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---
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.
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