| --- |
| 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. |
|
|
