gpiozero VS spleeter

It was a bit tricky to get CPU temperature of a host machine (Raspberry Pi) from within the running docker container. This Issue on GitHub has helped me :

No. The pi 4 is not a microcontroller. However, I would recommend gpiozero for a regular pi4

I run it in K3s on Raspberry Pi OS. The service is built on gpiozero so should work on other devices. It is a very helpful building block in my DIY home automation system. It's a general purpose connector so it can be configured for many different uses. At present I use it to:

Gpiozero is already installed in the current Raspberry Pi OS - if not, just run python3 -m pip gpiozero. The source code files can be accessed on Github. The official documentation on readthedocs.io provides rich information and plenty example code.

I tried to use it but I had some issues as others in the thread.

I have tried many sources and method over the years and settled on spleeter [0]. Works well even for 10+ minute songs, varying styles from flamenco to heavy metal.

[0] https://github.com/deezer/spleeter

Chopped from their website Simple Stems is a quick and easy way to decompose any audio into it’s constituent parts. The plugin uses the well established Spleeter algorithm by Deezer to deconstruct songs into 2, 4 or 5 stems. The results are stunning, though more complicated mixes and live recordings are not always perfectly decomposed.

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! :)

I'm chasing my white whale, which is to create a real-time version of the audio stem separation tool 'Spleeter' that I've been using for a few years now to remove instruments like drums/bass guitar from existing music so that I can play along at home.

I use Spleeter.

Some of you have probably heard of spleeter, a machine learning program developed by Deezer that isolates instruments. It was pretty good, but it had some obvious weaknesses. But what if I told you that there's something even better? Ultimate Vocal Remover is so good I audibly said "holy sh*t" when I listened to what it produced. It recently released a full-band model (UVR-MDX-NET Inst HQ 1), unlike spleeter which has an 11kHz cutoff.

I'm actually planning on using something like spleeter to create drumless tracks from youtube videos on a website where people can share the pre-synced music sheets and audio.

2) It's possible to separate vocals (and other stems) using software like Demucs, LALAL.AI, etc... While it isn't a perfect solution, the technology for this is getting better and better. I originally used Spleeter but now exclusively use Demucs (v4) since the results are highly impressive. If you're not comfortable setting up a python environment, there are some websites like AudioStrip that allow you to try it online with limitations.