Comments (4)
ZFTurbo
commented on June 1, 2024
2
I published my training code here:
https://github.com/ZFTurbo/Music-Source-Separation-Training
It gives ability to train 5 types of models: mdx23c, htdemucs, vitlarge23, bs_roformer and mel_band_roformer.
I also put some weights there to start training not from the scratch.
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commented on June 1, 2024
1
@lucidrains I believe in me. I thank you very much for this implementation.
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lucidrains
commented on June 1, 2024
1
@ZFTurbo thank you Roman for contributing the training code and weights! i've linked to your repository in the readme! 🙏
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lucidrains
commented on June 1, 2024
@maxcoo yes you can, i believe in you
training is just what i showed in the readme, within a for loop. or you can also use a variety of solutions million-dollar startups have tried to simplify for you (hf accelerate, lighting etc)
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Related Issues (20)
- Input tensor size HOT 1
- Flash Attention support HOT 2
- [bad assertion] strange bottleneck performance HOT 1
- MelBand doesn't work with stereo HOT 7
- Hop length HOT 7
- Hidden dim in mask estimation module HOT 5
- Removed
- How to use it for audio source separation? HOT 2
- MLP design in MaskEstimator HOT 7
- Hardly to train with 8s length audio for batch size of 2 HOT 6
- Cut frequencies at half of the Nyquist in the MelBand model HOT 7
- Potential bug with `num_stems > 1` HOT 3
- Feature request: decouple the loss function of the forward function
- Problem with DataParallel HOT 5
- MelRoformer parameters from paper HOT 16
- Gates in Attention module of bs_roformer.py HOT 5
- Linear Attention temperature initialization HOT 1
- Flash attention error in Linear Attention layer HOT 1
- Reproduce the results from the paper HOT 1
- Linear Attention
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