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Datasets:
SpeechAntiSpoofingBenchmarks
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ASVspoof5

Tasks:
Audio Classification
Modalities:
Audio
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
anti-spoofing
audio-deepfake-detection
speech
benchmark
arena-ready
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metadata 
license: odc-by
language:
  - en
pretty_name: ASVspoof 5 (track 1, eval)
task_categories:
  - audio-classification
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*.parquet
tags:
  - anti-spoofing
  - audio-deepfake-detection
  - speech
  - benchmark
  - arena-ready
arxiv:
  - '2408.08739'

ASVspoof 5 (track 1, eval)

Benchmark-ready packaging of the Track 1 (spoofing / deepfake detection) evaluation partition of the ASVspoof 5 challenge, for speech anti-spoofing and synthetic / deepfake voice detection.

Overview

Track 1 is binary classification: bonafide (genuine human speech) vs. spoof (synthetic / converted speech). This packaging contains the full track_1 evaluation set. The original challenge is at https://www.asvspoof.org/.

License & redistribution

Redistributed under the Open Data Commons Attribution License (ODC-By) v1.0. See LICENSE.txt. Labels and the evaluation protocol are unmodified; audio is the original 16 kHz mono FLAC, embedded bit-exactly (no re-encode — a full decode probe of all 680,774 clips passed cleanly).

Schema

Column Type Description
path string <utterance_id>.flac, unique
audio Audio(16000) 16 kHz mono FLAC
label ClassLabel "bonafide" (0) / "spoof" (1)
notes string JSON: utterance_id, speaker_id, gender, codec, codec_id, source_id, attack_condition, attack_id

notes example:

{"utterance_id": "E_0009538969", "speaker_id": "E_1607", "gender": "M", "codec": "C05", "codec_id": "2", "source_id": "E_0009486171", "attack_condition": "AC1", "attack_id": "A26"}

Quick Start 

from datasets import load_dataset

ds = load_dataset("SpeechAntiSpoofingBenchmarks/ASVspoof5", split="test")
print(ds[0])

Stats

Stat Value
Total trials 680,774
Bonafide 138,688
Spoof 542,086

Source provenance

Evaluation

For evaluation instructions and submission format, see submissions/README.md.

Citation 

@inproceedings{wang2024asvspoof5,
  title     = {{ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale}},
  author    = {Wang, Xin and Delgado, H{\'e}ctor and Tak, Hemlata and others},
  year      = {2024},
  booktitle = {ASVspoof Workshop 2024},
}

Maintainer

Maintained by Kirill Borodin (SpeechAntiSpoofingBenchmarks).