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AI large model voice development kit voice assistant chatbot

AI large model voice development kit voice assistant chatbot

AI large model voice development kit voice assistant chatbot to make your own chatbot. A whole electronic kit set ai module to DIY your smart home. Easy to access ChatGPT.Thinkcore Technology Co., Ltd. is a technical enterprise focusing on embedded hardware research and development, design, production and sales.

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Product Description

AI_VOICE Z01 is a cost-effective AI large model voice development kit voice assistant chatbot that can be used to quickly implement A1 voice applications and embedded A1 learning, and quickly develop and verify A1 voice products.

Built-in

 dual-channel high-sensitivity and high-SNR digital microphones,

 DVP universal camera interface,

 TF card slot,

 RGB colorful lights

 3.7V lithium battery interface

 Battery voltage measurement IC and other peripherals


The ESP32-S3R8/ESP32-S3R2 chip it uses integrates low-power WiFi and BLE5.0, external 16MB FIash and 8MB/2MB PSRAM, built-in hardware encryption accelerator, random number generator, HMAC and digital signature module, meeting users' development needs in low-cost, low-power, A1 large model toys, Internet of Things, mobile devices, outdoor monitoring, smart home and other fields.


 Dual-core high-performance main control 240MHZ

 Open source AILINKER backend service framework

 Dual-channel digital silicon microphone

 Support dual-microphone noise reduction algorithm

 Support VAD algorithm Support AEC algorithm

 Support custom access large model

 CHATGPT, Zhipu Qingyan, Tongyi Qianwen, Wenxin Yiyan, Doubao


Product Features

 Lightweight

 Safe

 Low latency

 Wake-up word customization

 Customized control instructions

 Small memory usage

 Fast calculation speed

 Local model

 Offline operation

 Quick response

 No network transmission delay

 Personalized customization

 Corpus collection

 Easy to add and delete commands

 Flexible binding actions


Core function parameters

 Local voice wake-up

 Low resource consumption of wake-up model

 Front-end acoustic algorithm

 High recognition accuracy

 Can support 5 wake-up words

 Internal RAM: 15~24 KB.

 CPU load 9%~30%(ESP32)

 Microphone array algorithm, echo cancellation,

 Noise reduction, sound activity detection




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