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RawBMamba

This repository provides the overall framework for training and evaluating audio anti-spoofing systems proposed in RawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection.

Mamba Installation

Mamba

Training

To train RawBMamba:

python train.py -o ./save_path/

Testing

To test RawBMamba on ASVspoof2019LA:

python ./ASVspoof2019LA_eval/19LA_test.py -o ./model/

To test RawBMamba on ASVspoof2021LA:

python ./ASVspoof2021LA_eval/21LA_test.py -o ./model/ -e ./model/21LA_eval.txt

To test RawBMamba on ASVspoof2021DF:

python ./ASVspoof2021DF_eval/21DF_test.py -o ./model/ -e ./model/21DF_eval.txt

Result

We found that there is variance in model training, which can sometimes result in better outcomes than those reported in the paper. These are our experimental findings.

<table> <tr> <th rowspan="2">Models</th> <th colspan="2">19LA</th> <th colspan="2">21LA</th> <th colspan="1">21DF</th> </tr> <tr> <th>EER(%)</th> <th>t-DCF</th> <th>EER(%)</th> <th>t-DCF</th> <th>EER(%)</th> </tr> <tr> <td>ours</td> <td>1.19</td> <td>0.0360</td> <td>3.39</td> <td>0.2726</td> <td>15.85</td> </tr> </table>

Pre-trained models

We provide pre-trained RawBMamba. Run the following code in the root directory of RawBMamba-main, and remember to modify the file paths accordingly.

To evaluate RawBMamba on ASVspoof2019LA:

python ./ASVspoof2019LA_eval/evaluate.py

To evaluate RawBMamba on ASVspoof2021LA:

bash ./ASVspoof2021LA_eval/evaluate.sh

To evaluate RawBMamba on ASVspoof2021DF:

bash ./ASVspoof2021DF_eval/evaluate.sh

References

@inproceedings{liu2023leveraging,
  title={Leveraging positional-related local-global dependency for synthetic speech detection},
  author={Liu, Xiaohui and Liu, Meng and Wang, Longbiao and Lee, Kong Aik and Zhang, Hanyi and Dang, Jianwu},
  booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

@article{mamba,
  title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces},
  author={Gu, Albert and Dao, Tri},
  journal={arXiv preprint arXiv:2312.00752},
  year={2023}
}

Citation

If you use this codebase, or otherwise find our work valuable, please cite RawBMamba.