The official implementation of the Interspeech 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution.

Related tags

Deep LearningWSRGlow
Overview

WSRGlow

The official implementation of the Interspeech 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution. Audio samples can be found here.

Feel free to create issues or send an email to [email protected] if you have problems running the code.

Before running the code, you need to install the dependicies by pip install -r requirements.txt.

The configs for model architecture and training scheme is saved in config.yaml. You can overwrite some of the attributes by adding the --hparams flag when running a command. The general way to run a python script is

python $SRC$ --config $CONFIG$ --hparams $KEY1$=$VALUE1$,$KEY2$=$VALUE2$,...

See hparams.py for more details.

To prepare data

Before training, you need to binarize the data first. The raw wav files should be put in the hparams['raw_data_path']. The binarized data would be put in the hparams['binary_data_path'].

Specifically, for the VCTK corpus, the file structure should be like

.
|--data
    |--raw
        |--VCTK-Corpus
            |--wav48
                |--$WAVS
|--checkpoints
    |--wsrglow
    

where the model checkpoints are in checkpoints/wsrglow.

The command to binarize is

python binarizer.py --config config.yaml

To modify the architecture of the model

The current WSRGlow model in model.py is designed for x4 super-resolution and takes waveform, spectrogram and phase information as input.

To train

Run python train.py --config config.yaml on a GPU.

To infer

Change the code in infer.py to specify the checkpoint you want to load and the sample inputs you want to use for inference. Run python infer.py --config config.yaml on a GPU, modify the code for the correct path of checkpoints and wav files.

Owner
Kexun Zhang
Interested in linguistics. Former participant in programming contests.
Kexun Zhang
Pytorch implementation of the DeepDream computer vision algorithm

deep-dream-in-pytorch Pytorch (https://github.com/pytorch/pytorch) implementation of the deep dream (https://en.wikipedia.org/wiki/DeepDream) computer

102 Dec 05, 2022
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation (CVPR 2022)

CCAM (Unsupervised) Code repository for our paper "CCAM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localizati

Computer Vision Insitute, SZU 113 Dec 27, 2022
Fast methods to work with hydro- and topography data in pure Python.

PyFlwDir Intro PyFlwDir contains a series of methods to work with gridded DEM and flow direction datasets, which are key to many workflows in many ear

Deltares 27 Dec 07, 2022
Label Mask for Multi-label Classification

LM-MLC 一种基于完型填空的多标签分类算法 1 前言 本文主要介绍本人在全球人工智能技术创新大赛【赛道一】设计的一种基于完型填空(模板)的多标签分类算法:LM-MLC,该算法拟合能力很强能感知标签关联性,在多个数据集上测试表明该算法与主流算法无显著性差异,在该比赛数据集上的dev效果很好,但是由

52 Nov 20, 2022
Trying to understand alias-free-gan.

alias-free-gan-explanation Trying to understand alias-free-gan in my own way. [Chinese Version 中文版本] CC-BY-4.0 License. Tzu-Heng Lin motivation of thi

Tzu-Heng Lin 12 Mar 17, 2022
KE-Dialogue: Injecting knowledge graph into a fully end-to-end dialogue system.

Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems This is the implementation of the paper: Learning Knowledge Bases with Par

CAiRE 42 Nov 10, 2022
Addition of pseudotorsion caclulation eta, theta, eta', and theta' to barnaba package

Addition to Original Barnaba Code: This is modified version of Barnaba package to calculate RNA pseudotorsion angles eta, theta, eta', and theta'. Ple

Mandar Kulkarni 1 Jan 11, 2022
Learning Compatible Embeddings, ICCV 2021

LCE Learning Compatible Embeddings, ICCV 2021 by Qiang Meng, Chixiang Zhang, Xiaoqiang Xu and Feng Zhou Paper: Arxiv We cannot release source codes pu

Qiang Meng 25 Dec 17, 2022
ICCV2021 Expert-Goal Trajectory Prediction

ICCV 2021: Where are you heading? Dynamic Trajectory Prediction with Expert Goal Examples This repository contains the code for the paper Where are yo

hz 21 Dec 12, 2022
An unreferenced image captioning metric (ACL-21)

UMIC This repository provides an unferenced image captioning metric from our ACL 2021 paper UMIC: An Unreferenced Metric for Image Captioning via Cont

hwanheelee 14 Nov 20, 2022
Official code for Score-Based Generative Modeling through Stochastic Differential Equations

Score-Based Generative Modeling through Stochastic Differential Equations This repo contains the official implementation for the paper Score-Based Gen

Yang Song 818 Jan 06, 2023
Collective Multi-type Entity Alignment Between Knowledge Graphs (WWW'20)

CG-MuAlign A reference implementation for "Collective Multi-type Entity Alignment Between Knowledge Graphs", published in WWW 2020. If you find our pa

Bran Zhu 28 Dec 11, 2022
CondNet: Conditional Classifier for Scene Segmentation

CondNet: Conditional Classifier for Scene Segmentation Introduction The fully convolutional network (FCN) has achieved tremendous success in dense vis

ycszen 31 Jul 22, 2022
An Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering

PC-SOS-SDP: an Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering PC-SOS-SDP is an exact algorithm based on the branch-and-bound techn

Antonio M. Sudoso 1 Nov 13, 2022
Toolbox to analyze temporal context invariance of deep neural networks

PyTCI A toolbox that estimates the integration window of a sensory response using the "Temporal Context Invariance" paradigm (TCI). The TCI method Int

4 Oct 23, 2022
OpenLT: An open-source project for long-tail classification

OpenLT: An open-source project for long-tail classification Supported Methods for Long-tailed Recognition: Cross-Entropy Loss Focal Loss (ICCV'17) Cla

Ming Li 37 Sep 15, 2022
Official implementation of "Learning Not to Reconstruct" (BMVC 2021)

Official PyTorch implementation of "Learning Not to Reconstruct Anomalies" This is the implementation of the paper "Learning Not to Reconstruct Anomal

Marcella Astrid 13 Dec 04, 2022
The pure and clear PyTorch Distributed Training Framework.

The pure and clear PyTorch Distributed Training Framework. Introduction Requirements and Usage Dependency Dataset Basic Usage Slurm Cluster Usage Base

WILL LEE 208 Dec 20, 2022
DTCN SMP Challenge - Sequential prediction learning framework and algorithm

DTCN This is the implementation of our paper "Sequential Prediction of Social Me

Bobby 2 Jan 24, 2022
Deep Crop Rotation

Deep Crop Rotation Paper (to come very soon!) We propose a deep learning approach to modelling both inter- and intra-annual patterns for parcel classi

Félix Quinton 5 Sep 23, 2022