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

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

A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.

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

Python re-implementation of the spectral clustering algorithm in the paper "Speaker Diarization with LSTM"

An Android ChatBot powered by Watson Services - Assistant, Speech-to-Text and Text-to-Speech on IBM Cloud.

End-to-End Neural Diarization

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

This repository contains audio samples and supplementary materials accompanying publications related to the speaker-id team at Google.

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

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

An Android ChatBot powered by IBM Watson Services (Assistant V1, Text-to-Speech, and Speech-to-Text with Speaker Recognition) on IBM Cloud.

Discriminative Neural Clustering for Speaker Diarisation

Speaker Diarization is the problem of separating speakers in an audio. There could be any number of speakers and final result should state when speaker starts and ends. In this project, we analyze given audio file with 2 channels and 2 speakers (on separate channels).

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

Scripts for LIUM SpkDiarization tools

🔭 Speaker diarization via transfer learning

speaker diarization system using an LSTM

A lightweight library to compute Diarization Error Rate (DER).

speaker diarization system using an LSTM

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

VB Diarization with Eigenvoice and HMM Priors, refactored

Audio based speaker diarization

Speaker Diarization is the first step in many early audio processing and aims to solve the problem ”who spoke when”. It therefore relies on efficient use of temporal information from extracted audio features.

Automated Multi Speaker diarization API for meetings, calls, interviews, press-conference etc.

Repository holding various implementation of specific NMF methods for speaker diarization

Collection of broadcast news video clips

Multimodal speaker diarization using pre-trained audio-visual synchronization model

A Speaker Diarization on Google Cloud machine learning project with Ted Bundy Audio Data