Welcome back! In the last lesson, you learned how to extract audio from video files using C# and the Xabe.FFmpeg library. We discussed why it is often better to work with audio files instead of video, especially when preparing content for transcription or speech recognition. You also saw how to use the right FFmpeg parameters to create audio files that are compatible with most APIs and services.

Today, we will build on these skills by addressing a common challenge: handling long-form or large audio files. Many real-world recordings — such as interviews, podcasts, or meeting recordings — can be much longer than what most transcription APIs will accept in a single upload. Even if you have already normalized and extracted the audio, you may still need to split it into smaller, manageable chunks before you can process or transcribe it.

By the end of this lesson, you will know how to split large audio files into smaller pieces using Xabe.FFmpeg in C#. This is a crucial step in any workflow that deals with long recordings, and it will help you avoid errors, stay within API limits, and keep your applications running smoothly.

Let’s take a moment to understand why splitting long audio files is so important. Most transcription APIs and cloud services have strict limits on the size or duration of audio files they will accept. For example, some services only allow files up to 25 MB or 30 seconds in length per request. If you try to upload a file that is too large or too long, you will likely get an error, or the service may simply reject your request.

Besides API limits, there are also performance reasons to split large files. Processing a long audio file in one go can be slow and may use a lot of memory. By breaking the audio into smaller chunks, you can process each piece independently, which is faster and more reliable. This approach also makes it easier to retry failed chunks without having to redo the entire file.

In summary, splitting long-form audio is not just about meeting technical requirements — it is also about making your workflow more efficient and robust.

Now, let’s look at how you can split an audio file into smaller chunks using Xabe.FFmpeg in C#. In your AudioProcessor class, you have a method called SplitAudioIntoChunksAsync. This method takes the path to your audio file and splits it into smaller files, each with a specified duration (for example, 30 seconds).

Here is the relevant code:

Let’s break down what is happening here. First, the method gets the total duration of the audio file using FFmpeg.GetMediaInfo. It then calculates how many chunks are needed by dividing the total duration by the desired chunk length. For each chunk, it uses FFmpeg’s -ss parameter to set the start time and -t to set the duration of the chunk. The -acodec copy parameter tells FFmpeg to copy the audio stream without re-encoding, which is faster and preserves quality.

For example, if you have a 90-second audio file and you want 30-second chunks, this method will create three files: chunk_1.wav (0-30s), chunk_2.wav (30-60s), and chunk_3.wav (60-90s). If the audio cannot be evenly divided into 30-second chunks — for example, if the total length is 95 seconds — the method will create a final chunk that contains the remaining audio. In this case, you would get three 30-second chunks and a fourth chunk with the last 5 seconds. This ensures that no part of the original audio is lost, even if the total duration is not a perfect multiple of the chunk size.

After running this method, you will get a list of file paths for all the chunks created. Here is what the output might look like:

Each of these files can now be processed or transcribed separately.

Now that you know how to split audio files, let’s see how this fits into your overall workflow. In a typical application, you might first extract audio from a video file, then split the audio into chunks, and finally send each chunk to a transcription service.

Here is how you can put it all together in your Program.cs:

In this example, you first initialize the audio processor and transcription service. Then, you split the audio file into 30-second chunks to stay within API limits. Each chunk is sent to the transcription service, and the transcriptions are collected.

Finally, the transcript for each chunk is printed to the console, making it easy to review the results and verify that each part of the audio was processed correctly. This approach helps you handle long recordings efficiently and ensures that you can process or debug each chunk individually if needed.

When working with large audio files, there are a few best practices to keep in mind. First, always check the output of your splitting process to make sure all chunks are created and have the expected duration. Sometimes, the last chunk may be shorter if the total duration is not a perfect multiple of your chunk size.

It is also important to handle errors gracefully. For example, if a chunk fails to process, your application should log the error and continue with the remaining chunks. This way, you do not lose all your progress if something goes wrong with just one part of the file.

Managing your files is another key consideration. Make sure to use clear and consistent naming for your chunks, and clean up any temporary files when you are done. This will help you avoid confusion and keep your workspace organized.

Finally, always test your workflow with different file sizes and formats to ensure it works reliably in all scenarios. This will help you catch edge cases and make your application more robust.

In this lesson, you learned why splitting long-form audio files is necessary and how to do it using Xabe.FFmpeg in C#. You saw how to use the SplitAudioIntoChunksAsync method to break large audio files into smaller pieces, making them easier to process and transcribe. You also learned how to integrate this step into your overall workflow, and you reviewed some best practices for handling large files in real-world applications.

You are now ready to practice these skills with hands-on exercises. Splitting audio files is a key part of working with long recordings, and mastering this technique will make your applications more flexible and reliable. In the next section, you will get a chance to try out these concepts for yourself. Good luck, and enjoy experimenting with audio splitting in C#!