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https://github.com/wq2012/awesome-diarization

A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.
https://github.com/wq2012/awesome-diarization
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A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.
Host: GitHub
URL: https://github.com/wq2012/awesome-diarization
Owner: wq2012
License: apache-2.0
Created: 2019-01-19T04:28:41.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-03-22T13:00:50.000Z (3 months ago)
Last Synced: 2024-05-21T12:20:05.309Z (about 1 month ago)
Topics: awesome, awesome-list, deep-learning, machine-learning, speaker-diarization, speech-processing, speech-recognition
Homepage: https://wq2012.github.io/awesome-diarization/
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Stars: 1,473
Watchers: 82
Forks: 225
Open Issues: 4
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README

        # Awesome Speaker Diarization [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) [![Contribution](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/wq2012/awesome-diarization/blob/master/CONTRIBUTING.md)

## Table of contents

* [Overview](#Overview)

* [Publications](#Publications)

* [Software](#Software)

  * [Framework](#Framework)

  * [Evaluation](#Evaluation)

  * [Clustering](#Clustering)

  * [Speaker embedding](#Speaker-embedding)

  * [Speaker change detection](#Speaker-change-detection)

  * [Audio feature extraction](#Audio-feature-extraction)

  * [Audio data augmentation](#Audio-data-augmentation)

  * [Other sotware](#Other-software)

* [Datasets](#Datasets)

  * [Diarization datasets](#Diarization-datasets)

  * [Speaker embedding training sets](#Speaker-embedding-training-sets)

  * [Augmentation noise sources](#Augmentation-noise-sources)

* [Conferences](#Conferences)

* [Other learning materials](#Other-learning-materials)

  * [Online courses](#Online-courses)

  * [Books](#Books)

  * [Tech blogs](#Tech-blogs)

  * [Video tutorials](#Video-tutorials)

* [Products](#Products)

## Overview

This is a curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.

The purpose of this repo is to organize the world’s resources for speaker diarization, and make them universally accessible and useful.

To add items to this page, simply send a pull request. ([contributing guide](CONTRIBUTING.md))

## Publications

### Special topics

#### Review & survey papers

* [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://arxiv.org/pdf/2101.09624.pdf), 2021

* [A review on speaker diarization systems and approaches](https://www.sciencedirect.com/science/article/abs/pii/S0167639312000696), 2012

* [Speaker diarization: A review of recent research](http://www.eurecom.fr/fr/publication/3152/download/mm-publi-3152.pdf), 2010

#### Large language model (LLM)

* [DiarizationLM: Speaker Diarization Post-Processing with Large Language Models](https://arxiv.org/abs/2401.03506), 2024

* [Enhancing Speaker Diarization with Large Language Models: A Contextual Beam Search Approach](https://arxiv.org/abs/2309.05248), 2023

* [Lexical speaker error correction: Leveraging language models for speaker diarization error correction](https://arxiv.org/abs/2306.09313), 2023

#### Supervised diarization

* [DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors](https://arxiv.org/abs/2312.04324), 2023

* [TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization](https://arxiv.org/abs/2303.05397), 2023

* [Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis](https://arxiv.org/abs/2211.10243), 2022

* [End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings](https://arxiv.org/abs/2105.02096), 2021

* [Supervised online diarization with sample mean loss for multi-domain data](https://arxiv.org/abs/1911.01266), 2019

* [Discriminative Neural Clustering for Speaker Diarisation](https://arxiv.org/abs/1910.09703), 2019

* [End-to-End Neural Speaker Diarization with Permutation-Free Objectives](https://arxiv.org/abs/1909.05952), 2019

* [End-to-End Neural Speaker Diarization with Self-attention](https://arxiv.org/abs/1909.06247), 2019

* [Fully Supervised Speaker Diarization](https://arxiv.org/abs/1810.04719), 2018

#### Joint diarization and ASR

* [A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings](https://arxiv.org/abs/2203.16834), 2022

* [Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection](https://arxiv.org/abs/2109.11641), 2021

* [Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR](https://arxiv.org/abs/2110.03151), 2021

* [Joint Speech Recognition and Speaker Diarization via Sequence Transduction](https://arxiv.org/abs/1907.05337), 2019

* [Says who? Deep learning models for joint speech recognition, segmentation and diarization](https://ieeexplore.ieee.org/abstract/document/8462375), 2018

#### Online speaker diarization

* [Speaker Diarization as a Fully Online Bandit Learning Problem in MiniVox](https://proceedings.mlr.press/v157/lin21c/lin21c.pdf), 2021

* [Online Speaker Diarization with Relation Network](https://arxiv.org/abs/2009.08162), 2020

* [VoiceID on the Fly: A Speaker Recognition System that Learns from Scratch](https://www.isca-speech.org/archive/pdfs/interspeech_2020/lin20b_interspeech.pdf), 2020

#### Challenges

* [M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge](https://arxiv.org/abs/2110.07393), 2022

