AI Kid Bot

A real-time voice-chat AI robot for kids, featuring conversational AI, speech-to-text, and text-to-speech capabilities. Designed for educational and fun interactions, supporting multiple languages and customizable voices.

Repository Description: AI-powered conversational robot for children with real-time voice interaction, using STT/TTS and LLMs. Built with Python backend and web-based PWA frontend for cross-device compatibility.

Features

• Real-Time Voice Chat: Continuous speech recognition and synthesis for natural conversations.
• Educational Content: Focuses on science, nature, space, dinosaurs, programming, and more.
• Multi-Provider LLM Support: Integrates with Groq, Ollama, and Gemini APIs.
• Customizable Voices: Supports various TTS voices via browser or server-side Piper.
• Web-Based Interface: Progressive Web App (PWA) for mobile and desktop.
• Modular Architecture: Separate STT, TTS, and LLM components for flexibility.
• Low-Resource Mode: Optimized for CPU-only systems with tiny models.

Architecture

The application follows a client-server architecture:

• Frontend (PWA): HTML5 Canvas-based robot face, WebSocket client for real-time communication. Built with vanilla JavaScript, CSS, and PWA manifest for offline/installable experience.
• Backend: Python FastAPI server with WebSocket support.
  • STT: Whisper (OpenAI) for speech-to-text.
  • TTS: Web Speech API (browser) or Piper (server-side) for text-to-speech.
  • LLM: Groq API (default), Ollama for local models, or Gemini API.
  • Audio Processing: Real-time audio streaming via WebSockets.
• Deployment: Runs locally or in containers (Docker). Supports GPU acceleration where available.

High-level flow: User speaks → STT transcribes → LLM generates response → TTS synthesizes → Audio played back.

Setup

Prerequisites

• Python 3.10+
• API keys for LLM providers (Groq recommended for speed)
• Microphone and speakers

Installation

1. Clone the repository:

git clone https://github.com/yourusername/ai-kid-bot.git
cd ai-kid-bot

2. Create virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

cp .env-example .env
# Edit .env with your API keys (GROQ_API_KEY, GEMINI_API_KEY if needed)

5. Download models (optional, auto-downloads on first run):

python -c "import whisper; whisper.load_model('tiny')"

Configuration

Edit config.json to customize:

• LLM provider and model
• STT/TTS providers
• Voice settings (rate, pitch, volume)

Usage

Running Locally

Start the server:

python run.py

Access at http://localhost:8000 or http://127.0.0.1:8000.

For low-resource systems:

python run.py --whisper tiny --no-pull

Mobile/Remote Access

• PWA: Install as app on mobile for fullscreen experience.
• Remote: Use --tunnel flag for ngrok tunneling and QR code access.
• iPad: Connect via chipbot.local (Bonjour) or Bluetooth pairing.

Docker

Build and run:

docker build -t ai-kid-bot .
docker run --net=host -v $(pwd):/data ai-kid-bot

For GPU support:

docker run --gpus all --net=host -v $(pwd):/data ai-kid-bot

Development

• Backend: run.py (FastAPI), brain/ (LLM logic), speech/ (TTS), transport/ (WebSockets).
• Frontend: avatar/ (HTML/JS/CSS).
• Models: Stored in models/ (Whisper, Piper voices).

Contributing

Contributions welcome! Please open issues for bugs or feature requests.

License

MIT License - see LICENSE file.