cvqluu/GE2E-Loss

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Created over 5 years ago
Updated over 5 years ago

cvqluu/GE2E-Loss

cvqluu

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Pytorch implementation of Generalized End-to-End Loss for speaker verification

Angular-Penalty-Softmax-Losses-Pytorch

Angular penalty loss functions in Pytorch (ArcFace, SphereFace, Additive Margin, CosFace)

TDNN

Time delay neural network (TDNN) implementation in Pytorch using unfold method

Factorized-TDNN

PyTorch implementation of the Factorized TDNN (TDNN-F) from "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks" and Kaldi

simple_diarizer

Simplified diarization pipeline using some pretrained models - audio file to diarized segments in a few lines of code

nn-similarity-diarization

Neural network based similarity scoring for diarization (pytorch implementation of "LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization")

MTL-Speaker-Embeddings

Code for the paper: "Leveraging speaker attribute information using multi task learning for speaker verification and diarization" presented at Interspeech 2021

dropclass_speaker

DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020

splitdim_disentangle

Disentangling speaker embeddings using multi-task learning and adversarial training