* [The Hitachi-JHU DIHARD III system: Competitive end-to-end neural diarization and x-vector clustering systems combined by DOVER-Lap](https://arxiv.org/abs/2102.01363)

* [Diarization is Hard: Some Experiences and Lessons Learned for the JHU

Team in the Inaugural DIHARD Challenge](https://www.isca-speech.org/archive/pdfs/interspeech_2018/sell18_interspeech.pdf), 2018

* [ODESSA at Albayzin Speaker Diarization Challenge 2018](https://www.isca-speech.org/archive/IberSPEECH_2018/pdfs/IberS18_AE-5_Patino.pdf), 2018

* [Joint Discriminative Embedding Learning, Speech Activity and Overlap Detection for the DIHARD Challenge](https://www.isca-speech.org/archive/Interspeech_2018/pdfs/2304.pdf), 2018

#### Audio-Visual Speaker Diarization

* [AVA-AVD: Audio-Visual Speaker Diarization in the Wild](https://dl.acm.org/doi/abs/10.1145/3503161.3548027), 2022

* [DyViSE: Dynamic Vision-Guided Speaker Embedding for Audio-Visual Speaker Diarization](https://ieeexplore.ieee.org/document/9948860), 2022

* [End-to-End Audio-Visual Neural Speaker Diarization](https://isca-speech.org/archive/interspeech_2022/he22c_interspeech.html), 2022

* [MSDWild: Multi-modal Speaker Diarization Dataset in the Wild](https://isca-speech.org/archive/interspeech_2022/liu22t_interspeech.html), 2022

### Other

#### 2021

* [Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation](https://arxiv.org/abs/2109.06483)

* [End-to-end speaker segmentation for overlap-aware resegmentation](https://www.isca-speech.org/archive/interspeech_2021/bredin21_interspeech.html)

* [DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding](https://arxiv.org/abs/2105.13802)

* [DOVER-Lap: A method for combining overlap-aware diarization outputs](https://arxiv.org/abs/2011.01997)

* [Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: Theory, implementation and analysis on standard tasks](https://www.sciencedirect.com/science/article/pii/S0885230821000619)

#### 2020

* [An End-to-End Speaker Diarization Service for improving Multimedia Content Access](https://nem-initiative.org/wp-content/uploads/2020/07/1-4-an_end_to_end_speaker_diarization_service_for_improving_multimedia_content_access.pdf)

* [Spot the conversation: speaker diarisation in the wild](https://arxiv.org/abs/2007.01216)

* [Speaker Diarization with Region Proposal Network](https://arxiv.org/abs/2002.06220)

* [Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario](https://arxiv.org/abs/2005.07272)

#### 2019

* [Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detection](https://arxiv.org/abs/1910.11646)

* [Speaker diarization using latent space clustering in generative adversarial network](https://arxiv.org/abs/1910.11398)

* [A study of semi-supervised speaker diarization system using gan mixture model](https://arxiv.org/abs/1910.11416)

* [Learning deep representations by multilayer bootstrap networks for speaker diarization](https://arxiv.org/abs/1910.10969)

* [Enhancements for Audio-only Diarization Systems](https://arxiv.org/abs/1909.00082)

* [LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization](https://arxiv.org/abs/1907.10393)

* [Meeting Transcription Using Virtual Microphone Arrays](https://www.microsoft.com/en-us/research/uploads/prod/2019/05/DenmarkTechReport-5ccb8b095c8f3.pdf)

* [Speaker diarisation using 2D self-attentive combination of embeddings](https://arxiv.org/abs/1902.03190)

* [Speaker Diarization with Lexical Information](https://arxiv.org/abs/2004.06756)

#### 2018

* [Neural speech turn segmentation and affinity propagation for speaker diarization](https://hal.archives-ouvertes.fr/hal-01912236/)

* [Multimodal Speaker Segmentation and Diarization using Lexical and Acoustic Cues via Sequence to Sequence Neural Networks](https://arxiv.org/abs/1805.10731)

* [Joint Speaker Diarization and Recognition Using Convolutional and Recurrent Neural Networks](https://ieeexplore.ieee.org/abstract/document/8461666)

#### 2017

* [Speaker Diarization with LSTM](https://arxiv.org/abs/1710.10468)

* [Speaker diarization using deep neural network embeddings](http://danielpovey.com/files/2017_icassp_diarization_embeddings.pdf)

* [Speaker diarization using convolutional neural network for statistics accumulation refinement](https://pdfs.semanticscholar.org/35c4/0fde977932d8a3cd24f5a1724c9dbca8b38d.pdf)

* [pyannote. metrics: a toolkit for reproducible evaluation, diagnostic, and error analysis of speaker diarization systems](https://www.isca-speech.org/archive/Interspeech_2017/pdfs/0411.PDF)

* [Speaker Change Detection in Broadcast TV using Bidirectional Long Short-Term Memory Networks](https://pdfs.semanticscholar.org/edff/b62b32ffcc2b5cc846e26375cb300fac9ecc.pdf)

* [Speaker Diarization using Deep Recurrent Convolutional Neural Networks for Speaker Embeddings](https://arxiv.org/abs/1708.02840)

