| --- |
| license: cc-by-4.0 |
| language: |
| - zh |
| pretty_name: CFAD |
| 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: |
| - "2207.12308" |
| --- |
| |
| # CFAD |
|
|
| Benchmark-ready packaging of the **CFAD** (Chinese Fake Audio Detection) **clean test** |
| set ([arXiv 2207.12308](https://arxiv.org/abs/2207.12308)), for speech anti-spoofing and |
| synthetic / deepfake voice detection on Mandarin Chinese speech. |
|
|
| ## Overview |
|
|
| CFAD is a large-scale Chinese fake-audio detection corpus. This repo packages the **clean** |
| version's two test partitions: |
|
|
| - **`test_seen`** — spoof systems and real corpora **also present** in the train/dev splits. |
| - **`test_unseen`** — spoof systems and real corpora held **out** of training, to measure |
| generalisation to unseen attacks. |
|
|
| The label is **binary**: **bonafide** (genuine human speech) vs. **spoof** (synthesized / |
| vocoded speech). It is derived from the source directory layout — clips under `fake_clean/` |
| are spoof; clips under `real_clean/` are bonafide. There is no separate protocol file. |
|
|
| | Partition | Side | Sources | n | |
| |-----------|------|---------|---| |
| | test_seen | bonafide | aishell1, aishell3, magicread, thchs30 | 14,000 | |
| | test_seen | spoof | gl, hifigan, lpcnet, mbmelgan, pwg, straight, stylegan, wavenet | 28,000 | |
| | test_unseen | bonafide | magicconversa, selfrecording | 7,000 | |
| | test_unseen | spoof | fasthifigan, partiallyfake, tacohifigan, world | 14,000 | |
|
|
| One aishell1 outlier (`BAC009S0764W0123.wav`, a 178 MB / ~97 min concatenation artifact) is |
| dropped, so the bonafide count is **20,999** rather than 21,000. |
|
|
| ## License & redistribution |
|
|
| CFAD is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** |
| license, which permits redistribution with attribution — see `LICENSE.txt`. Audio is |
| re-encoded to 16 kHz mono FLAC for a uniform schema; labels are unmodified. |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |--------|------|-------------| |
| | `path` | `string` | source-relative path (e.g. `test_seen_clean/fake_clean/gl/SSB07800001_gl.wav`), unique | |
| | `audio` | `Audio(16000)` | 16 kHz mono FLAC (re-encoded from the 16 kHz PCM source wav) | |
| | `label` | `ClassLabel` | `"bonafide"` (0) / `"spoof"` (1) | |
| | `notes` | `string` | JSON: `utterance_id`, `partition`, `source` | |
|
|
| `notes` example: |
| ```json |
| {"utterance_id": "CFAD_test_seen_clean_fake_clean_gl_SSB07800001_gl", "partition": "test_seen", "source": "gl"} |
| ``` |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("SpeechAntiSpoofingBenchmarks/CFAD", split="test") |
| print(ds[0]) |
| ``` |
|
|
| ## Stats |
|
|
| | Stat | Value | |
| |------|-------| |
| | Total trials | 62,999 | |
| | Bonafide | 20,999 | |
| | Spoof | 42,000 | |
|
|
| ## Source provenance |
|
|
| - Paper: https://arxiv.org/abs/2207.12308 |
| - Scope: the **clean** version's `test_seen_clean` + `test_unseen_clean` partitions |
| (the `codec`/`noisy` robustness variants and train/dev splits are not included). |
| - Labels derived from the source directory layout (`fake_clean/` = spoof, `real_clean/` = |
| bonafide). |
|
|
| ## Evaluation |
|
|
| For evaluation instructions and submission format, see [`submissions/README.md`](submissions/README.md). |
|
|
| ## Citation |
|
|
| > **Paper:** Haoxin Ma et al., *CFAD: A Chinese Dataset for Fake Audio Detection*, |
| > [arXiv:2207.12308](https://arxiv.org/abs/2207.12308) (2022). |
|
|
| ```bibtex |
| @article{ma2022cfad, |
| title = {CFAD: A Chinese Dataset for Fake Audio Detection}, |
| author = {Ma, Haoxin and Yi, Jiangyan and Wang, Chenglong and Yan, Xinrui and |
| Tao, Jianhua and Wang, Tao and Wang, Shiming and Fu, Ruibo}, |
| journal = {arXiv preprint arXiv:2207.12308}, |
| year = {2022} |
| } |
| ``` |
|
|
| ## Maintainer |
|
|
| Maintained by Kirill Borodin (SpeechAntiSpoofingBenchmarks). |
| - Email: ~~k.n.borodin@mtuci.ru~~ (deprecated — use kborodin.research@gmail.com) |
| - Telegram: [@korallll_ai](https://t.me/korallll_ai) |
|
|
