tt-vae-gan
Timbre transfer with variational autoencoding and cycle-consistent adversarial networks. Able to transfer the timbre of an audio source to that of another. (by RussellSB)
PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks. (by eriklindernoren)
tt-vae-gan | PyTorch-GAN | |
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4 | 7 | |
64 | 15,732 | |
- | - | |
1.8 | 0.0 | |
over 2 years ago | 3 days ago | |
Python | Python | |
- | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tt-vae-gan
Posts with mentions or reviews of tt-vae-gan.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-25.
- Use deep fake tech to say stuff with your favorite characters
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[Project] I've successfully applied a VAE-GAN model (initially for voice conversion ) to the problem of timbre transfer between musical instruments. This showcases the generalisability of the approach with the potential for more than just one audio style transfer problem.
Link for demonstration, code, and tutorial: https://github.com/RussellSB/tt-vae-gan
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[P] Voice Conversion VAE-Cycle-GAN on Melspectrograms
I've been working on an open source implementation for the past month. The link for it can be found here. But have since been struggling with a load of mode collapse - or the model outputting "blurry" spectrograms not quite capturing the same initial structure as they should.
PyTorch-GAN
Posts with mentions or reviews of PyTorch-GAN.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-07.
- How to run large GAN models on colab with Celeb dataset
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Cleansing Image for Cataloging(see description)
there are many image to image translation models you can choose from. A good starting point would be PyTorch-GAN. I guess you're able to pick one by experimenting yourself. Good luck in your interesting work, and report back once you're done.
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[P] Pythae: Unifying Generative AutoEncoders in Python - What's new ?
By the way, if you are interested in GANs, this repository may help you: https://github.com/eriklindernoren/PyTorch-GAN.
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Help learning GAN architecture
If you are familiar with pytorch, I highly recommand to take a look at https://github.com/eriklindernoren/PyTorch-GAN as they have papers and implementations
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[D] What is current SOTA in Image to Image Translation?
I also happen to know of a old (but it checks out) mass-port-to-pytorch github: https://github.com/eriklindernoren/PyTorch-GAN . That one is handy for more seeing what kind of models are out there, but they're probably not SOTA.
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[P] Voice Conversion VAE-Cycle-GAN on Melspectrograms
Hi just an update to whom ever faces a similar issue. I've fixed it.The key change was redefining the encoder loss - where kld is defined without logvar (only mu) and the reconstruction loss is defined with L1 rather than MSE. This was a helpful resource on the matter https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/unit/unit.py
- I created a GAN to generate Minecraft skins - http://perceptrons.tk:8501/
What are some alternatives?
When comparing tt-vae-gan and PyTorch-GAN you can also consider the following projects:
jukebox - Code for the paper "Jukebox: A Generative Model for Music"
contrastive-unpaired-translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
CoGAN
YourTTS - YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
StarGANv2-VC - StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion
autovc - AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
voice_conversion