Datasets:
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:
{"utterance_id": "en_1-4993-40677-0048", "language": "en", "speaker_id": "1", "system_id": "baidu_en"}
Quick Start
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.
Citation
@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