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.

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.

Official repository for RawNet, RawNet2, and RawNet3

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