README.md

---
language:
- en
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- audio-classification
pretty_name: 'SINE: Speech INfilling Edit Dataset'
tags:
- audio
- speech
- deepfake-detection
configs:
- config_name: preview
  data_files:
  - split: train
    path: preview/train-*
dataset_info:
  config_name: preview
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: filename
    dtype: string
  - name: category
    dtype: string
  - name: timestamp
    dtype: string
  - name: label
    dtype: int64
  - name: manipulation_type
    dtype: string
  splits:
  - name: train
    num_bytes: 10309938.0
    num_examples: 30
  download_size: 10039423
  dataset_size: 10309938.0
---

# SINE Dataset

## Overview

The Speech INfilling Edit (SINE) dataset is a comprehensive collection for speech deepfake detection and audio authenticity verification. This dataset contains ~87GB of audio data distributed across 32 splits, featuring both authentic and synthetically manipulated speech samples.

## Dataset Statistics

- **Total Size**: ~87GB
- **Number of Splits**: 32 (split-0.tar.gz to split-31.tar.gz)
- **Audio Format**: WAV files
- **Source**: Speech edited from LibriLight dataset with transcripts obtained from LibriHeavy

### Audio Statistics

| Audio Types | Subsets | # of Samples | # of Speakers | Durations (h) | Audio Lengths (s) |  |
|-------------|---------|--------------|---------------|---------------|-------------------|--|
|             |         |              |               |               | min | max |
| Real/Resyn  | train   | 26,547       | 70            | 51.82         | 6.00 | 8.00 |
|             | val     | 8,676        | 100           | 16.98         | 6.00 | 8.00 |
|             | test    | 8,494        | 900           | 16.60         | 6.00 | 8.00 |
| Infill/CaP  | train   | 26,546       | 70            | 51.98         | 5.40 | 9.08 |
|             | val     | 8,686        | 100           | 16.99         | 5.45 | 8.76 |
|             | test    | 8,493        | 903           | 16.64         | 5.49 | 8.85 |

## Data Structure

Each split (e.g., `split-0/`) contains:

```
split-X/
├── combine/                    # Directory containing all audio files (~11,076 files)
│   ├── dev_real_medium-*.wav          # Authentic audio samples
│   ├── dev_edit_medium-*.wav          # Edited audio samples
│   ├── dev_cut_paste_medium-*.wav     # Cut-and-paste manipulated samples
│   └── dev_resyn_medium-*.wav         # Resynthesized audio samples
├── medium_real.txt             # Labels for authentic audio (2,769 entries)
├── medium_edit.txt             # Labels for edited audio (2,769 entries)
├── medium_cut_paste.txt        # Labels for cut-paste audio (2,769 entries)
└── medium_resyn.txt            # Labels for resynthesized audio (2,769 entries)
```

## Audio Categories

### 1. Authentic Speech (`dev_real_medium-*`)
- Original, unmodified speech recordings from LibriVox audiobooks
- Labeled as class `1` (authentic)
- Simple time annotation format: `filename start-end-T label`

### 2. Resynthesized Speech (`dev_resyn_medium-*`)
- Speech regenerated from mel-spectrogram using HiFi-GAN vocoder
- Labeled as class `1` (authentic)
- Simple time annotation format

### 3. Edited Speech (`dev_edit_medium-*`)
- Audio samples with artificial modifications/edits
- Labeled as class `0` (manipulated)
- Complex time annotation with T/F segments indicating real/fake portions

### 4. Cut-and-Paste Speech (`dev_cut_paste_medium-*`)
- Audio created by cutting and pasting segments from different sources
- Labeled as class `0` (manipulated)
- Complex time annotation showing spliced segments

## Label Format

### Simple Format (Real/Resyn)
```
filename start_time-end_time-T label
```
Example:
```
dev_real_medium-100-emerald_city_librivox_64kb_mp3-emeraldcity_02_baum_64kb_21 0.00-7.92-T 1
```

### Complex Format (Edit/Cut-Paste)
```
filename time_segment1-T/time_segment2-F/time_segment3-T label
```
Example:
```
dev_edit_medium-100-emerald_city_librivox_64kb_mp3-emeraldcity_02_baum_64kb_21 0.00-4.89-T/4.89-5.19-F/5.19-8.01-T 0
```

Where:
- `T` = True/Authentic segment
- `F` = False/Manipulated segment
- `label`: `1` = Authentic, `0` = Manipulated

## Applications

This dataset is suitable for:

- **Speech Deepfake Detection**: Binary classification of authentic vs. manipulated speech
- **Temporal Localization**: Identifying specific time segments that contain manipulations
- **Manipulation Type Classification**: Distinguishing between different types of audio manipulation
- **Robustness Testing**: Evaluating detection systems across various manipulation techniques

## Citation

This is a joint work done by NVIDIA and National Taiwan University. If you use this dataset, please cite:

```bibtex
@inproceedings{huang2024detecting,
  title={Detecting the Undetectable: Assessing the Efficacy of Current Spoof Detection Methods Against Seamless Speech Edits},
  author={Huang, Sung-Feng and Kuo, Heng-Cheng and Chen, Zhehuai and Yang, Xuesong and Yang, Chao-Han Huck and Tsao, Yu and Wang, Yu-Chiang Frank and Lee, Hung-yi and Fu, Szu-Wei},
  booktitle={2024 IEEE Spoken Language Technology Workshop (SLT)},
  pages={652--659},
  year={2024},
  organization={IEEE}
}
```

## License

This dataset is released under the Apache 2.0 License.

---

**Note**: This dataset is intended for research purposes in speech authenticity verification and deepfake detection. Please use responsibly and in accordance with applicable laws and regulations.