A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)

A PyTorch-based Speech Toolkit

Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.

SincNet is a neural architecture for efficiently processing raw audio samples.

In defence of metric learning for speaker recognition

an open-source implementation of sequence-to-sequence based speech processing engine

🔈 Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

This project uses a variety of advanced voiceprint recognition models such as EcapaTdnn, ResNetSE, ERes2Net, CAM++, etc. It is not excluded that more models will be supported in the future. At the same time, this project also supports MelSpectrogram, Spectrogram data preprocessing methods

Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit

Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

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

speaker diarization by uis-rnn and speaker embedding by vgg-speaker-recognition

Aims to create a comprehensive voice toolkit for training, testing, and deploying speaker verification systems.

The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others.

This repository contains audio samples and supplementary materials accompanying publications by the "Speaker, Voice and Language" team at Google.

Deep speaker embeddings in PyTorch, including x-vectors. Code used in this work: https://arxiv.org/abs/2007.16196

使用Tensorflow实现声纹识别

Base on MFCC and GMM(基于MFCC和高斯混合模型的语音识别)

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