speech-separation

#计算机科学#A PyTorch-based Speech Toolkit

翻译 - 基于Pytorch的语音工具包

#计算机科学#End-to-End Speech Processing Toolkit

翻译 - 端到端语音处理工具包

#计算机科学#An AI-Powered Speech Processing Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Enhancement, Separation, and Target Speaker Extraction, etc.

#计算机科学#The PyTorch-based audio source separation toolkit for researchers

翻译 - 针对研究人员的基于PyTorch的音频源分离工具包||提供预训练的模型

💎 A list of accessible speech corpora for ASR, TTS, and other Speech Technologies

Unofficial PyTorch implementation of Google AI's VoiceFilter system

翻译 - Google AI的VoiceFilter系统的非官方PyTorch实施

A must-read paper for speech separation based on neural networks

A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).

PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

#计算机科学#This repo summarizes the tutorials, datasets, papers, codes and tools for speech separation and speaker extraction task. You are kindly invited to pull requests.

#计算机科学#Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement

UniSpeech - Large Scale Self-Supervised Learning for Speech

#计算机科学#Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation implemented by Pytorch

#计算机科学#The dataset of Speech Recognition

Tools for Speech Enhancement integrated with Kaldi

#计算机科学#Deep Recurrent Neural Networks for Source Separation

#计算机科学#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 a...

#计算机科学#Real-time GCC-NMF Blind Speech Separation and Enhancement

#计算机科学#Deep learning based speech source separation using Pytorch