omnizart

For MIDI: https://github.com/Music-and-Culture-Technology-Lab/omnizart and https://basicpitch.spotify.com/ They work better if you apply some source separation before (e.g, https://github.com/sigsep/open-unmix-pytorch, https://github.com/facebookresearch/demucs, or https://mvsep.com) Still, I think the best results are from proprietary models (specifically https://www.ableton.com/en/manual/converting-audio-to-midi/ and https://www.celemony.com/en/melodyne/what-is-melodyne)

This is called (multitrack) music transcription. There are some commercial solutions (AudioScore, AnthemScore, ...). For OSS, look at Omnizart [1] and magenta/mt3 [2]. I suppose these models are trained on western / pop music, so they may not work nicely on ethnic music. [1] https://github.com/Music-and-Culture-Technology-Lab/omnizart [2] https://github.com/magenta/mt3

I was going to post basic pitch from Spotify but it looks like billconan beat me to it. That said I can give you a bit more advice. The Spotify basic pitch model isn't too good at multi-track input. It's capable of it, but you may actually get better results if you separate out the tracks first and then run them individually through the basic pitch model. In order to do this you can use a source/stem separation model like spleeter (https://github.com/deezer/spleeter) and then run the basic pitch model (or any other midi transcription model). There's other you can try which may yield better results, for example: (https://github.com/Music-and-Culture-Technology-Lab/omnizart) Either way the key words you want to be looking for are "midi transcription" and "stem separation", should help you find more models to try for both steps. Good luck! :