Welcome to the final lesson of this course! So far, you have learned how to customize transcription settings and how to split and process large audio files into smaller chunks. In this lesson, we will take things a step further by making the transcription process even faster and more efficient using multithreading.

When you work with very large audio files, processing them one chunk at a time can be slow. Multithreading allows us to process several chunks at the same time, making the overall transcription much faster. This is especially useful when you have long recordings or need to transcribe many files quickly.

By the end of this lesson, you will know how to use Java's multithreading features to transcribe large audio files in parallel, manage resources, and clean up temporary files. This will help you build transcription tools that are both fast and reliable.

Let's start by understanding what multithreading is and how it helps us.

What is Multithreading?
Multithreading means running several tasks at the same time. In Java, this is done using threads. Each thread can work on a different part of a problem, so you can finish the whole task faster.

Why Use Multithreading for Transcription?
If you split a large audio file into three chunks, you can transcribe all three at once instead of waiting for each one to finish. This can make your program much faster, especially if you have a powerful computer.

Java Tools for Multithreading

• ExecutorService: This is a Java class that manages a pool of threads for you. You tell it how many threads you want, and it takes care of running your tasks.
• CompletableFuture: This class lets you run tasks in the background and get the results when they are ready. It's very useful for running several tasks at the same time and then combining the results.

Managing Resources and Errors
When you use multiple threads, you need to make sure you:

• Clean up any temporary files you create.
• Shut down your thread pool when you're done.
• Handle errors so that one failed chunk doesn't stop the whole process.

Let's build up the solution step by step. We'll see how to split the audio, transcribe each chunk in parallel, and combine the results.

First, we need a class that will manage our threads and handle the transcription using HTTP requests.

Explanation:

• executor is our thread pool manager that controls how many threads run at the same time.
• apiKey and baseUrl are loaded from environment variables for API access.
• httpClient is used to make HTTP requests to the OpenAI transcription API.

We split the large audio file into smaller chunks using the same splitter from the previous lesson:

We need a helper method to build the multipart form data for our HTTP requests:

For each chunk, we build an HTTP request and send it to the OpenAI API:

We need a method to clean up temporary audio chunk files:

Now we can put it all together in the main transcription method:

Key Points:

• We create a CompletableFuture for each chunk to run transcriptions in parallel
• CompletableFuture.allOf waits for all transcription tasks to complete
• We combine all results into a single transcript
• We clean up temporary files and shut down the thread pool

Here's how to use the ParallelTranscriber in your main method:

In this lesson, you learned how to use multithreading in Java to transcribe large audio files much faster by processing multiple chunks at the same time. You saw how to:

• Set up a thread pool with ExecutorService
• Transcribe each chunk in parallel using CompletableFuture
• Combine the results into a single transcript
• Clean up temporary files and shut down resources

Congratulations on reaching the end of this course! You now have the skills to build efficient, scalable transcription tools using OpenAI GPT-4o Mini in Java. Take a moment to review the code and concepts, and then try out the hands-on practice exercises to reinforce what you've learned. Well done!