kaldi-asr/kaldi is the official location of the Kaldi project.
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Updated
Oct 4, 2024 - Shell
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.
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
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