tuido VS youtube-cue

tuido: https://github.com/NiloCK/tuido - a tui that scrapes various text format files for things to do, with some nice value adds.

skuilder: https://github.com/NiloCK/vue-skuilder - a flexible SRS environment, which I currently use myself for harmony / ear training piano stuff, but also with my daughters who are learning to read.

Lately I've also made a couple of webextensions that layer some power tools on top of chatGPT (search past chats, faster keyboard based nav beween chats, faster keyboard based copying of gpt produced snippets). Also a cli utility to bundle the current directory into a zip file in the clipboard so that I can quickly share small projects with chatGPT.

(It feels a little weird to me that OpenAI's default offering doesn't offer more power-user friendly functionality off the shelf).

> CLI: I wanted to download songs from youtube, but they were often stitched as complete albums - so I wrote a youtube-cue generator that generates cuesheets that can then be used to split and tag the yt-dlp downloaded audio file. (https://github.com/captn3m0/youtube-cue)

Thanks for this! I need to do some testing, this might automate the last manual step of my own script for converting YT mixes into distinct tracks. The problem I faced is that often the timestamps are not in the description, but instead in a comment, sometimes not even the pinned/top voted comment. That is why I paste it in via stdin for now.

As this fits the thread topic, a short description of this script. I enjoy YT mixes and wanted to listen to them in my car. I can use an USB stick with media files and playlists which are displayed decently by the infotainment system. I wrote a script that takes in a YT URL (or anything supported by yt-dlp), downloads & converts it to mp3, splits the mp3 file based on a list of timestamps, recognizes (tries to anyway) the songs via SongRec [0], tags & names the files correctly and finally generates an M3U playlist in the format recognized by my car. I use song recognition instead of parsing out the names from the timestamped list as the format of Artist - Title is nearly always slightly different. It was easier to use SongRec instead and get everything I need for tagging with >90% hit rate.

The heavy lifting is done by calling out to yt-dlp, ffmpeg and SongRec. I just glued them together with Python. I like your approach of a do one thing well and might add youtube-cue to the toolset.

[0] https://github.com/marin-m/SongRec

Beets is amazing and comes with great defaults. I wrote code recently to generate CUE sheets from YouTube mixes[0] and beet imports it nicely and easily.

[0]: https://github.com/captn3m0/youtube-cue There is a bash snippet in readme to show the Beets integration.