fastapi-azure-auth VS spleeter

I'm under the impression that you work for a company that sells services related to FastAPI? https://github.com/Intility/fastapi-azure-auth

I maintain an open source library in my spare time for free, that you are welcome to ignore if you find better alternatives.

The documentation has a full tutorial in “Tiangolo-style”, which means it guided through setting up a project from scratch, and how to configure Azure appregs from scratch.

I think, in general, most FastAPI and Pydantic related libraries are heavily typed, use poetry, GitHub pipelines, black, isort, flake8 etc. so if you want to look at the ecosystem around a package I’ll recommend a few here, that has a smaller scope than the huge libraries Pydantic/FastAPI are. All packages listed below has all these things.

FastAPI-Azure-Auth [0] is a library to do authentication and authorization through Azure AD using tokens.

ASGI—Correlation-ID[1] is a package that utilizes contextvars to store information through the asyncio stack, in order to attach correlation/request ID to every log message from a request. Available for Django in [2].

Pydantic-factories [3] is an awesome library to mock data for your pydantic models.

[0] https://github.com/Intility/fastapi-azure-auth

If you want to use an external auth provider, I have written a library called FastAPI-Azure-Auth for authentication and authorization using Azure AD (which is free for something like 10.000 users). The tutorial should get you up and running quickly. Please note that this library is only intended to use for APIs (such as I sing a SPA frontend), so if you use jinja templates or render HTML from FastAPI this might not be the solution for you.

Hi! I’m the author of FastAPI-Azure-Auth, a package to handle Azure AD authentication and authorization for your FastAPI APIs. It’s a heavily tested package, supports trio, and the documentation has a full tutorial on how to set up both Azure and FastAPI from scratch.

I agree with you. Most my packages are around ~100 stars, and I'm met with a lot of respect and appreciatio.n[1][2]

My library for Correlation-IDs in Django[3] got implemented by AWX, which also was a nice experience![4] I maintain a lot of small packages, and maybe it is the Django/FastAPI community, but "you'll get a load of entitled users" is straight up not true in my experience.

[1] https://github.com/Intility/fastapi-azure-auth/issues/24

The documentation[1] contains a full tutorial on how to configure Azure AD and FastAPI for both single- and multi-tenant applications. It includes examples on how to lock down your APIs to certain scopes, tenants, roles etc.

[1] https://intility.github.io/fastapi-azure-auth/)

virtual dj and others stem separator is shrinked model of this https://github.com/deezer/spleeter you will get better results downloading original + their large model.

I have used multiple tools at this point. It depends on the scene. I use https://ultimatevocalremover.com/, https://github.com/deezer/spleeter/, iZotope RX. There are also multiple options online, I would personally recommend https://vocalremover.org/.

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

Spleeter is pretty good https://github.com/deezer/spleeter. Apparently it is used in some dj applications

Haha GitHub and command lines and all can be confusing, but it’s certainly worth the effort because it lets you do everything for free.. here’s the online tutorial: https://github.com/deezer/spleeter/wiki/1.-Installation

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! :)