| [](https://huggingface.co/datasets/CSALT/deepfake_detection_dataset_urdu) |
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| # Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset |
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| 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". |
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| 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. |
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| ### Dataset Statistics |
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| The dataset includes the following four parts: |
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| 1. Bonafide Part 1 |
| 2. Bonafide Part 2 |
| 3. Tacotron |
| 4. VITS TTS |
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| The statistics for each part are as follows: |
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| | **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 | |
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| ## Structure |
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| 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.). |
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| ## Usage |
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| 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. |
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