Tag Archives: Fusion HAT+

AI On the Edge, Tutorial

AI on the Edge LESSON 11: Control LED on Raspberry Pi With Voice Commands

In today’s lesson, we’re taking our first exciting step into giving our Raspberry Pi the ability to understand and respond to our voice. That’s right — we’re going to control a physical LED using nothing but spoken commands! This is a huge milestone in the class because it marks the beginning of building truly interactive AI projects that can listen to us and take action in the real world.

Using the SunFounder Fusion AI Hat’s built-in microphone and the excellent STT (Speech-to-Text) library, we create a simple but powerful voice assistant that can turn an LED on and off with commands like “on”, “off”, and “quit”. I walk you through every single line of the code so you can clearly see how we capture voice input, process the command, and control real hardware.

This lesson is intentionally straightforward because I want you to build a strong foundation. Once you understand how to take a voice command and turn it into physical action, we can start adding more complexity — like controlling multiple devices, adjusting brightness, or even combining voice control with computer vision in future lessons.

One of the things I love most about this project is how it makes the Raspberry Pi feel “alive.” Instead of clicking buttons or typing commands, you can now talk directly to your project. This is the kind of interaction that makes edge AI projects so much fun and so powerful.

By the end of this lesson, you’ll have a working voice-controlled LED and the confidence to start expanding your voice control skills. This is exactly the kind of capability we need as we move forward in the AI on the Edge journey — giving our intelligent systems natural, human-friendly ways to interact with us.

So grab your Fusion AI Hat, hook up that LED, and let’s turn your Raspberry Pi into a voice-controlled device! As always, I strongly encourage you to code along with me in the video and then play around with the program. Try adding more commands, control multiple LEDs, or even have it say something back to you.

This is where things start getting really fun. Let’s get that LED responding to your voice!

This is the schematic of the circuit we are using for our AI class. We go into great detail on this schematic in LESSON #5 if you want to learn more about it.

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

Now this is the code we developed in this lesson:

 

AI On the Edge

AI on the Edge LESSON 9: Make Your Raspberry Pi Talk with TTS

In today’s lesson, we’re going to make a huge leap forward in giving our Raspberry Pi some real personality. We’re going to teach it how to talk using Text-to-Speech (TTS). Instead of just blinking LEDs or printing text to the screen, our Pi will now speak out loud with a clear, natural-sounding voice.

This is a really fun and important lesson because one of the main goals of this class is to build intelligent systems that can interact with us in more human ways. Being able to make your Raspberry Pi speak opens up all kinds of exciting possibilities — whether you want your robot to tell you what it sees, have your AI assistant read sensor data out loud, or just add some personality and humor to your projects.

In this video, I show you how to use the TTS capabilities on the SunFounder Fusion AI Hat. You’ll learn how to install and set up the TTS engine, speak simple sentences, change voices, and control when the Pi talks. We’ll also look at how to make the speech sound more natural and how to integrate it smoothly into your programs without freezing everything else.

By the end of this lesson, your Raspberry Pi will be able to speak clearly and confidently — which is going to make the rest of our AI on the Edge journey a lot more exciting. Voice output combined with voice input (which we’ll work on soon) is what turns a simple circuit into a real interactive AI companion.

So go ahead and grab your Fusion AI Hat, plug in a speaker, and let’s give your Raspberry Pi a voice! As always, I encourage you to code along with me in the video and then experiment. Try making it say funny things, read temperatures, announce when it detects a face — whatever sparks your creativity.

This is where your projects start to feel truly alive.

I’m really excited for you on this one — let’s make your Raspberry Pi talk!

This is the schematic we are using on these projects;

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

This Schematic is explained in detail in LESSON #5.

Then this is the code we developed in today’s lesson.

 

AI On the Edge, Tutorial

AI on the Edge LESSON 8: Text to Speech (TTS) on the Raspberry Pi

In this video lesson I will show you how to get the Raspberry Pi to speak to you in plain English. This is our first dabbling with AI. In earlier lessons we have discussed that one of our first objectives will be to begin to audibly interact with our project through speech. The first step will be to get the Pi to talk to us. Then in future lessons we will show how to get the Pi to listen to us.

