Build a voice assistant with GPT-3 in python today

Build Voice Assistant With GPT-3 *Image Generated With DALL-E.

Welcome to our tutorial on how to build a voice assistant with GPT-3 in python today. In this tutorial, we’ll show you how to use the GPT-3 API to create a voice assistant that can understand and respond to natural language queries. We’ll also provide some tips on how to improve your voice assistant’s accuracy and performance.

To get started, you’ll need to sign up for a GPT-3 account and create a new project. Once you’ve done that, you can grab the API key from your project’s settings page.

With that out of the way, let’s get started! The first thing we need to do is install the gpt-3 SDK. We can do that with pip:

pip install gpt-3

Once that’s done, we can import the SDK into our project:

import gpt_3

Now that we have the SDK imported, we need to initialize it. We’ll need to provide our API key, as well as the project ID that we created earlier. We can do that like so:

gpt_3.init(api_key="your-api-key-here", project_id="your-project-id-here")

Now that the SDK is initialized, we can start building our voice assistant.

The first thing we need to do is define a function that will handle incoming queries. We’ll call this function handle_query():

def handle_query(query):
  # TODO: write code to handle queries here  
pass

Next, we need to write the code that will actually handle the queries. For our purposes, we’re going to use the GPT-3 API to query a pre-trained model. This model will take in a natural language query and return a response.

To do this, we’ll first need to create a new instance of the GPT-3Client class. We’ll need to provide our API key and project ID:

client = gpt_3.GPT_3Client(api_key="your-api-key-here", project_id="your-project-id-here")

Now that we have an instance of the GPT-3Client class, we can use the query() method to query the pre-trained model. We’ll need to provide the query string, as well as the name of the model that we want to use. For our purposes, we’re going to use the “default” model:

response = client.query(query_string="your-query-here", model_name="default")

Now that we have a response from the pre-trained model, we can return it to the caller of our handle_query() function:

return response

Now that we have our handle_query() function defined, we can start building the rest of our voice assistant.

We’ll need to write some code that will take in the audio input, convert it to text, and then pass it to our handle_query() function. We can do that with the following code:

import speech_recognition as sr  
r = sr.Recognizer()  
with sr.Microphone() as source:  
  print("Say something!")  
  audio = r.listen(source)  
try:  
  text = r.recognize_google(audio)  
  print("You said: " + text)  
except sr.UnknownValueError:  
  print("Could not understand audio")  
except sr.RequestError as e:  
  print("Error occurred during Recognition: {0}".format(e))
    
  query_result = handle_query(text)  
  print(query_result)

This code will use the SpeechRecognition library to take in the audio input, convert it to text, and then pass it to our handle_query() function.

Once we have our code in place, we can test it out!

Say something like “What’s the weather like today?” into your microphone and you should see a response from the pre-trained model.

You can also try asking your voice assistant other questions. With a bit of tweaking, you should be able to get your voice assistant to respond to almost any natural language query.

If you’re looking to improve your voice assistant’s accuracy, you can try training your own models with the GPT-3 API.

You can find more information on how to do that in the GPT-3 documentation.

With that, you now know how to build a voice assistant with GPT-3 in python.

You can use this knowledge to build all sorts of cool applications.

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Thanks for reading!