Jabberjay 0.0.8.post1

🦜 Synthetic Voice Detection

Jabberjay 🦜

One API. Every state-of-the-art synthetic voice detector.

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Why Jabberjay?

Synthetic voice detection is a fragmented landscape — state-of-the-art models are scattered across research repositories, each with its own dependencies, input formats, and output conventions. Jabberjay brings them all under one consistent Python API and CLI so you can detect AI-generated speech without wrestling with model internals.

• Seven model families — ViT, AST, Wav2Vec2, HuBERT, WavLM, RawNet2, and a classical baseline
• Unified output — every model returns the same DetectionResult with label, confidence, and scores
• Zero boilerplate — pass a file path, get a verdict; models are downloaded and cached automatically
• Flexible — use strings for quick experiments, enums for IDE autocomplete, or pre-load audio to run multiple models on the same clip

$ jabberjay interview.wav
Bonafide ✔️  (94.1% confidence, model=VIT)

$ jabberjay suspicious.wav -m HuBERT
Spoof ❌  (97.8% confidence, model=HuBERT)

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Installation

pip install jabberjay

Requires Python ≥ 3.10. Models are downloaded from Hugging Face Hub on first use and cached locally.

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Quickstart

from Jabberjay import Jabberjay

jj = Jabberjay()
result = jj.detect("audio.wav")

print(result)              # Bonafide ✔️ (94.1% confidence, model=VIT)
print(result.label)        # "Bonafide"
print(result.is_bonafide)  # True
print(result.confidence)   # 0.941

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Models

Vision Transformer (ViT)

Model	Dataset	Visualisation
MattyB95/VIT-ASVspoof2019-ConstantQ-Synthetic-Voice-Detection	ASVspoof2019	ConstantQ
MattyB95/VIT-ASVspoof2019-Mel_Spectrogram-Synthetic-Voice-Detection	ASVspoof2019	MelSpectrogram
MattyB95/VIT-ASVspoof2019-MFCC-Synthetic-Voice-Detection	ASVspoof2019	MFCC
MattyB95/VIT-ASVspoof5-ConstantQ-Synthetic-Voice-Detection	ASVspoof5	ConstantQ
MattyB95/VIT-ASVspoof5-Mel_Spectrogram-Synthetic-Voice-Detection	ASVspoof5	MelSpectrogram
MattyB95/VIT-ASVspoof5-MFCC-Synthetic-Voice-Detection	ASVspoof5	MFCC
MattyB95/VIT-VoxCelebSpoof-ConstantQ-Synthetic-Voice-Detection	VoxCelebSpoof	ConstantQ
MattyB95/VIT-VoxCelebSpoof-Mel_Spectrogram-Synthetic-Voice-Detection	VoxCelebSpoof	MelSpectrogram
MattyB95/VIT-VoxCelebSpoof-MFCC-Synthetic-Voice-Detection	VoxCelebSpoof	MFCC

Audio Spectrogram Transformer (AST)

Model	Dataset
MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection	ASVspoof2019
MattyB95/AST-ASVspoof5-Synthetic-Voice-Detection	ASVspoof5
MattyB95/AST-VoxCelebSpoof-Synthetic-Voice-Detection	VoxCelebSpoof

Wav2Vec2

Model	Dataset
Gustking/wav2vec2-large-xlsr-deepfake-audio-classification	ASVspoof2019

HuBERT

Model	Dataset
abhishtagatya/hubert-base-960h-itw-deepfake	In-The-Wild

WavLM

Model	Dataset
DavidCombei/wavLM-base-Deepfake_V2	Mixed

Other

Model	Paper	Codebase
Classical	—	Built-in KNN baseline
RawNet2	Tak et al., ICASSP 2021	rawnet2-antispoofing

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Usage

Command Line Interface

usage: jabberjay [-h] [-m {AST,Classical,HuBERT,RawNet2,VIT,Wav2Vec2,WavLM}]
                 [-d {ASVspoof2019,ASVspoof5,VoxCelebSpoof}]
                 [-vis {ConstantQ,MelSpectrogram,MFCC}] [-v]
                 audio

# Quickstart — VIT with ConstantQ on VoxCelebSpoof (defaults)
jabberjay audio.wav

# Self-contained models (no dataset or visualisation required)
jabberjay audio.wav -m Wav2Vec2
jabberjay audio.wav -m HuBERT
jabberjay audio.wav -m WavLM
jabberjay audio.wav -m RawNet2

