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
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