Upload readme.md

readme.md

@@ -0,0 +1,135 @@
+---
+title: Vocal Emotion Recognition
+emoji: 🎤
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 3.50.2
+app_file: app.py
+pinned: false
+---
+
+# Vocal Emotion Recognition System
+
+## 🎯 Project Overview
+
+A deep learning-based system for real-time emotion recognition from vocal input using state-of-the-art audio processing and transformer models.
+
+### Key Features
+- Real-time vocal emotion analysis
+- Advanced audio feature extraction
+- Pre-trained transformer model integration
+- User-friendly web interface
+- Comprehensive evaluation metrics
+
+## 🛠️ Technical Architecture
+
+### Components
+1. **Audio Processing Pipeline**
+   - Sample rate standardization (16kHz)
+   - Noise reduction and normalization
+   - Feature extraction (MFCC, Chroma, Mel spectrograms)
+
+2. **Machine Learning Pipeline**
+   - DistilBERT-based emotion classification
+   - Transfer learning capabilities
+   - Comprehensive evaluation metrics
+
+3. **Web Interface**
+   - Gradio-based interactive UI
+   - Real-time processing
+   - Intuitive result visualization
+
+## 📦 Installation
+
+1. **Clone the Repository**
+```bash
+git clone [repository-url]
+cd vocal-emotion-recognition
+```
+
+2. **Install Dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+3. **Environment Setup**
+- Python 3.8+ required
+- CUDA-compatible GPU recommended for training
+- Microphone access required for real-time analysis
+
+## 🚀 Usage
+
+### Starting the Application
+```bash
+python app.py
+```
+- Access the web interface at `http://localhost:7860`
+- Use microphone input for real-time analysis
+- View emotion classification results instantly
+
+### Training Custom Models
+```bash
+python model_training.py --data_path [path] --epochs [num]
+```
+
+## 📊 Model Performance
+
+The system utilizes various metrics for evaluation:
+- Accuracy, Precision, Recall, F1 Score
+- ROC-AUC Score
+- Confusion Matrix
+- MAE and RMSE
+
+## 🔧 Configuration
+
+### Model Settings
+- Base model: `bhadresh-savani/distilbert-base-uncased-emotion`
+- Audio sample rate: 16kHz
+- Batch size: 8 (configurable)
+- Learning rate: 5e-5
+
+### Feature Extraction
+- MFCC: 13 coefficients
+- Chroma features
+- Mel spectrograms
+- Spectral contrast
+- Tonnetz features
+
+## 📝 API Reference
+
+### Audio Processing
+```python
+preprocess_audio(audio_file)
+extract_features(audio_data)
+```
+
+### Model Interface
+```python
+analyze_emotion(audio_input)
+train_model(data_path, epochs)
+```
+
+## 🤝 Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Commit changes
+4. Push to the branch
+5. Open a pull request
+
+## 📄 License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## 🙏 Acknowledgments
+
+- HuggingFace Transformers
+- Librosa Audio Processing
+- Gradio Interface Library
+
+## 📞 Contact
+
+For questions and support, please open an issue in the repository.
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference