| [](https://huggingface.co/datasets/CSALT/deepfake_detection_dataset_urdu) | |
| # Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset | |
| This repository contains the Urdu Deepfake Audio Dataset introduced in the ACL 2024 paper "Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset". | |
| The dataset focuses on two spoofing attacks – Tacotron and VITS TTS – and includes bonafide audio samples for comparison. The dataset construction ensures phonemic cover and balance, making it suitable for training deepfake detection models in Urdu. | |
| ### Dataset Statistics | |
| The dataset includes the following four parts: | |
| 1. Bonafide Part 1 | |
| 2. Bonafide Part 2 | |
| 3. Tacotron | |
| 4. VITS TTS | |
| The statistics for each part are as follows: | |
| | **Metric** | **Bonafide Part 1** | **Bonafide Part 2** | **Tacotron** | **VITS TTS** | | |
| |------------------------------|---------------------|---------------------|--------------|--------------| | |
| | **Total Duration (mins)** | 1,302.66 | 1,271.65 | 1,061.96 | 1,340.79 | | |
| | **Max Sample Length (mins)** | 112.42 | 120.75 | 80.34 | 111.01 | | |
| | **Min Sample Length (mins)** | 61.73 | 56.45 | 44.64 | 65.53 | | |
| | **Avg Sample Length (mins)** | 76.63 | 74.80 | 62.47 | 78.87 | | |
| | **Files per Speaker** | 708 audio files | 495 audio files | 495 audio files | 495 audio files | | |
| ## Structure | |
| The dataset is organized into folders, each containing audio files for the respective parts mentioned above. Each folder is named according to its part (e.g., `Bonafide_Part1`, `Tacotron`, etc.). | |
| ## Usage | |
| The dataset is available on Huggingface through the following link: | |
| - Huggingface Dataset: https://huggingface.co/datasets/CSALT/deepfake_detection_dataset_urdu | |
| The code for this project is on Github: | |
| - https://github.com/CSALT-LUMS/urdu-deepfake-dataset | |
| ## Citation | |
| ``` | |
| @inproceedings{sheza-etal-2024-deepfake, | |
| title = "Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset", | |
| author = "Sheza Munir, Wassay Sajjad, Mukeet Raza, Emaan Mujahid Abbas, Abdul Hameed Azeemi, Ihsan Ayyub Qazi, and Agha Ali Raza", | |
| booktitle = "Findings of the Association for Computational Linguistics: ACL 2024", | |
| year = "2024", | |
| publisher = "Association for Computational Linguistics", | |
| } | |
| ``` | |
| ## Legal | |
| CC BY-NC 4.0 license for the data hosted on HuggingFace and Google Drive. | |