Datasets:
license: apache-2.0
language:
- en
- de
- fr
- es
- uk
- pl
- ru
- it
task_categories:
- audio-classification
tags:
- audio
- deepfake
- audio-deepfake-detection
- anti-spoofing
- voice
- voice-antispoofing
- MLAAD
pretty_name: 'MLAAD: The Multi-Language Audio Anti-Spoofing Dataset'
size_categories:
- 100K<n<1M
Introduction
Welcome to MLAAD: The Multi-Language Audio Anti-Spoofing Dataset -- a dataset to train, test and evaluate audio deepfake detection. See the paper for more information.
Download the dataset
# if needed, install git-lfs
sudo apt-get install git-lfs
git lfs install
# clone the repository
git clone https://huggingface.co/datasets/mueller91/MLAAD
Structure
The dataset is based on the M-AILABS dataset. MLAAD is structured as follows:
fake
|-language_1
|-language_2
|- ....
|- language_K
| - model_1_K
| - model_2_K
| - ....
| - model_L_K
| - meta.csv
| - audio_L_K_1.wav
| - audio_L_K_2.wav
| - audio_L_K_3.wav
| - ....
| - audio_L_K_1000.wav
The file 'meta.csv' contains the following identifiers. For more in these, please see the paper and our website.
path|original_file|language|is_original_language|duration|training_data|model_name|architecture|transcript
Proposed Usage
We suggest to use MLAAD either as new out-of-domain test data for existing anti-spoofing models, or as additional training resource. We urge to complement the fake audios in MLAAD with the corresponding authentic ones from M-AILABS, in order to obtain a balanced dataset. M-AILABS can be downloaded here. An antispoofing model trained on (among others) the MLAAD dataset is available here.
Bibtex
@article{muller2024mlaad,
title={MLAAD: The Multi-Language Audio Anti-Spoofing Dataset},
author={M{\"u}ller, Nicolas M and Kawa, Piotr and Choong, Wei Herng and Casanova, Edresson and G{\"o}lge, Eren and M{\"u}ller, Thorsten and Syga, Piotr and Sperl, Philip and B{\"o}ttinger, Konstantin},
journal={arXiv preprint arXiv:2401.09512},
year={2024}
}