🗣️ Convo-AI 🤖

A conversational AI system with voice + text input, powered by Ollama (local LLM), Whisper speech-to-text, and XTTS v2 text-to-speech.

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⚙️ Prerequisites

• 🐍 Python 3.8+
• 🎵 FFmpeg (for audio processing)
• 🦙 Ollama (for local LLM serving)
• 🎤 A working microphone (for voice input)

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📥 Installation

1. Clone the repo

git clone <repository-url>
cd Convo-Ai

2. Create a virtual environment

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

# Windows
python -m venv venv
.�env\Scripts�ctivate

3. Install dependencies

pip install -r requirements.txt

4. Install FFmpeg

# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt-get install ffmpeg

# Windows (Chocolatey)
choco install ffmpeg

5. Install & start Ollama

# Download: https://ollama.ai/download
ollama serve

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▶ Usage

1. Start the server

source venv/bin/activate   # or .\venv\Scripts\activate on Windows
python server.py

2. Run the client

source venv/bin/activate
python talk.py

3. Choose input mode

• 🎤 Voice → Press r to start/stop recording
• ⌨ Text → Type and press Enter

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✨ Core Features

• 🎤 Voice and text input modes
• 🤖 Natural language processing with Ollama
• 🔊 Text-to-speech output (XTTS v2)
• 📝 Conversation history & session logging
• 🎭 Mood analysis
• 🌐 Optional web interface (FastAPI + WebSocket)
• 🔒 Privacy-first: all processing runs locally

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⚙️ Configuration

Edit config.json to adjust:

• Voice model
• Speed & pitch
• WebSocket URL
• LLM settings

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🛠️ Tech Stack

• Backend → Python, FastAPI
• Speech-to-Text → OpenAI Whisper
• LLM → Ollama (local)
• TTS → XTTS v2
• Frontend → HTML + JavaScript
• Realtime → WebSocket

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📁 Project Structure

convo-ai-isolated/
├── src/
│   ├── server.py
│   └── talk.py
├── templates/
│   └── index.html
├── static/
├── logs/
├── tts_cache/
├── config.json
├── requirements.txt
├── README.md
└── HOWTO.md

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🛠️ Troubleshooting

• ✅ Virtual environment activated?
• ✅ Dependencies installed?
• ✅ FFmpeg installed?
• ✅ Ollama running?
• ✅ Microphone permissions granted?

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🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit (git commit -m 'Add some AmazingFeature')
4. Push (git push origin feature/AmazingFeature)
5. Open a Pull Request

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🙏 Acknowledgments

• Ollama — Local LLM
• Whisper — Speech-to-text
• XTTS — Text-to-speech
• FastAPI — Web framework

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