• Raspberry Pi.
• Raspberry Pi OS.
• Python 3.

• Speech-to-Text engine is a piece of software that interprets human voice into a string of text. It lets the computer know what is being said.
• Text-to-Speech engine converts text into sound. It allows the computer to speak, probably as a response to your command.

• Vosk as the Speech-to-Text engine.
• PYTTSX3 as the Text-to-Speech engine.

• Supports 20+ languages and dialects - English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish. More to come.
• Works offline, even on lightweight devices - Raspberry Pi, Android, iOS.
• Installs with simple pip3 install vosk.
• Portable per-language models are only 50Mb each, but there are much bigger server models available.
• Provides streaming API for the best user experience (unlike popular speech-recognition python packages).
• There are bindings for different programming languages, too - java/csharp/javascript etc.
• Allows quick reconfiguration of vocabulary for best accuracy.
• Supports speaker identification beside simple speech recognition.

• Speakers Plugged into the Raspberry Pi usingn Jack.
• Microphone Plugged ( Wired USB or Bluetooth ).

• First you need to install the necessary library packages which are:

  • pip install pyaudio.
  • pip install vosk.
  • pip install pyttsx3.

• or by using pip install -r requirments.txt

• After checking that the Speaker and Microphone works properly start using the Voice Control System using the Engine implementation.

Start using by importing Voice() which has seconds parameters that you can specity, default is 10 in your main application.

def voice(seconds = 10):
   start_time = time.time()
  
   stream.start_stream()
   print("Model Started ....")
   
   while True:

       current_time = time.time()
       elapsed_time = current_time - start_time
       
       data = stream.read(4000,exception_on_overflow = False)
       recogniser.AcceptWaveform(data)

       result = recogniser.Result()[14:-3]
       print(result)

       if elapsed_time > seconds:
           break

voice()

Start using by importing speak() in your main application.

def speak(audio):
   engine = pyttsx3.init()
   engine.setProperty('rate', 150)
   print("Assistant : " + audio)
   engine.say(audio)
   engine.runAndWait()

Note: If you use the code in another python script you need to import these libraries.

import pyttsx3
from vosk import Model, KaldiRecognizer
import pyaudio
import os
import time

It's that simple!