Mastering OpenAI Whisper: Integrating GPT-3 for Enhanced Speech Recognition

Mastering Whisper: OpenAI's Speech Recognition Powerhouse

Unveiling Whisper, OpenAI's trailblazing speech recognition system. Trained on an extensive multilingual dataset, it excels in deciphering accents, reducing background noise, and understanding technical language. It's your key to unlocking powerful applications in speech recognition. Dive into our Whisper tutorial and harness the power of GPT-3, transforming the way you interact with language and sound.

Whisper API Mastery: Tame the Text-Generating Giant, GPT-3

Dive into GPT-3, OpenAI's colossal Language Model, as you journey through our enlightening Whisper tutorial. Witness the incredible text generation and comprehension of this brilliant API, empowering you to craft outstanding AI applications and elevate your projects to unprecedented heights.

Embarking on the Whisper API Journey: A Step-Up Tutorial

Ready to elevate your Whisper API skills? This tutorial is a step-up from our previous Whisper API with Flask and Docker guide. If you're already familiar with that, let's dive deeper into the world of Whisper apps and GPT-3 applications!

Setting Up Your OpenAI API Key

If you don't have it already, please go to OpenAI and create an account to get your API key. Remember to never share your API key in a public repository!

Integrating with the OpenAI Package

We are adding the OpenAI package to our file. Create a new file called gpt3.py and add the following code to it. In the prompt, you can use the summary option to summarize the text, but feel free to customize it according to your needs. Modify the parameters as required.

Updating Imports

At the top of the gpt3.py, update your imports. Instead of MY_API_KEY, please insert the API key you created earlier.

Integrating the GPT-3 Function

We will integrate our new GPT3 function into the Flask route. When we receive the result from Whisper, we will pass it to the GPT-3 function and return the result.

How to Run the Container?

Open a terminal and navigate to the folder where you created the files. Run the following command to build the container:

docker build -t whisper-app .

Once built, run the following command to run the container:

docker run -p 5000:5000 whisper-app

How to Test the API?

You can test the API with a POST request to http://localhost:5000/whisper with a file included in it. The body should be in form-data format. To test this using curl, use the following command:

curl -X POST -F "file=@your_audio_file.wav" http://localhost:5000/whisper

If successful, you should receive a JSON object containing the transcript and summary.

How to Deploy the API?

This API can be deployed anywhere that supports Docker. Note that this setup currently uses CPU for processing audio files. If you want to leverage GPU for enhanced performance, you will need to modify the Dockerfile accordingly and share access to the GPU.

Accessing the Full Code

You can find the entire codebase on GitHub.

Put Your Whisper and GPT-3 Skills into Action: Join the AI Revolution

You've mastered the Whisper API and GPT-3; now it's time to put those skills to the test! Join lablab.ai's AI hackathons and collaborate with a community of over 52,000 AI enthusiasts. Together, we can create AI solutions that will make a real difference in the world!

Have more questions? Feel free to ask in the comments below or check out our other tutorials on OpenAI APIs.

Whether you are a seasoned developer or a newcomer, there’s always something new to learn in the world of AI! Stay tuned for more updates, and happy coding!