| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - audio-classification |
| language: |
| - en |
| - de |
| - fr |
| - zh |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - audio |
| - deepfake-detection |
| - anti-spoofing |
| - watermarking |
| - provenance |
| pretty_name: WASP |
| --- |
| # Paper: [https://arxiv.org/pdf/2606.23335](https://arxiv.org/pdf/2606.23335) |
|
|
| # WASP: Watermark-Shortcut Paired Speech Corpus |
|
|
| WASP is a paired speech corpus for studying the *watermark shortcut* in audio |
| deepfake detection: the spurious "watermark => fake" feature a detector picks up |
| when synthetic speech is watermarked by default and human speech is not. Every |
| utterance is provided clean and under each watermark, all applied **post hoc**, |
| so the clean and watermarked versions of an utterance differ only in the mark. |
| Any change in a detector's score between the two members of a pair is therefore |
| attributable to the watermark alone, with no confound from the generator. |
|
|
| This is the released corpus for the black-box study in the paper *The Watermark |
| Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection*. |
|
|
| ## Structure |
|
|
| ``` |
| {label}/{lang}/{source}/{model}/{watermark}/{utterance}.wav |
| e.g. spoof/en/mailabs/chatterbox/perth/ozma_of_oz_10_f000019.wav |
| spoof/en/mailabs/chatterbox/without/ozma_of_oz_10_f000019.wav # same base, no mark |
| ``` |
|
|
| - **label**: `spoof` (TTS output) or `bonafide` (genuine human speech). |
| - **lang**: `en`, `de`, `fr`, `zh`. |
| - **source**: `mailabs` (M-AILABS; en/de/fr) or `aishell` (AISHELL-3; zh). For `bonafide`, the model level is the literal `bonafide`. |
| - **model**: the TTS system (spoof only); see below. |
| - **watermark**: `without` (clean base), `perth`, `wavmark`, `audioseal`, `silentcipher`; `bonafide` additionally carries `all-WMs` (all marks stacked). |
|
|
| **Pairing.** A watermarked clip and its clean counterpart share the same |
| `{label}/{lang}/{source}/{model}/{utterance}` and differ only in the |
| `{watermark}` directory; the clean base lives under `without/`. |
|
|
| ## Synthesizers (spoof) |
|
|
| | Model | Default watermark | |
| |---|---| |
| | Chatterbox | PerTh | |
| | Chatterbox-Turbo | PerTh | |
| | DramaBox | PerTh | |
| | Kyutai | none | |
| | Orpheus | none (SilentCipher opt-in) | |
| | Sesame CSM | SilentCipher | |
|
|
| The built-in watermark of each model is disabled during generation to obtain a |
| clean base; all marks in the corpus are then applied post hoc. English carries |
| all six systems; German, French, and Mandarin are Chatterbox-led. |
|
|
| ## Watermark schemes |
|
|
| PerTh (Resemble AI), WavMark, AudioSeal (Meta), and SilentCipher (Sony), plus an |
| `all-WMs` stack for the bona-fide split. All operate on a finished waveform. |
|
|
| ## Size |
|
|
| About 7,000 clips derived from roughly 1,400 base utterances (~1,000 synthetic, |
| 400 genuine), across four languages. |
|
|
| ## Loading |
|
|
| ```python |
| # Build a Hugging Face datasets object with metadata parsed from the path |
| from huggingface_hub import snapshot_download |
| from datasets import Dataset, Audio |
| import glob, os |
| |
| local = snapshot_download("mueller91/WASP", repo_type="dataset") |
| rows = [] |
| for wav in glob.glob(os.path.join(local, "**", "*.wav"), recursive=True): |
| label, lang, source, model, watermark = os.path.relpath(wav, local).split(os.sep)[:5] |
| rows.append({"audio": wav, "label": label, "lang": lang, "source": source, |
| "model": model, "watermark": watermark, |
| "utterance": os.path.basename(wav)}) |
| ds = Dataset.from_list(rows).cast_column("audio", Audio()) |
| print(ds[0]) |
| ``` |
|
|
| ## Download |
|
|
| ```bash |
| # Option A - Hugging Face CLI |
| huggingface-cli download mueller91/WASP --repo-type dataset --local-dir ./WASP |
| ``` |
|
|
| ```python |
| # Option B - huggingface_hub (Python) |
| from huggingface_hub import snapshot_download |
| snapshot_download("mueller91/WASP", repo_type="dataset", local_dir="./WASP") |
| ``` |
|
|
| ```bash |
| # Option C - git clone (requires git-lfs) |
| git lfs install |
| git clone https://huggingface.co/datasets/mueller91/WASP |
| ``` |
|
|
| ## Intended use and license |
|
|
| Research on deepfake detection, watermark robustness, and shortcut learning. |
| Source audio derives from M-AILABS and AISHELL-3; downstream use is subject to |
| those corpora's terms in addition to the synthesizers' and watermark schemes' |
| licenses. Use for building or evaluating systems intended to deceive or to |
| falsely accuse is out of scope. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{mueller2026watermarkshortcut, |
| title = {The Watermark Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection}, |
| author = {M\"uller, Nicolas M. and Debus, Pascal}, |
| journal = {arXiv preprint arXiv:2606.23335}, |
| year = {2026}, |
| note = {Submitted to IEEE Signal Processing Letters} |
| } |
| ``` |
|
|
