Welcome back! In the previous lesson, you learned how to preprocess audio files using FFmpeg and the Xabe.FFmpeg library in C#. You explored how to normalize audio, convert it to a standard format, and prepare it for tasks like transcription. This foundational knowledge is essential for working with audio data in real-world applications.

Today, we will build on that foundation by introducing a new and very practical skill: extracting audio from video files. Many times, the content you need to process or transcribe is not in a standalone audio file but is embedded within a video — such as a recorded meeting, a lecture, or a podcast episode published as a video. Being able to extract just the audio track from these files is a key step in many media workflows.

By the end of this lesson, you’ll know how to extract audio from a video file using C# and Xabe.FFmpeg, making it ready for further processing or transcription. You’ll also understand why this step is often necessary and advantageous in modern workflows.

Extracting audio is useful for several reasons. Audio files are usually much smaller than their video counterparts, making them easier to upload, share, and process. Many APIs and cloud services for transcription or speech recognition enforce file size limits (e.g., 25MB per upload), so working with just the audio ensures you remain within those boundaries. Focusing on audio also reduces bandwidth costs and speeds up processing, especially when the video content isn’t needed.

When extracting audio from video, it’s important to produce output that is compatible with your downstream tasks, such as transcription. For best results, you want a mono, 16kHz WAV file—this is the standard for most speech recognition APIs.

Here’s what the key FFmpeg parameters do in this workflow:

• -i "input": Specifies the source video file.
• -vn: Ignores the video stream, processing audio only.
• -ar 16000: Sets audio sample rate to 16,000 Hz, which is standard for speech.
• -ac 1: Forces mono (single channel) output.
• -acodec pcm_s16le: Saves as 16-bit signed PCM in little-endian format, which is widely accepted for WAV files.

Additionally, you can extract only a specific segment of the audio by using the -ss (start time) and -t (duration) parameters:

• -ss {startTimeSeconds}: Start extracting from this timestamp (in seconds).
• -t {durationSeconds}: Extract audio for this duration (in seconds).

By using these parameters together, you create a clean, compact, and compatible audio file ready for transcription, and you can focus on just the segment you need.

To keep your code modular, you should implement audio extraction in a dedicated method within your AudioProcessor class. This approach is maintainable and lets you reuse the functionality across your projects.

This method requires you to provide startTimeSeconds and durationSeconds parameters. Only the specified segment of the audio will be extracted.

If you want to extract the entire audio track, you can create a separate method without the time segment parameters:

To use this feature in your project, initialize your processor and call the extraction method with the relevant file paths and the time segment you want to extract. Here’s how the workflow looks in your Program.cs:

If you want to extract the entire audio track, use the dedicated method:

Running this workflow will output:

This end-to-end example demonstrates extracting either the full audio or a specific segment, and then using your transcription service to process it.

In many real-world scenarios, you may receive media in video formats such as MP4, even if you only need the audio. While some APIs accept MP4 files directly, it is often better to extract and send just the audio. There are several reasons for this.

First, audio files are much smaller than video files, which makes them faster to upload and process. This is especially important when working with large or long-form content. Second, many APIs — including popular transcription services — have strict file size limits. For example, some APIs only accept files up to 25MB. By extracting the audio and saving it in a compact format, you can stay within these limits and avoid errors or rejections.

Additionally, sending only the audio reduces unnecessary overhead. Video data is not needed for speech recognition or transcription, so including it only wastes bandwidth and processing time. By focusing on the audio stream, you make your workflow more efficient and cost-effective.

In summary, while MP4 and other video formats are accepted by some services, extracting and sending just the audio is usually the best practice for transcription and similar tasks.

In this lesson, you learned how to extract audio from video files using C# and Xabe.FFmpeg, including how to extract only a specific segment using timestamps. You reviewed the importance of using the right FFmpeg parameters to produce high-quality, compatible audio files. You also saw how to implement and use the ExtractAudioFromVideoAsync and ExtractFullAudioFromVideoAsync methods in a real application, and you explored why extracting audio is often better than sending video files directly — especially when dealing with API size limits and efficiency concerns.

You are now ready to practice these skills with hands-on exercises. Extracting audio from video is a valuable feature in many applications, from transcription services to podcast platforms and beyond. As you move on to the practice section, remember that mastering these workflows will make you much more effective at handling large and complex media files in your own projects. Good luck, and enjoy experimenting with audio extraction in C#!