Welcome to the first lesson of the course, where we will explore how to make your audio transcriptions smarter and more accurate by customizing the transcription process. In many real-world situations, audio files can be in different languages or contain specific topics, names, or jargon. By customizing the transcription settings, you can help the model understand your audio better and produce more accurate results.

In this lesson, you will learn how to use custom parameters — specifically, the language and prompt options — when transcribing audio with OpenAI GPT-4o Mini in Java using direct HTTP requests. These options allow you to tell the model what language to expect and give it extra context about the audio, which can be very helpful for meetings, interviews, or technical discussions.

Before we dive into custom parameters, let's remind ourselves how a basic transcription works using direct HTTP requests. In a simple setup, you provide an audio file to the model via a multipart/form-data POST request, and it returns the text it hears using default settings.

For example, in previous lessons, you might have seen code like this:

This code sends an audio file to the model and returns the transcribed text. By default, the model tries to detect the language and does not use any extra context.

Now, let's look at how you can make your transcriptions even better by using two important parameters: language and prompt.

• language: This parameter tells the model what language to expect in the audio. For example, if your audio is in Spanish, you can set language to "es". This helps the model avoid mistakes in language detection and improves accuracy.

• prompt: This parameter lets you give the model extra information about the audio. For example, you can tell it, "This is a meeting about project planning," or provide a list of names or technical terms that might appear. This helps the model understand the context and transcribe tricky words more accurately.

Let's build up the code step by step to see how to use these custom parameters in your Java transcription project using direct HTTP requests.

First, ensure you have the required dependencies in your project. You'll need to add Jackson for JSON parsing to your Maven or Gradle configuration:

Maven (pom.xml):

Gradle (build.gradle):

Then add the necessary imports:

Next, you need to set up the HttpClient and load your configuration. This part loads your API key and base URL from a .env file and creates the HTTP client.

• Dotenv.configure().ignoreIfMissing().load() loads environment variables from a .env file using the dotenv-java library.
• apiKey is needed to authenticate with the OpenAI API.
• baseUrl specifies the API endpoint URL, which may be different from the default OpenAI endpoint.
• The HttpClient is the main object you use to send HTTP requests.
• ObjectMapper is Jackson's main class for parsing JSON responses safely.

Next, you need to specify the audio file you want to transcribe.

• audioFile is a Java File object pointing to your audio file.

Now, let's create the multipart/form-data with custom parameters. This is where we add the language and prompt fields to the form data.

Key points about this multipart form data construction:

• Each field is separated by a boundary marker (--boundary)
• The model field specifies which transcription model to use

Now, send the request to the API and handle the response with proper JSON parsing using Jackson.

Understanding the Boundary Generation:

The boundary ("----boundary" + System.currentTimeMillis()) is a unique string that separates different parts of the multipart form data. Here's why we use a timestamp:

• Uniqueness: System.currentTimeMillis() returns the current time in milliseconds since January 1, 1970, making each boundary unique across different requests
• Simplicity: It's a simple way to generate a unique identifier without additional dependencies
• HTTP Standard Compliance: The boundary must not appear in the actual data being sent, and using a timestamp makes this extremely unlikely

Alternative approaches you might see include:

• UUID: would provide better randomness but requires importing

Here's how it all fits together:

In this lesson, you learned how to improve your audio transcriptions by customizing the language and prompt parameters using direct HTTP requests to the OpenAI API with the GPT-4o Mini transcription model. You learned how to:

• Construct multipart/form-data requests manually
• Add optional parameters only when they have values
• Parse JSON responses safely using Jackson's ObjectMapper
• Handle API errors appropriately
• Generate unique boundaries for multipart requests using timestamps

Setting the correct language helps the model understand the audio better, and giving a prompt provides helpful context for more accurate results.

Next, you will get a chance to practice using these custom parameters yourself. You'll try different languages and prompts to see how they affect the transcription output. This hands-on practice will help you become comfortable with customizing your transcriptions for any situation.