spleeter
stylegan2-ada-pytorch
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spleeter | stylegan2-ada-pytorch | |
---|---|---|
230 | 30 | |
24,878 | 3,910 | |
1.4% | 1.7% | |
1.5 | 2.3 | |
about 1 month ago | 3 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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spleeter
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Are stems a good way of making mashups
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.
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Big News!
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/.
- Anybody here know what AI model does Steinberg's Spectralayers use to do stem separation?
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Show HN: Free AI-based music demixing in the browser
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
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AI tools list sorted by category in one place
Spleeter is pretty good https://github.com/deezer/spleeter. Apparently it is used in some dj applications
- Software to lower tracks?
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Where does one legally get stems for remixes?
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
- Audio and python help
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Are there any websites or programs that can separate vocals and drums from samples?
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.
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Ask HN: Is there an ML model that can go from an audio song to sheet music?
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! :)
stylegan2-ada-pytorch
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Samsung expected to report 80% profit plunge as losses mount at chip business
> there is really nothing that "normal" AI requires that is bound to CUDA. pyTorch and Tensorflow are backend agnostic (ideally...).
There are a lot of optimizations that CUDA has that are nowhere near supported in other software or even hardware. Custom cuda kernels also aren't as rare as one might think, they will often just be hidden unless you're looking at libraries. Our more well known example is going to be StyleGAN[0] but it isn't uncommon to see elsewhere, even in research code. Swin even has a cuda kernel[1]. Or find torch here[1] (which github reports that 4% of the code is cuda (and 42% C++ and 2% C)). These things are everywhere. I don't think pytorch and tensorflow could ever be agnostic, there will always be a difference just because you have to spend resources differently (developing kernels is time resource). We can draw evidence by looking at Intel MKL, which is still better than open source libraries and has been so for a long time.
I really do want AMD to compete in this space. I'd even love a third player like Intel. We really do need competition here, but it would be naive to think that there's going to be a quick catchup here. AMD has a lot of work to do and posting a few bounties and starting a company (idk, called "micro grad"?) isn't going to solve the problem anytime soon.
And fwiw, I'm willing to bet that most AI companies would rather run in house servers than from cloud service providers. The truth is that right now just publishing is extremely correlated to compute infrastructure (doesn't need to be but with all the noise we've just said "fuck the poor" because rejecting is easy) and anyone building products has costly infrastructure.
[0] https://github.com/NVlabs/stylegan2-ada-pytorch/blob/d72cc7d...
[1] https://github.com/microsoft/Swin-Transformer/blob/2cb103f2d...
[2] https://github.com/pytorch/pytorch/tree/main/aten/src
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[R] StyleGAN2-ADA on Power 9?!
I am talking about the original Nvidia implementation here: https://github.com/NVlabs/stylegan2-ada-pytorch
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This X Does Not Exist
I think you should be able to find a latent vector that returns a cat that is part of the original training data (or at least very close to it). Most of the outputs will not be real cats at all though. However, it's pretty simple to try and find the latent vector that reproduces a given image, e.g. https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/pr...
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[P] Frechet Inception Distance
One irritating flaw with FID is that scores are massively biased by the number of samples, that is, the fewer samples you use, the larger the score. So to make comparisons fair it's absolutely crucial to use the same number of samples. From what I've seen on standard benchmarks it's pretty common now to compute Inception features for every single data point, but only for 50k samples from generative models (for reference off the top of my head StyleGAN2-ADA does this, see Appendix A).
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generating images
You can follow the development of stylegan from NVIDIA: https://github.com/NVlabs/stylegan2-ada-pytorch They have formed datasets containing human faces, maybe you can use human faces with expressions as classes and train conditional GAN with your own classes.
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What is the best GAN architecture for image data augmentation?
Given the lack of data StyleGan 2 by Nvidia, which was specifically created to handle small datasets could be an option - https://github.com/NVlabs/stylegan2-ada-pytorch
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City Does Not Exist
First, you have to collect a few thousand images of the same thing (maybe more or less depending on how complex your thing is or how good the results should be). Then, you train a generative adversarial neural network on those images to generate new images. https://github.com/NVlabs/stylegan2-ada-pytorch works quite well. https://github.com/NVlabs/stylegan3 is supposedly even better, but I did not try it yet.
- Modern Propaganda (this person does not exist)
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From 53% to 95% acc - Real vs Fake Faces Classification | Fine-tuning EfficientNet (Github in comment)
What NVIDIA does when computing Perceptual Path Length is to center crop the faces before computing the metric. Here you can find the code to get an idea https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/metrics/perceptual_path_length.py
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StyleGAN2 ADA Pytorch ends after tick 0 with no errors.
I\m trying to train StyleGAN2 ADA Pytorch https://github.com/NVlabs/stylegan2-ada-pytorch on my own dataset.
What are some alternatives?
ultimatevocalremovergui - GUI for a Vocal Remover that uses Deep Neural Networks.
stylegan3 - Official PyTorch implementation of StyleGAN3
open-unmix-pytorch - Open-Unmix - Music Source Separation for PyTorch
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
demucs - Code for the paper Hybrid Spectrogram and Waveform Source Separation, but the goddamm motherfucker doesn't work.
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
SpleeterGui - Windows desktop front end for Spleeter - AI source separation
StyleFlow - StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021)
SpleetGUI - Spleeter GUI version
lucid-sonic-dreams
spleeter-web - Self-hostable web app for isolating the vocal, accompaniment, bass, and drums of any song. Supports Spleeter, D3Net, Demucs, Tasnet, X-UMX. Built with React and Django.
data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training