DIY ESP32 AI Voice Assistant with Xiaozhi MCP Framework

Voice-controlled smart devices have changed how we interact with technology, but most commercial assistants come with limitations such as privacy concerns, closed ecosystems, and limited customisation. This ESP32 AI Voice Assistant project demonstrates how you can build a fully functional, open-source, and customisable voice assistant from scratch using affordable hardware and modern embedded AI frameworks.

Built around Espressif’s powerful ESP32-S3 platform, this portable AI voice assistant combines on-device wake-word detection with cloud-based conversational AI, delivering natural voice interaction without relying on a smartphone.

This DIY AI voice assistant integrates Espressif’s Audio Front-End (AFE) framework with the Xiaozhi MCP chatbot system, creating a hybrid edge-and-cloud architecture. The ESP32-S3 handles real-time audio capture, noise suppression, and wake-word detection, while advanced natural language processing is performed by cloud-hosted large language models.

The result is a compact, always-on smart assistant capable of understanding voice commands, responding with natural speech, and controlling connected devices through standardised AI-to-hardware communication.

Core Hardware Components

• ESP32-S3-WROOM-1-N16R8 - Main controller with PSRAM and flash
• ICS-43434 MEMS microphones (×2) - Clear voice capture
• MAX98357A I²S amplifier -  Audio output
• BQ24250 Li-ion charger - Safe battery charging
• MAX20402 buck-boost converter - Stable 3.3V supply
• WS2812B RGB LEDs - Visual feedback
• USB-C connector - Power and programming

All components are selected to balance performance, power efficiency, and compact PCB design.

How the Voice Assistant Works

Wake-Word Detection

The ESP32-S3 continuously listens for a custom wake word using a low-power neural network

Audio Capture & Processing

Voice input is captured through the microphone array and processed using AFE for noise reduction and echo cancellation.

Cloud AI Interaction

Audio is streamed to the Xiaozhi backend, where speech-to-text, language model reasoning, and text-to-speech are performed.

Response Playback

The generated voice response is streamed back and played through the speaker in real time.

Hardware Control via MCP

Voice commands can trigger GPIO actions such as turning LEDs on or off, controlling relays, or interacting with sensors.

Firmware and Development

The firmware is developed using ESP-IDF (v5.4 or higher) in Visual Studio Code. Xiaozhi’s open-source framework allows easy configuration of wake words, AI backends, and MCP tools. The system supports multiple cloud AI models and can be adapted for different use cases without modifying the core firmware.

Enclosure and Design

A custom 3D-printed enclosure completes the project, designed to:

• Improve acoustic isolation between speaker and microphones
• Provide proper ventilation for power components
• Display LED status clearly
• Support desktop or wall-mounted use

The result is a polished, professional-looking AI assistant built entirely from scratch.

Applications

• Smart home voice control
• Hands-free personal assistant
• Embedded AI learning platform
• Accessibility support through voice interaction
• Custom AI experimentation with hardware integration

This ESP32 AI voice assistant project shows how far embedded AI has come. By combining edge-level audio processing with cloud-based intelligence, it’s now possible to build responsive, conversational devices on low-cost hardware. With full access to schematics, firmware, and PCB files, this open-source project empowers makers to explore AI, embedded systems, and smart device control without relying on closed commercial platforms.

Whether you’re an electronics enthusiast, IoT developer, or AI hobbyist, this project provides a complete roadmap for building your own intelligent voice assistant using ESP32-S3.