scotus-videos

This repository contains the code used to produce the videos for https://www.youtube.com/@SCOTUSOralArgument, which feature the transcript of oral arguments at the U.S. Supreme Court, displayed using a chat-like interface. The channel is managed by Dominik Peters in France as a side project. I do this because I enjoy listening to oral arguments, which feature both judges and advocates who are extremely well-prepared and are quick analytic thinkers, and I believe that these arguments should find a wider audience. You can contact me by email at mail@dominik-peters.de or you can leave feedback or suggestions as an issue on GitHub.

The videos are made by rendering the data using HTML/CSS, saving the frames to image files using puppeteer by screenshotting, and encoding the video using ffmpeg. The data is taken from the Oyez project.

Usage: Run

python preprocess.py https://www.oyez.org/cases/2023/22-915

to download the audio and transcript of a particular case.

Then run

./produce.sh

to build the video. Ensure that puppeteer is available. It will also attempt to upload to YouTube which will probably not work if you're not me.

Manual transcript alignment

Starting in the 2024 term, I've also been using the gentle forced aligner to align the audio to the transcript for some cases. This way I don't have to wait for the transcript to be available on Oyez. I do this for a few "hot-button" cases that I want to upload as soon as possible. The code I use for this is in the folder self-aligned. This is written specifically for my local machine (in particular using Acrobat Reader to get the plain text from the PDF transcript, which is done with AppleScript, and I'm using the mac binary of gentle since I couldn't get it to work any other way), so it may not be very useful for others, but you can take a look if you're interested. The entry point is python scotus-tool.py which leads the user through the different steps (downloading PDF and MP3 from supremecourt.gov, extracting plain text, running gentle, and post-processing the gentle output to get the final transcript format that I use for the videos which I then feed into self-aligned-preprocess.py).