Tag Archives: TTS

In Lesson 14 of AI on the Edge, we’re doing something really fun and powerful — we’re building a voice-controlled RGB LED that listens to you, changes colors on command, and even talks back with some personality! This is true edge AI running 100% locally on your Raspberry Pi with the Fusion HAT. No cloud, no internet, just fast, private, and responsive voice interaction right on your desk.

You simply speak a color — red, green, blue, cyan, magenta, yellow, off, or even quit — and the RGB LED instantly springs to life with beautiful color. But that’s not all. Every time you give a command, the system replies with a fun, playful spoken response using the Piper text-to-speech engine. It turns your Raspberry Pi into a charming little LED companion that feels alive and interactive.In this lesson, you’ll learn how to combine local Speech-to-Text with the STT library and natural-sounding Text-to-Speech with Piper. You’ll master PWM control of a full-color RGB LED through the Fusion HAT, and you’ll see how to use Python threading plus a queue to keep the voice listening running smoothly in the background without ever locking up your main program. The code is clean, well-structured, and includes proper startup greetings, graceful shutdown, and excellent resource cleanup — exactly the kind of solid practices we love in this series.What makes this project extra special is how it brings everything together. You get real-time voice recognition, instant hardware response, and spoken feedback — all happening locally on the edge. It’s fast, it’s private, and it’s incredibly satisfying to watch that LED light up exactly as you command while your Pi chats back at you.

Go ahead and watch the full Lesson 14 video, grab the complete code from the description, and build this project step by step with me. Once you have it running, I want you to play with it! Add new colors, create your own funny responses, or start thinking about how you could combine this voice control with sensors or other hardware in future projects.

This is the kind of hands-on, creative AI application that makes learning so exciting. You’re not just watching — you’re building real, useful skills that put you in the driver’s seat with artificial intelligence.

Fire up that Raspberry Pi, get your Fusion HAT ready, and let’s make some colors shine while the Pi talks back. I can’t wait to see what you create with this one!

Happy building, everyone — I’ll see you in the next lesson!

This is the schematic we are using for the project:

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

This is the code we developed in the video:

from fusion_hat.pwm import PWM
from fusion_hat.stt import STT
import threading
from queue import Queue
from time import sleep
from fusion_hat.tts import Piper
tts = Piper()
tts.set_model('en_US-kristin-medium')
msg = 'Speak your favorite color, and your wish is my command'
tts.say(msg,stream=False)

redPin = 5
greenPin = 6
bluePin = 7

redLED= PWM(redPin)
greenLED=PWM(greenPin)
blueLED=PWM(bluePin)

rVal=0
gVal=0
bVal=0

stt = STT('en-us')
running=True
colorQ = Queue()

def getColor():
    print("Input Thread is Running")
    global running
    while running:
        print('What color: red, green, blue, cyan, magenta, yellow, off, quit')
        myColor = stt.listen(stream=False)
        myColor=myColor.strip()
        if myColor == 'quit':
            running = False
            msg = 'So Sorry to See you go, Please Call Me Again Soon'
            tts.say(msg, stream=False)
            break
        colorQ.put(myColor)
    print("Thread is Terminated")
colorThread= threading.Thread(target=getColor,daemon=True)
colorThread.start()
print("Main Program is Started")
try:
    while running:
        if colorQ.empty() == False:
            myColor = colorQ.get()
            print('Color: ',myColor)
            if myColor == 'off':
                rVal = 0
                gVal = 0
                bVal = 0
                msg = 'Please bring back your beautiful colors!'
                tts.say(msg, stream=False)
            if myColor == 'red' or myColor=='read':
                rVal = 100
                gVal = 0
                bVal = 0
                msg = 'I can not get you out of my head, so I will turn it red'
                tts.say(msg, stream=False)
            if myColor == 'green':
                rVal = 0
                gVal = 100
                bVal = 0
                msg = 'Because I want you to be seen, I will turn it green'
                tts.say(msg, stream=False)
            if myColor == 'blue':
                rVal = 0
                gVal = 0
                bVal = 100
                msg = 'Because I love you, I will turn it blue'
                tts.say(msg, stream=False)
            if myColor == 'cyan':
                rVal = 0
                gVal = 100
                bVal = 25
                msg = 'You turn my world cyan and bright, what a beautiful sight!'
                tts.say(msg, stream=False)
            if myColor == 'magenta':
                rVal = 100
                gVal = 0
                bVal = 100
                msg = 'You Light my Magenta Fire, You are my burning Desire'
                tts.say(msg, stream=False)
            if myColor == 'yellow':
                rVal = 100
                gVal = 25
                bVal = 0
                msg = 'You are such a handsome fellow, I will turn it yellow'
                tts.say(msg, stream=False)          
            myColor = 'null'
            redLED.pulse_width_percent(rVal)
            greenLED.pulse_width_percent(gVal)
            blueLED.pulse_width_percent(bVal)
    redLED.pulse_width_percent(0)
    greenLED.pulse_width_percent(0)
    blueLED.pulse_width_percent(0)
    redLED.enable(False)
    greenLED.enable(False)
    blueLED.enable(False)
    print("LEDs are Released")
    print("Program is Terminated")            
         
