speech-enhancement
There are 217 repositories under speech-enhancement topic.
speechbrain/speechbrain
A PyTorch-based Speech Toolkit
espnet/espnet
End-to-End Speech Processing Toolkit
asteroid-team/asteroid
The PyTorch-based audio source separation toolkit for researchers
Rikorose/DeepFilterNet
Noise supression using deep filtering
resemble-ai/resemble-enhance
AI powered speech denoising and enhancement
haoheliu/voicefixer
General Speech Restoration
JusperLee/Speech-Separation-Paper-Tutorial
A must-read paper for speech separation based on neural networks
nanahou/Awesome-Speech-Enhancement
A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.
k2kobayashi/sprocket
Voice Conversion Tool Kit
breizhn/DTLN
Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.
Audio-WestlakeU/FullSubNet
PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."
anicolson/DeepXi
Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.
funcwj/setk
Tools for Speech Enhancement integrated with Kaldi
double22a/speech_dataset
The dataset of Speech Recognition
schmiph2/pysepm
Python implementation of performance metrics in Loizou's Speech Enhancement book
yongxuUSTC/sednn
deep learning based speech enhancement using keras or pytorch, make it easy to use
shahules786/mayavoz
Pytorch based speech enhancement toolkit.
haoxiangsnr/Wave-U-Net-for-Speech-Enhancement
Implement Wave-U-Net by PyTorch, and migrate it to the speech enhancement.
seanwood/gcc-nmf
Real-time GCC-NMF Blind Speech Separation and Enhancement
jzi040941/PercepNet
Unofficial implementation of PercepNet: A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech
aishoot/LSTM_PIT_Speech_Separation
Two-talker Speech Separation with LSTM/BLSTM by Permutation Invariant Training method.
haoxiangsnr/A-Convolutional-Recurrent-Neural-Network-for-Real-Time-Speech-Enhancement
A minimum unofficial implementation of the "A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement" (CRN) using PyTorch
haoheliu/voicefixer_main
General Speech Restoration
fgnt/pb_bss
Collection of EM algorithms for blind source separation of audio signals
yxlu-0102/MP-SENet
MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra
ictnlp/StreamSpeech
StreamSpeech is an “All in One” seamless model for offline and simultaneous speech recognition, speech translation and speech synthesis.
huyanxin/phasen
A unofficial Pytorch implementation of Microsoft's PHASEN
AkojimaSLP/Beamforming-for-speech-enhancement
simple delaysum, MVDR and CGMM-MVDR
jtkim-kaist/Speech-enhancement
Deep neural network based speech enhancement toolkit
sekiguchi92/SoundSourceSeparation
The code for multi-channel source separation and dereverberation such as FastMNMF1, FastMNMF2, and AR-FastMNMF2.
echocatzh/MTFAA-Net
Multi-Scale Temporal Frequency Convolutional Network With Axial Attention for Speech Enhancement
madhavmk/Noise2Noise-audio_denoising_without_clean_training_data
Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples.
james34602/SpleeterRT
Real time monaural source separation base on fully convolutional neural network operates on Time-frequency domain.
helianvine/fdndlp
A speech dereverberation algorithm, also called wpe
funcwj/aps
A personal toolkit for single/multi-channel speech recognition & enhancement & separation.
KyleZhang1118/Voice-Separation-and-Enhancement
A framework for quick testing and comparing multi-channel speech enhancement and separation methods, such as DSB, MVDR, LCMV, GEVD beamforming and ICA, FastICA, IVA, AuxIVA, OverIVA, ILRMA, FastMNMF.