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)
