AI Innovation Projects Hub

A collection of cutting-edge AI projects spanning speech processing, content generation, social media automation, and specialized tools

Project Overview

This initiative consists of multiple independent projects, each aiming to develop advanced AI tools and assistants with various capabilities. The projects range from speech-to-speech assistants to social media agents and content generation tools. All teams are expected to follow common development practices and deliver functional end products.

Google Colab Prototyping

Initial development using Google Colab for phase one

Functional End Product

Build usable versions with graphical user interfaces

Git Collaboration

Regular commits with clear version control

Multilingual Support

Focus on Tamil language capabilities for speech projects

Project Categories

Team 1: ElevenLabs Speech to Speech

  • Use ElevenLabs for high-quality text-to-speech output
  • Use Whisper (or similar) for speech-to-text
  • Connect with an LLM like GPT for processing
  • Support same-language conversations (English to English, Tamil to Tamil)
  • Build a GUI for testing and real-world use

Team 2: Native Speech Processing Models

  • Implement true speech-to-speech models with native speech processing (no separate ASR)
  • Explore and implement Moshi by Kyutai Labs - a speech-text foundation model with full-duplex spoken dialogue
  • Experiment with Ultravox - an open-source Speech Language Model that extends LLMs with multimodal projectors
  • Evaluate Meta Spirit LM for native speech-text integration with emotional expressivity
  • Compare performance across models for Tamil language support and multilingual capabilities
  • Build a comprehensive GUI for testing different models and approaches
  • Focus on local deployment options that can run on consumer hardware

Team 3: Tamil and Mixed-Language Speech to Speech

  • Prioritize Tamil language support
  • Handle: English → Tamil, Tamil → Tamil, Tamil → English (optional)
  • Use translation APIs or multilingual models
  • Build a GUI focused on Tamil language scenarios

Team 4: Sesame-based Speech Assistant

  • Use the open-source Sesame model by Meta
  • Explore seamless integration of conversational AI with expressive speech
  • Study its performance in Tamil and mixed-language cases
  • Compare quality with other approaches (ElevenLabs, Coqui)
  • Build a complete GUI application using Sesame

Development Environment

Required Tools & Skills

Development Environment

  • WSL (Windows Subsystem for Linux) on Windows
  • Warp terminal for enhanced productivity
  • Visual Studio Code with extensions
  • GitHub Copilot for AI-assisted coding
  • Continue extension for VSCode - AI code completion
  • Cline extension for VSCode - enhanced productivity
  • Trae IDE for specialized development
  • Cursor editor for AI-powered coding
  • Windsurf for UI development

Services & Tools

  • Firebase Studio for backend services
  • Lovable for design collaboration
  • bolt.new for rapid prototyping
  • Ollama for local LLM deployment

Programming Skills

  • JavaScript
  • HTML
  • CSS
  • Python

Recommended Additional Tools

  • Aider in the terminal - AI pair programming assistant
  • Advanced Git workflows with GitHub CLI
  • Docker for containerization

Required Accounts

AI Services

  • ChatGPT (OpenAI)
  • Deepseek
  • Openrouter
  • LmArena
  • Grok AI
  • Groq.com
  • Genspark.ai
  • Google Gemini

Design Tools

  • Canva
  • Figma

Project-Specific Technologies(Learn as needed for your project)

AI & LLM Technologies

  • Langchain - Framework for developing LLM applications
  • Langgraph - Building complex AI workflows and agents
  • RAG (Retrieval Augmented Generation) - Enhancing LLMs with external knowledge
  • Vector databases (Pinecone, Weaviate, etc.) - For semantic search and embeddings

AI Media Generation

  • ComfyUI - Node-based interface for stable diffusion workflows
  • Stable Diffusion - Image generation model and ecosystem
  • Automatic1111 - Web UI for Stable Diffusion

Backend Frameworks

  • Express.js - Web framework for Node.js
  • FastAPI - Modern, fast web framework for Python
  • Flask - Lightweight WSGI web application framework for Python

Financial ML Technologies

  • XGBoost - Gradient boosting framework for time series prediction
  • LSTM (Long Short-Term Memory) - Neural network architecture for sequence data
  • Prophet - Time series forecasting library
  • Pandas-ta - Technical analysis library for financial data
  • yfinance - Yahoo Finance market data downloader
  • Alpha Vantage API - Real-time and historical financial data

Collaboration Requirements

Git Collaboration

All team members MUST collaborate through Git on a regular basis.

  • Regular commits with descriptive messages
  • Use feature branches and pull requests
  • Code reviews before merging

LinkedIn Updates

For their own professional growth, team members MUST post updates on LinkedIn on a daily basis.

  • Share project progress and milestones
  • Highlight technical challenges and solutions
  • Engage with industry professionals

Task Tracking

All tasks MUST be entered inside Spark Synergy to:

  • Maintain score and track progress
  • Ensure accountability and transparency
  • Qualify for a high-profile certificate upon completion

Project Workflow

1

Prototype Phase

Google Colab development of basic project pipeline

Initial setup and prototyping
2

Development Phase

Build full functionality with GUI interfaces

Implementation with specified technologies
3

Delivery Phase

Finalize and document the completed project

User guide and video demonstration

Deliverables for All Teams

Functional Project Pipeline

Complete working system with all required functionality

Graphical User Interface

Intuitive web or desktop interface for end users

Git Repository

Regular commits with clear messages showing progress

Weekly Updates

Regular reporting on project status and milestones

User Guide

Documentation explaining how to use the system

Video Demo

Short demonstration showcasing functionality