except KeyboardInterrupt:
    running = False
    redLED.pulse_width_percent(0)
    greenLED.pulse_width_percent(0)
    blueLED.pulse_width_percent(0)
    redLED.enable(False)
    greenLED.enable(False)
    blueLED.enable(False)
    print("LEDs are Released")
    print("Program is Terminated")

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from fusion_hat.pwm import PWM

from fusion_hat.stt import STT

import threading

from queue import Queue

from time import sleep

from fusion_hat.tts import Piper

tts = Piper()

tts.set_model('en_US-kristin-medium')

msg = 'Speak your favorite color, and your wish is my command'

tts.say(msg,stream=False)

redPin = 5

greenPin = 6

bluePin = 7

redLED= PWM(redPin)

greenLED=PWM(greenPin)

blueLED=PWM(bluePin)

rVal=0

gVal=0

bVal=0

stt = STT('en-us')

running=True

colorQ = Queue()

def getColor():

print("Input Thread is Running")

global running

while running:

print('What color: red, green, blue, cyan, magenta, yellow, off, quit')

myColor = stt.listen(stream=False)

myColor=myColor.strip()

if myColor == 'quit':

running = False

msg = 'So Sorry to See you go, Please Call Me Again Soon'

tts.say(msg, stream=False)

break

colorQ.put(myColor)

print("Thread is Terminated")

colorThread= threading.Thread(target=getColor,daemon=True)

colorThread.start()

print("Main Program is Started")

try:

while running:

if colorQ.empty() == False:

myColor = colorQ.get()

print('Color: ',myColor)

if myColor == 'off':

rVal = 0

gVal = 0

bVal = 0

msg = 'Please bring back your beautiful colors!'

tts.say(msg, stream=False)

if myColor == 'red' or myColor=='read':

rVal = 100

gVal = 0

bVal = 0

msg = 'I can not get you out of my head, so I will turn it red'

tts.say(msg, stream=False)

if myColor == 'green':

rVal = 0

gVal = 100

bVal = 0

msg = 'Because I want you to be seen, I will turn it green'

tts.say(msg, stream=False)

if myColor == 'blue':

rVal = 0

gVal = 0

bVal = 100

msg = 'Because I love you, I will turn it blue'

tts.say(msg, stream=False)

if myColor == 'cyan':

rVal = 0

gVal = 100

bVal = 25

msg = 'You turn my world cyan and bright, what a beautiful sight!'

tts.say(msg, stream=False)

if myColor == 'magenta':

rVal = 100

gVal = 0

bVal = 100

msg = 'You Light my Magenta Fire, You are my burning Desire'

tts.say(msg, stream=False)

if myColor == 'yellow':

rVal = 100

gVal = 25

bVal = 0

msg = 'You are such a handsome fellow, I will turn it yellow'

tts.say(msg, stream=False)

myColor = 'null'

redLED.pulse_width_percent(rVal)

greenLED.pulse_width_percent(gVal)

blueLED.pulse_width_percent(bVal)

redLED.pulse_width_percent(0)

greenLED.pulse_width_percent(0)

blueLED.pulse_width_percent(0)

redLED.enable(False)

greenLED.enable(False)

blueLED.enable(False)

print("LEDs are Released")

print("Program is Terminated")

except KeyboardInterrupt:

running = False

redLED.pulse_width_percent(0)

greenLED.pulse_width_percent(0)

blueLED.pulse_width_percent(0)

redLED.enable(False)

greenLED.enable(False)

blueLED.enable(False)

print("LEDs are Released")

print("Program is Terminated")

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

April 26, 2026 admin

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;

This Schematic is explained in detail in LESSON #5.

