Silent Supporter - AI Therapy Platform

Scalable multimodal therapy platform with advanced AI capabilities

Project Overview

Silent Supporter is an innovative AI-powered therapy platform developed in collaboration with Tsinghua AIR, featuring advanced multimodal capabilities including real-time emotion visualization and personalized music therapy.

Key Achievements

  • Performance Optimization: Reduced real-time generation latency by 80% through WebSocket implementation
  • AI Model Enhancement: Achieved 3x inference speed improvement via ONNX Runtime graph optimization
  • Custom AI Training: Fine-tuned InspireMusic 1.5B model for targeted music style transfer
  • Real-time Visualization: Developed WebGL engine for dynamic conversational emotion display

Technical Architecture

Backend Infrastructure

  • Node.js: High-performance JavaScript runtime for scalable backend services
  • PostgreSQL: Robust relational database for primary data storage
  • Redis: In-memory caching for session management and performance optimization
  • RabbitMQ: Event-driven architecture for asynchronous multimodal generation

AI & Machine Learning

  • Custom Model Fine-tuning: InspireMusic 1.5B model adapted for therapeutic music generation
  • ONNX Runtime: Graph optimization for 3x faster inference speeds
  • Multimodal Processing: Real-time audio, text, and visual data integration

Frontend & Visualization

  • WebGL Engine: Custom-built real-time emotion visualization system
  • Real-time Updates: WebSocket-powered live emotional state tracking
  • Responsive Design: Cross-platform compatibility for therapy sessions

Innovation Highlights

This project represents a significant advancement in digital therapy platforms, combining cutting-edge AI research with practical therapeutic applications. The integration of real-time emotion visualization with personalized music therapy creates a uniquely immersive therapeutic experience.

Research Collaboration

Developed in partnership with Tsinghua AIR, this project contributes to ongoing research in AI-assisted mental health interventions and demonstrates the practical application of advanced machine learning techniques in healthcare technology.