ultimatevocalremovergui VS stemroller

Compare ultimatevocalremovergui vs stemroller and see what are their differences.

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ultimatevocalremovergui stemroller
82 37
14,928 2,406
- 1.7%
8.7 6.2
10 days ago 17 days ago
Python Svelte
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

ultimatevocalremovergui

Posts with mentions or reviews of ultimatevocalremovergui. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-02.

stemroller

Posts with mentions or reviews of stemroller. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-15.

What are some alternatives?

When comparing ultimatevocalremovergui and stemroller you can also consider the following projects:

spleeter - Deezer source separation library including pretrained models.

demucs - Code for the paper Hybrid Spectrogram and Waveform Source Separation, but the goddamm motherfucker doesn't work.

Retrieval-based-Voice-Conversion-WebUI - Easily train a good VC model with voice data <= 10 mins!

demucs-cxfreeze

vocal-remover - Vocal Remover using Deep Neural Networks

tortoise-tts-fast - Fast TorToiSe inference (5x or your money back!)

so-vits-svc-fork - so-vits-svc fork with realtime support, improved interface and more features.

demucs4max - Demucs as a max4live device

jukebox - Code for the paper "Jukebox: A Generative Model for Music"

music-source-separation-using-Unets - This repo explores the concept of blind source separation by training a U-Net model that separated a song into its vocal and accompaniments