How do I make a voice assistant with ChatGPT? If you want to convert ChatGPT to an advanced voice assistant, then this post is for you.
ChatGPT is a powerful and versatile artificial intelligence chatbot that can generate natural language responses to various inputs. It is based on the GPT-3.5 model, which is one of the most advanced language models available. ChatGPT can be used for a variety of natural language processing tasks, such as generating human-like responses to questions and prompts, summarizing text, and translating between languages.
But what if you want to interact with ChatGPT using your voice, instead of typing? What if you want to create your own voice assistant that can leverage the capabilities of ChatGPT and answer your queries in a conversational manner?
What you need to create a ChatGPT Voice Assistant
To create a ChatGPT voice assistant, you will need the following:
- A ChatGPT account and API key. You can sign up for a free ChatGPT account and get an API key from the settings page. You will need this key to access the ChatGPT API and send requests to the chatbot.
- A Python environment with the following libraries installed: requests, speech_recognition, pyttsx3, and gradio. You can use pip or conda to install these libraries. Request is a library for making HTTP requests in Python. Speech_recognition is a library for performing speech recognition using various engines and APIs. Pyttsx3 is a library for text-to-speech conversion using offline engines. Gradio is a library for creating interactive web interfaces for machine learning models.
- A microphone and speakers (or headphones) for recording and playing audio.
How to create a ChatGPT Voice Assistant: Step-by-Step
Now that you have everything you need, let’s start creating our ChatGPT voice assistant. We will use Python as our programming language and write our code in a Jupyter notebook. You can also use any other IDE or text editor of your choice.
See also: How To Build An AI Voice Assistant In Python Using OpenAI ChatGPT API
Step 1: Import the Libraries
The first step is to import the libraries that we will use in our code. We will use the following import statements:
import requests # for making HTTP requests
import speech_recognition as sr # for speech recognition
import pyttsx3 # for text-to-speech
import gradio as gr # for creating web interfaceStep 2: Define the ChatGPT API URL and Key
The next step is to define the ChatGPT API URL and key that we will use to communicate with the chatbot. We will store them in two variables: URL and key. The URL is the endpoint of the ChatGPT API, which is https://api.openai.com/v1/engines/chatgpt/completions. The key is the API key that we obtained from the ChatGPT settings page, which starts with sk-. We will prepend it with Bearer to form the authorization header.
url = "https://api.openai.com/v1/engines/chatgpt/completions" # ChatGPT API URL
key = "Bearer sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # ChatGPT API keyStep 3: Define the Speech Recognition Function
The next step is to define a function that will perform speech recognition using the speech_recognition library. We will use the recognize_google method, which uses the Google Web Speech API to recognize speech from audio data. The function will take an audio file as input and return a string of text as output. If there is an error or no speech is detected, the function will return an empty string.
def recognize_speech(audio_file):
"""Recognize speech from audio file using Google Web Speech API"""
r = sr.Recognizer() # create a recognizer object
with sr.AudioFile(audio_file) as source: # open the audio file
audio_data = r.record(source) # read the audio data
try:
text = r.recognize_google(audio_data) # recognize speech using Google Web Speech API
except sr.UnknownValueError: # if speech is unintelligible or not detected
text = ""
except sr.RequestError: # if there is an error with the request
text = ""
return text # return the recognized textStep 4: Define the Text-to-Speech Function
The next step is to define a function that will perform text-to-speech conversion using the pyttsx3 library. We will use the init method to create an engine object, which can be used to set properties and synthesize speech from text. The function will take a string of text as input and return an audio file as output. The function will also play the audio file using the say and runAndWait methods.
def synthesize_speech(text):
"""Synthesize speech from text using pyttsx3"""
engine = pyttsx3.init() # create an engine object
engine.setProperty("rate", 150) # set the speech rate
engine.setProperty("volume", 1.0) # set the speech volume
engine.save_to_file(text, "output.wav") # save the speech to an audio file
engine.say(text) # say the text
engine.runAndWait() # wait until the speech is done
return "output.wav" # return the audio fileStep 5: Define the ChatGPT function
The next step is to define a function that will communicate with the ChatGPT chatbot using the requests library. We will use the post method to send a POST request to the ChatGPT API URL with the authorization header and a JSON payload. The payload will contain the following parameters:
`prompt`: The input text that we want to send to the chatbot. This can be a question, a statement, or a conversation starter.
`max_tokens`: The maximum number of tokens that we want the chatbot to generate as a response. A token is a unit of text, such as a word or a punctuation mark. We will set this to 150, which is equivalent to about 30 words.
`temperature`: The randomness of the response. A higher temperature means more creativity and diversity, while a lower temperature means more coherence and consistency. We will set this to 0.9, which is a high value that allows for more interesting and varied responses.
`frequency_penalty`: The penalty for repeating words or phrases in the response. A higher penalty means less repetition, while a lower penalty means more repetition. We will set this to 0.5, which is a moderate value that avoids excessive repetition but still allows for some natural repetition.
`presence_penalty`: The penalty for using words or phrases that are not relevant to the prompt or the context. A higher penalty means more relevance, while a lower penalty means more irrelevance. We will set this to 0.6, which is a high value that ensures that the response is related to the input and the previous conversation.
