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Gravity: Offline Language Learning Voice Recognition Sensor for micro:bit / Arduino / ESP32 - I2C & UART

SKU:SEN0539-EN Brand:DFRobot Reward Points: 169
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Introduction

The Offline Speech Recognition Module is an efficient solution ideal for human-computer interaction, machine learning, voice assistants, and smart home voice control projects. It is easy to use and compatible with Arduino, micro:bit, and ESP32, with detailed tutorials provided. It offers plug-and-play functionality through I2C and UART. The module features 121 built-in command words commonly used in education and smart home settings and supports 17 customized commands (e.g., recording a cat's meow for an automatic feeder). The module is highly integrated, including an onboard speaker and microphone, providing real-time voice feedback.


Easy to Use

The offline voice module is simple and compatible with existing platforms like Arduino UNO, micro:bit, and ESP32, with detailed Arduino IDE tutorials and Makecode tutorials. It supports plug-and-play through I2C and UART communication methods, allowing easy integration into projects.

Compatible with Arduino, micro:bit, Raspberry Pi, ESP32


121 Ready-to-Use Words

The built-in commands are commonly used in educational and smart home scenarios, allowing for immediate use without any configuration, such as "Play music," "Open the door," "Turn on the light," and "Close the window." Here is the full list of command words.

Offline Speech Recognition Module Built-in 121 command words


Self-Learning Function

The self-learning function is user-friendly, allowing users to add 17 customized command words. Any language or sound can be trained as a command, providing great flexibility for interactive audio projects.

Teaching the device new commands is simple. Activate the learning mode with the wake-up word, then say "Learning command word," and repeat the desired command three times. After these steps, the device will have learned the new command. Detailed instructions are provided to ensure an easy learning process.

Offline Speech Recognition Module Self-Learning Function


No Internet Connection, More Secure

Users need not worry about their conversations being recorded and uploaded to the cloud. The module functions seamlessly in outdoor projects, schools, and other environments with unstable networks. Unlike existing online voice assistants that rely on cloud platforms and require internet access, potentially compromising device data privacy during use.


High Integration, Saving More Space

The offline voice module integrates an onboard speaker and microphone. High integration saves space and eliminates the hassle of complicated wiring, enhancing the project's neatness and stability.

Offline Speech Recognition Module High Integration, Saving More Space


Real-Time Voice Feedback

Users can immediately receive recognition results during use, improving user experience and convenience.


The Offline Speech Recognition Module is an easy-to-use, highly integrated solution perfect for various projects, offering 121 built-in commands, 17 customized commands, real-time voice feedback, and secure offline operation without the need for an internet connection.

offline voice recognition module features

Features

  • User-Friendly: Plug and play, compatible with Arduino UNO, micro:bit, and ESP32.
  • 121 Pre-Programmed Commands: Ready for immediate use without any setup.
  • Self-Learning Capability: Supports the addition of 17 customized command words.
  • Offline Operation, Enhanced Privacy: No network required.
  • High Integration: Includes an onboard speaker and microphone.
  • Instant Voice Feedback: Provides real-time recognition results.
  • Applications

  • Voice recognition interaction
  • Voice-controlled terminal
  • Competition and project development
  • Specification

  • Operating Voltage: 3.3 - 5V
  • Maximum Operating Current: ≤370 mA (5V)
  • Communication: I2C/UART
  • I2C Address: 0x64
  • Fixed Commands: 121
  • Fixed Wake-up Command: 1
  • Custom Commands: 17
  • Learning Activation Command: 1
  • Onboard Microphone Sensitivity: -28dB
  • Module Size: 49×32 mm / 1.93×1.26”
  • Operating Temperature: 0-70℃
  • Projects

    Project: Getting Started with Offline Voice Recognition Sensor

    Introduction: This video introduces how to use the offline voice recognition sensor, covering both hardware connections and software setup. It also shows how to use the sensor with Arduino and Raspberry Pi, including installing libraries and modifying code to achieve various voice control functions.


    Project: How to Build a Simple Voice-controlled Automation Project

    Introduction: The video shows how to connect the module to a homemade Arduino and write code to control LEDs via voice commands.



    Project: Smart Waiter - Intelligent Voice Assistant in Restaurant

    Introduction: Restaurant/Hotel Food ordering System using Voice Assistant. Using DFRobot Offline Language Learning Voice Recognition Sensor


    Project: Revolutionize Your Automation Game with Blues& DFRobot

    Introduction: Build your own home automation system using the Blues Notecard, Notehub, Node-RED, and Qubitro.


    Project: Raspberry Pi Pico Tutorial - Voice Recognition Module Sensor

    Introduction: This tutorial explores the use of a voice recognition sensor with a Raspberry Pi Pico. The sensor allows interaction with any microcontroller using voice commands. The video demonstrates how to connect the sensor to the Raspberry Pi Pico, create custom command words, and control a relay module board. The sensor's versatility is highlighted, with potential applications in home automation and studio control. The tutorial concludes with a demonstration of voice-controlled relay activation and deactivation.

    Documents

    Shipping List

  • Gravity: Voice Recognition Module - I2C & UART x1
  • Gravity-4P I2C/UART Sensor Connector x1
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    Gravity: Offline Language Learning Voice Recognition Sensor for micro:bit / Arduino / ESP32 - I2C & UART

    $16.90
    In Stock
    QTY DISCOUNT
    • 3-4$16.50
    • 5-9$16.00
    • 10+$15.50
    Quantity:

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