Live Transcription with Multimodal LLMs

Overview

Implement real-time audio streaming and transcription using Multimodal Large Language Models with low latency and automatic error correction.

Project Details

• Complexity: Large
• Estimated Time: 100-120 hours
• Mentors: Navas (BE), Bijoy (BE)
• Project Links:
  • Backend: https://github.com/medispeak/medispeak-backend
  • Frontend: https://github.com/medispeak/medispeak-app

Skills Required

• Python
• WebSocket
• Audio processing
• Machine Learning
• Performance optimization
• Ruby on Rails
• Real-time systems
• LLM Integration

Acceptance Criteria

1. Implement real-time audio streaming
2. Support continuous mode
3. Add automatic punctuation and formatting
4. Implement context-aware error correction
5. Enable streaming stability
6. Support multiple audio formats
7. Leverage multimodal capabilities for improved accuracy

Milestones

Phase 1: Streaming Setup (30-35 hours)

• Implement audio streaming
• Set up WebSocket connection
• Configure LLM integration
• Create basic transcription pipeline
• Add performance monitoring

Phase 2: Real-time Processing (25-30 hours)

• Optimize latency
• Implement continuous mode
• Leverage LLM for enhanced punctuation
• Create context-aware formatting engine

Phase 3: Error Handling (25-30 hours)

• Implement intelligent error detection
• Add LLM-based correction system
• Create recovery mechanism
• Build stability features

Phase 4: Optimization (20-25 hours)

• Performance optimization
• Write comprehensive tests
• Create documentation
• System stress testing