---
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

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)