File size: 2,076 Bytes
c32578d 25783c5 c32578d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | # 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)**
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