#### 2016

* [A Speaker Diarization System for Studying Peer-Led Team Learning Groups](https://arxiv.org/pdf/1606.07136.pdf)

#### 2015

* [Diarization resegmentation in the factor analysis subspace](https://engineering.jhu.edu/hltcoe/wp-content/uploads/sites/92/2016/10/Sell_Garcia-Romero_2015A.pdf)

#### 2014

* [A study of the cosine distance-based mean shift for telephone speech diarization](https://www.researchgate.net/profile/Patrick_Kenny/publication/260661427_A_Study_of_the_Cosine_Distance-Based_Mean_Shift_for_Telephone_Speech_Diarization/links/0c96053270d2eaa133000000.pdf)

* [Speaker diarization with PLDA i-vector scoring and unsupervised calibration](https://ieeexplore.ieee.org/abstract/document/7078610)

* [Artificial neural network features for speaker diarization](https://ieeexplore.ieee.org/abstract/document/7078608)

#### 2013

* [Unsupervised methods for speaker diarization: An integrated and iterative approach](http://groups.csail.mit.edu/sls/publications/2013/Shum_IEEE_Oct-2013.pdf)

#### 2011

* [PLDA-based Clustering for Speaker Diarization of Broadcast Streams](https://pdfs.semanticscholar.org/0175/a752c5c72cadc7c0b899fd15f2f6b93c3335.pdf)

* [Speaker diarization of meetings based on speaker role n-gram models](https://publications.idiap.ch/downloads/papers/2011/Valente_ICASSP2011_2011.pdf)

#### 2009

* [Speaker Diarization for Meeting Room Audio](https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/ListenSemester2_2009_10/sun_IS2009_SpDia_meeting.PDF)

#### 2008

* [Stream-based speaker segmentation using speaker factors and eigenvoices](https://www.researchgate.net/profile/Pietro_Laface/publication/224313019_Stream-based_speaker_segmentation_using_speaker_factors_and_eigenvoices/links/5770fe8608ae10de639dc121.pdf)

#### 2006

* [An overview of automatic speaker diarization systems](https://alize.univ-avignon.fr/doc/publis/06_IEEE-TASP_Tranter.pdf)

* [A spectral clustering approach to speaker diarization](http://www.ifp.illinois.edu/~hning2/papers/Ning_spectral.pdf)

## Software

### Framework

| Link | Language | Description |

| ---- | -------- | ----------- |

| [FunASR](https://github.com/alibaba-damo-academy/FunASR) ![GitHub stars](https://img.shields.io/github/stars/alibaba-damo-academy/FunASR?style=social) | Python & PyTorch | FunASR is an open-source speech toolkit based on PyTorch, which aims at bridging the gap between academic researchs and industrial applications. |

| [MiniVox](https://github.com/doerlbh/MiniVox) ![GitHub stars](https://img.shields.io/github/stars/doerlbh/MiniVox?style=social) | MATLAB | MiniVox is an open-source evaluation system for the online speaker diarization task. |

| [SpeechBrain](https://github.com/speechbrain/speechbrain) ![GitHub stars](https://img.shields.io/github/stars/speechbrain/speechbrain?style=social) | Python & PyTorch | SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch. |

| [SIDEKIT for diarization (s4d)](https://projets-lium.univ-lemans.fr/s4d/) | Python | An open source package extension of SIDEKIT for Speaker diarization. |

| [pyAudioAnalysis](https://github.com/tyiannak/pyAudioAnalysis) ![GitHub stars](https://img.shields.io/github/stars/tyiannak/pyAudioAnalysis?style=social) | Python | Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications. |

| [AaltoASR](https://github.com/aalto-speech/speaker-diarization) ![GitHub stars](https://img.shields.io/github/stars/aalto-speech/speaker-diarization?style=social) | Python & Perl | Speaker diarization scripts, based on AaltoASR. |

| [LIUM SpkDiarization](https://projets-lium.univ-lemans.fr/spkdiarization/) | Java | LIUM_SpkDiarization is a software dedicated to speaker diarization (i.e. speaker segmentation and clustering). It is written in Java, and includes the most recent developments in the domain (as of 2013). |

| [kaldi-asr](https://github.com/kaldi-asr/kaldi/tree/master/egs/callhome_diarization) [![Build Status](https://travis-ci.com/kaldi-asr/kaldi.svg?branch=master)](https://travis-ci.com/kaldi-asr/kaldi) | Bash | Example scripts for speaker diarization on a portion of CALLHOME used in the 2000 NIST speaker recognition evaluation. |

| [Alize LIA_SpkSeg](https://alize.univ-avignon.fr/) | C++ | ALIZÉ is an opensource platform for speaker recognition. LIA_SpkSeg is the tools for speaker diarization. |

| [pyannote-audio](https://github.com/pyannote/pyannote-audio) ![GitHub stars](https://img.shields.io/github/stars/pyannote/pyannote-audio?style=social) | Python | Neural building blocks for speaker diarization: speech activity detection, speaker change detection, speaker embedding. |

