Building a Voice-Controlled AI Agent Using Whisper and Ollama

N A Asgar Basha — Mon, 13 Apr 2026 15:10:56 +0000

Introduction

In this project, I built a Voice-Controlled AI Agent that can take audio input, convert it into text, understand user intent, and perform actions like file creation, code generation, and summarization.

This project demonstrates how AI can automate tasks using voice commands in a fully local environment.

Architecture Overview

The system follows a simple pipeline:

Audio Input
User provides input through an audio file or microphone.
Speech-to-Text
The audio is converted into text using Whisper.
Intent Detection
The transcribed text is analyzed using a local LLM (Ollama) to detect user intent.
Tool Execution
Based on the detected intent, the system performs actions such as:
Creating files
Writing code
Summarizing text
General chat
User Interface
A Streamlit-based UI displays:
Transcribed text
Detected intent
Executed action
Final output

Technologies Used

Python
Whisper (Speech-to-Text)
Ollama (Local LLM)
Streamlit (Frontend UI)

Example Workflow

User Input:
"Create a Python file with hello world code"

System Execution:

Converts speech to text
Detects intent: write_code
Generates code
Saves file in output folder
Displays result in UI

Challenges Faced

Running models locally required good system performance
Managing correct intent classification was tricky
Handling audio formats and errors
Integrating multiple components smoothly

Solutions

Used lightweight Whisper model
Structured prompts for better intent detection
Restricted file operations to a safe output folder
Modularized code for better debugging

Future Improvements

Real-time microphone input
Multiple command support
Better UI experience
Memory and chat history

Conclusion

This project shows how voice interfaces and AI can be combined to create powerful automation tools. Running everything locally ensures better privacy and control.

Author

Asgar Basha

DEV Community: N A Asgar Basha

Building a Voice-Controlled AI Agent Using Whisper and Ollama