Summarizing Youtube video transcripts with language models

yt-dlp
yt-dlp

You can download a Youtube video transcript with yt-dlp.

Terminal window
yt-dlp --write-auto-sub --skip-download --sub-format vtt --output transcript "<video_url>"

This will output a file called transcript.en.vtt. That file can be cleaned like this, to remove all formatting and metadata except the transcript text.

Terminal window
cat transcript.en.vtt | grep : -v | awk '!seen[$0]++'

This approach is useful for simple way to pipe the contents of a Youtube video into an LLM, my motivation for finding a way to accomplish this task.

Here is a script that pulls the transcript of a video then summarizes it using llm.

#!/bin/zsh


if [ $# -eq 0 ]
  then
    echo "No arguments supplied. Please provide a YouTube URL as an argument."
    exit 1
fi
yt-dlp --write-auto-sub --skip-download --sub-format vtt --output transcript "$1" >/dev/null 2>&1
cat transcript.en.vtt | grep : -v | awk '!seen[$0]++' | llm "write a short summary of the contents of this youtube video transcript"
rm transcript.en.vtt

Run it like this

Terminal window
./summarize.sh <youtube_url>

Adding support for questions

To further customize the script, you can add support for a second argument to the command, which can serve as an additional prompt (usually some kind of question about the prior context) to the model.

#!/etc/profiles/per-user/danielcorin/bin/zsh


if [ $# -eq 0 ]
  then
    echo "No arguments supplied. Please provide a YouTube URL as an argument."
    exit 1
fi


youtube_url="$1"
prompt="write a short summary of the contents of this youtube video transcript"


if [ $# -eq 2 ]
  then
    prompt="$2"
fi


yt-dlp --write-auto-sub --skip-download --sub-format vtt --output transcript "$youtube_url" >/dev/null 2>&1
cat transcript.en.vtt | grep : -v | awk '!seen[$0]++' | llm -m claude-3-haiku "$prompt"
rm transcript.en.vtt

For example:

Terminal window
❯ yt-summary "https://www.youtube.com/watch?v=8SF_h3xF3cE" "does the lecturer discuss how to withhold a test set using the fastai library?"
Yes, the lecturer mentions the importance of withholding a validation set when training machine learning models. Specifically, he states:


"It's so critical that fast ai won't let you train a model with that one. So you actually have to tell it how to create a validation set, how to set aside some data."


He explains that the validation set is used to evaluate the accuracy of the trained model, separate from the training data. This is a key concept in machine learning to avoid overfitting to the training data.


The lecturer doesn't go into the specific details of how to create the validation set using the fastai library, but he does emphasize that it's a critical step that the fastai library handles for you.
raw edit

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