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End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection

This repository contains our implementation of the paper published in the ASVspoof 2021 workshop, "End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection". This work demonstrates the effectivness of end-to-end spectro-temporal graph attention network (GAT) which learns the relationship between cues spanning different sub-bands and temporal intervals for anti-spoofing and speech deepfake detection. Paper link here

Installation

First, clone the repository locally, create and activate a conda environment, and install the requirements :

$ git clone https://github.com/eurecom-asp/RawGAT-ST-antispoofing.git
$ conda create --name RawGAT_ST_anti_spoofing python=3.8.8
$ conda activate RawGAT_ST_anti_spoofing
$ conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
$ pip install -r requirements.txt

Experiments

Dataset

Our experiments are done in the logical access (LA) partition of the ASVspoof 2019 dataset, which can can be downloaded from here.

Training

To train the model run:

python main.py --track=logical --loss=WCE   --lr=0.0001 --batch_size=10

Testing

To evaluate your own model on LA evaluation dataset:

python main.py --track=logical --loss=WCE --is_eval --eval --model_path='/path/to/your/best_model.pth' --eval_output='eval_CM_scores_file.txt'

We also provide a pre-trained models. To use it you can run:

python main.py --track=logical --loss=WCE --is_eval --eval --model_path='Pre_trained_models/RawGAT_ST_mul/Best_epoch.pth' --eval_output='RawGAT_ST_mul_LA_eval_CM_scores.txt'

If you would like to compute scores on development dataset simply run:

python main.py --track=logical --loss=WCE --eval --model_path='/path/to/your/best_model.pth' --eval_output='dev_CM_scores_file.txt'

Compute the min t-DCF and EER(%) on development dataset

python tDCF_python_v2/evaluate_tDCF_asvspoof19_eval_LA.py  dev  'dev_CM_scores_file.txt'

Compute the min t-DCF and EER(%) on evaluation dataset

python tDCF_python_v2/evaluate_tDCF_asvspoof19_eval_LA.py  Eval  'eval_CM_scores_file.txt'

Acknowledgements

This repository is built on our End-to-end RawNet2 (ASVspoof2021 Challenge baseline).

AASIST (Extended work of RawGAT-ST paper) is available here.

Authors would like to acknowledge other repositories as well.

Contact

For any query regarding this repository, please contact:

  • Hemlata Tak: tak[at]eurecom[dot]fr

Citation

If you use RawGAT-ST, AASIST and RawNet2 for anti-spoofing please use the following citations:

[1] End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection

@inproceedings{tak21_asvspoof,
  author={Hemlata Tak and Jee-weon Jung and Jose Patino and Madhu Kamble and Massimiliano Todisco and Nicholas Evans},
  title={{End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection}},
  year=2021,
  booktitle={Proc. 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge},
  pages={1--8},
  doi={10.21437/ASVSPOOF.2021-1}
}

[2] AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks

@INPROCEEDINGS{Jung2021AASIST,
  author={Jung, Jee-weon and Heo, Hee-Soo and Tak, Hemlata and Shim, Hye-jin and Chung, Joon Son and Lee, Bong-Jin and Yu, Ha-Jin and Evans, Nicholas},
  booktitle={arXiv preprint arXiv:2110.01200}, 
  title={AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks}, 
  year={2021}

[3] End-to-End anti-spoofing with RawNet2

@INPROCEEDINGS{Tak2021End,
  author={Tak, Hemlata and Patino, Jose and Todisco, Massimiliano and Nautsch, Andreas and Evans, Nicholas and Larcher, Anthony},
  booktitle={Proc. ICASSP}, 
  title={End-to-End anti-spoofing with RawNet2}, 
  year={2021},
  pages={6369-6373}
}