open-unmix-pytorch
Our great sponsors
| music_source_separation | open-unmix-pytorch | |
|---|---|---|
| 3 | 11 | |
| 1,186 | 1,149 | |
| 2.8% | 1.7% | |
| 0.0 | 0.0 | |
| over 1 year ago | about 1 month ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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.
music_source_separation
-
Cassiopeia: New Audio Splitter Superior to Spleeter and UMX
There's maybe something useful for you here?
https://github.com/facebookresearch/demucs
https://github.com/sigsep/open-unmix-pytorch
-
Last week AI, ML, and data-related original articles summary
Music Source Separation
-
[R] Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation
github: https://github.com/bytedance/music_source_separation
open-unmix-pytorch
-
Show HN: Improved freemusicdemixer (AI music demixing in the browser)
In my first post, quite a lot of alternatives were discussed: https://news.ycombinator.com/item?id=36707877
The model I'm using is called Open-Unmix (https://github.com/sigsep/open-unmix-pytorch). In 2021, there was an update to Open-Unmix to include new weights, UMX-L, which made it perform better than it used to on the older weights (UMXHQ).
In the grand landscape of music demixing, I don't think UMX-L is near the top anymore.
_However_, the demixing performance of freemusicdemixer.com is very close to the full PyTorch performance of Open-Unmix UMX-L, despite the tricks I needed to get it working in the browser, such as splitting up the inference to operate on segments of the song, or making the LSTM operate on streaming segments rather than holding the entire track in the LSTM memory.
In my first release, I loaded and did inference on the entire track at once (like the PyTorch model), which frequently crashed or exceeded the 4GB WASM memory for medium or large-size tracks.
-
Show HN: Free AI-based music demixing in the browser
* Post-processing step (bigger impact)
I tried to tackle the post-processing step in my C++ code (which would win ~1 dB in quality across all targets) but it's too tricky for now [2]. Maybe some other day.
1: https://github.com/sevagh/free-music-demixer/blob/main/examp...
2: https://github.com/sigsep/open-unmix-pytorch/blob/master/ope...
-
Splitter.fm: Listen to the individual instrument/vocal tracks (known as "stems") for over 700 songs uploaded by 150+ artists
Or open-unmix. I've found it performs better than spleeter in some of my tests.
- I've created a website that extracts audio stems from songs using Spleeter, Demucs3, and Open Unmix for free.
-
Cassiopeia: New Audio Splitter Superior to Spleeter and UMX
There's maybe something useful for you here?
https://github.com/facebookresearch/demucs
https://github.com/sigsep/open-unmix-pytorch
- [N] Music Demixing (Audio Source Separation) Competition by Sony | ISMIR 2021
- [N Music Demixing (Audio Source Separation) Competition by Sony | ISMIR 2021
-
[Research] Music Source Separation with AI networks: Comparison Tests incl. Spleeter, Lalal.ai, OpenUnmix and Extended Unmix
OpenUnmix ( is a neural network solution from Yuki Mitsufuji and Stefan Uhlich, music industry luminaries that work in Sony's core divisions.
What are some alternatives?
demucs - Code for the paper Hybrid Spectrogram and Waveform Source Separation, but the goddamm motherfucker doesn't work.
spleeter - Deezer source separation library including pretrained models.
free-music-demixer - free website for client-side music demixing with Demucs + WebAssembly
ai-research-code
ultimatevocalremovergui - GUI for a Vocal Remover that uses Deep Neural Networks.
1000sharks.xyz - AI "metal artist" with SampleRNN (mirror from GitLab)
flask-website-template
EfficientAT - This repository aims at providing efficient CNNs for Audio Tagging. We provide AudioSet pre-trained models ready for downstream training and extraction of audio embeddings.
stemroller - Isolate vocals, drums, bass, and other instrumental stems from any song
umx.cpp - C++17 port of Open-Unmix-PyTorch with streaming LSTM inference, ggml, quantization, and Eigen