In this lesson we demonstrated simple Text to Speech (TTS) with this code.

Remember this program requires use of the AI Educational OS we flashed in LESSON #2.

As we say in the video, the first time you run the program you will get a permission error. This is because the model folders are inside a system folder and must be created as a ‘superuser’ using ‘sudo’. As shown in the video, you need to open a terminal window, and type in these commands at the command prompt (Put them in one at a time):

You only need to do that one time. Next time you run the program, all will work properly.

Then, in order to hear all the different voice models Piper offers, you can run this program, and each voice will introduce itself to you.

 

Remember in these early lessons we are using this circuit to demo our programs. Please leave this circuit put together.

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class
AI On the Edge

AI on the Edge LESSON 6: Digitial Out, Servos, Analog In and PWM on the Fusion HAT+

n Lesson 6, we’re really starting to get our hands dirty with real hardware control. Today we dive into the core fundamentals of physical computing on the SunFounder Fusion AI Hat — learning how to use Digital Outputs, control Servos, read Analog Inputs, and generate PWM signals.

This is a big lesson because it bridges the gap between writing simple Python scripts and actually making the Raspberry Pi interact with the physical world. You’ll learn how to turn LEDs on and off using digital outputs, precisely control the position of a servo motor, read values from a potentiometer using the Analog-to-Digital Converter, and smoothly adjust brightness using PWM (Pulse Width Modulation).

I take my time in this video to explain not just how to do these things, but why they work the way they do. Understanding PWM is especially important because it’s a technique we’ll use heavily later in the class when controlling motors, adjusting LED brightness, generating audio tones, and more.

By the end of this lesson, you’ll have a solid foundation in hardware control using the Fusion HAT. These skills are critical as we move forward in the AI on the Edge journey — because no matter how smart your AI code is, it eventually has to do something useful in the real world, whether that’s moving a camera, turning on lights, or controlling a robot.

This lesson marks the point where we shift from just blinking LEDs to building real, useful control systems. The combination of reading sensors (Analog In) and controlling actuators (Servos + PWM) is exactly what intelligent edge devices need to sense and act.

So grab your Fusion AI Hat, hook up an LED, a servo, and a potentiometer, and let’s start giving your Raspberry Pi real physical superpowers!

As always, I strongly encourage you to code along with me in the video. Try different servo angles, change the PWM frequency, and experiment with mapping the potentiometer to different outputs. That hands-on practice is where the real learning happens.

You’re doing great — we’re building something special here. Let’s keep going!

The schematic for the circuit we will be using in today’s lesson if below. We describe it in more detail in the video. The schematic is:

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

In the video lesson we demonstrated the following programs:

Digital output to blink an LED:

PWM Example to Control RGB LED Color and Brightness.

Reading Analog Voltages on the Pi 5 Using the Fusion HAT+

Controlling Servos With the Fusion AI HAT+

 

AI On the Edge

AI on the Edge LESSON 2: Raspberry Pi Operating System for Artificial Intelligence

The major challenge we face in this AI on the Edge class is getting a Raspberry Pi 5 configures where you have all the AI Models, Libraries, Modules and Methods installed, and where they all play nicely together. Often, when you add a new model, the old model becomes broken. This is because when you install something new, it often times updates the dependencies. That means it updates a library already on your system. For example, lets say you have numpy 14, working with YOLO 11. Now you install mediapipe, and it updates numpy 14 to numpy 15. This then ‘Breaks’ your YOLO, as it wanted a different version of numpy.  Likely you will get frustrated and quit before you get the dependency problems solved. In order to get around this, you can use a special education version of the Bookworm OS, which has all the needed libraries installed already and working nicely with each other. The video above shows you how to install this OS. Once you do, no not update it, do not upgrade it, do not touch it. Use it to develop your programs and projects for this class. If you want to do something else with your pi, have a separate SD card.