PyTorch implementation of MuseMorphose, a Transformer-based model for music style transfer.

Overview

MuseMorphose

This repository contains the official implementation of the following paper:

Shih-Lun Wu, Yi-Hsuan Yang
MuseMorphose: Full-Song and Fine-Grained Music Style Transfer with One Transformer VAE
ArXiv preprint, May 2021 [arXiv] [demo website]

Prerequisites

Python >= 3.6
Install dependencies

pip3 install -r requirements.txt

GPU with >6GB RAM (optional, but recommended)

Preprocessing

# download REMI-pop-1.7K dataset
wget -O remi_dataset.tar.gz https://zenodo.org/record/4782721/files/remi_dataset.tar.gz?download=1
tar xzvf remi_dataset.tar.gz
rm remi_dataset.tar.gz

# compute attributes classes
python3 attributes.py

Training

python3 train.py [config file]

e.g.

python3 train.py config/default.yaml

Or, you may download the pretrained weights straight away

wget -O musemorphose_pretrained_weights.pt https://zenodo.org/record/5119525/files/musemorphose_pretrained_weights.pt?download=1

Generation

python3 generate.py [config file] [ckpt path] [output dir] [num pieces] [num samples per piece]

e.g.

python3 generate.py config/default.yaml musemorphose_pretrained_weights.pt generations/ 10 5

This script will randomly draw the specified # of pieces from the test set.
For each sample of a piece, the rhythmic intensity and polyphonicity will be shifted entirely and randomly by [-3, 3] classes for the model to generate style-transferred music.
You may modify random_shift_attr_cls() in generate.py or write your own function to set the attributes.

Customized Generation (To Be Added)

We welcome the community's suggestions and contributions for an interface on which users may

upload their own MIDIs, and
set their desired bar-level attributes easily

Citation BibTex

If you find this work helpful and use our code in your research, please kindly cite our paper:

@article{musemorphose21arxiv,
    title={{MuseMorphose}: Full-Song and Fine-Grained Music Style Transfer with One {Transformer VAE}},
    author={Shih-Lun Wu and Yi-Hsuan Yang},
    year={2021},
    journal={arXiv preprint arXiv:2105.04090},
}

You might also like...

Instant Real-Time Example-Based Style Transfer to Facial Videos

FaceBlit: Instant Real-Time Example-Based Style Transfer to Facial Videos The official implementation of FaceBlit: Instant Real-Time Example-Based Sty

131 Dec 19, 2022

StyleTransfer - Open source style transfer project, based on VGG19

9 Dec 13, 2021

Neural style transfer in PyTorch.

style-transfer-pytorch An implementation of neural style transfer (A Neural Algorithm of Artistic Style) in PyTorch, supporting CPUs and Nvidia GPUs.

395 Jan 6, 2023

Neural style transfer as a class in PyTorch

pt-styletransfer Neural style transfer as a class in PyTorch Based on: https://github.com/alexis-jacq/Pytorch-Tutorials Adds: StyleTransferNet as a cl

31 Jun 27, 2022

This is the official Pytorch implementation of the paper "Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model"

Diverse Motion Stylization (Official) This is the official Pytorch implementation of this paper. Diverse Motion Stylization for Multiple Style Domains

28 Dec 16, 2022

Comments

'scipy' is missing

'scipy' is required while computing attributes classes

Traceback (most recent call last):
  File "/home/tanch/workspace/MuseMorphose/attributes.py", line 1, in <module>
    from utils import pickle_load
  File "/home/tanch/workspace/MuseMorphose/utils.py", line 3, in <module>
    from scipy.spatial import distance
ModuleNotFoundError: No module named 'scipy'

opened by tanchihpin0517 1

Generation for arbitrary length

Hello,

Maybe you could help me with my problem)When i run your generation.py script with defaults parameters it generates correctly 16 bars output for any midi file. But when i tried to change the length from 1280 to length of my midi-file (11134) and 16 bars to 76 bars the model output the mistake. Do you know how to fix it? Thank you in advance!

[FATAL] model stuck, exiting ... Traceback (most recent call last): File "generate.py", line 299, in temperature=config['generate']['temperature'], ValueError: too many values to unpack (expected 3)

opened by SashaBurashnikova 14

PyTorch implementation of MuseMorphose, a Transformer-based model for music style transfer.

Related tags

Overview

MuseMorphose

Prerequisites

Preprocessing

Training

Generation

Customized Generation (To Be Added)

Citation BibTex

You might also like...

Instant Real-Time Example-Based Style Transfer to Facial Videos

StyleTransfer - Open source style transfer project, based on VGG19

Neural style transfer in PyTorch.

Neural style transfer as a class in PyTorch

This is the official Pytorch implementation of the paper "Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model"

PyTorch implementation of ECCV 2020 paper "Foley Music: Learning to Generate Music from Videos "

An implementation of "Optimal Textures: Fast and Robust Texture Synthesis and Style Transfer through Optimal Transport"

Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer"

Official Implementation of Domain-Aware Universal Style Transfer

Comments

'scipy' is missing

Generation for arbitrary length

Owner

Yating Music, Taiwan AI Labs

Only a Matter of Style: Age Transformation Using a Style-Based Regression Model

PyTorch implementation of neural style transfer algorithm

Official PyTorch implementation of "ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows"

Pytorch implementation of Nueral Style transfer

Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

PyTorch implementation of paper: AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer, ICCV 2021.

Implementation of Neural Style Transfer in Pytorch

Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization'

Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Fast Neural Style for Image Style Transform by Pytorch