BS-RoFormer 1.2.4

BS-RoFormer

Implementation of Band Split Roformer, SOTA Attention network for music source separation out of ByteDance AI Labs. They beat the previous first place by a large margin. The technique uses axial attention across frequency (hence multi-band) and time. They also have experiments to show that rotary positional encoding led to a huge improvement over learned absolute positions.

It also includes support for stereo training and outputting multiple stems.

Please join if you are interested in replicating a SOTA music source separator out in the open

Update: This paper has been replicated by Roman and weight open sourced here

Update 2: Used for this Katy Perry remix! Hatsune Miku!

Update 3: Kimberley Jensen has open sourced a MelBand Roformer trained on vocals here!

Appreciation

• StabilityAI and 🤗 Huggingface for the generous sponsorship, as well as my other sponsors, for affording me the independence to open source artificial intelligence.

• Roee and Fabian-Robert for sharing their audio expertise and fixing audio hyperparameters

• @chenht2010 and Roman for working out the default band splitting hyperparameter!

• Max Prod for reporting a big bug with Mel-Band Roformer with stereo training!

• Roman for successfully training the model and open sourcing his training code and weights at this repository!

• Christopher for fixing an issue with multiple stems in Mel-Band Roformer

• Iver Jordal for identifying that the default stft window function is not correct

• @firebirdblue23 for suggested fixes to MelBand Roformer

Install

$ pip install BS-RoFormer

Usage

import torch
from bs_roformer import BSRoformer

model = BSRoformer(
    dim = 512,
    depth = 12,
    time_transformer_depth = 1,
    freq_transformer_depth = 1,
    use_pope = False # turn this on to try a successor to rotary embeddings
)

x = torch.randn(2, 352800)
target = torch.randn(2, 352800)

loss = model(x, target = target)
loss.backward()

# after much training

out = model(x)

To use the Mel-Band Roformer proposed in a recent follow up paper, simply import MelBandRoformer instead

import torch
from bs_roformer import MelBandRoformer

model = MelBandRoformer(
    dim = 32,
    depth = 1,
    time_transformer_depth = 1,
    freq_transformer_depth = 1,
    use_pope = False # turn this on to try a successor to rotary embeddings
)

x = torch.randn(2, 352800)
target = torch.randn(2, 352800)

loss = model(x, target = target)
loss.backward()

# after much training

out = model(x)

You can also use a Flow-Matching variant of the BS-Roformer, FlowBSRoformer, which replaces masking with predicting the flow between pure noise and the target audio.

import torch
from bs_roformer import FlowBSRoformer

model = FlowBSRoformer(
    dim = 512,
    depth = 12,
    time_transformer_depth = 1,
    freq_transformer_depth = 1
)

x = torch.randn(2, 352800)
target = torch.randn(2, 352800)

loss = model(x, target = target)
loss.backward()

# after much training

out = model.sample(x)

Todo

• get the multiscale stft loss in there
• figure out what n_fft should be
• review band split + mask estimation modules

Citations

@inproceedings{Lu2023MusicSS,
    title   = {Music Source Separation with Band-Split RoPE Transformer},
    author  = {Wei-Tsung Lu and Ju-Chiang Wang and Qiuqiang Kong and Yun-Ning Hung},
    year    = {2023},
    url     = {https://api.semanticscholar.org/CorpusID:261556702}
}

@inproceedings{Wang2023MelBandRF,
    title   = {Mel-Band RoFormer for Music Source Separation},
    author  = {Ju-Chiang Wang and Wei-Tsung Lu and Minz Won},
    year    = {2023},
    url     = {https://api.semanticscholar.org/CorpusID:263608675}
}

@misc{ho2019axial,
    title  = {Axial Attention in Multidimensional Transformers},
    author = {Jonathan Ho and Nal Kalchbrenner and Dirk Weissenborn and Tim Salimans},
    year   = {2019},
    archivePrefix = {arXiv}
}

@misc{su2021roformer,
    title   = {RoFormer: Enhanced Transformer with Rotary Position Embedding},
    author  = {Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu},
    year    = {2021},
    eprint  = {2104.09864},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}

@inproceedings{dao2022flashattention,
    title   = {Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness},
    author  = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
    booktitle = {Advances in Neural Information Processing Systems},
    year    = {2022}
}

@article{Bondarenko2023QuantizableTR,
    title   = {Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing},
    author  = {Yelysei Bondarenko and Markus Nagel and Tijmen Blankevoort},
    journal = {ArXiv},
    year    = {2023},
    volume  = {abs/2306.12929},
    url     = {https://api.semanticscholar.org/CorpusID:259224568}
}

@inproceedings{ElNouby2021XCiTCI,
    title   = {XCiT: Cross-Covariance Image Transformers},
    author  = {Alaaeldin El-Nouby and Hugo Touvron and Mathilde Caron and Piotr Bojanowski and Matthijs Douze and Armand Joulin and Ivan Laptev and Natalia Neverova and Gabriel Synnaeve and Jakob Verbeek and Herv{\'e} J{\'e}gou},
    booktitle = {Neural Information Processing Systems},
    year    = {2021},
    url     = {https://api.semanticscholar.org/CorpusID:235458262}
}

@inproceedings{Zhou2024ValueRL,
    title   = {Value Residual Learning For Alleviating Attention Concentration In Transformers},
    author  = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273532030}
}

@misc{gopalakrishnan2025decouplingwhatwherepolar,
    title   = {Decoupling the "What" and "Where" With Polar Coordinate Positional Embeddings},
    author  = {Anand Gopalakrishnan and Robert Csordás and Jürgen Schmidhuber and Michael C. Mozer},
    year    = {2025},
    eprint  = {2509.10534},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2509.10534},
}

@misc{li2025basicsletdenoisinggenerative,
    title   = {Back to Basics: Let Denoising Generative Models Denoise},
    author  = {Tianhong Li and Kaiming He},
    year    = {2025},
    eprint  = {2511.13720},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV},
    url     = {https://arxiv.org/abs/2511.13720},
}