Lilly Tech Systems
Build a Voice Assistant
Build a complete, end-to-end voice assistant from scratch. You will capture speech with Whisper ASR, process conversations with an LLM brain, synthesize responses with ElevenLabs/OpenAI TTS, stream audio over WebSockets, and deploy the entire system with Docker — all in 5 hands-on steps plus enhancements.
What You Will Build
A fully functional voice assistant that listens to your speech, understands your intent through an LLM, and responds with natural-sounding speech. The system captures microphone audio, transcribes it with Whisper, sends the transcript to an OpenAI-powered conversation engine, and streams synthesized audio back to the browser in real time.
Speech Recognition
Real-time audio capture and transcription using OpenAI Whisper. Handles noise, accents, and streaming audio chunks with high accuracy.
LLM Conversation Engine
An intelligent brain powered by GPT-4o with dialog management, conversation memory, and tool-use capabilities for actions like setting timers or searching the web.
Text-to-Speech
Natural voice synthesis using ElevenLabs and OpenAI TTS APIs. Streaming audio output with voice selection and low-latency playback.
Web Interface
A browser-based push-to-talk interface with WebSocket audio streaming, waveform visualization, and real-time conversation display.
Tech Stack
Production-grade components with generous free tiers. Total cost to run: $0 for development, under $10/month in production.
Python 3.11+
The core language for the backend server, audio processing, and API integrations.
FastAPI + WebSockets
Async web framework with native WebSocket support for real-time bidirectional audio streaming.
OpenAI Whisper
State-of-the-art automatic speech recognition. Use the API ($0.006/min) or run locally with whisper.cpp for zero cost.
OpenAI GPT-4o
The conversation brain. Handles intent understanding, dialog management, and tool calling at $2.50/1M input tokens.
ElevenLabs / OpenAI TTS
High-quality voice synthesis with streaming support. ElevenLabs for premium voices, OpenAI TTS for cost-effective output.
Docker
Containerized deployment with docker-compose for reproducible builds across dev, staging, and production.
Prerequisites
Make sure you have these installed before starting.
Required
- Python 3.11 or higher
- Docker and docker-compose
- An OpenAI API key (get one at
platform.openai.com) - An ElevenLabs API key (free tier at
elevenlabs.io) - A microphone-equipped device for testing
- A modern browser (Chrome, Firefox, Edge)
Helpful but Not Required
- Experience with FastAPI or async Python
- Familiarity with WebSockets
- Basic understanding of audio formats (WAV, PCM, MP3)
- HTML/CSS/JavaScript basics for the frontend step
Build Steps
Follow these lessons in order. Each step builds on the previous one. By the end, you will have a fully deployable voice assistant.
1. Project Setup & Architecture
Understand the ASR-LLM-TTS pipeline, set up the project structure, install dependencies, and configure API keys for Whisper, OpenAI, and ElevenLabs.
2. Speech Recognition
Integrate OpenAI Whisper for speech-to-text. Handle streaming audio capture, noise filtering, silence detection, and real-time transcription.
3. LLM Conversation Engine
Build the AI brain with dialog management, conversation memory, tool-use capabilities, and streaming response generation.
4. Text-to-Speech
Integrate ElevenLabs and OpenAI TTS for natural voice synthesis with streaming audio output, voice selection, and SSML support.
5. Web Interface
Create a browser-based voice UI with WebSocket audio streaming, push-to-talk controls, waveform visualization, and conversation history.
6. Deploy to Production
Containerize the stack with Docker, optimize latency, handle concurrent WebSocket sessions, and set up monitoring.
7. Enhancements & Next Steps
Add wake word detection, multi-language support, telephony integration, and explore advanced voice assistant patterns. Includes a comprehensive FAQ.