Tennis Attribution Trainer

A web application for training machine learning models to attribute tennis actions and movements to specific players.

Features

• 🎾 File Upload: Upload tennis videos, images, and training datasets
• 🎤 Audio Transcription: AI-powered audio transcription using OpenAI Whisper API
  • Automatic compression for files >25MB
  • Automatic splitting for very large files
  • Supports MP3, WAV, M4A, Opus, and more
• 📊 Training Management: Start and monitor attribution model training
• 🔒 Security: Built with security best practices using Helmet.js
• 📁 Static Files: Serve frontend assets efficiently
• 🌐 CORS Support: Configurable cross-origin request handling
• 📝 Logging: Request logging with Morgan
• ⚡ Fast Development: Hot reload with nodemon

Quick Start

1. Install dependencies:

   npm install

2. Set up environment:

   cp .env.example .env
   # Edit .env with your configuration, including OpenAI API key

3. Configure OpenAI API Key:

   # Add your OpenAI API key to .env file
   OPENAI_API_KEY=your_actual_openai_api_key_here

4. Install ffmpeg (Optional but recommended for large files):

   # Windows (using Chocolatey)
   choco install ffmpeg
   
   # macOS (using Homebrew)
   brew install ffmpeg
   
   # Linux (Ubuntu/Debian)
   sudo apt-get install ffmpeg
   
   # Verify installation
   ffmpeg -version

   Note: ffmpeg is required for automatic compression and splitting of large audio files (>25MB). Without it, files must be manually compressed.

5. Start development server:

   npm run dev

6. Visit the application: Open http://localhost:3000 in your browser

Project Structure

tennis-attribution-trainer/
├── public/                 # Frontend static files
│   ├── index.html         # Main HTML page
│   └── app.js             # Frontend JavaScript
├── server/                # Backend services
│   ├── services/          # Business logic services
│   │   ├── attributionService.js
│   │   └── transcription.js    # Audio transcription service
│   └── data/              # Data storage
│       ├── uploads/       # Uploaded files
│       ├── models/        # Trained models
│       └── processed/     # Processed data
├── server.js              # Main server file
├── package.json           # Dependencies and scripts
├── .env.example           # Environment variables template
└── README.md              # This file

API Endpoints

Health Check

• GET /api/health - Server health status

Audio Transcription

• POST /api/transcribe - Transcribe audio files using OpenAI Whisper
  • Supports files up to 500MB (auto-compresses/splits if >25MB)
  • Requires ffmpeg for large file handling (optional but recommended)

File Upload

• POST /api/upload - Upload single file
• POST /api/upload-multiple - Upload multiple files

Training

• GET /api/training-data - Get training data information
• POST /api/train - Start model training

Audio Transcription

The application now supports AI-powered audio transcription using OpenAI's Whisper API.

Supported Audio Formats

• MP3 - MPEG Audio Layer III
• WAV - Waveform Audio File Format
• M4A - MPEG-4 Audio
• MP4 - MPEG-4 Video (audio track)
• MPEG - MPEG Audio
• MPGA - MPEG Audio
• WebM - WebM Audio

Transcription Features

• Automatic language detection or manual language selection
• Timestamps for segments and individual words
• Context prompts to improve accuracy
• Temperature control for creativity vs. accuracy
• Verbose JSON format with detailed metadata
• File size limit: 50MB per audio file

Usage Example

# Upload an audio file for transcription
curl -X POST http://localhost:3000/api/transcribe \
  -F "audio=@interview.mp3" \
  -F "language=en" \
  -F "prompt=Tennis interview with technical terminology"

Environment Variables

Create a .env file based on .env.example:

PORT=3000
NODE_ENV=development
ALLOWED_ORIGINS=http://localhost:3000
OPENAI_API_KEY=your_openai_api_key_here
MAX_FILE_SIZE=52428800
UPLOAD_DIR=server/data/uploads

Required for Transcription

• OPENAI_API_KEY - Your OpenAI API key for Whisper access

File Upload

Supported file types:

• Images: JPEG, PNG, GIF
• Videos: MP4, MOV, AVI
• Data: CSV, JSON, TXT
• Audio: MP3, WAV, M4A, MP4, MPEG, MPGA, WebM

Maximum file size: 50MB

Development

Available Scripts

• npm start - Start production server
• npm run dev - Start development server with hot reload
• npm test - Run tests (placeholder)

Security Features

• Helmet.js for security headers
• CORS configuration
• File type validation
• File size limits
• Input sanitization
• Automatic file cleanup after transcription

Dependencies

Core:

• Express.js - Web framework
• Multer - File upload handling
• OpenAI - AI API integration
• fs-extra - Enhanced file system operations

Security:

• Helmet - Security headers
• CORS - Cross-origin resource sharing

Development:

• Nodemon - Development auto-reload
• Morgan - Request logging

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

MIT License - see LICENSE file for details