---
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