| --- |
| license: cc-by-4.0 |
| language: |
| - de |
| - en |
| - fr |
| - it |
| - zh |
| pretty_name: CVoiceFake (small) |
| task_categories: [audio-classification] |
| size_categories: [100K<n<1M] |
| configs: |
| - config_name: default |
| data_files: |
| - {split: test, path: "data/test-*.parquet"} |
| tags: |
| - anti-spoofing |
| - audio-deepfake-detection |
| - speech |
| - benchmark |
| - arena-ready |
| paperswithcode_id: |
| arxiv: |
| - "2409.09272" |
| --- |
| |
| # CVoiceFake (small) |
|
|
| Benchmark-ready packaging of the **CVoiceFake (small)** multilingual audio-deepfake |
| detection set introduced with **SafeEar** ([arXiv 2409.09272](https://arxiv.org/abs/2409.09272)), |
| for speech anti-spoofing / synthetic-voice detection. |
|
|
| ## Overview |
|
|
| CVoiceFake is a multilingual deepfake-audio dataset built on top of Mozilla |
| **CommonVoice**: genuine clips are re-synthesised by a bank of vocoders to produce |
| matched spoof audio. This repo packages the public **small** subset — a random ~10% |
| of the full collection — across **five languages** (German, English, French, Italian, |
| Chinese). It is a binary classification benchmark: **bonafide** (genuine CommonVoice |
| speech) vs. **spoof** (vocoder re-synthesis). |
|
|
| The spoof side aggregates **five synthesis systems**, each applied to the same |
| bonafide utterances: |
|
|
| - **Griffin-Lim** (classical phase reconstruction) |
| - **WORLD** (vocoder) |
| - **Parallel WaveGAN** |
| - **Multi-band MelGAN** |
| - **Style MelGAN** |
|
|
| (The SafeEar paper additionally describes a DiffWave system; it is **not present** in |
| this public small subset, which ships the five systems above.) |
|
|
| ## License & redistribution |
|
|
| Released under **CC BY 4.0** (Creative Commons Attribution 4.0 International), the |
| upstream license on the CVoiceFake Zenodo record — see `LICENSE.txt` (verbatim) and |
| `RIGHTS.md` for provenance. CC BY 4.0 permits redistribution and derivatives |
| (including the format normalisation done here) with attribution, so this rehost is |
| legally clean. Underlying speech is from Mozilla CommonVoice. |
|
|
| ## Schema |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | path | string | Source-relative path (`<lang>/<system>/<file>.mp3`), unique. | |
| | audio | Audio(16kHz mono) | Source MP3 bytes embedded verbatim (already 16 kHz mono). | |
| | label | ClassLabel[bonafide, spoof] | Index 0 = bonafide, 1 = spoof. | |
| | notes | string (JSON) | `utterance_id`, `system_id`, `language`, `source`. | |
|
|
| `notes` example (spoof): |
| `{"utterance_id": "CVF_de_world_generated_common_voice_de_38024994_Gen", "system_id": "world_generated", "language": "de", "source": "CommonVoice"}` |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("SpeechAntiSpoofingBenchmarks/CVoiceFake_small", split="test") |
| ``` |
|
|
| ## Stats |
|
|
| | n_total | n_bonafide | n_spoof | total duration | |
| |---|---|---|---| |
| | 138,136 | 23,544 | 114,592 | ~218 hours | |
| |
| Per-language (bonafide / spoof): |
| |
| | Lang | Bonafide | Spoof | Total | |
| |------|----------|-------|-------| |
| | de | 7,251 | 36,064 | 43,315 | |
| | en | 4,315 | 21,438 | 25,753 | |
| | fr | 3,589 | 17,685 | 21,274 | |
| | it | 3,865 | 18,052 | 21,917 | |
| | zh-CN | 4,524 | 21,353 | 25,877 | |
| |
| All clips are 16 kHz mono. The spoof side dominates because each bonafide clip is |
| re-synthesised by up to five systems. |
| |
| ## Source provenance |
| |
| - **Bonafide:** genuine speech from Mozilla **CommonVoice** (per language). |
| - **Spoof:** vocoder re-synthesis of the bonafide clips (Griffin-Lim, WORLD, |
| Parallel WaveGAN, Multi-band MelGAN, Style MelGAN). |
| - **Upstream:** CVoiceFake / SafeEar — Zenodo record |
| [11124319](https://zenodo.org/records/11124319); |
| project page <https://safeearweb.github.io/Project/>. |
| |
| ## Evaluation |
| |
| See `eval.yaml` and `submissions/README.md`. Primary metric: **EER (%)** over all |
| 138,136 utterances. |
| |
| ## Citation |
| |
| **Original paper**: SafeEar: Content Privacy-Preserving Audio Deepfake Detection |
| ([arXiv 2409.09272](https://arxiv.org/abs/2409.09272)). |
| |
| ```bibtex |
| @inproceedings{li2024safeear, |
| title = {SafeEar: Content Privacy-Preserving Audio Deepfake Detection}, |
| author = {Li, Xinfeng and Li, Kai and Zheng, Yifan and Yan, Chen and Ji, Xiaoyu and Xu, Wenyuan}, |
| booktitle = {Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS)}, |
| year = {2024}, |
| doi = {10.1145/3658644.3670285} |
| } |
| ``` |
| |
| ## Maintainer |
| |
| Packaged for the Speech Anti-Spoofing Arena (`SpeechAntiSpoofingBenchmarks`). |
| Questions: k.n.borodin@mtuci.ru |
| |
