DeepFake-Audio-Rangers/Arabic_Audio_Deepfake

---
dataset_info:
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': fake
'1': real
splits:
- name: train
num_bytes: 1322891516.5301797
num_examples: 15648
- name: test
num_bytes: 331978521.95082015
num_examples: 3913
download_size: 1567148799
dataset_size: 1654870038.481
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- audio-to-audio
- audio-classification
- automatic-speech-recognition
language:
- ar
tags:
- synthetic
size_categories:
- 10K<n<100K
pretty_name: ArAD
license: odc-by
---
### ArAD Dataset (Arabic Audio DeepFake Dataset)

**Dataset Summary**
This dataset contains Arabic deepfake audio samples, focusing mainly on Levantine dialect with some examples in Standard Arabic. It was created using the RVC v2 framework, fine-tuned on a custom dataset of multi-dialect Arabic speech. The goal is to simulate real-world deepfake audio attacks by generating synthetic speech from actual recordings and voice messages.
One of the first datasets to include real-world deepfake audio arabic speech.

**Dataset Creation**
The process involved collecting speech audio for each speaker (with a minimum of 5 minutes per speaker) and training a unique model for each speaker. These models were then used to generate fake audio, ensuring no speaker's model was used to generate their own voice. Speech clips were segmented based on silence and limited to a maximum of 3 seconds in length.

**Audio Specifications**
- Format: WAV
- Sample Rate: 16KHz (resampled from original recordings at various sample rates)
- Audio cleaning: Major disturbances were cut, audio was cleaned using Ultimate Vocal Remover or the Adobe Podcast platform.
- Resampling: Audio was resampled to 40KHz for model generation, then downsampled to 16KHz using the Librosa Python library.

**Intended Use**
This dataset is valuable for research in deepfake detection, voice cloning, and related AI tasks. It is a resource for exploring vulnerabilities in voice systems.

**Licensing and Citation**
This data is made available under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/