youtube-cue VS soundfingerprinting

> 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.

Started 10 years ago as an open-source project, building an algorithm for audio fingerprinting. Added a commercial offering, selling storage built specifically for audio fingerprints, targeting enterprise customers. Since the offering was too technical (it's hard to sell solutions to problems that are too narrow and domain-specific), pivoted to more "business-oriented problems". This last year's pivot is a chance to finally grow. Running a business in single-player mode is, at times, too stressful. Aside from the technical part, which I very much enjoy, I need to wear marketing, sales, and customer support hats.

[1] - https://emysound.com

The OP said elsewhere they are using this[1] library, which allows you to specify minimum seconds to match, so you'd presumably set it to match 20 seconds or whatever minimum length podcast commercials usually are.

Most other audio fingerprinting libraries I've seen allow you to specify min/max time, as well.

HTH.

1. https://github.com/AddictedCS/soundfingerprinting

My goal for the next year is just to work fewer hours. Covid pushed my work habit to the extreme, and I need to rebalance.

1. Read more books, less social media/news.

2. Spend more time with my friends. I haven't seen some of them IRL for more than a year.

3. Exercise more, play tennis with my daughter, spend quality time with my kids.

4. Spend more time with my parents. They've become visibly older in the last years, an observation that scares me.

5. Focus more on marketing for the businesses that I've bootstrapped. All the shiny new features that are developed are not as important as getting more people to use your product.

6. Promote open-source project to 1k GitHub stars[1]. I know it isn't very meaningful, but it's just nice to receive a bit of recognition from the community.

7. Enjoy life, don't stress about all the little things that happen along the way.

Happy new year!

[1]: https://github.com/AddictedCS/soundfingerprinting