Building a Voice-Controlled AI Agent for Real-Time Intent Execution
🚀 Overview
I built a voice-controlled AI agent that can take audio input, understand user intent, execute local actions, and display results through a web interface.
The goal was to design an end-to-end system that connects speech processing with intelligent execution.
🧠 Architecture
This modular pipeline design allows each component (STT, LLM, execution) to be independently optimized and replaced, which is a common approach in production voice AI systems.
The system follows a simple pipeline:
Audio → Speech-to-Text → Intent Classification → Tool Execution → UI
Each component is modular and communicates sequentially, making the system easy to debug and extend.
🎤 Speech-to-Text
For converting audio to text, I used Groq’s Whisper-based API.
Although the assignment preferred local models, I initially attempted to run local Whisper models but faced RAM limitations. To ensure stable performance, I switched to an API-based solution, which provided fast and reliable transcription.
🤖 Intent Understanding
The transcribed text is processed using a language model to classify intent into:
- Create file
- Write code
- Summarize text
- General chat
I also added simple rule-based overrides to improve accuracy for code-related requests.
⚙️ Tool Execution
Based on the detected intent, the system performs actions such as:
- Creating files (restricted to a safe output folder)
- Generating executable code using an LLM
- Summarizing text
- Handling conversational queries
This layer connects AI decisions with real system operations.
🖥️ User Interface
The frontend is built using Streamlit and displays:
- Transcription
- Detected intent
- Action details
- Final output
This ensures full transparency of the pipeline.
🔥 Key Enhancements
- Human-in-the-Loop: Confirmation before file operations
- Session Memory: Tracks past interactions
- Context-Aware Chat: Maintains conversational continuity
- Error Handling: Graceful failure management
⚡ Challenges
- Running local models under hardware constraints
- Ensuring clean code generation without extra formatting
- Designing reliable intent classification
- Handling audio input and system safety
🎯 Conclusion
This project demonstrates how to design a practical AI agent by combining speech processing, language understanding, and real-world execution. It highlights the importance of modular architecture, system safety, and user interaction in building reliable AI systems.
🔗 Links
- GitHub: github.com/uditjainofficial/assignment-voice-controlled-ai-agent
- Demo Video: youtube.com/watch?v=6frrIILn5BQ&t=5s
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