Welcome back! In our previous lessons, we've explored using basic transcribing techniques with OpenAI's Whisper API, as well as calculating the media duration using FFmpeg. Today, we'll shift our focus to transcribing large files with OpenAI Whisper and FFmpeg. 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 FFmpeg's capabilities.

OpenAI Whisper has a file size limitation of 25 MB, which poses a challenge when attempting to transcribe large audio or video 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 FFmpeg's capabilities to split the files into segments that fall within the permissible size limit. This will ensure compatibility with OpenAI Whisper 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 consider Python code to achieve this, ensuring all steps are easily comprehensible. First, let's revisit how we retrieve the media's length using FFmpeg:

This section of the code employs ffprobe to determine an audio file's duration. ffprobe is a component of FFmpeg that fetches file data without altering it. The command is carefully structured to extract only the duration, allowing us to calculate how to split the file accordingly.

Now, let's implement one more helper function. Splitting a media file into chunks is a time-consuming process, and FFmpeg will produce its logs as a stream - they will iteratively appear as it keeps processing the file. In order for us to process that efficiently, we should implement a way to stream these logs to the console in Python:

This helper function allows us to run commands and stream outputs in real time. By setting up a subprocess, it captures output line-by-line, ensuring you keep track of the progress during long operations, a critical feature when managing large files.

The process of splitting media files into smaller chunks involves key FFmpeg commands that work together to extract segments without re-encoding. Let's break down the code to see how it operates:

Code Explanation:

1. Initialize Variables:

   • We first determine the duration of the media file using the helper get_audio_duration function.
   • The file_size is retrieved to calculate the proper chunk duration that fits within the specified chunk_size_mb limit (which is by default 20Mb).

2. Calculate Chunks:

Running the code (e.g., split_media('resources/sample_video.mp4', 1)) will print something like this:

The sample_video.mp4 video file size is around 2Mb, so splitting it into chunk_size_mb produces 2 chunks of 1 Mb, both of which are properly extracted with FFmpeg and saved as separate temporary files.

Congratulations on mastering the process of splitting large media files using FFmpeg! In this lesson, you've learned how to leverage FFmpeg's capabilities to efficiently break down large files into smaller, manageable chunks. By understanding the intricacies of file handling, 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!