MindSync 2.0 🧠✨

Real-time AI-powered meeting transcription and smart assistance platform

MindSync 2.0 is an intelligent meeting assistant that provides real-time transcription, smart suggestions, and comprehensive meeting management with voice cloning capabilities.

🚀 Features

Core Capabilities

• Real-time Transcription: Live audio-to-text using VOSK and Whisper AI
• AI Assistant Mode: Intelligent suggestions and responses during meetings
• Voice Cloning: Personalized TTS with voice synthesis
• Meeting Management: Complete CRUD operations for meeting records
• Chat Interface: Interactive AI-powered conversation
• Multi-format Audio: Support for various audio formats and real-time streaming

Technical Highlights

• Dual Transcription Engine: VOSK for real-time + Whisper for accuracy
• WebSocket Communication: Real-time bidirectional data flow
• Vector Search: Semantic search across meeting content
• Pronunciation Training: Interactive pronunciation coaching
• REST API: Comprehensive backend API with FastAPI
• Modern Frontend: React with TypeScript and Vite

� Screenshots

Main Interface

Modern sidebar navigation with organized sections and clean interface

AI Assistant Mode

Intelligent AI suggestions and responses during meetings

Interactive Chat Interface

AI-powered conversation with context awareness

Voice Cloning Technology

Personalized voice profile management and creation

TTS Testing & Voice Synthesis

Real-time text-to-speech testing with custom voice profiles

�📋 Prerequisites

System Requirements

• Python: 3.11+ (for backend)
• Node.js: 18+ (for frontend)
• FFmpeg: For audio processing
• Ollama: For local LLM support (optional)

Platform Support

• macOS (tested)
• Linux (Docker recommended)
• Windows (Docker recommended)

🛠️ Quick Start

Option 1: One-Command Deployment (Recommended)

# Clone the repository
git clone <repository-url>
cd MindSync2.0

# Make scripts executable
chmod +x start.sh stop.sh dev.sh

# Start everything in background
./start.sh

Option 2: Docker Deployment

# Using Docker Compose
docker-compose up -d

# Check status
docker-compose ps

Option 3: Manual Development Setup

# Backend setup
cd meeting-summarizer-app/backend
python -m venv venv311
source venv311/bin/activate  # On Windows: venv311\Scripts\activate
pip install -r requirements.txt

# Frontend setup
cd ../frontend
npm install

# Run backend (Terminal 1)
cd meeting-summarizer-app/backend
python run_server.py

# Run frontend (Terminal 2)
cd meeting-summarizer-app/frontend
npm run dev

🎯 Usage

Accessing the Application

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs

Key Features Walkthrough

1. Real-time Transcription

1. Click "Start Recording" on the main interface
2. Speak into your microphone
3. See live transcription appear in real-time
4. AI suggestions will appear automatically

2. Meeting Management

• Create: Upload audio files or start live recording
• View: Browse all meetings with search and filters
• Edit: Update meeting details and transcriptions
• Delete: Remove meetings and associated data

3. Voice Cloning

1. Navigate to TTS section
2. Upload reference audio (your voice)
3. Enter text to synthesize
4. Generate personalized speech

4. Chat Interface

• Ask questions about meeting content
• Get AI-powered insights and summaries
• Interactive conversation with context awareness

🔧 Management Commands

Service Management

# Start services in background
./start.sh

# Stop all services
./stop.sh

# Development toolkit
./dev.sh status    # Check service status
./dev.sh logs      # View real-time logs
./dev.sh test      # Test API endpoints
./dev.sh clean     # Clean up logs and PIDs

Docker Management

# Start with Docker
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

# Rebuild containers
docker-compose up --build -d

Production Deployment

# Install as system service (Linux/macOS)
sudo cp mindsync.service /etc/systemd/system/
sudo systemctl enable mindsync
sudo systemctl start mindsync

