Welcome to the project roadmap for VoiceHAC! In this blog, I’ll be documenting my journey as I work on an ambitious project that involves integrating Mycroft, an open-source voice assistant, with Natural Language Processing (NLP) capabilities using the nltk library, as well as harnessing the power of GPT-2 and GPT-4 models from PyTorch and OpenAI, respectively. The ultimate goal is to create an intelligent and context-aware voice assistant that can interact with my Home Assistant setup and provide seamless control over my smart home devices.

The roadmap below outlines the various steps and milestones involved in this project. As I make progress, I’ll be sharing updates, challenges, and lessons learned through blog posts. Stay tuned to follow along and learn from my experiences as I embark on this exciting journey!

Software Architecture

  • Define a detailed Software Architecture for the project, including general software architecture considerations and Python-specific ones.
    • Define high-level system overview, architectural styles or patterns, and non-functional requirements.
    • Create component and deployment diagrams.
    • Outline data models and database design, security architecture, and interfaces.
    • Detail error handling and logging, and maintenance and scalability plans.
    • Organize Python code into modules and packages for reusability and maintainability.
    • Define objects and data structures, use of libraries and frameworks, and data flow.
    • Document concurrency and parallelism models used, and testing and error handling strategies.
    • Document integration with other systems, environment and dependencies, and deployment and distribution strategies.

Mycroft Voice Assistant

  • Implement a general fallback skill in Mycroft to process all requests. Customize it further as needed.
  • Configure and integrate Mycroft’s ability to handle key tasks like STT, TTS, wake word detection, and managing audio hardware.

Home Assistant

  • Configure Home Assistant to interact with various home automation devices.
  • Set up Home Assistant as the centralized data server.

NLP Component

  • Implement NER microservice for identifying and categorizing entities in the user’s speech.
  • Develop HADirect, dividing it into multiple modules each handling specific types of entities like doors, lights, windows, heating, locks, etc.
  • Set up local GPT-2.0 AI for processing complex or Home Assistant specific queries.
  • Implement cloud-based GPT-4.0 AI for handling general knowledge queries.
  • Create a To-Do Handling microservice for managing to-do lists and related requests.
  • Develop a Grocery List microservice for handling grocery list-related requests.
  • Set up a Media Player microservice for processing all media playing requests.

Context Manager

  • Design a data structure to store conversation history.
  • Implement functions for adding new interactions and retrieving context information.
  • Implement logic for handling follow-up questions and providing context-aware responses.

Orchestrator

  • Design and implement the Orchestrator to manage the different microservices.
    • Implement routing logic to forward requests to the appropriate service.
    • Ensure proper communication between the services.

Integration

  • Connect Mycroft to the NLP component.
  • Integrate the NLP component with the Home Assistant.
  • Implement logic to route queries to the appropriate AI model (GPT-2.0 or GPT-4.0) based on context and intent.
  • Integrate the Context Manager with the NLP component, GPT models, and Mycroft.

Testing and Debugging

  • Test the system with various user queries and scenarios.
  • Debug and fix any issues that arise during testing.
  • Optimize the system’s performance based on test results.