Models

Jabberjay bundles ten model families. Each is downloaded from Hugging Face Hub on first use and cached locally, then kept in memory for the lifetime of the process so repeated detect() calls for the same model skip both the download and the reload.

Choosing a model

Model	Type	Datasets	Requires
VIT	Vision Transformer	ASVspoof2019, ASVspoof5, VoxCelebSpoof	`dataset`, `visualisation`
AST	Audio Spectrogram Transformer	ASVspoof2019, ASVspoof5, VoxCelebSpoof	`dataset`
Spectra0	Wav2Vec2 + ECAPA-TDNN	ASVspoof19/21, ASVspoof5, In-the-Wild	—
SpectraAASIST	Wav2Vec2 + AASIST	ASVspoof19/21, ASVspoof5, In-the-Wild	—
SpectraAASIST3	Wav2Vec2 + KAN-AASIST	ASVspoof19/21, ASVspoof5, In-the-Wild	—
Wav2Vec2	Self-supervised transformer	ASVspoof2019	—
HuBERT	Self-supervised transformer	In-The-Wild	—
WavLM	Self-supervised transformer	Mixed deepfake	—
RawNet2	End-to-end CNN	ASVspoof 2021	—
Classical	KNN classifier	ASVspoof2019	—

Simple rule of thumb:

For the lowest error rate on In-the-Wild audio — use SpectraAASIST3 (EER 0.961%)
For a quick, general-purpose result — use WavLM or HuBERT
For a lightweight baseline with no deep learning — use Classical
To sweep all models and compare — use jj.load() once, then call jj.detect() for each

VIT

Vision Transformer classifiers that convert the audio to a 2D image (spectrogram) and classify it visually.

Nine variants, covering three visualisation types × three training datasets:

Visualisation	ASVspoof2019	ASVspoof5	VoxCelebSpoof
ConstantQ	✓	✓	✓
MelSpectrogram	✓	✓	✓
MFCC	✓	✓	✓

jj.detect("audio.wav", model="VIT", dataset="VoxCelebSpoof", visualisation="ConstantQ")
jj.detect("audio.wav", model="VIT", dataset="ASVspoof2019", visualisation="MFCC")
jj.detect("audio.wav", model="VIT", dataset="ASVspoof5", visualisation="MelSpectrogram")

Visualisations:

ConstantQ — Constant-Q transform; good frequency resolution across the full spectrum
MelSpectrogram — Mel-scaled spectrogram; perceptually motivated, widely used in speech
MFCC — Mel-frequency cepstral coefficients; compact and speech-focused

AST

Audio Spectrogram Transformer. Applies a transformer directly to a patch-based spectrogram without a CNN backbone.

Available for ASVspoof2019, ASVspoof5, and VoxCelebSpoof datasets.

jj.detect("audio.wav", model="AST")  # defaults to VoxCelebSpoof
jj.detect("audio.wav", model="AST", dataset="VoxCelebSpoof")
jj.detect("audio.wav", model="AST", dataset="ASVspoof2019")

Note

dataset is optional for AST and defaults to VoxCelebSpoof if not specified.

Spectra0

lab260/spectra_0

SSL encoder (Wav2Vec2-XLS-R-300M) → MLP projection → ECAPA-TDNN binary classifier, trained across multiple anti-spoofing benchmarks.

Benchmark	EER (%)
ASVspoof19 LA	0.181
ASVspoof21 LA	6.475
ASVspoof21 DF	5.410
ASVspoof5	14.426
ADD2022	14.716
In-the-Wild	1.026

jj.detect("audio.wav", model="Spectra0")

Note

On first run, Spectra0 downloads both its own weights and the facebook/wav2vec2-xls-r-300m SSL encoder (~1.2 GB total). Both are cached by HuggingFace Hub after the first download.

SpectraAASIST

lab260/Spectra-AASIST

SSL encoder (Wav2Vec2-XLS-R-300M) → MLP projection → AASIST graph attention network with standard linear layers.

Benchmark	EER (%)
ASVspoof19 LA	0.159
ASVspoof21 LA	5.164
ASVspoof21 DF	2.568
ASVspoof5	14.056
ADD2022	15.205
In-the-Wild	1.461

jj.detect("audio.wav", model="SpectraAASIST")

Note

Downloads facebook/wav2vec2-xls-r-300m on first run (~1.2 GB total, cached by HuggingFace Hub).

SpectraAASIST3

lab260/Spectra-AASIST3

SSL encoder (Wav2Vec2-XLS-R-300M) → MLP projection → AASIST graph attention network with KAN (Kolmogorov-Arnold Network) linear layers — the strongest model in the Spectra family on In-the-Wild audio.

Benchmark	EER (%)
ASVspoof19 LA	0.723
ASVspoof21 LA	4.506
ASVspoof21 DF	1.998
ASVspoof5	13.820
ADD2022	15.187
In-the-Wild	0.961

jj.detect("audio.wav", model="SpectraAASIST3")

Note

Downloads facebook/wav2vec2-xls-r-300m on first run (~1.2 GB total, cached by HuggingFace Hub).

Wav2Vec2

Gustking/wav2vec2-large-xlsr-deepfake-audio-classification

Wav2Vec2-XLSR-300M fine-tuned on ASVspoof2019. EER 4.01%.

jj.detect("audio.wav", model="Wav2Vec2")

HuBERT

abhishtagatya/hubert-base-960h-itw-deepfake

HuBERT-base fine-tuned on the In-The-Wild dataset. EER 1.43%.

jj.detect("audio.wav", model="HuBERT")

WavLM

DavidCombei/wavLM-base-Deepfake_V2

WavLM-base fine-tuned on a mixed deepfake dataset.

jj.detect("audio.wav", model="WavLM")

RawNet2

End-to-end anti-spoofing network via rawnet2-antispoofing, with weights from ASVspoof 2021. Operates directly on raw waveforms — no feature extraction step.

jj.detect("audio.wav", model="RawNet2")

Note

scores is None for RawNet2 — only label, is_bonafide, and confidence are populated.

Classical

Feature-based KNN classifier trained on ASVspoof2019. Extracts hand-crafted audio features (MFCCs, spectral features) and classifies with k-nearest neighbours. Fast and dependency-light compared to the transformer models.

jj.detect("audio.wav", model="Classical")

Note

scores is None for Classical — only label, is_bonafide, and confidence are populated.