DeepFake-Audio-Rangers/Arabic_Audio_Deepfake

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/