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2021-ismir-mss-challenge-cws-presunet

Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.

adjustable-real-time-style-transfer

adjustautocorrelation

Adjusting for Autocorrelated Errors in Neural Networks for Time Series

afrcnn-for-speech-separation

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

asteroid

The PyTorch-based audio source separation toolkit for researchers

attentionaugmentedconvlstm

Implementation of TAAConvLSTM and SAAConvLSTM used in "Attention Augmented ConvLSTM for Environment Prediction"

audiogpt

AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head

awesome-large-audio-models

Collection of resources on the applications of Large Language Models (LLMs) in Audio AI.

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.

awesome-speech-enhancement-1

speech enhancement\speech seperation\sound source localization

bs-roformer

Implementation of Band Split Roformer, SOTA Attention network for music source separation out of ByteDance AI Labs

cdiffuse

Conditional Diffusion Probabilistic Model for Speech Enhancement

chinese_speech_pretrain

chinese speech pretrained models

citisen

clarity

Clarity Challenge toolkit - software for building Clarity Challenge systems

cleanunet

Official PyTorch Implementation of CleanUNet (ICASSP 2022)

cmgan

Conformer-based Metric GAN for speech enhancement

cntk

Computational Network Toolkit (CNTK)

cntkx

Deep learning library that builds on and extends Microsoft CNTK

complex_networks

Contain some activations, dropout, BN, LN, Linear, Conv, ConvTranspose, LSTM(LN)

complexpytorch

A high-level toolbox for using complex valued neural networks in PyTorch

conformer

PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)

connear_cochlea

crowdsourcing_design_for_enhancement

cruse

a lightweight network for monaural speech enhancement

deep_complex_networks

Implementation related to the Deep Complex Networks

deepcomplexcrn

deepfilternet

Noise supression using deep filtering

denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.

discretespeechmetrics

Reference-aware automatic speech evaluation toolkit