OpenAI Whisper tutorial: Updating our Whisper API with GPT-3

2022-10-13T13:02:46.841Z by Flafi
OpenAI Whisper tutorial: Updating our Whisper API with GPT-3

What is Whisper?

Whisper is an automatic State-of-the-Art speech recognition system from OpenAI that has been trained on 680,000 hours of multilingual and multitask supervised data collected from the web. This large and diverse dataset leads to improved robustness to accents, background noise and technical language. In addition, it enables transcription in multiple languages, as well as translation from those languages into English. OpenAI released the models and code to serve as a foundation for building useful applications that leverage speech recognition.

What is GPT-3?

GPT-3 is a language model from OpenAI that can generate text. It is trained on a large dataset of text from the web.

How to start?

This tutorial is an upgrade of the previous tutorial Whisper API with Flask and Docker.

So please first check that out :) If you already did that you can continue here.

OpenAI API key

If you don't have it already please go to OpenAI and create an account. And create your API key. Never share your API key in public repository!

Updates to requirement.txt

We are adding openai package to our file

Creating file for gpt3 function

We will create a new file called and add the following code to it. In the prompt I was using the summary option to summarize the text, but you can use anything you want. And you can tweak the parameters as well.

import openai

def gpt3complete(speech):
    # Completion function call engine: text-davinci-002

    Platformresponse = openai.Completion.create(
        prompt="Write a short summary of this text: {}".format(speech),

    return Platformresponse.choices[0].text


On the top we will update our imports. Instead of "MY_API_KEY" please insert the API key you created earlier.

import openai
import gpt3
# GPT-3 API Key
openai.api_key = "MY_API_KEY"

Update the /whisper route

We will integrate our new GPT3 function into the route. So when we are getting the result from whisper we will pass it to the gpt3 function and return the result.

@app.route('/whisper', methods=['POST'])
def handler():
    if not request.files:
        # If the user didn't submit any files, return a 400 (Bad Request) error.

    # For each file, let's store the results in a list of dictionaries.
    results = []

    # Loop over every file that the user submitted.
    for filename, handle in request.files.items():
        # Create a temporary file.
        # The location of the temporary file is available in ``.
        temp = NamedTemporaryFile()
        # Write the user's uploaded file to the temporary file.
        # The file will get deleted when it drops out of scope.
        # Let's get the transcript of the temporary file.
        result = model.transcribe(
        text = result['text']
        # Let's get the summary of the soundfile
        summary = gpt3.gpt3complete(text)
        # Now we can store the result object for this file.
            'filename': filename,
            'transcript': text.strip(),
            'summary': summary.strip(),

    # This will be automatically converted to JSON.
    return {'results': results}

How to run the container?

  1. Open a terminal and navigate to the folder where you created the files.
  2. Run the following command to build the container:
docker build -t whisper-api .
  1. When built is ready, run the following command to run the container:
docker run -p 5000:5000 whisper-api

How to test the API?

  1. You can test the API by sending a POST request to the route http://localhost:5000/whisper with a file in it. Body should be form-data.
  2. You can use the following curl command to test the API:
curl -F "file=@/path/to/file" http://localhost:5000/whisper
  1. In result you should get a JSON object with the transcript and summary in it.

How to deploy the API?

This API can be deployed anywhere where Docker can be used. Just keep in mind that this setup currently using CPU for processing the audio files. If you want to use GPU you need to change Dockerfile and share the GPU. I won't go into this deeper as this is an introduction. Docker GPU

You can find the whole code here

Thank you for reading! If you enjoyed this tutorial you can find more and continue reading on our tutorial page

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