# Full LibriSpeech Copy–Move Forgery Dataset

## 📘 Overview

The **Full LibriSpeech Copy–Move Forgery Dataset** is designed for advancing research in **audio forgery detection** and **tampering localization**. It focuses on the challenging task of **copy–move forgeries**, where segments from a single audio recording are duplicated and relocated within the same file.

The dataset provides speaker-disjoint splits, detailed temporal annotations, and multiple levels of forgery intensity to ensure reproducible and fair evaluation of modern deep learning models.

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## 🧩 Dataset Details

| Property           | Description                                  |
| ------------------ | -------------------------------------------- |
| **Source**         | Derived from the clean subset of LibriSpeech |
| **Total Samples**  | 57,078                                       |
| **Forgery Levels** | 3 (weak, medium, strong)                     |
| **Splits**         | Train / Validation / Test (speaker-disjoint) |
| **Annotations**    | Start–end timestamps for forged regions      |
| **Features**       | Mel-spectrograms                             |
| **Size**           | ~25 GB                                       |

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## ⚙️ Usage

You can directly load this dataset using the `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("TheAnalyzer/Full-LibriSpeech-CopyMove-Forgery-Dataset")
print(dataset)
```

Or clone the dataset using Git LFS:

```bash
git lfs install
git clone https://huggingface.co/datasets/TheAnalyzer/Full-LibriSpeech-CopyMove-Forgery-Dataset
```

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## 🧠 Baseline Implementation

The baseline CNN-based model, preprocessing pipeline, and evaluation scripts can be found in the companion GitHub repository:

👉 [https://github.com/RDisCoding/Full-LibriSpeech-CopyMove-Forgery-Dataset](https://github.com/RDisCoding/Full-LibriSpeech-CopyMove-Forgery-Dataset)

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## 📬 Contact

For questions or collaborations, please contact: **[rdiscoding@gmail.com](mailto:rdiscoding@gmail.com)**