The function will take an input text as input and return a response text as output. If there is an error with the request or the response, the function will return an empty string.
def chatgpt(input_text):
"""Communicate with ChatGPT chatbot using requests"""
headers = {"Authorization": key} # create authorization header with API key
payload = { # create JSON payload with parameters
"prompt": input_text,
"max_tokens": 150,
"temperature": 0.9,
"frequency_penalty": 0.5,
"presence_penalty": 0.6
}
try:
response = requests.post(url, headers=headers, json=payload) # send POST request to ChatGPT API URL
response_text = response.json()["choices"][0]["text"] # get response text from JSON response
except requests.exceptions.RequestException: # if there is an error with the request
response_text = ""
except KeyError: # if there is an error with the response
response_text = ""
return response_text # return the response textStep 6: Define the Voice Assistant function
The next step is to define a function that will combine all the previous functions and create our voice assistant. The function will take an audio file as input and return an audio file as output. The function will also display the input and output texts on the screen. The function will do the following steps:
- Recognize speech from the input audio file using the recognize_speech function and store it in a variable called input_text.
- Print the input_text on the screen with a prefix of “You said:”.
- If the input_text is not empty, communicate with the ChatGPT chatbot using the chatgpt function and store it in a variable called output_text.
- Print the output_text on the screen with a prefix of “ChatGPT said:”.
- If the output_text is not empty, synthesize speech from the output_text using the synthesize_speech function and store it in a variable called output_audio.
- Return the output_audio as the output of the function.
def voice_assistant(input_audio):
"""Create a voice assistant using speech recognition, ChatGPT, and text-to-speech"""
input_text = recognize_speech(input_audio) # recognize speech from input audio file
print(f"You said: {input_text}") # print input text on screen
if input_text: # if input text is not empty
output_text = chatgpt
(input_text) # communicate with ChatGPT chatbot using chatgpt function and store it in a variable called output_text
print(f"ChatGPT said: {output_text}") # print output text on screen
if output_text: # if output text is not empty
output_audio = synthesize_speech(output_text) # synthesize speech from output text using synthesize_speech function and store it in a variable called output_audio
return output_audio # return output audio as the output of the function
else: # if input text is empty
return None # return None as the output of the functionStep 7: Create the Web Interface
The final step is to create a web interface for our voice assistant using the gradio library. We will use the Interface class to create an interface object, which can be used to launch a web app that allows us to interact with our voice assistant. The interface object takes the following arguments:
`fn`: The function that we want to use as the core of our interface. In our case, this is the voice_assistant function that we defined in the previous step.
`inputs`: The input component that we want to use for our interface. In our case, this is a `Microphone` component, which allows us to record audio from our microphone and send it to our function.
`outputs`: The output component that we want to use for our interface. In our case, this is an `Audio` component, which allows us to play audio from our function and hear the response from our voice assistant.
`title`: The title of our interface. We can give it any name we want, but we will call it “ChatGPT Voice Assistant” for simplicity.
`description`: The description of our interface. We can give it any text we want, but we will write a brief introduction about what our voice assistant can do and how to use it.
We will also use the launch method to launch our interface in a new tab in our browser. The method takes an optional argument called share, which determines whether we want to share our interface with others or not. If we set it to True, we will get a public URL that we can share with anyone who wants to try out our voice assistant. If we set it to False, we will only be able to access our interface locally.
# create a web interface for voice assistant using gradio
interface = gr.Interface(
fn=voice_assistant, # use voice_assistant function as core of interface
inputs=gr.inputs.Microphone(), # use microphone component as input
outputs=gr.outputs.Audio(), # use audio component as output
title="ChatGPT Voice Assistant", # set title of interfacedescription=”A voice assistant that uses ChatGPT chatbot to generate natural language responses. You can ask any question or start a conversation with ChatGPT by recording your voice and clicking submit. You will hear ChatGPT’s response in your speakers or headphones.” # set description of interface
)
# launch web interface in browser
interface.launch(share=True) # launch interface with public URL
See also: What Is The Best Free AI Voice Generator Now?
FAQs
What is ChatGPT?
ChatGPT is a powerful and versatile artificial intelligence chatbot that can generate natural language responses to various inputs. It is based on the GPT-3.5 model, which is one of the most advanced language models available.
How do I get a ChatGPT account and API key?
Sign up for a free account at [ChatGPT] and get an API key from the settings page. You will need this key to access the ChatGPT API and send requests to the chatbot.
How do I use the ChatGPT voice assistant?
You can use the ChatGPT voice assistant by recording your voice and clicking submit on the web interface. You will hear ChatGPT’s response in your speakers or headphones. You can ask any question or start a conversation with ChatGPT by using your voice.
How do I change the parameters of the ChatGPT chatbot?
To change the parameters of the ChatGPT chatbot, modify the payload in the chatgpt function. The parameters are max_tokens, temperature, frequency_penalty, and presence_penalty. You can adjust them according to your preference and see how they affect the chatbot’s response.
How do I share the ChatGPT voice assistant with others?
You can share the ChatGPT voice assistant with others by setting the share argument to True in the launch method. You will get a public URL that you can share with anyone who wants to try out your voice assistant. Alternatively, you can also download the code and run it on your own machine.
Conclusion
We have successfully created a ChatGPT voice assistant using Python and some open-source libraries. We have learned how to use speech recognition, ChatGPT, and text-to-speech to create a natural language interaction between us and the chatbot. We have also learned how to create a web interface for our voice assistant using gradio. We can now enjoy chatting with ChatGPT using our voice and hear its responses in a conversational manner.