| [pyBK](https://github.com/josepatino/pyBK) ![GitHub stars](https://img.shields.io/github/stars/josepatino/pyBK?style=social) | Python | Speaker diarization using binary key speaker modelling. Computationally light solution that does not require external training data. |

| [Speaker-Diarization](https://github.com/taylorlu/Speaker-Diarization) ![GitHub stars](https://img.shields.io/github/stars/taylorlu/Speaker-Diarization?style=social) | Python | Speaker diarization using uis-rnn and GhostVLAD. An easier way to support openset speakers. |

| [EEND](https://github.com/hitachi-speech/EEND) ![GitHub stars](https://img.shields.io/github/stars/hitachi-speech/EEND?style=social) | Python & Bash & Perl | End-to-End Neural Diarization. |

| [VBx](https://github.com/BUTSpeechFIT/VBx) ![GitHub stars](https://img.shields.io/github/stars/BUTSpeechFIT/VBx?style=social) | Python | Variational Bayes HMM over x-vectors diarization. x-vector extractor [recipe](https://github.com/phonexiaresearch/VBx-training-recipe) |

| [RE-VERB](https://github.com/team-re-verb/RE-VERB) ![GitHub stars](https://img.shields.io/github/stars/team-re-verb/RE-VERB?style=social) | Python & JavaScript | RE: VERB is speaker diarization system, it allows the user to send/record audio of a conversation and receive timestamps of who spoke when. |

| [StreamingSpeakerDiarization](https://github.com/juanmc2005/StreamingSpeakerDiarization/) ![GitHub stars](https://img.shields.io/github/stars/juanmc2005/StreamingSpeakerDiarization?style=social) | Python | Streaming speaker diarization, extends [pyannote.audio](https://github.com/pyannote/pyannote-audio) to online processing |

| [simple_diarizer](https://github.com/cvqluu/simple_diarizer) | Python | Simplified diarization pipeline using some pretrained models. Made to be a simple as possible to go from an input audio file to diarized segments. |

| [Picovoice Falcon](https://github.com/Picovoice/falcon) ![GitHub stars](https://img.shields.io/github/stars/Picovoice/falcon?style=social) | C & Python | A [lightweight, accurate, and fast](https://picovoice.ai/docs/benchmark/speaker-diarization/#accuracy) speaker diarization engine written in C and available in Python, running on CPU with minimal overhead. |

| [DiaPer](https://github.com/BUTSpeechFIT/DiaPer) ![GitHub stars](https://img.shields.io/github/stars/BUTSpeechFIT/DiaPer?style=social) | Python | Pytorch implementation for [DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors](https://arxiv.org/pdf/2312.04324.pdf) including models pre-trained on free and public data. |

### Evaluation

| Link | Language | Description |

| ---- | -------- | ----------- |

| [pyannote-metrics](https://github.com/pyannote/pyannote-metrics) ![GitHub stars](https://img.shields.io/github/stars/pyannote/pyannote-metrics?style=social) [![Build Status](https://travis-ci.org/pyannote/pyannote-metrics.svg?branch=master)](https://travis-ci.org/pyannote/pyannote-metrics)  | Python| A toolkit for reproducible evaluation, diagnostic, and error analysis of speaker diarization systems. |

| [SimpleDER](https://github.com/wq2012/SimpleDER) ![GitHub stars](https://img.shields.io/github/stars/wq2012/SimpleDER?style=social) [![Build Status](https://travis-ci.org/wq2012/SimpleDER.svg?branch=master)](https://travis-ci.org/wq2012/SimpleDER) | Python | A lightweight library to compute Diarization Error Rate (DER). |

| NIST md-eval | Perl | (1) modified [md-eval.pl](http://www1.icsi.berkeley.edu/~knoxm/dia/) from [Mary Tai Knox](http://www1.icsi.berkeley.edu/~knoxm); (2) [md-eval-v21.pl](https://github.com/jitendrab/btp/blob/master/c_code/single_diag_gaussian_no_viterbi/md-eval-v21.pl) from [jitendra](https://github.com/jitendrab); (3) [md-eval-22.pl](https://github.com/nryant/dscore/blob/master/scorelib/md-eval-22.pl) from [nryant](https://github.com/nryant) |

| [dscore](https://github.com/nryant/dscore) ![GitHub stars](https://img.shields.io/github/stars/nryant/dscore?style=social) | Python & Perl | Diarization scoring tools. |

| [Sequence Match Accuracy](https://github.com/google/uis-rnn/blob/master/uisrnn/evals.py) | Python | Match the accuracy of two sequences with Hungarian algorithm. |

| [spyder](https://github.com/desh2608/spyder) ![GitHub stars](https://img.shields.io/github/stars/desh2608/spyder?style=social) | Python & C++ | Simple Python package for fast DER computation. |

