Datasets:
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license: cc-by-nc-4.0
language:
- en
- zh
pretty_name: SONAR
task_categories: [audio-classification]
size_categories: [1K<n<10K]
configs:
- config_name: default
data_files:
- {split: test, path: "data/test-*.parquet"}
tags:
- anti-spoofing
- audio-deepfake-detection
- speech
- benchmark
- arena-ready
arxiv:
- "2410.04324"
---
# SONAR
Benchmark-ready packaging of the **SONAR** synthetic-audio detection evaluation set
([arXiv 2410.04324](https://arxiv.org/abs/2410.04324)), for speech anti-spoofing and
synthetic / deepfake voice detection.
## Overview
SONAR is a binary-classification benchmark: **bonafide** (genuine human speech)
vs. **spoof** (AI-synthesized speech). The spoof side aggregates clips from **eight modern
speech-synthesis systems**, deliberately spanning architectures and providers so that a
detector cannot win by memorising one generator's artifacts. The bonafide side is real
clips sampled from the **LibriTTS** clean-test set. Original code and data:
https://github.com/Jessegator/SONAR.
| Side | Source | n |
|------|--------|---|
| bonafide | LibriTTS clean-test | 2274 |
| spoof | OpenAI TTS | 600 |
| spoof | xTTS | 600 |
| spoof | FlashSpeech | 118 |
| spoof | VoiceBox | 104 |
| spoof | AudioGen | 100 |
| spoof | VALL-E | 95 |
| spoof | NaturalSpeech 3 | 32 |
| spoof | PromptTTS 2 | 25 |
> **Seed-TTS excluded (2026-06-07).** The upstream `seedtts_testset/` directory holds the
> SeedTTS *reference prompts* — real Common Voice audio (`common_voice_en_<prompt>-<target>.wav`),
> not synthesized speech (the actual SeedTTS-generated outputs are not redistributed on disk).
> Labeling those 600 real clips as spoof was incorrect, so they are dropped from this package.
> This matches the SONAR authors' own released eval set (3948 clips).
## License & redistribution
This **packaging** is released under the **Creative Commons Attribution-NonCommercial 4.0
International (CC BY-NC 4.0)** license — see `LICENSE.txt`. SONAR is an aggregate of audio
from multiple synthesis systems and from LibriTTS, each carrying its own upstream terms; the
original authors note that the constituent sources "may be subject to different distribution
licenses and usage restrictions." This repackaging is provided **for non-commercial research
use only**; the underlying sources retain their own terms, and downstream users should
consult the respective upstream providers' usage policies. Audio is re-encoded to 16 kHz mono
FLAC for a uniform schema; labels are unmodified.
## Schema
| Column | Type | Description |
|--------|------|-------------|
| `path` | `string` | source-relative path (e.g. `OpenAI/alloy_69.wav`), unique |
| `audio` | `Audio(16000)` | 16 kHz mono FLAC (re-encoded from heterogeneous source wav/mp3) |
| `label` | `ClassLabel` | `"bonafide"` (0) / `"spoof"` (1) |
| `notes` | `string` | JSON: `utterance_id`, `system`, `language` |
`notes` example:
```json
{"utterance_id": "SONAR_OpenAI_alloy_69", "system": "OpenAI", "language": "en"}
```
## Quick Start
```python
from datasets import load_dataset
ds = load_dataset("SpeechAntiSpoofingBenchmarks/SONAR", split="test")
print(ds[0])
```
## Stats
| Stat | Value |
|------|-------|
| Total trials | 3948 |
| Bonafide | 2274 |
| Spoof | 1674 |
## Source provenance
- Paper: https://arxiv.org/abs/2410.04324
- Code / data: https://github.com/Jessegator/SONAR
- Labels derived from the source directory layout (`real_samples/` = bonafide; the eight
synthesis-system directories = spoof; `seedtts_testset/` excluded — real reference prompts).
## Evaluation
For evaluation instructions and submission format, see [`submissions/README.md`](submissions/README.md).
## Citation
```bibtex
@article{li2024sonar,
title = {Where are we in audio deepfake detection? A systematic analysis over
generative and detection models},
author = {Li, Xiang and Chen, Pin-Yu and Wei, Wenqi},
journal = {arXiv preprint arXiv:2410.04324},
year = {2024}
}
```
## Maintainer
Maintained by Kirill Borodin (SpeechAntiSpoofingBenchmarks).
- Email: ~~k.n.borodin@mtuci.ru~~ (deprecated — use kborodin.research@gmail.com)
- Telegram: [@korallll_ai](https://t.me/korallll_ai)
|