Baseline for the Spoofing-aware Speaker Verification Challenge 2022

Related tags

Deep LearningSASVC2022_Baseline
Overview

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].
  • 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

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

  1. create new file under ./models/.
  2. create a new configuration file under ./configs
  3. in the new configuration, modify model_arch and add required arguments in model_config.
  4. 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.txt or
    • protocols/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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Comments
  • About the extracted embeddings.

    About the extracted embeddings.

    When we installed the git-lfs and step to pull the embeddings data, an error like:

    batch response: This repository is over its data quota. Account responsible for LFS bandwidth should purchase more data packs to restore access.
error: failed to fetch some objects from 'https://github.com/sasv-challenge/SASVC2022_Baseline.git/info/lfs

    was appeared.

    What should I do? How can I download the embeddings data?

    opened by ikou-austin 3
  • Reproducing baseline1

    Reproducing baseline1

    Thanks for providing the code for pre-trained models and baseline2. I am reproducing baseline1 based on your description in the evaluation plan, but I got very different results on the development set. I am also curious why the SPF-EER on the development set is much worse than that on the evaluation set in your results. Could you please provide the code for reproducing your baseline1 result? Thank you so much!

    opened by yzyouzhang 3
  • omegaconf.errors.ConfigAttributeError: Missing key

    omegaconf.errors.ConfigAttributeError: Missing key

    I encounter the following error when I run main.py with the Baseline2 configuration.

    omegaconf.errors.ConfigAttributeError: Missing key

    There are in total three keys missing. min_req_mem gradient_clip reload_every_n_epoch

    I fixed these missing keys one by one by setting them to 0 or None. I am curious what are the default values for these. Thank you very much.

    opened by yzyouzhang 3
  • speaker_loss.weight is not in the model.

    speaker_loss.weight is not in the model.

    Thanks for your repo. I have successfully replicated the baseline2 performance. I encounter the following messages when I run python save_embeddings.py. It did not crash the program but I wonder where is the second line printed from since I did not find it. I am also not sure if it will cause potential issues.

    Device: cuda speaker_loss.weight is not in the model. Getting embedgins from set trn...

    Thanks.

    opened by yzyouzhang 1
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