| --- |
| license: other |
| license_name: vouch-bench-mixed |
| license_link: https://huggingface.co/datasets/NagaYu/vouch-bench/blob/main/LICENSES.md |
| language: |
| - en |
| - ja |
| task_categories: |
| - audio-classification |
| tags: |
| - anti-spoofing |
| - deepfake-detection |
| - voice-conversion |
| - text-to-speech |
| - benchmark |
| size_categories: |
| - 1K<n<10K |
| pretty_name: vouch-bench (release subset) |
| configs: |
| - config_name: default |
| data_files: metadata.csv |
| --- |
| |
| # vouch-bench: synthetic-speech detection under messaging-app conditions |
|
|
| **What this is.** A small benchmark of real and synthetic speech, shaped like messaging-app voice notes: 2-10 s clips, |
| Opus at 16 and 24 kbps, a phone-microphone simulation, and English plus Japanese. Every clip comes in four renderings |
| (clean, Opus 16k, Opus 24k, phone mic). The folds test whether a detector generalises to a generator it has never seen. |
|
|
| **What it can measure.** |
| - Detection with all generators seen, under each messaging condition. |
| - Leave-one-generator-out generalisation, separately within the voice-conversion (VC) lineage and across lineages. |
| - How a detector trained on older generators treats a modern neural TTS (Kokoro-82M) that sits in a **test-only |
| partition** and must never be used for training, validation, normalisation statistics or model selection. |
|
|
| **What it cannot measure.** |
| - **Unseen VC lineage is at or below chance for every detector we tried.** With no VC in training, the reference |
| detector reaches AUC 0.503 on the research benchmark and |
| 0.351 on this release subset (decisions invert). The benchmark exposes the |
| failure; it cannot tell you how to fix it. |
| - **Zero-shot voice cloning, commercial TTS APIs and phone-call audio are not included.** Results do not transfer to them. |
| - **Japanese results are exploratory.** FLEURS has no speaker ids, so speaker-disjointness relies on six coarse |
| (split, gender) groups, and the Japanese test side is female only. |
| - The data is small (1,000 base clips), so treat differences of a few points of EER as noise. |
|
|
| ## Composition |
|
|
| 4,000 clips = 1,000 base clips x 4 messaging profiles. Base hours: 2.02. |
|
|
| | label | generator | lang | base clips | clips (x4 profiles) | |
| |---|---|---|---|---| |
| | fake | espeak_ng | en | 100 | 400 | |
| | fake | espeak_ng | ja | 100 | 400 | |
| | fake | freevc | en | 100 | 400 | |
| | fake | freevc | ja | 100 | 400 | |
| | fake | knn_vc | en | 100 | 400 | |
| | fake | knn_vc | ja | 100 | 400 | |
| | fake | kokoro_82m | en | 100 | 400 | |
| | fake | kokoro_82m | ja | 100 | 400 | |
| | real | - | en | 100 | 400 | |
| | real | - | ja | 100 | 400 | |
|
|
| `test_only = true` marks the 800 Kokoro-82M clips. **They are a hold-out. Do not train, validate, tune or select on them.** |
|
|
| macOS system-voice clips from the research benchmark are excluded from this release because their redistribution terms |
| have not been cleared. The "say unseen" condition of the research benchmark therefore cannot be evaluated here. |
|
|
| ## Folds (`folds/*.json`) |
| |
| Speakers and synthetic voices never cross between train and test. VC fakes take the identity of their target speaker, |
| so they follow that speaker's side. All four renderings of a base clip stay on the same side. |
| |
| | fold | meaning | train clips | test clips | |
| |---|---|---|---| |
| | `speaker` | All generators seen | 2164 | 1036 | |
| | `logo-espeak_ng` | espeak-ng unseen (= TTS lineage unseen) | 1588 | 1052 | |
| | `logo-freevc` | FreeVC unseen (kNN-VC in train) | 1868 | 532 | |
| | `logo-knn_vc` | kNN-VC unseen (FreeVC in train) | 1868 | 532 | |
| | `family-tts` | TTS lineage unseen | 1588 | 1052 | |
| | `family-vc` | VC lineage unseen (both VCs held out) | 1348 | 812 | |
| |
| Only one TTS generator (espeak-ng) is trainable in this release, so `family-tts` and `logo-espeak_ng` hold out the same |
| generator and have identical test sets. `folds/holdout.json` lists the test-only Kokoro clips. |
| |
| ## Usage |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("NagaYu/vouch-bench", split="train") # all rows; use folds/*.json to split |
| ``` |
| |
| Or with pandas: `pd.read_parquet("manifest.parquet")`. The `file_name` column is relative to the repository root. |
| For a fold, select `clip_id`s from `folds/<fold>.json` (`train` / `test`). The test-only hold-out is always |
| `test_only == True`. |
| |
| Key columns: `label` (real/fake), `lang`, `speaker_id`, `generator_id` (null for real), `generator_family` (tts/vc), |
| `codec_profile`, `duration`, `source_id`, `source_clip_id`, `paired_clip_id`, `test_only`, `license`, `sha256`, |
| `gen_params`. |
| |
| ## Reference results |
| |
| Benchmark tables (research setting, which also includes macOS-say clips in training) and the release-checkpoint table |
| are in the model card: https://huggingface.co/NagaYu/vouch-hubert-lr. |
| |
| ## Licences |
| |
| There is no single licence. See [LICENSES.md](LICENSES.md) for the component table and full attribution: |
| LibriSpeech, FLEURS and VCTK are CC BY 4.0; kNN-VC and FreeVC are MIT; Kokoro-82M is Apache-2.0; eSpeak NG is GPL |
| software whose audio output the GPL does not cover. Each manifest row records its own source, generator and licence. |
| |
| ## How it was made |
| |
| See [REPRODUCE.md](REPRODUCE.md). |
| |
| ## Contact |
| |
| Portfolio: https://nagayu-portfolio.nagayu.workers.dev/en |
| |
