Learn to Build a ChatGPT Voice Assistant in 8 Minutes!

Table of Contents

1. Introduction
2. Setting up the Environment
3. Importing the Required Libraries
4. Setting Up the OpenAI API Key
5. Creating the Text-to-Speech Engine
6. Transcribing Voice Commands into Text
7. Generating Responses from the GPT-3 API
8. Speaking the Responses
9. Structuring the Logic of the Program
10. Running the Voice Assistant
11. Converting the Python Program into a Website
12. Troubleshooting: Module Not Found Error
13. Conclusion

Introduction

In this article, we will explore how to create a voice assistant powered by OpenAI's GPT-3 using Python. We will guide you through the step-by-step process of setting up the environment, importing the necessary libraries, transcribing voice commands into text, generating responses from the GPT-3 API, and speaking the responses. We will also discuss how you can convert your Python program into a website so that it can be accessed by anyone with an internet connection. So let's dive into the code and create our own voice assistant!

Setting up the Environment

Before we begin, let's make sure we have the necessary tools in place. Open your Python environment and create a new Python file for our project.

Importing the Required Libraries

To access the GPT-3 API, we need to import the openai library. Additionally, we'll import the pyttsx3 library for text-to-speech conversion and the speech_recognition library for transcribing audio to text. Let's import these libraries:

import openai
import pyttsx3
import speech_recognition as sr

Setting Up the OpenAI API Key

To use the GPT-3 API, we need to set up our OpenAI API key. Replace the dummy API key with your own OpenAI API key, which you can obtain for free from the OpenAI website. Here's how you can set up your API key:

openai.api_key = 'YOUR_API_KEY'

Creating the Text-to-Speech Engine

Next, let's set up the text-to-speech engine. We will use the pyttsx3 library for this purpose. Create an instance of the text-to-speech engine using the init method. Here's how you can create the engine:

engine = pyttsx3.init()

Transcribing Voice Commands into Text

To make our Python program understand voice commands, we need to transcribe audio to text. We'll use the speech_recognition library for this task. Let's define a function called transcribe_audio_to_text to transcribe voice commands:

def transcribe_audio_to_text(file_name):
    recognizer = sr.Recognizer()

    with sr.AudioFile(file_name) as source:
        audio = recognizer.record(source)

    try:
        text = recognizer.recognize_google(audio)
        return text
    except sr.UnknownValueError:
        print("Audio could not be transcribed.")
        return ""

In the transcribe_audio_to_text function, we create an instance of the Recognizer class from the speech_recognition module. We use the with statement to open the audio file specified by file_name using the AudioFile class. Then, we record the audio using the record method of the recognizer object. Finally, we transcribe the recorded audio to text using the recognize_google method of the recognizer object. In case of any error during transcription, an exception will be raised and an error message will be printed.

Generating Responses from the GPT-3 API

Now, let's create a function to generate responses from the GPT-3 API. We'll call this function generate_response. It will take a prompt as an argument, which represents the input text for generating a response. Here's how you can generate a response using the GPT-3 API:

def generate_response(prompt):
    response = openai.Completion.create(
        engine='davinci',
        prompt=prompt,
        max_tokens=4000,
        temperature=0.5
    )

    return response.choices[0].text.strip()

In the generate_response function, we use the openai.Completion.create method to generate a response based on the given prompt. We specify the engine as 'davinci', which represents the GPT-3 model. The max_tokens variable limits the number of characters in the response to 4000. You can adjust this limit as per your requirements. The temperature parameter controls the creativity or randomness of the generated text. A value of 0.5 is often a good starting point. Finally, we return the generated response.

Speaking the Responses

To make our voice assistant interactive, we'll create a function called speak_text to convert the text response to speech. Here's how you can define the speak_text function:

def speak_text(text):
    engine.say(text)
    engine.runAndWait()

In the speak_text function, we use the engine.say method to specify the text to be spoken and the engine.runAndWait method to play the speech.

Structuring the Logic of the Program

Now, let's structure the logic of our program. We'll create a main function to do so. Inside the main function, we'll add a while loop that will run continuously until the program is stopped. This loop will allow our program to listen to the user's voice commands, generate responses, and speak them. Here's how you can structure the logic of your program:

def main():
    while True:
        print("Say 'genius' to start recording your question:")
        # Code for recording audio
        # Code for transcribing audio to text
        # Code for generating response
        # Code for speaking the response

Running the Voice Assistant

To run our voice assistant, we'll call the main function at the end of the script. Here's how you can do it:

if __name__ == '__main__':
    main()

By adding this code, we ensure that the main function is only executed if the script is run directly, not when it is imported as a module.

Converting the Python Program into a Website

To convert your Python program into a website, you'll need to use a web framework such as Flask or Django. These frameworks allow you to create web applications that can be hosted on the internet and accessed by anyone with an internet connection. You'll also need to create a web interface for your voice assistant so that users can interact with it. Additionally, you'll need to set up a server to host your application. Once you have all these components in place, you can make your Python program into a website.

Troubleshooting: Module Not Found Error

If your Python code returns a "No module named 'pyttsx3'" error, it is possible that the code is looking for a specific module that is not found. Make sure that you have installed the pyttsx3 module correctly and that it is compatible with your version of Python. You can install the module using the command pip install pyttsx3.

Conclusion

In this article, we have learned how to create a voice assistant powered by OpenAI's GPT-3 using Python. We have explored the step-by-step process of setting up the environment, importing the necessary libraries, transcribing voice commands into text, generating responses from the GPT-3 API, and speaking the responses. We have also discussed how you can convert your Python program into a website so that it can be accessed by anyone with an internet connection. With this knowledge, you can now create your own voice assistant and explore the possibilities of AI-powered interactions.