🎙️ WhispererAI 🤖

An intelligent voice-based AI assistant that transcribes speech and answers questions in real-time using OpenAI's Whisper and Llama models.

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✨ Core Features

• 🎤 Real-time Audio Recording & Transcription: Capture and convert speech to text instantly.
• 🧠 Local Speech Recognition: Utilizes the Whisper Base model for efficient on-device processing.
• 💡 AI-Powered Responses: Leverages Llama (via Ollama) for intelligent question answering.
• 🔊 High-Quality Audio Processing: Includes noise filtering for clearer audio input.
• 🚀 CUDA Acceleration: Supports GPU acceleration for faster performance.
• 💻 Cross-Platform Compatibility: Works on Windows, Linux, and macOS.

🛠️ Tech Stack

• Programming Language: Python 3.8+
• Speech-to-Text: OpenAI Whisper (Base model)
• Language Model: Llama (via Ollama)
• Core Libraries:
  • PyTorch
  • Transformers
  • SoundFile
• Audio Backend: FFMPEG

📋 Prerequisites

• 🐍 Python 3.8 or higher.
• 🎮 CUDA-capable GPU (Optional, but highly recommended for performance).
• 🎞️ FFMPEG installed and accessible in your system's PATH.
• 🦙 Ollama installed and running locally.
• 🎧 A compatible audio input device (Defaults to HyperX Cloud Stinger Core Wireless on Windows, or system default otherwise).

🚀 Getting Started: Installation

1. Clone the Repository:

   git clone https://github.com/yourusername/WhispererAI.git
   cd WhispererAI

2. Set Up a Virtual Environment:

   python -m venv venv

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Install and Run Ollama:

   • Download and install Ollama from ollama.com.
   • Ensure the Ollama service is running.
   • Pull the Llama model you intend to use (e.g., ollama pull llama3.2).

💻 How to Use

1. Launch the Application:

   python app.py

2. Interact with the Assistant:

   • Press R to Start Recording your voice.
   • Press S to Stop Recording and process the audio.
   • Press C to Clear the terminal screen.
   • Press Q to Quit the application.

⚙️ Configuration Details

The application comes with the following default settings:

• Audio Sample Rate: 48kHz
• Audio Channels: Mono
• Whisper Model: openai/whisper-base
• LLM (via Ollama): llama3.2 (Ensure this model is available in your Ollama setup)
• Processing Device: CUDA (if available), otherwise CPU.
• Audio Filters:
  • High-pass: 50Hz
  • Low-pass: 15kHz
  • Volume Boost: 1.5x

🎤 Audio Device Setup

• Windows: Attempts to automatically detect "Microphone (HyperX Cloud Stinger Core Wireless DTS)".
• Linux/macOS: Uses the default system audio input device.
• ℹ️ If the preferred device isn't found, the application will list available audio devices. You may need to modify app.py to specify your device.

🤝 Contributing

Contributions are highly encouraged and welcome! If you have improvements or bug fixes, please:

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

⚠️ Important Notes

• Ensure Ollama is running with the specified model before starting WhispererAI.
• Configure your audio input device in app.py if the default settings don't work for your setup.
• For the best performance, a CUDA-capable GPU is recommended.

📝 License

This project is licensed under the MIT License. See the LICENSE file for more details (assuming a LICENSE file exists or will be created).

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Happy Whispering! 💬