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SpeechAntiSpoofingBenchmarks
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DFADD

Tasks:
Audio Classification
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anti-spoofing
audio-deepfake-detection
speech
benchmark
arena-ready
diffusion
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metadata 
license: mit
language:
  - en
pretty_name: DFADD — Diffusion and Flow-Matching Based Audio Deepfake Dataset
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
  - diffusion
  - flow-matching
arxiv:
  - '2409.08731'

DFADD — Diffusion and Flow-Matching Based Audio Deepfake Dataset

Benchmark-ready packaging of the DFADD test (eval) split (DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset, arXiv 2409.08731), a VCTK-derived dataset targeting the newest generation of high-quality TTS spoofing built on diffusion and flow-matching synthesizers.

Overview

Binary classification: bonafide (genuine VCTK recordings) vs. spoof (text-to-speech generated from VCTK speakers). Label is the top-level source directory. The spoof side spans five modern TTS systems:

Generator Family Spoof clips (test)
Grad-TTS diffusion 600
Matcha-TTS flow-matching 600
NaturalSpeech 2 latent diffusion 600
PFlow-TTS flow-matching 600
StyleTTS 2 diffusion (style) 600

The bonafide side is the genuine VCTK audio held out for the test split (755 clips).

License & redistribution

Released under the MIT License (per the upstream DFADD release). Redistribution is permitted with attribution; see LICENSE.txt. The underlying speech derives from the VCTK Corpus (CC BY 4.0) — attribute both DFADD and VCTK. Audio is the original 16 kHz mono FLAC/WAV, embedded bit-exactly (no re-encode — a full decode probe of all test clips passed cleanly).

Schema

Column Type Description
path string source-relative path, e.g. DATASET_GradTTS/test/p227_001_GradTTS.flac, unique
audio Audio(16000) 16 kHz mono
label ClassLabel "bonafide" (0) / "spoof" (1)
notes string JSON: utterance_id, generator, speaker, attack

notes example:

{"utterance_id": "gradtts__p227_001_GradTTS", "generator": "gradtts", "speaker": "p227", "attack": "gradtts"}

utterance_id is <generator>__<filename-stem> — the bare stem repeats across generators (the same VCTK texts are synthesized), so the generator prefix is what makes ids unique. Bonafide ids use the vctk__ prefix.

Quick Start 

from datasets import load_dataset

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

Stats

Stat Value
Total trials 3,755
Bonafide (VCTK) 755
Spoof (TTS) 3,000
Generators Grad-TTS, Matcha-TTS, NaturalSpeech 2, PFlow-TTS, StyleTTS 2 (600 each)
Sample rate 16 kHz mono

Source provenance

  • Paper: DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset, arXiv 2409.08731 (https://arxiv.org/abs/2409.08731).
  • Underlying speech: the VCTK Corpus (CC BY 4.0).

Evaluation

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

Citation 

@article{du2024dfadd,
  title   = {{DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset}},
  author  = {Du, Jiawei and others},
  journal = {arXiv preprint arXiv:2409.08731},
  year    = {2024},
}

Maintainer

Maintained by Kirill Borodin (SpeechAntiSpoofingBenchmarks).