Building a Voice-Controlled AI Agent with Speech Recognition and LLMs

Yenugu Sujithreddy — Mon, 13 Apr 2026 08:15:28 +0000

GITHUB LINK: https://github.com/SUJITH-REDDY-YENUGU/VOICE_AI_AGENT

Introduction

In this project, I built a voice-controlled local AI agent capable of understanding spoken commands, classifying user intent, executing tasks on the local machine, and displaying results through a user interface. The system integrates speech-to-text models, large language models, and local tool execution into a single pipeline.

The goal was to simulate a real-world AI assistant that can interact with users through voice and perform meaningful actions such as creating files, generating code, and summarizing text.

System Architecture

The system follows a modular pipeline:

Audio Input → Speech-to-Text → Intent Classification → Tool Execution → UI Display

Each component is designed to be independent and replaceable, allowing flexibility in choosing models and tools.

Components and Implementation

1. Audio Input

The system accepts audio in two ways:

Live microphone input
Uploading audio files in formats such as .wav or .mp3

This ensures flexibility for both real-time interaction and testing scenarios.

2. Speech-to-Text

The audio input is converted into text using a speech recognition model. I used a Whisper-based model for transcription due to its strong accuracy across different accents and noise conditions.

If running locally is not feasible, API-based alternatives can be used, but local inference was preferred to maintain system independence.

3. Intent Understanding

After transcription, the text is passed to a large language model to determine the user’s intent.

The system supports the following intents:

File creation
Code generation and writing
Text summarization
General conversation

The model analyzes the input and outputs a structured intent label, which is then used to trigger the appropriate action.

4. Tool Execution

Based on the detected intent, the system executes corresponding actions on the local machine.

File operations:

Creates files and directories inside a restricted output folder

Code generation:

Generates code using the language model and writes it into a file

Text processing:

Summarizes user-provided content

Safety was ensured by restricting all file operations to a dedicated output directory to prevent unintended system modifications.

5. User Interface

The system includes a user interface built using a web-based framework. The UI provides a clear view of the entire pipeline and displays:

The transcribed text from the audio input
The detected user intent
The action performed by the system
The final output or result

This makes the system transparent and easy to interact with.

Example Workflow

User input:
"Create a Python file with a retry function"

System execution:

Audio is transcribed into text
Intent is classified as file creation and code generation
The system generates the required Python code
A file is created inside the output directory
The UI displays all intermediate and final results

Challenges Faced

One of the main challenges was integrating multiple components into a smooth pipeline. Ensuring that speech recognition, intent classification, and tool execution worked seamlessly required careful handling of data flow between modules.

Another challenge was running models locally with limited hardware resources. This required selecting lightweight models or using APIs as fallbacks.

Handling ambiguous user inputs was also difficult, as the system needs to correctly interpret intent even when commands are not clearly defined.

Conclusion

This project demonstrates how multiple AI components can be combined to create a practical voice-controlled assistant. By integrating speech recognition, language models, and local execution tools, the system is able to perform meaningful real-world tasks.

The modular design allows for easy improvements, such as adding more intents, improving model accuracy, or enhancing the user interface.

Future Improvements

Support for compound commands
Improved intent classification with fine-tuned models
Persistent memory for maintaining context
Better error handling for unclear audio inputs
Performance optimization for faster local inference

Final Thoughts

Building this system provided hands-on experience with designing end-to-end AI pipelines. It highlights the importance of combining multiple technologies to create intelligent and interactive applications.

DEV Community: Yenugu Sujithreddy