---
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