AASIST

This repository provides the overall framework for training and evaluating audio anti-spoofing systems proposed in 'AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks'

Getting started

requirements.txt must be installed for execution. We state our experiment environment for those who prefer to simulate as similar as possible.

• Installing dependencies

pip install -r requirements.txt

• Our environment (for GPU training)
  • Based on a docker image: pytorch:1.6.0-cuda10.1-cudnn7-runtime
  • GPU: 1 NVIDIA Tesla V100
    • About 16GB is required to train AASIST using a batch size of 24
  • gpu-driver: 418.67

Data preparation

We train/validate/evaluate AASIST using the ASVspoof 2019 logical access dataset [4].

python ./download_dataset.py

(Alternative) Manual preparation is available via

• ASVspoof2019 dataset: https://datashare.ed.ac.uk/handle/10283/3336
  1. Download LA.zip and unzip it
  2. Set your dataset directory in the configuration file

Training

The main.py includes train/validation/evaluation.

To train AASIST [1]:

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

To train AASIST-L [1]:

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

Training baselines

We additionally enabled the training of RawNet2[2] and RawGAT-ST[3].

To Train RawNet2 [2]:

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

To train RawGAT-ST [3]:

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

Pre-trained models

We provide pre-trained AASIST and AASIST-L.

To evaluate AASIST [1]:

• It shows EER: 0.83%, min t-DCF: 0.0275

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

To evaluate AASIST-L [1]:

• It shows EER: 0.99%, min t-DCF: 0.0309
• Model has 85,306 parameters

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

Developing custom models

Simply by adding a configuration file and a model architecture, one can train and evaluate their models.

To train a custom model:

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}

License

Copyright (c) 2021-present NAVER Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.

Acknowledgements

This repository is built on top of several open source projects.

• ASVspoof 2021 baseline repo
• min t-DCF implementation

The repository for baseline RawGAT-ST model will be open

• https://github.com/eurecom-asp/RawGAT-ST-antispoofing

The dataset we use is ASVspoof 2019 [4]

• https://www.asvspoof.org/index2019.html

References

[1] 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}

[2] 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}
}

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

@inproceedings{tak21_asvspoof,
  author={Tak, Hemlata and Jung, Jee-weon and Patino, Jose and Kamble, Madhu and Todisco, Massimiliano and Evans, Nicholas},
  booktitle={Proc. ASVSpoof Challenge},
  title={End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection},
  year={2021},
  pages={1--8}

[4] ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech

@article{wang2020asvspoof,
  title={ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech},
  author={Wang, Xin and Yamagishi, Junichi and Todisco, Massimiliano and Delgado, H{\'e}ctor and Nautsch, Andreas and Evans, Nicholas and Sahidullah, Md and Vestman, Ville and Kinnunen, Tomi and Lee, Kong Aik and others},
  journal={Computer Speech \& Language},
  volume={64},
  pages={101114},
  year={2020},
  publisher={Elsevier}
}