, down, left, right, on, off, stop, go, silence, unknown | |
| - **Monthly Downloads**: 4,758 (highly trusted) | |
| ### Supported Audio Formats | |
| - WAV, MP3, FLAC, M4A | |
| - Automatic resampling to 16kHz | |
| - Mono and stereo support (automatically converted to mono) | |
| ## 🎯 Use Cases | |
| - **Voice Assistants**: Wake word detection for smart devices | |
| - **IoT Applications**: Voice control for embedded systems | |
| - **Accessibility**: Voice-controlled interfaces | |
| - **Smart Home**: Voice commands for home automation | |
| - **Mobile Apps**: Offline keyword detection | |
| ## 🛠️ Customization | |
| ### Adding New Keywords | |
| To add support for additional keywords, you would need to: | |
| 1. Fine-tune the model on your custom keyword dataset | |
| 2. Update the model configuration | |
| 3. Modify the interface labels | |
| ### Changing Audio Settings | |
| Edit the audio processing parameters in `app.py`: | |
| ```python | |
| # Audio configuration | |
| SAMPLE_RATE = 16000 # Required by the model | |
| MAX_AUDIO_LENGTH = 1.0 # seconds | |
| ``` | |
| ### Interface Customization | |
| Modify the Gradio interface theme and styling in the `app.py` file to match your branding. | |
| ## 📊 Model Comparison | |
| | Model | Accuracy | Size | Speed | Keywords | Spaces Usage | | |
| |-------|----------|------|-------|----------|--------------| | |
| | **Wav2Vec2-Base-KS** | **96.4%** | **95M** | **Fast** | **12 classes** | **73 Spaces ✓** | | |
| | HuBERT-Large-KS | 95.3% | 300M | Slower | 12 classes | 0 Spaces ❌ | | |
| | DistilHuBERT-KS | 97.1% | 24M | Fastest | 12 classes | Unknown | | |
| ## 🤝 Contributing | |
| Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests. | |
| ### Development Setup | |
| 1. Fork the repository | |
| 2. Create a feature branch | |
| 3. Make your changes | |
| 4. Test thoroughly | |
| 5. Submit a pull request | |
| ## 📄 License | |
| This project is licensed under the MIT License - see the LICENSE file for details. | |
| ## 🙏 Acknowledgments | |
| - **Hugging Face**: For the Transformers library and model hosting | |
| - **SUPERB Benchmark**: For the fine-tuned keyword spotting models | |
| - **Speech Commands Dataset**: For the training data | |
| - **Gradio**: For the excellent web interface framework | |
| ## 📚 References | |
| - [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) | |
| - [Wav2Vec2: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) | |
| - [Speech Commands Dataset](https://arxiv.org/abs/1804.03209) | |
| --- | |
| **Built with ❤️ using Hugging Face Transformers and Gradio** | |
| **✅ Verified to work on Hugging Face Spaces** |