Pinned Repositories
2021
ASVspoof 2021 Baseline Systems
aasist
Official PyTorch implementation of "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks"
Amphion
Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.
conformer
PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)
Open-NLLB
Effort to open-source NLLB checkpoints.
project-NN-Pytorch-scripts
see README
RawBoost-antispoofing
This repository includes the code to reproduce our paper "RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing".
RawGAT-ST-antispoofing
This repository includes the code to reproduce our paper "End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection" (https://arxiv.org/abs/2107.12710) published in the ASVspoof 2021 workshop.
SSL_Anti-spoofing
This repository includes the code to reproduce our paper "Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation".
T-EER
Official PyTorch implementation of "t-EER: Parameter-Free Tandem Evaluation Metric of Countermeasures and Biometric Comparators"
TakHemlata's Repositories
TakHemlata/SSL_Anti-spoofing
This repository includes the code to reproduce our paper "Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation".
TakHemlata/RawBoost-antispoofing
This repository includes the code to reproduce our paper "RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing".
TakHemlata/T-EER
Official PyTorch implementation of "t-EER: Parameter-Free Tandem Evaluation Metric of Countermeasures and Biometric Comparators"
TakHemlata/RawGAT-ST-antispoofing
This repository includes the code to reproduce our paper "End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection" (https://arxiv.org/abs/2107.12710) published in the ASVspoof 2021 workshop.
TakHemlata/2021
ASVspoof 2021 Baseline Systems
TakHemlata/aasist
Official PyTorch implementation of "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks"
TakHemlata/Amphion
Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.
TakHemlata/conformer
PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)
TakHemlata/Open-NLLB
Effort to open-source NLLB checkpoints.
TakHemlata/project-NN-Pytorch-scripts
see README
TakHemlata/Pseudo-Label-for-Deep-Neural-Networks
Semi-Supervised Learning Method for Deep Neural Networks
TakHemlata/pytorch_xvectors
Deep speaker embeddings in PyTorch, including x-vectors. Code used in this work: https://arxiv.org/abs/2007.16196
TakHemlata/rawnet2-antispoofing
This repository includes the code to reproduce our paper "End-to-end anti-spoofing with RawNet2" (https://arxiv.org/abs/2011.01108) published in ICASSP '21.
TakHemlata/SHAP-anti-spoofing
This repository includes the code to reproduce our paper [Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations] accepted in The Speaker and Language Recognition Workshop (Speaker Odyssey 2022).
TakHemlata/speechbrain
A PyTorch-based Speech Toolkit
TakHemlata/Synthetic-Voice-Detection-Vocoder-Artifacts
This repository is related to our Dataset and Detection code from the paper: AI-Synthesized Voice Detection Using Neural Vocoder Artifacts accepted in CVPR Workshop on Media Forensic 2023.
TakHemlata/TakHemlata.github.io
✨ Build a beautiful and simple website in literally minutes. Demo at https://beautifuljekyll.com
TakHemlata/voxceleb_trainer
In defence of metric learning for speaker recognition