### Clustering

| Link | Language | Description |

| ---- | -------- | ----------- |

| [uis-rnn](https://github.com/google/uis-rnn) ![GitHub stars](https://img.shields.io/github/stars/google/uis-rnn?style=social) [![Build Status](https://travis-ci.org/google/uis-rnn.svg?branch=master)](https://travis-ci.org/google/uis-rnn) | Python & PyTorch | Google's Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, for Fully Supervised Speaker Diarization. This clustering algorithm is **supervised**. |

| [uis-rnn-sml](https://github.com/DonkeyShot21/uis-rnn-sml) ![GitHub stars](https://img.shields.io/github/stars/DonkeyShot21/uis-rnn-sml?style=social) | Python & PyTorch | A variant of UIS-RNN, for the paper Supervised Online Diarization with Sample Mean Loss for Multi-Domain Data. |

| [DNC](https://github.com/FlorianKrey/DNC) ![GitHub stars](https://img.shields.io/github/stars/FlorianKrey/DNC?style=social) | Python & ESPnet | Transformer-based Discriminative Neural Clustering (DNC) for Speaker Diarisation. Like UIS-RNN, it is **supervised**. |

| [SpectralCluster](https://github.com/wq2012/SpectralCluster) ![GitHub stars](https://img.shields.io/github/stars/wq2012/SpectralCluster?style=social) [![Build Status](https://travis-ci.org/wq2012/SpectralCluster.svg?branch=master)](https://travis-ci.org/wq2012/SpectralCluster) | Python | Spectral clustering with affinity matrix refinement operations, auto-tune, and speaker turn constraints. |

| [sklearn.cluster](https://scikit-learn.org/stable/modules/clustering.html) [![Build Status]( https://api.travis-ci.org/scikit-learn/scikit-learn.svg?branch=master)](https://travis-ci.org/scikit-learn/scikit-learn) | Python | scikit-learn clustering algorithms. |

| [PLDA](https://github.com/RaviSoji/plda) ![GitHub stars](https://img.shields.io/github/stars/RaviSoji/plda?style=social) | Python | Probabilistic Linear Discriminant Analysis & classification, written in Python. |

| [PLDA](https://github.com/mrouvier/plda) ![GitHub stars](https://img.shields.io/github/stars/mrouvier/plda?style=social) | C++ | Open-source implementation of simplified PLDA (Probabilistic Linear Discriminant Analysis). |

| [Auto-Tuning Spectral Clustering](https://github.com/tango4j/Auto-Tuning-Spectral-Clustering.git) ![GitHub stars](https://img.shields.io/github/stars/tango4j/Auto-Tuning-Spectral-Clustering?style=social) | Python | Auto-tuning Spectral Clustering method that does not need development set or supervised tuning. |

### Speaker embedding

| Link | Method | Language | Description |

| ---- | ------ | -------- | ----------- |

| [resemble-ai/Resemblyzer](https://github.com/resemble-ai/Resemblyzer) ![GitHub stars](https://img.shields.io/github/stars/resemble-ai/Resemblyzer?style=social) | d-vector | Python & PyTorch | PyTorch implementation of generalized end-to-end loss for speaker verification, which can be used for voice cloning and diarization. |

| [Speaker_Verification](https://github.com/Janghyun1230/Speaker_Verification) ![GitHub stars](https://img.shields.io/github/stars/Janghyun1230/Speaker_Verification?style=social) | d-vector | Python & TensorFlow | Tensorflow implementation of generalized end-to-end loss for speaker verification. |

| [PyTorch_Speaker_Verification](https://github.com/HarryVolek/PyTorch_Speaker_Verification) ![GitHub stars](https://img.shields.io/github/stars/HarryVolek/PyTorch_Speaker_Verification?style=social) | d-vector | Python & PyTorch | PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al. With UIS-RNN integration. |

| [Real-Time Voice Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) ![GitHub stars](https://img.shields.io/github/stars/CorentinJ/Real-Time-Voice-Cloning?style=social) | d-vector | Python & PyTorch | Implementation of "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis" (SV2TTS) with a vocoder that works in real-time. |

| [deep-speaker](https://github.com/philipperemy/deep-speaker) ![GitHub stars](https://img.shields.io/github/stars/philipperemy/deep-speaker?style=social) | d-vector |Python & Keras | Third party implementation of the Baidu paper Deep Speaker: an End-to-End Neural Speaker Embedding System. |

| [x-vector-kaldi-tf](https://github.com/hsn-zeinali/x-vector-kaldi-tf) ![GitHub stars](https://img.shields.io/github/stars/hsn-zeinali/x-vector-kaldi-tf?style=social) | x-vector | Python & TensorFlow & Perl | Tensorflow implementation of x-vector topology on top of Kaldi recipe. |

| [kaldi-ivector](https://github.com/idiap/kaldi-ivector) ![GitHub stars](https://img.shields.io/github/stars/idiap/kaldi-ivector?style=social) | i-vector | C++ & Perl |  Extension to Kaldi implementing the standard i-vector hyperparameter estimation and i-vector extraction procedure. |

| [voxceleb-ivector](https://github.com/swshon/voxceleb-ivector) ![GitHub stars](https://img.shields.io/github/stars/swshon/voxceleb-ivector?style=social) | i-vector |Perl | Voxceleb1 i-vector based speaker recognition system. |

