kaldi-asr/kaldi is the official location of the Kaldi project.

A PyTorch-based Speech Toolkit

Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node

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

Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)

DELTA is a deep learning based natural language and speech processing platform. LF AI & DATA Projects: https://lfaidata.foundation/projects/delta/

A Repository for Single- and Multi-modal Speaker Verification, Speaker Recognition and Speaker Diarization

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

In defence of metric learning for speaker recognition

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)

Deep learning for audio processing

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

UniSpeech - Large Scale Self-Supervised Learning for Speech

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

Official repository for RawNet, RawNet2, and RawNet3

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.

Tensorflow implementation of "Generalized End-to-End Loss for Speaker Verification"

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

Speaker embedding (d-vector) trained with GE2E loss