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#   README.md
parents feb18807 b9d3cf6a
# AASIST
# Aasist website project
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'](https://arxiv.org/abs/2110.01200)
The goal of this project is to allow to use an existing project 'Aasist project' on git, to transform it to use it on a local web page.
To do this I used the NodeJs technology, in order to execute python files from a website.
I then had to modify the existing python project, so that instead of evaluating all the documents, the user can choose one of the files and test it alone.
The website returns the score of the audio file, as well as its description if it is spoof or bonafide.
### 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
## Getting started
### Training
The `main.py` includes train/validation/evaluation.
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
To train AASIST [1]:
```
python main.py --config ./config/AASIST.conf
```
To train AASIST-L [1]:
```
python main.py --config ./config/AASIST-L.conf
```
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
#### Training baselines
## Add your files
We additionally enabled the training of RawNet2[2] and RawGAT-ST[3].
- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
To Train RawNet2 [2]:
```
python main.py --config ./config/RawNet2_baseline.conf
cd existing_repo
git remote add origin https://gitlab.eurecom.fr/fougeres/aasist-website-project.git
git branch -M main
git push -uf origin main
```
To train RawGAT-ST [3]:
```
python main.py --config ./config/RawGATST_baseline.conf
```
## Integrate with your tools
### Pre-trained models
We provide pre-trained AASIST and AASIST-L.
- [ ] [Set up project integrations](https://gitlab.eurecom.fr/fougeres/aasist-website-project/-/settings/integrations)
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
```
## Collaborate with your team
- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
### Developing custom models
Simply by adding a configuration file and a model architecture, one can train and evaluate their models.
## Test and Deploy
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}
```
Use the built-in continuous integration in GitLab.
### 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.
```
- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
### Acknowledgements
This repository is built on top of several open source projects.
- [ASVspoof 2021 baseline repo](https://github.com/asvspoof-challenge/2021/tree/main/LA/Baseline-RawNet2)
- [min t-DCF implementation](https://www.asvspoof.org/resources/tDCF_python_v2.zip)
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
```bibtex
@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
```bibtex
@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}
}
```
# Editing this README
[3] End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection
```bibtex
@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}
```
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
[4] ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
```bibtex
@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}
}
```
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