An official implementation of Mamba-TasNet and dual-path Mamba for speech separation.

1. Download WSJ0 corpus and follow an example instruction to create WSJ0-2Mix.

2. Install Packages.

conda create --name Slytherin python=3.9
conda activate Slytherin
pip install -r requirements.txt

You may need to install lower or higher versions of torch, torchaudio, causal-conv1d and mamba-ssm based on your hardware and system. Make sure they are compatible.

python train_wsj0mix.py hparams/WSJ0Mix/{mambatasnet, dpmamba}_{XS, S, M, L}.yaml \
--data_folder </yourpath/wsj0-mix/2speakers> \
--dynamic_mixing True \
--base_folder_dm </yourpath/wsj0_processed/si_tr_s> \
--precision bf16

You might encounter numerical instablity in training L-sized model.

We recommend training with fp32 if GPU memory permits.

Please check a related issue and Section 6.4 in the Jamba paper on stabilizing loss.

We acknowledge the wonderful work of Mamba and Vision Mamba. We borrowed their implementation of Mamba and bidirectional Mamba. The training recipes are adapted from SpeechBrain.

If you find this work helpful, please consider citing:

@article{jiang2024speechslytherin,
      title={Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis}, 
      author={Xilin Jiang and Yinghao Aaron Li and Adrian Nicolas Florea and Cong Han and Nima Mesgarani},
      year={2024},
      eprint={2407.09732},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2407.09732}, 
}

@misc{jiang2024dual,
  title={Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation},
  author={Jiang, Xilin and Han, Cong and Mesgarani, Nima},
  journal={arXiv preprint arXiv:2403.18257},
  year={2024}
}

You may also like our Mamba for speech recognition : https://github.com/xi-j/Mamba-ASR