Extracting Audio from Video with Python, NotebookLM, and Claude

Do you have a video file that you’d like to turn into text? Whether it’s an interview, a podcast episode, or a lecture, extracting the audio and getting a transcript can be incredibly useful. Today, I’ll show you how to use Python to extract the audio track from a video and then easily transcribe it with NotebookLM. Let’s dive in!

Step 1: Extract Audio from a Video Using Python

Python makes it straightforward to extract audio from a video, thanks to the awesome moviepy library. This library allows us to manipulate videos and audio without a lot of hassle. Below, I’ll walk you through a simple script that takes a video file and extracts the audio track, saving it as an MP3.

Make sure you have the moviepy library installed. You can install it with:

pip install moviepy

Next, you can use the following Python code to extract the audio:

from moviepy import VideoFileClip

# Load the video file
video = VideoFileClip("Blog Tag Creation.mp4")

# Write the audio to a file
video.audio.write_audiofile("Blog Tag Creation.mp3")

This code does exactly what we need:

  • It loads the video file named Blog Tag Creation.mp4.
  • Then it writes the audio part to a new file called Blog Tag Creation.mp3.

After running this script, you should have an MP3 file containing all the audio from your video.

Going Deeper: Understanding the Code

Let’s take a closer look at the components of this code:

  • Importing VideoFileClip: The VideoFileClip class from moviepy is the core tool we use to load the video. It reads the video file and provides access to its audio.
  • Loading the Video: When we create a VideoFileClip object, Python reads the video and stores information about it, such as duration, resolution, and audio track. In this example, we load the file named "Blog Tag Creation.mp4". You can replace this with any video file on your system.
  • Extracting Audio: The audio.write_audiofile() function extracts the audio track from the loaded video and saves it as a separate file. Here, we specify "Blog Tag Creation.mp3" as the output filename, but you could save it in different formats like .wav if desired.

Step 2: Transcribe the Audio Using NotebookLM

Now that you’ve got an MP3 file, the next step is to turn it into a text transcript. One of the easiest ways to do this is by using NotebookLM, which can help you quickly create a transcript from an audio file.

To get started:

  1. Upload the MP3 File to NotebookLM: Simply go to your NotebookLM instance, and upload the Blog Tag Creation.mp3 file. NotebookLM is designed to be user-friendly, and the upload process is quite straightforward. The file size and duration may affect processing time, but most common formats are supported without issues.
  2. Generate the Transcript: NotebookLM will process the file and generate a transcript of your audio. This makes it easy to search for specific topics discussed in the video or use the content for your notes, articles, or future blog posts. The accuracy of the transcript depends on the clarity of the audio, so here are a few tips to improve transcription quality:
    • Minimize Background Noise: The clearer the audio, the more accurate the transcription. Try to minimize background noise during recording.
    • High-Quality Audio Format: Using higher quality formats like .wav instead of .mp3 can also improve transcription accuracy, as it retains more of the original audio detail.

Example Prompts for NotebookLM

  • Basic Transcript: “Please transcribe the audio file Blog Tag Creation.mp3 into text.”
  • Summarize Key Points: “Transcribe the audio and provide a summary of the main topics discussed.”
  • Highlight Quotes: “Identify any important quotes or noteworthy comments made in the audio file and transcribe them clearly.”

These prompts can help guide NotebookLM to create the most useful transcription output based on your needs.

Step 3: Refining the Transcript Using Claude

Once you have your initial transcript from NotebookLM, it’s often a good idea to refine it for better accuracy and readability. You can use AI tools like Claude to help clean up and polish your transcript.

To refine your transcript:

  1. Upload or Paste the Transcript into Claude: Start by copying the transcript text and providing it to Claude. Claude is designed to help make text clearer and more coherent. It’s particularly effective at handling casual or conversational language, making it ideal for refining transcripts that may include pauses, filler words, or slang.
  2. Edit and Improve: Use Claude to refine grammar, remove filler words, and make the transcript flow more naturally. This is especially useful if the audio contains a lot of conversational speech or background noise. Claude can also help structure the transcript logically, ensuring that the final version reads smoothly.

Example Prompts for Claude

  • Polish Grammar: “Refine this transcript to correct grammar and improve readability.”
  • Remove Filler Words: “Edit this transcript to remove filler words like ‘um’, ‘uh’, and ‘you know’, and make it more concise.”
  • Reorganize for Clarity: “Reorganize the transcript to enhance clarity and readability, ensuring logical flow between ideas.”

These prompts can help you get the most refined and professional transcript possible, saving you time on manual editing.

Making the Most Out of Your Transcript

Refining your transcript with Claude can be a transformative step:

  • Create Content Faster: Use the refined transcript to quickly draft articles, blog posts, or even social media content. A polished transcript saves time and provides a solid foundation for further content creation.
  • Professional Presentation: Whether you’re sharing meeting notes or publishing an interview, presenting a refined, well-structured document helps convey professionalism and attention to detail.

Wrapping Up

With just a few lines of Python code and the power of NotebookLM, you can easily extract audio from videos and transcribe them into text, completely free. This process can be a game-changer for anyone who needs to convert spoken content into written form, be it for blogs, research, or personal records.

Next Steps

If you’re interested in automating everyday workflows like this, get in touch. At Aido4Me, we help businesses and individuals leverage automation to make tasks like extracting audio, transcribing, and refining content more efficient.

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