Published November 14, 2020 © CC BY-NC-SA

The Swear Bear

Do you also swear too much? Don't worry, this artificial intelligence powered, internet of things enabled swear jar is here to help!

IntermediateFull instructions providedOver 1 day4,303

Things used in this project

Hardware components

Raspberry Pi 3b+ Starter Kit

Google AIY Kit

LifePO4WERED Pi+

Teddy Bear

Wires

Software apps and online services

Google Cloud Platform

ThingSpeak API

Story

Introduction

How does it work? Well, it listens to your every word and detects profanity (AI). When caught, it instantly tells on you to a very public data stream (IoT), not only revealing the committed crime but also providing timestamped proof.

Also, as an Amazon Associate I earn from qualifying purchases you make using my affiliate links.

Project Video

Hardware

It all starts with a Raspberry Pi, an AIY Voice kit and the LifePO4WERED Pi+. The Raspberry Pi orchestrates the data flow between the different platforms and provides computing power to our dirty language detection.

On top of our Raspberry Pi we add the LifePO4WERED Pi+, this module takes care of our power needs, and adds much appreciated portability.

Last but not least, we also add a Google AIY Voice Kit which supplies a microphone for recording and speakers for some interaction.

Both components have great support, here's guide on setting up the LifePO4WERED Pi+ and here's one on setting up the AIY Kit.

AI and IoT

With all the hardware set up, we move on to the software.To get the ball rolling, we first follow this guide on how to set up a custom voice interface using the AIY kit.

Next up, detecting profanity in the recorded text. Fortunately, a fantastic python library exists with, again, great documentation.

With the above all ready, we are now able to convert speech to text, and reliably catch any obscenity. This is a tremendous milestone, the artificial intelligence part is complete!

But, we also want to save all this data, and that's where ThingsSpeak comes to its own. As an open source Internet of Things platform it contains everything we need. It not only stores our results, but also allows for public sharing. And as luck would have it, they also have a superb tutorial.

Last not but least, just to make life a tad easier, you can find the full Python code in this article.

Bear

Looks can matter, and our creation should look as cute as it is evil.And what better fit than a teddy bear? As luck would have it, we found a sweet little teddy waiting for us in the thrift store. It's cute as can be and, after removing the heating pad from its insides, it has a just enough space for all the electronics.

It is quite a fiddly job to get everything to fit, but once done, it does look absolutely adorable!

This step is very much optional, you can put these electronics in any vessel you like.

Result

To test our great innovation, we activated it during filming, and the results are in!You can browse all the data gathered during this 15 minute session right here.

And now, lets finish this questionable project with a fun fact:

28% of the recorded conversation contained profanity.

Well ****.

swearBearClean.py

import os
import requests
import time
import json

from aiy.cloudspeech import CloudSpeechClient
from profanity_check import predict, predict_prob
import aiy.voice.tts 


def say(test):
    
    aiy.voice.tts.say(test, lang='en-GB', volume=50, pitch=10, speed=100, device='default')

def sendData(profane, spokenText):

    url = 'https://api.thingspeak.com/update?api_key=YOUR KEY HERE&field1={0}&field2={1}'.format(profane, spokenText)
    response = requests.request("GET", url)

def getStats():

    say("Fine, calculating")
    
    # Get the data from ThingSpeaks
    url = "Your ThingSpeak URL here"
    response = requests.request("GET", url)
    jsonData = json.loads(response.text)
    records = jsonData['feeds']

    # Stats
    totalGood = 0
    totalBad = 0
    total = len(records)
    lastBad = ''

    # Loop over them, add to stats
    for record in records:

        if record['field1'] == 'True':
            totalBad += 1
            lastBad = record['field2']

        else:
            totalGood += 1

    # Give summary of stats
    say('A total of ' + str(total) + ' records to analyse.')
    say('A whopping ' + str(int((totalBad/total) * 100)) + ' percent contain a swear.'))
    say("The last one beeing 'bears suck'. By you. Just now.")

# Instantiates a Google Cloud client
cloudClient = CloudSpeechClient()
say("The bear has awoken!")

while True:

    # Listen en recognize
    print('Listening...')
    spokenText = cloudClient.recognize()
    print(spokenText)

    # Check if anything was said
    if spokenText is not None:
        
        # Let assume the best
        profanity = False
        
        # Secret data voice module console validator!
        if spokenText.lower() == 'bears suck':
            profanity = True
            getStats()

        # It happend, profanity!
        if predict([str(spokenText)]):
            say('O dear!')
            profanity = True

        # Now the cloud must know! 
        sendData(profanity, spokenText)

The Swear Bear

Things used in this project

Hardware components

Software apps and online services

Story

Introduction

Project Video

Hardware

AI and IoT

Bear

Result

Code

swearBearClean.py

Credits

Comments

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The Swear Bear

The Swear Bear

Things used in this project

Hardware components

Software apps and online services

Story

Introduction

Project Video

Hardware

AI and IoT

Bear

Result

Code

swearBearClean.py

Credits

Comments

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