Benchmark-ready packaging of the Round 1 (R1) evaluation partition of Track 3 (Audio Fake Game / FG) from the ADD 2022 challenge (arXiv 2202.08433). Binary anti-spoofing: bonafide (genuine human speech) vs. spoof (synthesized / fake speech).

⚠️ Labels only — the audio is not redistributed here

The ADD 2022 audio is licensed CC BY-NC-ND 4.0 (NonCommercial-NoDerivatives), which does not permit us to redistribute the waveforms. This repo therefore ships only data/labels.parquet (utterance_id + label) — no audio at all. That is everything the Arena needs: scoring is reproduced from a model's scores.txt plus these labels and never transfers audio.

load_dataset(...) returns only the id/label table here (the viewer is disabled); it is not the audio access path.

How to obtain the audio

Download the original ADD 2022 Track 3 audio from the source release:

Zenodo: https://zenodo.org/records/12188035

Extract the R1 test split into a track3test/ directory of ADD_E3_*.wav files (16 kHz mono WAV). The accompanying protocol is track3_R1_label.txt (<file>.wav <genuine|fake>), which is exactly what data/labels.parquet was derived from (see _build_labels.py).

How to compute scores locally

Once you have licensed access to the audio, run your anti-spoofing model over the local audio directory and emit a scores.txt (<utterance_id> <score>, higher = more bonafide):

python _score_add22.py \
    --audio-dir /path/to/add22track31test/track3test \
    --model random-baseline \
    --out scores.txt

_score_add22.py is a small, model-pluggable driver: it lists the audio directory, decodes each clip with soundfile (sorted by id — spinning-disk friendly), calls your model's score(audio, sr), and writes <utterance_id> <score>. Swap --model for your own module:Class implementing the package's SimpleAntiSpoofingModel interface. Then submit scores.txt to the Arena (the labels here verify it) — see the package's docs/submitting/.

Schema (`data/labels.parquet`)

Column	Type	Description
utterance_id	string	Audio filename stem, e.g. `ADD_E3_00000000`
label	int8	`0` = bonafide (genuine), `1` = spoof (fake)

utterance_id is the audio file's stem (no .wav). A submitter's scores.txt keys by this id.

Stats

Stat	Value
Total trials	112861
Bonafide (genuine)	20776
Spoof (fake)	92085

Arena scoring

Standard EER (eer_percent, lower is better), computed over all 112 861 utterances. The seeded random-baseline scores ≈ 50 % EER by construction.

Source & citation

Original audio: https://zenodo.org/records/12188035 (CC BY-NC-ND 4.0)
Protocol: track3_R1_label.txt
Paper: ADD 2022 — the first Audio Deep Synthesis Detection Challenge, arXiv 2202.08433

@inproceedings{yi2022add,
  title     = {{ADD} 2022: the first Audio Deep Synthesis Detection Challenge},
  author    = {Yi, Jiangyan and Fu, Ruibo and Tao, Jianhua and Nie, Shuai and
               Ma, Haoxin and Wang, Chenglong and Wang, Tao and Tian, Zhengkun and
               Bai, Ye and Fan, Cunhang and Liang, Shan and Wang, Shiming and
               Zhang, Shuai and Yan, Xinrui and Xu, Le and Wen, Zhengqi and Li, Haizhou},
  booktitle = {ICASSP},
  pages     = {9216--9220},
  year      = {2022}
}