| [pytorch_xvectors](https://github.com/manojpamk/pytorch_xvectors) ![GitHub stars](https://img.shields.io/github/stars/manojpamk/pytorch_xvectors?style=social) | x-vector | Python & PyTorch | PyTorch implementation of Voxceleb x-vectors. Additionaly, includes meta-learning architectures for embedding training. Evaluated with speaker diarization and speaker verification. |

| [ASVtorch](https://gitlab.com/ville.vestman/asvtorch) | i-vector | Python & PyTorch | ASVtorch is a toolkit for automatic speaker recognition. |

| [asv-subtools](https://github.com/Snowdar/asv-subtools) ![GitHub stars](https://img.shields.io/github/stars/Snowdar/asv-subtools?style=social) | i-vector & x-vector | Kaldi & PyTorch | ASV-Subtools is developed based on Pytorch and Kaldi for the task of speaker recognition, language identification, etc. The 'sub' of 'subtools' means that there are many modular tools and the parts constitute the whole. |

| [WeSpeaker](https://github.com/wenet-e2e/wespeaker.git) ![GitHub stars](https://img.shields.io/github/stars/wenet-e2e/wespeaker?style=social) | x-vector & r-vector | Python & C++ & PyTorch | WeSpeaker is a research and production oriented speaker verification, recognition and diarization toolkit, which supports very strong recipes with on-the-fly data preparation, model training and evaluation, as well as runtime C++ codes. |

### Speaker change detection

| Link  | Language | Description |

| ----  | -------- | ----------- |

| [change_detection](https://github.com/yinruiqing/change_detection) ![GitHub stars](https://img.shields.io/github/stars/yinruiqing/change_detection?style=social) | Python & Keras | Code for Speaker Change Detection in Broadcast TV using Bidirectional Long Short-Term Memory Networks. |

### Audio feature extraction

| Link  | Language | Description |

| ----  | -------- | ----------- |

| [LibROSA](https://github.com/librosa/librosa) ![GitHub stars](https://img.shields.io/github/stars/librosa/librosa?style=social) | Python | Python library for audio and music analysis. https://librosa.github.io/ |

| [python_speech_features](https://github.com/jameslyons/python_speech_features) ![GitHub stars](https://img.shields.io/github/stars/jameslyons/python_speech_features?style=social) | Python | This library provides common speech features for ASR including MFCCs and filterbank energies. https://python-speech-features.readthedocs.io/en/latest/ |

| [pyAudioAnalysis](https://github.com/tyiannak/pyAudioAnalysis) ![GitHub stars](https://img.shields.io/github/stars/tyiannak/pyAudioAnalysis?style=social) | Python | Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications. |

### Audio data augmentation

| Link  | Language | Description |

| ----  | -------- | ----------- |

| [pyroomacoustics](https://github.com/LCAV/pyroomacoustics) ![GitHub stars](https://img.shields.io/github/stars/LCAV/pyroomacoustics?style=social) | Python | Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios. https://pyroomacoustics.readthedocs.io |

| [gpuRIR](https://github.com/DavidDiazGuerra/gpuRIR) ![GitHub stars](https://img.shields.io/github/stars/DavidDiazGuerra/gpuRIR?style=social) | Python | Python library for Room Impulse Response (RIR) simulation with GPU acceleration |

| [rir_simulator_python](https://github.com/sunits/rir_simulator_python) ![GitHub stars](https://img.shields.io/github/stars/sunits/rir_simulator_python?style=social) | Python | Room impulse response simulator using python |

| [WavAugment](https://github.com/facebookresearch/WavAugment) ![GitHub stars](https://img.shields.io/github/stars/facebookresearch/WavAugment?style=social) | Python & PyTorch | WavAugment performs data augmentation on audio data. The audio data is represented as pytorch tensors |

| [EEND_dataprep](https://github.com/BUTSpeechFIT/EEND_dataprep) ![GitHub stars](https://img.shields.io/github/stars/BUTSpeechFIT/EEND_dataprep?style=social) | Bash & Python | Recipes for generating [simulated conversations](https://arxiv.org/abs/2204.00890) used to train end-to-end diarization models. |

### Other software

| Link | Language | Description |

| ---- | -------- | ----------- |

| [VB Diarization](https://github.com/wq2012/VB_diarization) ![GitHub stars](https://img.shields.io/github/stars/wq2012/VB_diarization?style=social) [![Build Status](https://travis-ci.org/wq2012/VB_diarization.svg?branch=master)](https://travis-ci.org/wq2012/VB_diarization) | Python | VB Diarization with Eigenvoice and HMM Priors. |

| [DOVER-Lap](https://github.com/desh2608/dover-lap) ![GitHub stars](https://img.shields.io/github/stars/desh2608/dover-lap?style=social) | Python | Python package for combining diarization system outputs |

