# 🎤 Building a Real-Time Voice AI Assistant Using Open Source Tools

> Source: <https://dev.to/kailashdev/building-a-real-time-voice-ai-assistant-using-open-source-tools-1gcj>
> Published: 2026-05-26 22:05:49+00:00

I built a real-time Voice AI assistant that listens, thinks, and talks back — using entirely open-source tools and APIs.

No ChatGPT wrappers.

No expensive SDKs.

Just raw engineering.

🚀 Live Demo

🌐 Try it here:

[https://huggingface.co/spaces/Kailashalgo/voice-ai-chat](https://huggingface.co/spaces/Kailashalgo/voice-ai-chat)

Press and hold the mic button → speak → AI replies out loud.

🧠 What This Project Does

The app creates a full voice conversation pipeline:

You speak into the browser

Whisper converts speech → text

LLaMA 3.3 70B generates a response

gTTS converts text → speech

Audio plays back instantly

It feels surprisingly natural and fast.

🛠️ Tech Stack

Layer Tool

🎤 Speech to Text Whisper Large V3 Turbo (Groq API)

🧠 LLM LLaMA 3.3 70B

🔊 Text to Speech gTTS

⚡ Backend FastAPI + Python

🌐 Frontend Vanilla HTML/CSS/JS

🐳 Deployment Docker

☁️ Hosting HuggingFace Spaces

⚡ Why I Built This

Most AI voice demos online are:

expensive,

closed-source,

or heavily abstracted.

I wanted to understand how real-time voice AI systems actually work under the hood.

This project helped me explore:

streaming workflows,

latency optimization,

speech pipelines,

browser audio APIs,

and LLM orchestration.

🧩 System Architecture

The complete flow:

User Voice

→ Whisper STT

→ LLaMA Processing

→ gTTS Voice Generation

→ Browser Playback

Simple architecture — but extremely powerful.

📂 Project Structure

voice-ai-chat/

├── backend/

│ ├── main.py

│ ├── stt.py

│ ├── tts.py

│ └── requirements.txt

├── frontend/

│ └── index.html

├── Dockerfile

├── .env.example

└── README.md

⚙️ Running Locally

Clone the repository

git clone [https://github.com/kailashv2/voice-ai-chat.git](https://github.com/kailashv2/voice-ai-chat.git)

cd voice-ai-chat

Create virtual environment

python -m venv venv

Install dependencies

pip install -r requirements.txt

Add Groq API key

GROQ_API_KEY=your_key_here

Start FastAPI server

uvicorn main:app --reload

🐳 Docker Support

docker build -t voice-ai-chat .

docker run -p 7860:7860 -e GROQ_API_KEY=your_key voice-ai-chat

💸 Cost

Completely free to build and deploy.

Groq free tier

Whisper via Groq

gTTS

HuggingFace Spaces free hosting

🔥 What I Learned

The hardest part wasn't the AI.

It was reducing latency and making conversations feel natural.

Voice interfaces are fundamentally different from text chat:

response speed matters more,

interruptions matter,

audio processing matters,

UX matters a lot.

This project gave me a much deeper understanding of production-grade AI interaction systems.

🌐 Live Project

Demo:

[https://huggingface.co/spaces/Kailashalgo/voice-ai-chat](https://huggingface.co/spaces/Kailashalgo/voice-ai-chat)

GitHub:

[https://github.com/kailashv2/voice-ai-chat](https://github.com/kailashv2/voice-ai-chat)

👨💻 Built By

Kailash

Building AI systems, full-stack products, and agentic workflows.

If you found this useful, consider starring the repo ⭐