MCIT-ML-UseCases

This is repo for solutions of ML use cases in MCIT training

jarvis code

Requires PyAudio and PySpeech.

import speech_recognition as sr from time import ctime import time import os from gtts import gTTS

def speak(audioString): print(audioString) tts = gTTS(text=audioString, lang='en') tts.save("audio.mp3") os.system("mpg321 audio.mp3")

def recordAudio(): # Record Audio r = sr.Recognizer() with sr.Microphone() as source: print("Say something!") audio = r.listen(source)

# Speech recognition using Google Speech Recognition
data = ""
try:
    # Uses the default API key
    # To use another API key: `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
    data = r.recognize_google(audio)
    print("You said: " + data)
except sr.UnknownValueError:
    print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
    print("Could not request results from Google Speech Recognition service; {0}".format(e))

return data

def jarvis(data): if "how are you" in data: speak("I am fine")

if "what time is it" in data:
    speak(ctime())

if "where is" in data:
    data = data.split(" ")
    location = data[2]
    speak("Hold on Frank, I will show you where " + location + " is.")
    os.system("chromium-browser https://www.google.nl/maps/place/" + location + "/&amp;")

initialization

time.sleep(2) speak("Hi Frank, what can I do for you?") while 1: data = recordAudio() jarvis(data)

Salah856/MCIT-ML-UseCases

MCIT-ML-UseCases

jarvis code

Requires PyAudio and PySpeech.

initialization