ESP32 NodeMCU LyraT Voice Recognition AI Board (Audio player & Smart Speaker)

Description

ESP-LyraT Development Board
An open-source development board,
supporting Espressif Systems’ ADF
and featuring voice wake-up,
a wake-up button and an audio player.
Designed for smart speakers and
smart-home applications.
Overview
ESP32-LyraT is an open-source development board for Espressif Systems’ Audio Development Framework (ADF). It is designed for smart speakers and smart-home applications.  The dev board consists  of the ESP32-WROVER module, a Micro SD card, expansion interfaces, touch buttons and several function keys. The ESP32-LyraT development board is designed for the speech and voice recognition market. It integrates the ESP32-WROVER module, which includes a dual-core processor and 4.5 MB of operating memory. With this development board, only few peripheral devices are required for implementing a highly-integrated audio solution.
Wide Applicability
With a great variety of voice commands, interactive voice functions and a rich peripheral set, ESP32-LyraT allows the fast development of applications relating to consumer electronics, wearables, smart home and industrial automation.
Excellent Performance
It facilitates the quick and easy development of dual-mode (Bluetooth + Wi-Fi) audio solutions , also supporting  one-key  Wi-Fi configuration, a wake-up button, voice wake-up, voice recognition, cloud platform access, and an audio player.
Key Supported Features:
•
Various mainstream, both lossy and lossless, compressed audio formats, including M4A, AAC, FLAC, OGG, OPUS, MP3, etc.
•
One-key configuration and wake-up from the standby mode.
•
SoftAP and Station mode.
•
Various wireless protocols：Wi-Fi 802.11b/g/n, Classic BT and BLE.
•
A series of audio inputs, including Wi-Fi, BT-audio, DLNA, Line-in, etc.
•
BLE network configuration, and smart network configuration with apps, such as WeChat.
•
Two microphones for the development of near-field and far-field voice recognition applications.
•
Peripherals for differentiated demands.
User Guide and
Audio Development Framework
/espressif/esp-adf @ github
ESP32-LyraTD-MSC Development Board
Designed for smart speakers and AI applications. Supports Acoustic Echo Cancellation (AEC), Automatic Speech Recognition (ASR), Wake-up Interrupt and Voice Interaction.
Overview
ESP32-LyraTD-MSC is designed for smart speakers and AI applications. This audio development board consists of two parts: the upper board, which provides a three-microphone array, function keys and LED lights; and the lower board, which integrates ESP32-WROVER, a MicroSemi Digital Signal Processing (DSP) chip, and a power management module.
Wide Applicability
ESP32-LyraTD-MSC implements voice interaction and cloud-on-demand services for smart speaker applications, human-machine interaction and image recognition for AI applications, as well as interconnectivity  and real-time feedback for smart home devices.
Excellent Performance
ESP32-LyraTD-MSC facilitates the quick and easy development of dual-mode (Bluetooth + Wi-Fi) audio solutions, as it supports one-key Wi-Fi network  configuration, Acoustic Echo Cancellation (AEC), near/far-field voice wake-up, cloud platform access, voice recognition, wake-up interrupt and audio decoding.
Key Supported Features:
•
A lightweight, low-power and cost-effective smart audio solution.
•
Access to multiple cloud platforms including DuerOS, Amazon, Tmall Genie, Turing, JD and iFLYTEK.
•
HTTP live streaming, such as Internet radio and Ximalaya.
•
Voice wake-up optimized with speech recognition and echo cancellation.
•
Three digital MICs support far-field voice pick-up (from a distance of one to five meters).
•
The dual-board design integrates an LED light strip and fully-functional buttons.
•
Audio inputs over Wi-Fi, BT, DLNA, SD-Card.
•
A variety of network configurations, such as Smartconfig, BLE, and Air-kiss.
•
Wi-Fi 802.11b/g/n, Classic BT and BLE in the 2.4GHz band.
•
Multiple audio formats including M4A, AAC, FLAC, OGG, OPUS, MP3, AMR.
User Guide and
Audio Development Framework
/espressif/esp-adf @ github