A python package to build AI-powered real-time audio applications

Speaker embedding (d-vector) trained with GE2E loss

Keras implementation of ‘’Deep Speaker: an End-to-End Neural Speaker Embedding System‘’ (speaker recognition)

PyTorch implementation of Densely Connected Time Delay Neural Network

A pipeline to read lips and generate speech for the read content, i.e Lip to Speech Synthesis.

Luigi pipeline to download VoxCeleb(2) audio from YouTube and extract speaker segments

On-device speaker recognition engine powered by deep learning

PyTorch implementation of the 1D-Triplet-CNN neural network model described in Fusing MFCC and LPC Features using 1D Triplet CNN for Speaker Recognition in Severely Degraded Audio Signals by A. Chowdhury, and A. Ross.

Speaker embedding for VI-SVC and VI-SVS, alse for VITS; Use this to replace the ID to implement voice clone.

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

Official implementation of the ICASSP 2024 paper: Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Speaker Verification

Angular triplet center loss implementation in Pytorch.

simple version of our torch kaldi toolkit, developed at the LIA by 2 apprentices. (@Chaanks & @vbrignatz)

This project partially embodies the state-of-the-art practices in speaker verification technology up until 2020, while attaining the state-of-the-art performance on the VoxCeleb1 test sets.

Code for the paper: Improving Speaker Representations Using Contrastive Losses on Multi-scale Features

说话人识别仓库-说话人表征-ResNet/VGGVox || a ready-to-use repo for Speaker Verification / Speaker Embedding with xvector

For further release go to: https://git-lium.univ-lemans.fr/speaker/sidekit

PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al.

说话人识别仓库-说话人表征-dvector || a ready-to-use repo for Speaker Verification / Speaker Embedding with dvector

Speaker identification on audio files using the pyannote/embedding model.