How I Built a Voice-Controlled Local AI Agent with Python and Groq

What I Built I built a voice-controlled AI agent that can take spoken input and convert it into meaningful actions. The system: Accepts input via microphone or audio file Converts speech to text using Whisper (via Groq API) Uses an LLM to understand what the user wants Executes the appropriate action locally — like creating files, generating code, summarizing content, or responding conversationally This project was developed as part of the Mem0 AI/ML Generative AI Developer Intern assignment. Live demo: [your streamlit URL here] GitHub: [your github URL here] Architecture Overview The application follows a simple but effective pipeline: Audio Input → Speech-to-Text → Intent Detection → Action Execution → UI Output Tech stack used: Streamlit — for building the UI quickly Groq API — Whisper (speech-to-text) + LLM (intent understanding) faster-whisper — local fallback for transcription Python — core logic and tool execution Intent Classification Approach Instead of training a separate mod