# Check service status
sudo systemctl status mindsync

📁 Project Structure

MindSync2.0/
├── README.md                          # This file
├── DEPLOYMENT.md                      # Detailed deployment guide
├── docker-compose.yml                # Docker orchestration
├── Dockerfile                        # Container definition
├── start.sh                         # Main startup script
├── stop.sh                          # Shutdown script
├── dev.sh                           # Development toolkit
├── mindsync.service                 # Systemd service
├── meeting-summarizer-app/
│   ├── backend/                     # FastAPI backend
│   │   ├── app/
│   │   │   ├── main.py             # FastAPI application
│   │   │   ├── models/             # Database models
│   │   │   ├── routers/            # API endpoints
│   │   │   ├── services/           # Business logic
│   │   │   └── utils/              # Utility functions
│   │   ├── config.py               # Configuration
│   │   ├── requirements.txt        # Python dependencies
│   │   └── run_server.py           # Server entry point
│   ├── frontend/                   # React frontend
│   │   ├── src/
│   │   │   ├── App.tsx            # Main application
│   │   │   ├── components/        # React components
│   │   │   └── assets/           # Static assets
│   │   ├── package.json           # Node dependencies
│   │   └── vite.config.ts         # Vite configuration
│   └── vosk-model/                # Speech recognition model
└── uploads/                       # User uploaded files

🔌 API Endpoints

Core Endpoints

• GET /docs - API documentation
• POST /upload-audio - Upload audio for transcription
• GET /meetings - List all meetings
• POST /meetings - Create new meeting
• WebSocket /ws/real-time-transcribe - Real-time transcription

Specialized Endpoints

• POST /chat - AI chat interface
• POST /tts/synthesize - Text-to-speech synthesis
• POST /pronunciation/score - Pronunciation scoring
• GET /audio/{filename} - Serve audio files

🛡️ Configuration

Environment Variables

# Backend Configuration
BACKEND_HOST=0.0.0.0
BACKEND_PORT=8000
DATABASE_URL=sqlite:///uploads/meetings.db

# Frontend Configuration
FRONTEND_HOST=localhost
FRONTEND_PORT=3000

# AI Configuration
OLLAMA_BASE_URL=http://localhost:11434
LLM_MODEL=llama3.2:latest

Audio Configuration

• Sample Rate: 16kHz (VOSK), 16kHz (Whisper)
• Channels: Mono
• Format: PCM, WAV, MP3, WebM supported
• Chunk Size: 1024 bytes for real-time processing

🐛 Troubleshooting

Common Issues

Services Won't Start

# Check port availability
./dev.sh test

# View detailed logs
./dev.sh logs

# Clean up and restart
./dev.sh clean
./start.sh

Audio Issues

• Ensure microphone permissions are granted
• Check browser audio settings
• Verify FFmpeg installation: ffmpeg -version

Docker Issues

# Rebuild containers
docker-compose down
docker-compose up --build -d

# Check container logs
docker-compose logs backend
docker-compose logs frontend

WebSocket Connection Problems

• Check firewall settings
• Verify backend is running on port 8000
• Test with: curl http://localhost:8000/docs

Performance Optimization

• Use Docker for consistent performance
• Ensure adequate RAM (4GB+ recommended)
• SSD storage recommended for large audio files
• Close unnecessary browser tabs during recording

🤝 Contributing

Development Setup

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make changes and test thoroughly
4. Run the test suite: ./dev.sh test
5. Submit a pull request

Code Standards

• Python: Follow PEP 8, use type hints
• TypeScript: Use strict mode, proper typing
• Git: Conventional commit messages
• Testing: Maintain test coverage > 80%

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• VOSK: Open-source speech recognition
• OpenAI Whisper: Advanced transcription accuracy
• FastAPI: Modern Python web framework
• React: Frontend user interface
• TTS (Text-to-Speech): Voice synthesis capabilities

📞 Support

Documentation

• Deployment Guide: See DEPLOYMENT.md
• API Reference: http://localhost:8000/docs
• Development Tools: Use ./dev.sh for common tasks

Getting Help

1. Check the troubleshooting section above
2. Review logs with ./dev.sh logs
3. Test connectivity with ./dev.sh test
4. Create an issue with detailed error information

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Built with ❤️ for seamless meeting experiences