| --- |
| license: cc-by-nc-nd-4.0 |
| language: |
| - zh |
| pretty_name: ADD 2022 Track 3 R1 Test (labels only) |
| task_categories: |
| - audio-classification |
| size_categories: |
| - 100K<n<1M |
| viewer: false |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/labels.parquet |
| tags: |
| - anti-spoofing |
| - audio-deepfake-detection |
| - speech |
| - benchmark |
| - arena-ready |
| arxiv: |
| - "2202.08433" |
| --- |
| |
| # ADD 2022 — Track 3 (Audio Fake Game), Round 1 Test · labels only |
|
|
| 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](https://arxiv.org/abs/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): |
|
|
| ```bash |
| 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](https://arxiv.org/abs/2202.08433) |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
| ## Maintainer |
|
|
| Maintained by Kirill Borodin (SpeechAntiSpoofingBenchmarks). |
| - Email: kborodin.research@gmail.com |
| - Telegram: [@korallll_ai](https://t.me/korallll_ai) |
|
|