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

from fusion_hat.adc import ADC
from fusion_hat.pwm import PWM
from fusion_hat.tts import Piper
from time import sleep
tts = Piper()
tts.set_model('en_US-amy-low')
potPin = 0
myPot = ADC(potPin)
redPin = 5
redLED = PWM(redPin)
rangeOld = 100

while True:
    potVal = myPot.read()
    writeVal=100*(potVal/4095) - .005
    if writeVal < 1:
        msg = "Your LED is Off"
        range = 0
    if writeVal >=1 and writeVal<10:
        msg = "Your LED is Brightness 1"
        range =  1
    if writeVal >=10 and writeVal<20:
        msg = "Your LED is Brightness 2"
        range=2
    if writeVal >=20 and writeVal<30:
        msg = "Your LED is Brightness 3"
        range=3
    if writeVal >=30 and writeVal<40:
        msg = "Your LED is Brightness 4"
        range=4
    if writeVal >=40 and writeVal<50:
        msg = "Your LED is Brightness 5"
        range=5
    if writeVal >=50 and writeVal<60:
        msg = "Your LED is Brightness 6"
        range=6
    if writeVal >=60 and writeVal<70:
        msg = "Your LED is Brightness 7"
        range=7
    if writeVal >=70 and writeVal<80:
        msg = "Your LED is Brightness 8"
        range=8
    if writeVal >=80 and writeVal<90:
        msg = "Your LED is Brightness 9"
        range=9
    if writeVal >=90 and writeVal<99:
        msg = "Your LED is Brightness 10"
        range=10
    if writeVal >=99:
        msg = "Your LED is full Brightness"
        range=11
    if rangeOld != range:
        tts.say(msg, stream = False)
    rangeOld=range
    redLED.pulse_width_percent(int(writeVal))
    sleep(.1)

from fusion_hat.adc import ADC

from fusion_hat.pwm import PWM

from fusion_hat.tts import Piper

from time import sleep

tts = Piper()

tts.set_model('en_US-amy-low')

potPin = 0

myPot = ADC(potPin)

redPin = 5

redLED = PWM(redPin)

rangeOld = 100

while True:

potVal = myPot.read()

writeVal=100*(potVal/4095) - .005

if writeVal < 1:

msg = "Your LED is Off"

range = 0

if writeVal >=1 and writeVal<10:

msg = "Your LED is Brightness 1"

range = 1

if writeVal >=10 and writeVal<20:

msg = "Your LED is Brightness 2"

range=2

if writeVal >=20 and writeVal<30:

msg = "Your LED is Brightness 3"

range=3

if writeVal >=30 and writeVal<40:

msg = "Your LED is Brightness 4"

range=4

if writeVal >=40 and writeVal<50:

msg = "Your LED is Brightness 5"

range=5

if writeVal >=50 and writeVal<60:

msg = "Your LED is Brightness 6"

range=6

if writeVal >=60 and writeVal<70:

msg = "Your LED is Brightness 7"

range=7

if writeVal >=70 and writeVal<80:

msg = "Your LED is Brightness 8"

range=8

if writeVal >=80 and writeVal<90:

msg = "Your LED is Brightness 9"

range=9

if writeVal >=90 and writeVal<99:

msg = "Your LED is Brightness 10"

range=10

if writeVal >=99:

msg = "Your LED is full Brightness"

range=11

if rangeOld != range:

tts.say(msg, stream = False)

rangeOld=range

redLED.pulse_width_percent(int(writeVal))

sleep(.1)

Jetson Nano

AI on the Jetson Nano LESSON 61: Image Recognition and Speech (TTS) on the Nano

September 26, 2020 admin

In this video lesson we learn how to add speech to our NVIDIA Jetson Nano we demonstrate how the Jetson can not only recognize an item, but can audibly speak the item it sees. The video takes you through the process step-by-step, and shows you how to make it all work together properly. For your convenience, the code we developed is included below.

