1000sharks.xyz VS umx.cpp

OK, so, tangentially related: I tried to do something once - I took small chunks of songs generated by SampleRNN in an attempt to stitch together the ones that sounded the most similar.

The script [1] uses Essentia Chromaprint [2] to "grade" the similarity of audio tracks, and combine the ones with the closest chromaprint.

I have a track on Soundcloud which uses the above technique (mashing together short generated clips by their chromagram), trained on Cannibal Corpse [3]

1: https://github.com/sevagh/1000sharks.xyz/blob/master/sampler...

2: https://essentia.upf.edu/reference/std_Chromaprinter.html

3: https://soundcloud.com/user-167126026/1000sharks-domainal-sk...

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