README.md

---
license: cc-by-nc-4.0
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
---



<p align="center" style="width: 50%">
  <img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F651bba9c00137407015e0bdf%2FDDRTGPCGGr-d0rQ_M-GwG.png%26quot%3B%3C%2Fspan%3E />
</p>

# Introduction
Welcome to MLAAD: The Multi-Language Audio Anti-Spoofing Dataset -- a dataset to train, test and evaluate audio deepfake detection. See
[the paper](https://arxiv.org/pdf/2401.09512.pdf) for more information.

# License:
Starting from MLAADv8, this dataset will be published under a non-commercial license (CC-BY-NC 4.0).
If you want to use this dataset for commercial purposes, you need to either:
- use a previous version (MLAAD v1 - v7)
- contact us to obtain a commercial license (nicolas.mueller@aisec.fraunhofer.de)

# Download the dataset
#### Option 1: Hugging Face datasets library

Install the datasets package:
```bash
pip install datasets
```

Login with your Hugging Face account:
```bash
huggingface-cli login
```

Then load the dataset in Python:
```python
from datasets import load_dataset

# Download from HF and cache
ds = load_dataset("mueller91/MLAAD")

# Optionally: Save the dataset to your own directory
ds.save_to_disk("MLAAD_local")
```

This will automatically handle authentication and download.

#### Option 2: Git + git-lfs

If you prefer to clone with git, you must first login via Hugging Face:
```bash
huggingface-cli login
```

Then clone:
```bash
git lfs install
git clone https://huggingface.co/datasets/mueller91/MLAAD
```


# Structure
The dataset is based on the [M-AILABS](https://github.com/imdatceleste/m-ailabs-dataset) 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](https://arxiv.org/pdf/2401.09512) and [our website](https://deepfake-total.com/mlaad).
```
path|original_file|language|is_original_language|duration|training_data|model_name|architecture|transcript|reference_speaker
```
where `reference_speaker` may or not be present - this key has been introduced only in v8.


# 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](https://github.com/imdatceleste/m-ailabs-dataset). 
An antispoofing model trained on (among others) the MLAAD dataset is available [here](https://deepfake-total.com/).

# 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}
}
```

# Contact Emails
- nicolas.mueller@aisec.fraunhofer.de
- piotr.kawa@pwr.edu.pl