Welcome back! In our previous lessons, we've explored using basic transcribing techniques with OpenAI's gpt-4o-transcribe API, as well as calculating media duration using PyDub. Today, we'll shift our focus to transcribing large files with OpenAI gpt-4o-transcribe and PyDub. Managing large audio or video files by splitting them into manageable pieces ensures that tasks like transcription can be performed efficiently and without errors. This lesson will empower you to handle these files smoothly, leveraging PyDub's capabilities.

OpenAI's gpt-4o-transcribe has file size limitations, which pose a challenge when attempting to transcribe large audio files. To work around this constraint, we need a method to divide these large files into smaller, manageable chunks that can be processed sequentially. Our strategy involves leveraging PyDub's capabilities to split the files into segments that fall within the permissible size limit. This will ensure compatibility with OpenAI's gpt-4o-transcribe while maintaining the quality and integrity of the original content. By breaking down large files, we facilitate efficient transcription, allowing for smooth and accurate processing of each smaller segment.

Let's see how we can retrieve the duration of an audio file using PyDub. This is much simpler than using command-line tools, as PyDub provides a high-level interface:

This function uses PyDub's AudioSegment.from_file() method, which automatically detects and loads the appropriate file format. The duration is then easily accessed through the .duration_seconds property, which gives us the total playback time in seconds.

Now, let's see how to split a media file into smaller chunks using PyDub's simple and intuitive slicing API:

Code Explanation:

1. Initialize Variables:

   • We load the audio file using PyDub's AudioSegment.from_file() method.
   • We retrieve the file size using Python's os.path.getsize() to calculate the appropriate chunk duration.

2. Calculate Chunks:

   • chunk_duration_ms calculates how long each chunk should be in milliseconds, based on the desired chunk size in megabytes.
   • num_chunks determines the total number of chunks needed.

3. Create Each Chunk:

   • We iterate through each chunk, calculating the start and end times in milliseconds.
   • PyDub allows us to slice the audio using a simple bracket notation: audio[start_time:end_time].

4. Save Each Chunk:

   • We create a unique temporary file for each chunk using Python's tempfile and uuid modules.
   • We export the audio chunk using PyDub's .export() method, which handles the file format automatically.

5. Return Chunk Paths:

   • We store and return the paths to all the temporary files we created.

This approach provides a clean, Pythonic way to split audio files without having to deal with complex command-line parameters.

Running the code in a Python application looks like this:

When executed, you'll see output similar to this:

The sample_audio.mp3 audio file size is around 2Mb, so splitting it with chunk_size_mb set to 1 produces 2 chunks of approximately 1 Mb each. PyDub handles the extraction and export with just a few lines of Python code, making the process much more straightforward than using lower-level tools.

Congratulations on mastering the process of splitting large media files using PyDub in Python! In this lesson, you've learned how to leverage PyDub's capabilities to efficiently break down large files into smaller, manageable chunks. By understanding the intuitive API of PyDub and Python's file handling capabilities, you can now enhance file operations, reduce memory overhead, and enable parallel processing for improved performance, all while maintaining content quality. You're now well-equipped to tackle large-scale multimedia tasks with confidence and precision!