readme.md

metadata

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

git clone [repository-url]
cd vocal-emotion-recognition

2. Install Dependencies

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

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

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

preprocess_audio(audio_file)
extract_features(audio_data)

Model Interface

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