# AST with a specific dataset
jabberjay audio.wav -m AST -d ASVspoof2019

# VIT with full options
jabberjay audio.wav -m VIT -d ASVspoof5 -vis MelSpectrogram

# Verbose output
jabberjay audio.wav -v

Python API

All public names are importable from the top-level package:

from Jabberjay import Jabberjay, DetectionResult, Model, Dataset, Visualisation

Choosing a model

String names and enum values are both accepted:

jj = Jabberjay()

# Self-contained models — no extra arguments needed
result = jj.detect("audio.wav", model="Wav2Vec2")
result = jj.detect("audio.wav", model="HuBERT")
result = jj.detect("audio.wav", model="WavLM")
result = jj.detect("audio.wav", model="RawNet2")
result = jj.detect("audio.wav", model="Classical")

# AST — requires a dataset
result = jj.detect("audio.wav", model="AST", dataset="VoxCelebSpoof")

# VIT — requires a dataset and a visualisation
result = jj.detect("audio.wav", model="VIT", dataset="ASVspoof5", visualisation="MFCC")

# Enums give IDE autocomplete and catch typos at import time
result = jj.detect(
    "audio.wav",
    model=Model.VIT,
    dataset=Dataset.ASVspoof5,
    visualisation=Visualisation.MFCC,
)

DetectionResult

Every call to detect() returns a DetectionResult regardless of the model used:

Attribute	Type	Description
label	str	"Bonafide" or "Spoof"
is_bonafide	bool	True if the audio is classified as genuine
confidence	float	Confidence score for the top prediction (0.0–1.0)
model	Model	The model that produced this result
scores	list[dict] | None	Full label/score breakdown for VIT, AST, Wav2Vec2, HuBERT, and WavLM (sorted highest-first); None for Classical and RawNet2

if result.is_bonafide:
    print(f"Genuine voice detected with {result.confidence:.1%} confidence")
else:
    print(f"Synthetic voice detected with {result.confidence:.1%} confidence")

# Full per-label scores
if result.scores:
    for entry in result.scores:
        print(f"  {entry['label']}: {entry['score']:.3f}")

Pre-loading audio

Use load() when running multiple models on the same clip to avoid re-reading the file:

audio = jj.load("audio.wav")  # returns (samples, sample_rate)

results = [
    jj.detect(audio, model="Wav2Vec2"),
    jj.detect(audio, model="HuBERT"),
    jj.detect(audio, model="VIT", dataset="VoxCelebSpoof", visualisation="ConstantQ"),
]

Discovering available options

jj.list_models()         # prints and returns list[Model]
jj.list_datasets()       # prints and returns list[Dataset]
jj.list_visualisations() # prints and returns list[Visualisation]

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Examples

The examples/ directory contains focused, runnable scripts:

Script	What it shows
quickstart.py	Minimum viable usage
choosing_a_model.py	Every model family, string and enum APIs
preloading_audio.py	Efficient multi-model runs with jj.load()
exploring_results.py	All DetectionResult fields and score breakdown
run_all.py	Exhaustive sweep across every model combination

just example                           # run quickstart
just run-example preloading_audio      # run a specific example
just run-all                           # full sweep (slow — downloads all models)

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Developer Setup

Requires uv and just.

git clone https://github.com/MattyB95/Jabberjay.git
cd Jabberjay
just install   # install all dependencies including dev tools

Command	Description
just test	Run the test suite
just check	Lint, format check, and type check
just fix	Auto-fix lint issues and reformat
just detect audio.wav	Run the CLI against a file
just build	Build the package

See just --list for all available commands.

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Contributing

Contributions are welcome — especially new models! See CONTRIBUTING.md for a full guide.

The quickest way to make an impact is to open a model request issue with a HuggingFace link and licence details.

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Support

If Jabberjay saves you time, consider supporting its development:

• GitHub Sponsors — recurring or one-off support, directly on GitHub
• Ko-fi — buy me a coffee

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Citation

If you use Jabberjay in your research, please cite it. GitHub's "Cite this repository" button (in the sidebar) will generate APA or BibTeX automatically from the CITATION.cff file, or you can use the entry below directly:

@software{boakes_jabberjay_2026,
  author  = {Boakes, Matthew},
  title   = {Jabberjay},
  year    = {2026},
  url     = {https://github.com/MattyB95/Jabberjay},
  version = {0.0.8.post1},
  doi     = {10.5281/zenodo.19057457},
}

Archived versions are available on Zenodo. The concept DOI (10.5281/zenodo.19057457) always resolves to the latest release.

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Acknowledgement

This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation [INV-001309].