pip install -r requirements.txt

python ./download_dataset.py

python main.py --config ./config/AASIST.conf

python main.py --config ./config/AASIST-L.conf

python main.py --config ./config/RawNet2_baseline.conf

python main.py --config ./config/RawGATST_baseline.conf

python main.py --eval --config ./config/AASIST.conf

python main.py --eval --config ./config/AASIST-L.conf

1. Define your model
  - The model should be a class named "Model"
2. Make a configuration by modifying "model_config"
  - architecture: filename of your model.
  - hyper-parameters to be tuned can be also passed using variables in "model_config"
3. run python main.py --config {CUSTOM_CONFIG_NAME}

Copyright (c) 2021-present NAVER Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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@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}, 
  title={AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks}, 
  year={2021},
  pages

@INPROCEEDINGS{Tak2021End,
  author={Tak, Hemlata and Patino, Jose and Todisco, Massimiliano and Nautsch, Andreas and Evans, Nicholas and Larcher, Anthony},
  booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={End-to-End anti-spoofing with RawNet2}, 
  year={2021},
  pages={6369-6373}
}

@inproceedings{tak21_asvspoof,
  author={Tak, Hemlata and Jung, Jee-weon and Patino, Jose and Kamble, Madhu and Todisco, Massimiliano and Evans, Nicholas},
  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}