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Welcome to Vixevia's learning guide! This document aims to provide you, with insights into how Vixevia works and how you can contribute to the project.

System Architecture

Vixevia utilizes a multi-layered architecture for processing user interactions and generating responses:

1. Input Layer:

  • Speech Recognition: Captures user speech and converts it to text.
  • Vision Processing: Analyzes images from a webcam using OpenCV and generates descriptive text through the vision model.

2. Processing Layer:

  • Gemini Language Model: The core of Vixevia, powered by Google's Gemini, interprets the combined text input from both speech and vision, understands context, and generates responses.
  • Dialogue Management: Maintains conversation history and manages the flow of dialogue.

3. Output Layer:

  • Text-to-Speech Synthesis: Converts generated text responses to audio using gTTS and So-VITS-SVC for a more natural-sounding voice.
  • Visual Output: While currently not implemented, there's potential for displaying Vixevia's expressions or reactions through a virtual avatar.

Key Components

  • Google Generative AI (Gemini): Responsible for natural language processing, understanding user queries, and generating contextually relevant responses.
  • SpeechRecognition: Handles speech-to-text conversion.
  • so-vits-svc-fork: Provides advanced text-to-speech synthesis with realistic voice generation.
  • PyAudio & simpleaudio: Facilitate audio input and output functionalities.
  • Torch & Transformers: Libraries essential for deep learning and natural language processing tasks.
  • gTTS: Text-to-speech library for basic audio generation.
  • opencv-python: Enables image processing and computer vision tasks.
  • FastAPI & Uvicorn: Framework for building the web application and server.

Contribution Opportunities

  • Live2D Model Integration: Develop or integrate a Live2D model to provide Vixevia with a visually engaging and expressive avatar.
  • Enhanced Vision Capabilities: Improve object recognition and scene understanding in the vision processing component.
  • Multi-language Support: Extend Vixevia's capabilities to understand and respond in multiple languages.
  • Personality Refinement: Fine-tune the system prompt and training data to create a more nuanced and engaging personality for Vixevia.
  • Web Interface Development: Improve the user interface and user experience of the web application.
  • Additional Features: Explore and implement new features, such as sentiment analysis, emotion detection, and more diverse response formats (e.g., images, videos).

Getting Involved

  1. Fork the repository: Create your own copy of the project on GitHub.
  2. Explore the codebase: Familiarize yourself with the existing code structure and functionality.
  3. Identify an area of interest: Choose a specific feature or aspect you want to contribute to.
  4. Develop and test your changes: Implement your improvements and test them thoroughly.
  5. Submit a pull request: Share your contributions with the community for review and potential inclusion in the main project.

We appreciate your interest in Vixevia and welcome your valuable contributions to make her even more intelligent and interactive!