| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| - zh |
| pretty_name: DECRO (eval) |
| task_categories: |
| - audio-classification |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: "data/test-*.parquet" |
| tags: |
| - anti-spoofing |
| - audio-deepfake-detection |
| - speech |
| - benchmark |
| - arena-ready |
| arxiv: |
| - "10.1145/3543507.3583222" |
| --- |
| |
| # DECRO (eval) |
|
|
| Benchmark-ready packaging of the **evaluation partition** of the **DECRO** (DEepfake CROss-lingual) dataset — a cross-lingual (English + Chinese) speech anti-spoofing / synthetic-voice detection benchmark from *Transferring Audio Deepfake Detection Capability across Languages* (TheWebConf / WWW 2023). |
|
|
| ## Overview |
|
|
| DECRO pairs an English and a Chinese subset whose spoofed speech is generated with the **same** synthesis algorithms, so it isolates the effect of language on deepfake detection. The task is binary classification: **bonafide** (genuine human speech) vs. **spoof** (TTS / VC synthetic speech). This packaging contains the **eval** split of **both** language subsets, combined into a single test set. The original dataset is at https://github.com/petrichorwq/DECRO-dataset. |
|
|
| ## License & redistribution |
|
|
| Redistributed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license — the upstream DECRO license. See `LICENSE.txt`. CC BY 4.0 permits redistribution and derivative works with attribution. Labels and the evaluation protocol are unmodified; the audio was decoded and re-encoded to canonical 16 kHz mono FLAC (the source mixes sampling rates — 16 / 22.05 / 24 / 44.1 kHz — and a few FLOAT-subtype WAVs, so a uniform 16 kHz re-encode is required for evaluation). |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |--------|------|-------------| |
| | `path` | `string` | `<utterance_id>.flac`, unique | |
| | `audio` | `Audio(16000)` | 16 kHz mono FLAC | |
| | `label` | `ClassLabel` | `"bonafide"` (0) / `"spoof"` (1) | |
| | `notes` | `string` | JSON: `utterance_id`, `language`, `speaker_id`, `system_id` | |
|
|
| `utterance_id` is the **language-prefixed** filename stem (`en_<stem>` / `ch_<stem>`); raw stems are not globally unique across the two subsets, so the prefix is the stable join key. `system_id` is the spoof algorithm (e.g. `hifigan`, `vits`, `baidu_en`) or, for bonafide, the source corpus (e.g. `asv19`, `aishell1`). |
|
|
| `notes` example: |
| ```json |
| {"utterance_id": "en_1-4993-40677-0048", "language": "en", "speaker_id": "1", "system_id": "baidu_en"} |
| ``` |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("SpeechAntiSpoofingBenchmarks/DECRO", split="test") |
| print(ds[0]) |
| ``` |
|
|
| ## Stats |
|
|
| | Stat | Value | |
| |------|-------| |
| | Total trials | 37,314 | |
| | Bonafide | 10,415 | |
| | Spoof | 26,899 | |
| | English (en_eval) | 19,190 (4,306 bonafide / 14,884 spoof) | |
| | Chinese (ch_eval) | 18,124 (6,109 bonafide / 12,015 spoof) | |
|
|
| ## Source provenance |
|
|
| - Original repository: https://github.com/petrichorwq/DECRO-dataset |
| - Protocols: `en_eval.txt`, `ch_eval.txt` (format: `SPEAKER_ID AUDIO_FILE_NAME - SYSTEM_ID KEY`) |
| - Bonafide: ASVspoof2019 LA (English); Aidatatang/Aishell/freeST/MagicData (Chinese). Spoof: WaveFake, FAD, and TTS/VC systems (Tacotron, FastSpeech2, VITS, StarGANv2-VC, NVC-Net, HiFiGAN, MB-MelGAN, PWG, Baidu, Xunfei). |
|
|
| ## Evaluation |
|
|
| For evaluation instructions and submission format, see [`submissions/README.md`](submissions/README.md). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{ba2023transferring, |
| title = {Transferring Audio Deepfake Detection Capability across Languages}, |
| author = {Ba, Zhongjie and Wen, Qing and Cheng, Peng and Wang, Yuwei and Lin, Feng and Lu, Li and Liu, Zhenguang}, |
| booktitle = {Proceedings of the ACM Web Conference 2023 (WWW '23)}, |
| year = {2023}, |
| doi = {10.1145/3543507.3583222}, |
| } |
| ``` |
|
|
| ## Maintainer |
|
|
| Contact: k.n.borodin@mtuci.ru |
|
|
