Datasets:
df_eval
Public evaluation-only speech deepfake detection dataset, organized like Common Voice language configs: each config is a standard eval protocol (ASVspoof, ADD, In-the-Wild, …) with embedded audio.
Companion code: github.com/Shuo-H/df_eval
Configs are added as uploads complete. Declared configs below match currently available parquet shards on the Hub.
Load
from datasets import load_dataset
ds = load_dataset("shuohann/df_eval", name="sonar", split="test")
print(ds[0]["audio"], ds[0]["label"], ds[0]["file_id"])
Schema
| column | type | description |
|---|---|---|
| audio | Audio | waveform |
| label | ClassLabel(bonafide, spoof) | ground-truth |
| file_id | string | basename of original file |
| source | string | config name |
| path | string | original protocol path (metadata) |
Extra columns on some configs:
in_the_wild:speaker(celebrity name from the official In-the-Wildmeta.csv, 54 speakers)speechfake:generator,model,speaker,languagectrsvdd:corpus(kising/m4singer/acesinger),speaker,attack(A09–A14, or-for bona fide)
Split: test only.
Planned configs
Aligned with Speech DF Arena protocol files:
asvspoof_2019,asvspoof_2021_la,asvspoof_2021_df,asvspoof_2024add_2022_track_1,add_2022_track_3,add_2023_round_1,add_2023_round_2codecfake,dfadd,fake_or_real,in_the_wild,librisevoc,sonar,speechfake,ctrsvdd
License / redistribution notice
This Hub dataset repackages evaluation audio from multiple third-party corpora. Each source has its own license (often research / non-commercial). Upstream sources include:
- ASVspoof 2019
- ASVspoof 2021 LA/DF
- ASVspoof 2024
- ADD 2022 / 2023
- FakeOrReal
- Codecfake
- DFADD
- LibriSeVoc
- SONAR
- In The Wild
- CtrSVDD (CC-BY-NC-ND 4.0)
You are responsible for complying with each original license. Prefer citing and downloading from the original authors when required. The uploader does not claim ownership of the underlying audio.
Citation
Please cite the original dataset papers for any config you use, and Speech DF Arena if you use their protocol definitions.
- CtrSVDD: Zhang et al., SVDD 2024; eval audio Zenodo 12703261 (CC-BY-NC-ND 4.0)
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