umx.cpp VS EfficientAT

Good question! So, I wasn't even thinking about WASM to begin with. When I saw llama.cpp and whisper.cpp on the front page of HN, I found the idea exciting - instead of neural networks being magic, I wanted to copy the ggml idea of parsing the PyTorch weights file myself and rewriting the inference code in a lower-level language than Python (or, it's even more accurate to say PyTorch, since there is so much matrix heavy lifting e.g. broadcasting or reshaping that is done for you automatically).

That's when I wrote umx.cpp [1] (which is what this site is based on).

On an unrelated project, a friend of mine mentioned WASM, and as I looked into it a bit more I thought trying to compile umx.cpp to WASM would be a great idea, since I only use Eigen (which is a header-only library that only depends on std).

1: https://github.com/sevagh/umx.cpp

Interesting, I attempted to do the same as you but stopped just shy of BPM matching.

However I did get sound similarity working using an audio tagging neural net [1]. I chopped off the first and last 15 seconds of every song in my collection and ran them all through this analysis which produces a ~520 dimensional vector. I then targeted specific endings I wanted to match and used Euclidian distance to find the closest matching song beginning.

YMMV but I thought it actually worked pretty well, I just never got to automating the BPM matching. I can try to look for my old script if you're interested :)

[1] https://github.com/fschmid56/EfficientAT