## Datasets

### Diarization datasets

| Audio | Diarization ground truth | Language | Pricing | Additional information |

| ----- | ------------------------ | -------- | ------- | ---------------------- |

| [2000 NIST Speaker Recognition Evaluation](https://catalog.ldc.upenn.edu/LDC2001S97) | [Disk-6 (Switchboard)](https://github.com/google/speaker-id/tree/master/publications/LstmDiarization/evaluation/NIST_SRE2000/Disk6_ground_truth), [Disk-8  (CALLHOME)](https://github.com/google/speaker-id/tree/master/publications/LstmDiarization/evaluation/NIST_SRE2000/Disk8_ground_truth) | Multiple | $2400.00 | [Evaluation Plan](https://www.nist.gov/sites/default/files/documents/2017/09/26/spk-2000-plan-v1.0.htm_.pdf) |

| [2003 NIST Rich Transcription Evaluation Data](https://catalog.ldc.upenn.edu/LDC2007S10) | Together with audios | en, ar, zh | $2000.00 | telephone speech, broadcast news |

| [CALLHOME American English Speech](https://catalog.ldc.upenn.edu/LDC97S42) | [CALLHOME American English Transcripts](https://catalog.ldc.upenn.edu/LDC97T14) | en | $1500.00 + $1000.00| [CH109 whitelist](https://github.com/google/speaker-id/blob/master/publications/LstmDiarization/evaluation/CALLHOME_American_English/ch109_whitelist.txt) |

| [The ICSI Meeting Corpus](http://groups.inf.ed.ac.uk/ami/icsi/) | Together with audios | en | Free | [License](http://groups.inf.ed.ac.uk/ami/icsi/license.shtml) |

| [The AMI Meeting Corpus](http://groups.inf.ed.ac.uk/ami/corpus/) | Together with audios (need to be processed) | Multiple | Free | [License](http://groups.inf.ed.ac.uk/ami/corpus/license.shtml) |

| [Fisher English Training Speech Part 1 Speech](https://catalog.ldc.upenn.edu/LDC2004S13) | [Fisher English Training Speech Part 1 Transcripts](https://catalog.ldc.upenn.edu/LDC2004T19)| en | $7000.00 + $1000.00 |

| [Fisher English Training Part 2, Speech](https://catalog.ldc.upenn.edu/LDC2005S13) | [Fisher English Training Part 2, Transcripts](https://catalog.ldc.upenn.edu/LDC2005T19) | en | $7000.00 + $1000.00 |

| [VoxConverse](https://github.com/joonson/voxconverse) | TBD | TBD | Free | VoxConverse is an audio-visual diarisation dataset consisting of over 50 hours of multispeaker clips of human speech, extracted from YouTube videos |

| [MiniVox Benchmark](https://github.com/doerlbh/MiniVox) | [MiniVox Benchmark](https://github.com/doerlbh/MiniVox) | en | Free | MiniVox is an automatic framework to transform any speaker-labelled dataset into continuous speech datastream with episodically revealed label feedbacks. |

| [The AliMeeting Corpus](https://github.com/yufan-aslp/AliMeeting) | Together with audios | zh | Free |  |

### Speaker embedding training sets

| Name | Utterances | Speakers | Language | Pricing | Additional information |

| ---- | ---------- | -------- | -------- | ------- | ---------------------- |

| [TIMIT](https://catalog.ldc.upenn.edu/LDC93S1) | 6K+ | 630 | en | $250.00 | Published in 1993, the TIMIT corpus of read speech is one of the earliest speaker recognition datasets. |

| [VCTK](https://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html) | 43K+ | 109 | en | Free | Most were selected from a newspaper plus the Rainbow Passage and an elicitation paragraph intended to identify the speaker's accent. |

| [LibriSpeech](http://www.openslr.org/12) | 292K | 2K+ | en | Free | Large-scale (1000 hours) corpus of read English speech. |

| [Multilingual LibriSpeech (MLS)](http://openslr.org/94/) | ? | ? | en, de, nl, es, fr, it, pt, po | Free | Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. |

| [LibriVox](https://librivox.org/) | 180K | 9K+ | Multiple | Free | Free public domain audiobooks. LibriSpeech is a processed subset of LibriVox. Each original unsegmented utterance could be very long. |

| [VoxCeleb 1&2](http://www.robots.ox.ac.uk/~vgg/data/voxceleb/) | 1M+ | 7K | Multiple | Free | VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube. |

| [The Spoken Wikipedia Corpora](https://nats.gitlab.io/swc/) | 5K | 879 | en, de, nl | Free | Volunteer readers reading Wikipedia articles. |

| [CN-Celeb](http://www.openslr.org/82/) | 130K+ | 1K | zh | Free | A Free Chinese Speaker Recognition Corpus Released by CSLT@Tsinghua University. |

| [BookTubeSpeech](https://users.wpi.edu/~jrwhitehill/BookTubeSpeech/index.html) | 8K | 8K | en | Free | Audio samples extracted from BookTube videos - videos where people share their opinions on books - from YouTube. The dataset can be downloaded using [BookTubeSpeech-download](https://github.com/wq2012/BookTubeSpeech-download). |

| [DeepMine](http://data.deepmine.ir/en/index.html) | 540K | 1850 | fa, en | Unknown | A speech database in Persian and English designed to build and evaluate speaker verification, as well as Persian ASR systems. |

| [NISP-Dataset](https://github.com/iiscleap/NISP-Dataset) | ? | 345 | hi, kn, ml, ta, te (all Indian languages) | Free | This dataset contains speech recordings along with speaker physical parameters (height, weight, ... ) as well as regional information and linguistic information. |

