Jabberjay 0.0.3

🦜 Synthetic Voice Detection

Jabberjay

🦜 Synthetic Voice Detection

Models

Vision Transformer

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

Audio Spectrogram Transformer

Name	Model	Dataset	Model
MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection	AST	ASVspoof2019	Hugging Face
MattyB95/AST-VoxCelebSpoof-Synthetic-Voice-Detection	AST	VoxCelebSpoof	Hugging Face

Other

Name	Paper	Codebase	Model
Classical	Placeholder	Placeholder	Placeholder
RawNet2	End-to-End anti-spoofing with RawNet2	rawnet2-antispoofing	pre_trained_DF_RawNet2.zip

Usage

Command Line Interface

usage: Jabberjay [-h] [-m {AST,Classical,RawNet2,VIT}]
                 [-d {ASVspoof2019,VoxCelebSpoof}]
                 [-vis {ConstantQ,MelSpectrogram,MFCC}] [-v]
                 audio

Python API

from Jabberjay.Utilities.enum_handler import Visualisation, Model, Dataset
from Jabberjay.jabberjay import Jabberjay

jabberjay = Jabberjay()

bonafide = jabberjay.load(filename="../res/bonafide/bonafide.flac")
spoof = jabberjay.load(filename="../res/spoof/spoof.flac")

jabberjay.detect(audio=bonafide, model=Model.VIT, visualisation=Visualisation.ConstantQ, dataset=Dataset.VoxCelebSpoof)
jabberjay.detect(audio=spoof, model=Model.VIT, visualisation=Visualisation.ConstantQ, dataset=Dataset.VoxCelebSpoof)