Update README.md

README.md

@@ -7,133 +7,7 @@ sdk: gradio
 sdk_version: 5.32.1
 app_file: app.py
 pinned: false
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-
-🎤 Voice Recognition Security System
-A sophisticated voice recognition system built with PyTorch and deployed on Hugging Face Spaces using Gradio. This system uses transfer learning with ResNet18 and advanced audio processing techniques for secure voice-based access control.
-🚀 Features
-
-Advanced Voice Recognition: Uses transfer learning with ResNet18 for high-accuracy voice identification
-Security-Focused: Implements confidence thresholds and authorization checks
-Data Augmentation: Trained with comprehensive audio augmentation techniques
-Real-time Processing: Fast inference for real-time voice recognition
-User-Friendly Interface: Clean Gradio interface for easy interaction
-
-🏗️ Model Architecture
-
-Base Model: ResNet18 with transfer learning
-Input: MFCC features (40 coefficients, 174 time frames)
-Output: Multi-class classification for voice identification
-Security: Confidence-based access control with authorized user validation
-
-📊 Technical Details
-Audio Processing
-
-Feature Extraction: 40 MFCC coefficients
-Sample Rate: Flexible (auto-detected)
-Window Length: 174 time frames (standardized)
-Augmentation: Noise addition, time shifting, pitch shifting, time stretching
-
-Model Training
-
-Transfer Learning: Pre-trained ResNet18 backbone
-Optimization: Adam optimizer with learning rate scheduling
-Regularization: Dropout (0.5) and weight decay
-Batch Size: 32
-Epochs: 25 with early stopping capability
-
-Security Features
-
-Authorization List: Predefined authorized users (user1-user7)
-Confidence Threshold: Configurable (default: 0.7)
-False Acceptance Rate: Minimized through strict thresholding
-Access Control: Binary grant/deny decisions with detailed logging
-
-🔧 Usage
-Online Demo
-Visit the Hugging Face Space to try the system: [Your Space URL]
-Local Installation
-bash# Clone the repository
-git clone [your-repo-url]
-cd voice-recognition-security
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the application
-python app.py
-API Usage
-The system accepts audio files in various formats (.wav, .mp3, .flac, .ogg, .m4a, .aac) and returns:
-
-Access decision (Granted/Denied)
-Predicted user
-Confidence score
-Detailed status information
-
-📝 Model Performance
-Training Results
-
-Overall Accuracy: ~95%+ on test set
-False Acceptance Rate: <0.05
-False Rejection Rate: <0.10
-Security Score: >85%
-
-Supported Audio Formats
-
-WAV (recommended)
-MP3
-FLAC
-OGG
-M4A
-AAC
-
-🛠️ Technical Implementation
-Data Augmentation Techniques
-
-White/Pink Noise Addition: Improves robustness to background noise
-Time Shifting: Handles timing variations in speech
-Pitch Shifting: Accounts for natural voice variations
-Time Stretching: Adapts to different speaking speeds
-Volume Changes: Normalizes for different recording levels
-Frequency/Time Masking: SpecAugment for better generalization
-
-Security Measures
-
-Multi-factor Authentication: Voice + confidence scoring
-Threshold-based Rejection: Configurable confidence thresholds
-Authorization Validation: Whitelist-based access control
-Anomaly Detection: Low-confidence sample rejection
-
-🔒 Security Considerations
-This system is designed for demonstration purposes. For production deployment:
-
-Encrypt Model Files: Protect model weights from unauthorized access
-Secure Audio Transmission: Use HTTPS and audio encryption
-Rate Limiting: Implement request throttling
-Audit Logging: Log all access attempts
-Regular Retraining: Update model with new voice samples
-
-📋 Requirements
-
-Python 3.8+
-PyTorch 2.1.0+
-Gradio 4.44.0+
-Librosa 0.10.1+
-Scikit-learn 1.3.2+
-NumPy, SciPy, Matplotlib
 
-🤝 Contributing
-Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
-📄 License
-This project is licensed under the MIT License - see the LICENSE file for details.
-🙏 Acknowledgments
 
-PyTorch Team: For the excellent deep learning framework
-Librosa Developers: For comprehensive audio processing tools
-Hugging Face: For providing the deployment platform
-Gradio Team: For the intuitive interface framework
 
-📞 Contact
-For questions, issues, or collaboration opportunities, please open an issue on GitHub or contact [your-contact-info].
 
-Built with ❤️ using PyTorch, Gradio, and Hugging Face Spaces