Control Your Appliances with Voice Commands using Coral Dev Board Mini

Control Your Appliances with Voice Commands using Coral Dev Board Mini

Table of Contents

  1. Introduction
  2. The Coral Dev Board Mini
  3. Building an AI Architecture
  4. Controlling Appliances with Voice Commands
  5. Using the Edge TPU for Keyphrase Classification
  6. Controlling Lights with Python
  7. Bringing it All Together: Implementing a Callback Function
  8. Testing the System
  9. Conclusion

Introduction

The idea of adding locally running AI to our appliances and controlling them with just our voice is an exciting prospect. With the new Coral Dev Board Mini, this is now a possibility. In this article, we will explore how to build an AI architecture that allows us to control our appliances with voice commands, without needing an internet connection.

The Coral Dev Board Mini

The Coral Dev Board Mini is a single port computer for prototyping locally running AI and ML. It is smaller than the Coral Dev Port and only slightly larger than the Coral USB Accelerator. The board has a quad-Core ARM CPU, 2GB of RAM, Wi-Fi 5, and Bluetooth 5. It also has a 3.5mm headphone and mic combo jack, a speaker output terminal, camera and display connectors, a micro HDMI port, and two USB-C ports, both of which can be used to power the port. The board also has an Edge TPU machine learning accelerator, which runs at 4 trillion operations per Second.

Building an AI Architecture

To build an AI architecture that allows us to control our appliances with voice commands, we need to first understand how it works. We start by saying key phrases, such as "switch on" or "Switch Off," which are recorded by the on-board microphone. We then pass the recording to the Edge TPU, which runs a keyphrase classification machine learning model. We get the detected key phrases and execute actions Based on those. The action can be to send an API call to our equipment.

Controlling Appliances with Voice Commands

To control our appliances with voice commands, we need to first test the keyword inference with an example application. We can use the pre-trained model that comes with the Coral Dev Board Mini, which can recognize around 140 key phrases. We can pick and choose keywords that we need, such as "switch on" and "switch off" for the lights. We can also use keywords for turning up and down the brightness. We can test the keyword inference with this example application, which will Record our voice, pass it through the Edge TPU running the pre-trained model, and execute the corresponding action.

Using the Edge TPU for Keyphrase Classification

To use the Edge TPU for keyphrase classification, we need to download the Google Coral example project for detecting audio key phrases. We can then test the keyword inference with the keywords we selected. The default brightness for the lights can also be set using a keyphrase. We can use the on-board microphone to record our voice and run a voice classification machine learning model to detect what We Are saying and then control other appliances.

Controlling Lights with Python

To control our lights programmatically, we can use a Python module called "Elgato Key Light." We can install it with pip and then import it into our code. We can then make a "leg light" object and execute "elgato.off" to switch off the lights. We can switch them back on by executing "elgato.on." For lights that require a mechanical power switch, we can use a smart switch that has Wi-Fi and a Python client.

Bringing it All Together: Implementing a Callback Function

To bring everything together, we can implement a callback function that executes an operating system command based on the command that we received. The mapping between the keywords and the shell commands to execute are in a config file. We can configure what command we want to execute based on the keyword that was detected.

Testing the System

To test the system, we can execute the light control script manually and tell it to increase the brightness. We can then bring it back down to the default brightness. We can then start the app on the Coral Dev Port Mini and say our keywords, such as "switch off," "switch on," "turn up," "turn down," and "position 11." This will allow us to control our appliances with voice commands.

Conclusion

In conclusion, the Coral Dev Board Mini is a powerful tool for building an AI architecture that allows us to control our appliances with voice commands. By using the Edge TPU for keyphrase classification and Python for controlling our lights, we can bring everything together to Create a system that is both powerful and easy to use.

Highlights

  • The Coral Dev Board Mini is a single port computer for prototyping locally running AI and ML.
  • We can use the on-board microphone to record our voice and run a voice classification machine learning model to detect what we are saying and then control other appliances.
  • To control our lights programmatically, we can use a Python module called "Elgato Key Light."
  • We can implement a callback function that executes an operating system command based on the command that we received.
  • By using the Edge TPU for keyphrase classification and Python for controlling our lights, we can create a system that is both powerful and easy to use.

FAQ

Q: Can the Coral Dev Board Mini be used without an internet connection? A: Yes, the Coral Dev Board Mini can be used without an internet connection.

Q: How many key phrases can the pre-trained model recognize? A: The pre-trained model can recognize around 140 key phrases.

Q: What Python module can be used to control the Elgato Key Light? A: The "Elgato Key Light" Python module can be used to control the Elgato Key Light.

Q: What is a callback function? A: A callback function is a function that is passed as an argument to another function and is executed after some event has occurred.

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