CSALT/deepfake_detection_dataset_urdu

[](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.

原始信息汇总

数据集概述

许可协议

• 本数据集遵循MIT许可协议。