Introduction
This repository contains several materials that supplements the Spoofing-Aware Speaker Verification (SASV) Challenge 2022 including:
- calculating metrics;
- extracting speaker/spoofing embeddings from pre-trained models;
- training/evaluating Baseline2 in the evaluation plan.
More information can be found in the webpage and the evaluation plan
Prerequisites
Load ECAPA-TDNN & AASIST repositories
git submodule init
git submodule update
Install requirements
pip install -r requirements.txt
Data preparation
The ASVspoof2019 LA dataset [1] can be downloaded using the scipt in AASIST [2] repository
python ./aasist/download_dataset.py
Speaker & spoofing embedding extraction
Speaker embeddings and spoofing embeddings can be extracted using below script. Extracted embeddings will be saved in ./embeddings.
- Speaker embeddings are extracted using the ECAPA-TDNN [3].
- Implmented by https://github.com/TaoRuijie/ECAPATDNN
- Spoofing embeddings are extracted using the AASIST [2].
- We also prepared extracted embeddings.
- To use prepared emebddings, git-lfs is required. Please refer to https://git-lfs.github.com for further instruction. After installing git-lfs use following command to download the embeddings.
git-lfs install git-lfs pull
- To use prepared emebddings, git-lfs is required. Please refer to https://git-lfs.github.com for further instruction. After installing git-lfs use following command to download the embeddings.
python save_embeddings.py
Baseline 2 Training
Run below script to train Baseline2 in the evaluation plan.
- It will reproduce Baseline2 described in the Evaluation plan.
python main.py --config ./configs/baseline2.conf
Developing own models
- Currently adding...
Adding custom DNN architecture
- create new file under
./models/. - create a new configuration file under
./configs - in the new configuration, modify
model_archand add required arguments inmodel_config. - run
python main.py --config {USER_CONFIG_FILE}
Using only metrics
Use get_all_EERs in metrics.py to calculate all three EERs.
- prediction scores and keys should be passed on using
protocols/ASVspoof2019.LA.asv.dev.gi.trl.txtorprotocols/ASVspoof2019.LA.asv.eval.gi.trl.txt
References
[1] 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}
}
[2] AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
@inproceedings{Jung2022AASIST,
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={Proc. ICASSP},
title={AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks},
year={2022}
[3] ECAPA-TDNN: Emphasized Channel Attention, propagation and aggregation in TDNN based speaker verification
@inproceedings{desplanques2020ecapa,
title={{ECAPA-TDNN: Emphasized Channel Attention, propagation and aggregation in TDNN based speaker verification}},
author={Desplanques, Brecht and Thienpondt, Jenthe and Demuynck, Kris},
booktitle={Proc. Interspeech 2020},
pages={3830--3834},
year={2020}
}
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