AI assistant

I've built a robust, local AI assistant that leverages three key machine learning components: Speech-to-Text (STT), a Large Language Model (LLM), and Text-to-Speech (TTS). The process starts with a custom wake word model that activates the STT module, which converts spoken language into text using advanced acoustic and linguistic models. This transcribed text is then processed by an LLM, capable of deep natural language understanding and context-aware responses, ensuring precise comprehension and dynamic interactions. Finally, the TTS module converts the generated textual responses back into natural-sounding speech. Running entirely on local hardware, this architecture not only optimizes performance but also prioritizes security by eliminating reliance on external servers, showcasing a strong grasp of integrated machine learning solutions.