
ESP32-S3 Korvo 2 Audio Dev Board
Espressif audio and multimedia development board with ESP32-S3, dual-microphone array for far-field wake-word detection, camera support, LCD interface, microSD, and Wi-Fi + BLE.
$49.95
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Specifications
Components
Two MEMS microphones for near/far-field wake-word detection.
Bus Interfaces
GPIO Map
Overview
The ESP32-S3 Korvo 2 is Espressif's dedicated audio and multimedia development board, designed for building voice assistant, smart speaker, and audio streaming applications. At its core is the ESP32-S3 SoC — dual-core Xtensa LX7 at 240 MHz with AI acceleration and Wi-Fi 4 + Bluetooth 5 LE — paired with a dual-microphone array optimized for near and far-field wake-word detection and voice command recognition.
The board supports 24-pin 8-bit camera modules (OV5640, OV2640) for video capture, an LCD display interface for visual output, and a microSD card slot for audio file storage and logging. JPEG video processing is supported directly in firmware. The ESP-ADF (Audio Development Framework) provides high-level APIs for audio pipelines, equalizers, and streaming codecs, while ESP-SR handles offline speech recognition with multi-language wake-word support.
Connectivity over USB Micro-B enables programming and serial debugging. The Korvo 2 is the development platform for IoT voice interfaces, smart home hubs, and intercom systems, and integrates with ESP RainMaker for cloud connectivity. Hardware is open-source via the Espressif esp-adf repository.
Use Cases
5Voice Assistant
Offline and cloud-connected voice assistant, wake-word detection, and speech recognition projects.
Audio
Audio playback, streaming, amplification, digital signal processing, and music projects.
IoT
Internet of Things applications including connected sensors, actuators, and smart devices that communicate over WiFi, BLE, LoRa, or other wireless protocols.
Smart Home
Home automation devices for controlling lighting, climate, security, and appliances.
AI / Machine Learning
Edge AI inference, TinyML, TensorFlow Lite, and on-device machine learning applications.
Where to Buy
Available Software
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