Update README.md

README.md

@@ -1,51 +1,59 @@
-# 🎤 Voice Recognition Security System
+---
+title: Voice Recognition Security System
+emoji: 🎤
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: "4.44.0"
+app_file: app.py
+pinned: false
+---
 
+🎤 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
 
-## 🚀 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
 
-- **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
 
-## 🏗️ 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
 
-- **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
 
-## 📊 Technical Details
+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
 
-### 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
 
-### 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
+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
+Security Features
 
-## 🔧 Usage
+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
 
-### Online Demo
+🔧 Usage
+Online Demo
 Visit the Hugging Face Space to try the system: [Your Space URL]
-
-### Local Installation
-```bash
-# Clone the repository
+Local Installation
+bash# Clone the repository
 git clone [your-repo-url]
 cd voice-recognition-security
 
@@ -54,85 +62,78 @@ pip install -r requirements.txt
 
 # Run the application
 python app.py
-```
-
-### API Usage
+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
-1. **White/Pink Noise Addition**: Improves robustness to background noise
-2. **Time Shifting**: Handles timing variations in speech
-3. **Pitch Shifting**: Accounts for natural voice variations
-4. **Time Stretching**: Adapts to different speaking speeds
-5. **Volume Changes**: Normalizes for different recording levels
-6. **Frequency/Time Masking**: SpecAugment for better generalization
-
-### Security Measures
-1. **Multi-factor Authentication**: Voice + confidence scoring
-2. **Threshold-based Rejection**: Configurable confidence thresholds
-3. **Authorization Validation**: Whitelist-based access control
-4. **Anomaly Detection**: Low-confidence sample rejection
-
-## 🔒 Security Considerations
 
-This system is designed for demonstration purposes. For production deployment:
+Access decision (Granted/Denied)
+Predicted user
+Confidence score
+Detailed status information
 
-1. **Encrypt Model Files**: Protect model weights from unauthorized access
-2. **Secure Audio Transmission**: Use HTTPS and audio encryption
-3. **Rate Limiting**: Implement request throttling
-4. **Audit Logging**: Log all access attempts
-5. **Regular Retraining**: Update model with new voice samples
+📝 Model Performance
+Training Results
 
-## 📋 Requirements
+Overall Accuracy: ~95%+ on test set
+False Acceptance Rate: <0.05
+False Rejection Rate: <0.10
+Security Score: >85%
 
-- Python 3.8+
-- PyTorch 2.1.0+
-- Gradio 4.44.0+
-- Librosa 0.10.1+
-- Scikit-learn 1.3.2+
-- NumPy, SciPy, Matplotlib
+Supported Audio Formats
 
-## 🤝 Contributing
+WAV (recommended)
+MP3
+FLAC
+OGG
+M4A
+AAC
 
-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.
+🛠️ Technical Implementation
+Data Augmentation Techniques
 
-## 📄 License
+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
 
-This project is licensed under the MIT License - see the LICENSE file for details.
+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
 
-## 🙏 Acknowledgments
+🔒 Security Considerations
+This system is designed for demonstration purposes. For production deployment:
 
-- **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
+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
 
-## 📞 Contact
+📋 Requirements
 
-For questions, issues, or collaboration opportunities, please open an issue on GitHub or contact [your-contact-info].
+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*
+Built with ❤️ using PyTorch, Gradio, and Hugging Face Spaces