Codecfake Dataset

Dataset Overview

Dataset Name

• Codecfake Dataset

Dataset Description

• Codecfake Dataset is a dataset for universal detection of deep‑fake audio, composed of multiple subsets including training, development, and test sets.

Dataset Subsets

Dataset License

• CC BY‑NC‑ND 4.0

Dataset Structure

• The dataset should be organized as follows:

  ├── Codecfake │ ├── label │ │ └── *.txt │ ├── train │ │ └── *.wav (740,747 samples) │ ├── dev │ │ └── *.wav (92,596 samples) │ ├── test │ │ └── C1 │ │ └── *.wav (26,456 samples) │ │ └── C2 │ │ └── *.wav (26,456 samples) │ │ └── C3 │ │ └── *.wav (26,456 samples) │ │ └── C4 │ │ └── *.wav (26,456 samples) │ │ └── C5 │ │ └── *.wav (26,456 samples) │ │ └── C6 │ │ └── *.wav (26,456 samples) │ │ └── C7 │ │ └── *.wav (145,505 samples) │ │ └── A1 │ │ └── *.wav (8,902 samples) │ │ └── A2 │ │ └── *.wav (8,902 samples) │ │ └── A3 │ │ └── *.wav (99,112 samples)

Usage Recommendations

• If you wish to jointly train with the ASVspoof2019 dataset, first download the corresponding training, development, and evaluation sets from the ASVspoof2019 LA Database.

Pre‑trained Models

• Several pre‑trained models are provided, including Vocoder‑trained ADD, Codec‑trained ADD, and Co‑trained ADD models, stored in the ./pretrained_model directory.

Citation

• When using this dataset, please cite:

  @article{xie2024codecfake, title={The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio}, author={Xie, Yuankun and Lu, Yi and Fu, Ruibo and Wen, Zhengqi and Wang, Zhiyong and Tao, Jianhua and Qi, Xin and Wang, Xiaopeng and Liu, Yukun and Cheng, Haonan and others}, journal={arXiv preprint arXiv:2405.04880}, year={2024} }