import jetson.inference
import jetson.utils
import numpy as np 
import time
import os
from gtts import gTTS
import threading

speak=True
item='Welcome to My Identify. Are you Ready to Rumble?'
confidence=0
itemOld=''


import cv2
print(cv2.__version__)
width=1280
height=720
flip=2
#Uncomment These next Two Line for Pi Camera
#camSet='nvarguscamerasrc !  video/x-raw(memory:NVMM), width=3264, height=2464, format=NV12, framerate=21/1 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'
#cam= cv2.VideoCapture(camSet)

def sayItem():
    global speak
    global item
    while True:
        if speak ==True:
            output=gTTS(text=item, lang='en',slow=False)
            output.save('output.mp3')
            os.system('mpg123 output.mp3')
            speak=False
x=threading.Thread(target=sayItem, daemon=True)
x.start()

#Or, if you have a WEB cam, uncomment the next line
#(If it does not work, try setting to '1' instead of '0')
cam=cv2.VideoCapture('/dev/video1')
cam.set(cv2.CAP_PROP_FRAME_WIDTH,width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)
net=jetson.inference.imageNet('googlenet')
font=cv2.FONT_HERSHEY_SIMPLEX
timeMark=time.time()
fpsFilter=0

while True:
    ret, frame = cam.read()
    img=cv2.cvtColor(frame,cv2.COLOR_BGR2RGBA).astype(np.float32)
    img=jetson.utils.cudaFromNumpy(img)
    if speak==False:
        classID, confidence = net.Classify(img,width,height)
        if confidence>=.5:
            item=net.GetClassDesc(classID)
            if item!=itemOld:
                speak=True
        if confidence<.5:
            item=''
        itemOld=item
    dt=time.time()-timeMark
    timeMark=time.time()
    fps=1/dt
    fpsFilter=.95*fpsFilter + .05 *fps
    cv2.putText(frame,str(round(fpsFilter,1))+'  fps  '+item+'   '+str(round(confidence,2)),(0,30),font,1,(0,0,255),2)
    cv2.imshow('nanoCam',frame)
    cv2.moveWindow('nanoCam',0,0)
    if cv2.waitKey(1)==ord('q'):
        break
cam.release()
cv2.destroyAllWindows()

import jetson.inference

import jetson.utils

import numpy as np

import time

import os

from gtts import gTTS

import threading

speak=True

item='Welcome to My Identify. Are you Ready to Rumble?'

confidence=0

itemOld=''

import cv2

print(cv2.__version__)

width=1280

height=720

flip=2

#Uncomment These next Two Line for Pi Camera

#camSet='nvarguscamerasrc ! video/x-raw(memory:NVMM), width=3264, height=2464, format=NV12, framerate=21/1 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'

#cam= cv2.VideoCapture(camSet)

def sayItem():

global speak

global item

while True:

if speak ==True:

output=gTTS(text=item, lang='en',slow=False)

output.save('output.mp3')

os.system('mpg123 output.mp3')

speak=False

x=threading.Thread(target=sayItem, daemon=True)

x.start()

#Or, if you have a WEB cam, uncomment the next line

#(If it does not work, try setting to '1' instead of '0')

cam=cv2.VideoCapture('/dev/video1')

cam.set(cv2.CAP_PROP_FRAME_WIDTH,width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)

net=jetson.inference.imageNet('googlenet')

font=cv2.FONT_HERSHEY_SIMPLEX

timeMark=time.time()

fpsFilter=0

while True:

ret, frame = cam.read()

img=cv2.cvtColor(frame,cv2.COLOR_BGR2RGBA).astype(np.float32)

img=jetson.utils.cudaFromNumpy(img)

if speak==False:

classID, confidence = net.Classify(img,width,height)

if confidence>=.5:

item=net.GetClassDesc(classID)

if item!=itemOld:

speak=True

if confidence<.5:

item=''

itemOld=item

dt=time.time()-timeMark

timeMark=time.time()

fps=1/dt

fpsFilter=.95*fpsFilter + .05 *fps

cv2.putText(frame,str(round(fpsFilter,1))+' fps '+item+' '+str(round(confidence,2)),(0,30),font,1,(0,0,255),2)

cv2.imshow('nanoCam',frame)

cv2.moveWindow('nanoCam',0,0)

if cv2.waitKey(1)==ord('q'):

break

cam.release()

cv2.destroyAllWindows()

Technology Tutorials

Tag Archives: TTS

AI on the Edge LESSON 14: Control LED Color With Voice Commands on Raspberry Pi 5

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

AI on the Jetson Nano LESSON 61: Image Recognition and Speech (TTS) on the Nano

Making The World a Better Place One High Tech Project at a Time. Enjoy!