### Augmentation noise sources

| Name | Utterances | Pricing | Additional information |

| ---- | ---------- | ------- | ---------------------- |

| [AudioSet](https://research.google.com/audioset/) | 2M | Free | A large-scale dataset of manually annotated audio events. |

| [MUSAN](https://www.openslr.org/17/) | N/A | Free | MUSAN is a corpus of music, speech, and noise recordings. |

## Conferences

| Conference/Workshop | Frequency | Page Limit  | Organization | Blind Review |

| ------------------- | --------- | ----------  | ------------ | ------------ |

| ICASSP              | Annual    | 4 + 1 (ref) | IEEE         | No           |

| InterSpeech         | Annual    | 4 + 1 (ref) | ISCA         | No           |

| Speaker Odyssey     | Biennial  | 8 + 2 (ref) | ISCA         | No           |

| SLT                 | Biennial  | 6 + 2 (ref) | IEEE         | Yes          |

| ASRU                | Biennial  | 6 + 2 (ref) | IEEE         | Yes          |

| WASPAA              | Biennial  | 4 + 1 (ref) | IEEE         | No           |

| IJCB                | Annual    | 8       | IEEE & IAPR TC-4 | Yes          |

## Other learning materials

### Online courses

* Course on Udemy: [A Tutorial on Speaker Diarization](https://www.udemy.com/course/diarization/?referralCode=21D7CC0AEABB7FE3680F)

### Books

* [Voice Identity Techniques: From core algorithms to engineering practice (Chinese)](https://github.com/wq2012/VoiceIdentityBook) by Quan Wang, 2020

### Tech blogs

* [Literature Review For Speaker Change Detection](https://hedonistrh.github.io/2018-07-09-Literature-Review-for-Speaker-Change-Detection/)

  by [Halil Erdoğan](https://github.com/hedonistrh)

* [Speaker Diarization: Separation of Multiple Speakers in an Audio File](https://medium.com/datadriveninvestor/speaker-diarization-22121f1264b1) by [Jaspreet Singh](https://medium.com/@jaspreetuseducation)

* [Speaker Diarization with Kaldi](https://towardsdatascience.com/speaker-diarization-with-kaldi-e30301b05cc8) by [Yoav Ramon](https://towardsdatascience.com/@yoavramon)

* [Who spoke when! How to Build your own Speaker Diarization Module](https://medium.com/saarthi-ai/who-spoke-when-build-your-own-speaker-diarization-module-from-scratch-e7d725ee279) by Rahul Saxena

### Video tutorials

* [pyannote audio: neural building blocks for speaker diarization](https://www.youtube.com/watch?v=37R_R82lfwA) by Hervé Bredin

* [Google's Diarization System: Speaker Diarization with LSTM](https://www.youtube.com/watch?v=pjxGPZQeeO4) by Google

* [Fully Supervised Speaker Diarization: Say Goodbye to clustering](https://www.youtube.com/watch?v=pGkqwRPzx9U) by Google

* [Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection](https://www.youtube.com/watch?v=U79Aw1ky7ag) by Google

* [Speaker Diarization: Optimal Clustering and Learning Speaker Embeddings](https://www.youtube.com/watch?v=vcyB8xb1-ys) by Microsoft Research

* [Robust Speaker Diarization for Meetings: the ICSI system](https://www.youtube.com/watch?v=kEcUcfLmIS0) by Microsoft Research

* [【机器之心&博文视点】入门声纹技术｜第二讲：声纹分割聚类与其他应用](https://www.youtube.com/watch?v=HE9JW8yKYRk) by Quan Wang

## Products

| Company | Product                                                                                                                                      |

| ------- |----------------------------------------------------------------------------------------------------------------------------------------------|

| Google  | [Recorder app](https://support.google.com/pixelphone?p=recorder_speaker_labels)                                                              |

| Google  | [Google Cloud Speech-to-Text API](https://cloud.google.com/speech-to-text/docs/multiple-voices)                                              |

| Amazon  | [Amazon Transcribe](https://aws.amazon.com/transcribe)                                                                                       |

| IBM     | [Watson Speech To Text API](https://www.ibm.com/watson/services/speech-to-text)                                                              |

| DeepAffects | [Speaker Diarization API](https://www.deepaffects.com/diarization-api)                                                                       |

| Alibaba | [Tingwu (听悟)](https://tingwu.aliyuncs.com/trans)                                                                                             |

| Microsoft | [Azure Conversation Transcription API](https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/conversation-transcription) |

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