The official implementation of Theme Transformer

Ian Shih

Last update: Dec 8, 2022

Related tags

Overview

Theme Transformer

This is the official implementation of Theme Transformer.

Checkout our demo and paper : Demo | arXiv

Environment:

using python version 3.6.8
install python dependencies:

pip install -r requirements.txt

To train the model with GPU:

python train.py --cuda

To generate music from theme

python inference.py --cuda --theme <theme midi file> --out_midi <output midi file>

Details of the files in this repo

.
├── ckpts                   For saving checkpoints while training
├── data_pkl                Stores train and val data
│   ├── train_seg2_512.pkl
│   └── val_seg2_512.pkl
├── inference.py            For generating music. (Detailed usage are written in the file)
├── logger.py               For logging
├── mymodel.py              The overal Theme Transformer Architecture
├── myTransformer.py        Our transformer revision code 
├── parse_arg.py            Some arguments for training
├── preprocess              For data preprocessing  
│   ├── music_data.py       Theme Transformer pytorch dataset definition
│   └── vocab.py            Our vocabulary for transformer
├── randomness.py           For fixing random seed
├── readme.txt              Readme
├── tempo_dict.json         The original tempo information from POP909 (used in inference time)
├── theme_files/            The themes from our testing set.
├── trained_model           The model we trained.
│   └── model_ep2311.pt
└── train.py                Code for training Theme Transformer

Citation

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

@article{shih2021theme,
      title={Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer}, 
      author={Yi-Jen Shih and Shih-Lun Wu and Frank Zalkow and Meinard Müller and Yi-Hsuan Yang},
      year={2021},
      eprint={2111.04093},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

Comments

data issue

Hi Ian Shih, Thank for your amazing job. I would like to train this model using my data, could you tell my how I make the data like your 'train_seg2_512.pkl' data? I will appreciate that if I can your reply.

Thanks.
documentation

opened by meadow163 6
A question about input dimensions

Hi Ian Shih, Thank for your amazing job. In my memories，the input dimesions of Transformer is [batch_size, seq_len,d_model]. So I wonder that why your input dimesions of model is seq_len, batch_size, d_model? I will appreciate that if I can your reply.

Thanks.
question

opened by DaiZhenrong 5

Install requirements fails

Running pip install -r requirements.txt from root fails with

Collecting cycler==0.10.0
  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)
ERROR: Could not find a version that satisfies the requirement dataclasses==0.8 (from versions: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6)
ERROR: No matching distribution found for dataclasses==0.8

Is the version pinning required for training the model? If not, using >= would be better.

Thanks!

bug

opened by afrozas 3

Data processing problems

Hello, thank you very much for your work

I'm trying to use my own data to train your model, but because my MIDI data is single track, I want to know how you get the boundary_ track，melody_ boundary_tracks, theme info tracks，are they added manually？

thank you!

opened by dedededefo 2
MIDI data problems and training problems

Thank you for your excellent work! I have a few questions to ask you. How can I convert my own MIDI data into input suitable for the model? I haven't found a solution in your article. And can I use my own data to train a new model? What should I do?

opened by dedededefo 2
How to generate training pkls and theme files from POP909

Hello, your work is so brilliant and I've completed the inference process for some provided theme files (e.g., 875_theme.mid). But I feel a little confused how to generate the similar theme file from POP909 and how to obtain the training pkl files (train_seg2_512.pkl & val_seg2_512.pkl) from scratch. If possible, could you pls share some related ideas or scripts? Thank you~~
documentation question

opened by 2000222 2
Data process in prepareDataPkl.py
Hi, Ian! Thank you for your excellent job. I want to process my data from *.midi to *.pkl using your code prepareDataPkl.py lately, and maybe I found some little errors in it. in line 20 - 25

all_mids = sorted(glob.glob(MIDI_FILES)) for _midiFile in all_midis: # convert midi files to token representation and save as .pkl file output_pkl_fp = midifp.replace(".mid",".pkl") remi_seq = myvocab.midi2REMI(midifp,include_bridge=False,bar_first=False,verbose=False)

maybe it's for _midiFile in all_mids: and output_pkl_fp = _midifile.replace(".mid",".pkl")? And in line 40 and line 45, the definition of training_data is missing.
opened by ZZDoog 1

Owner

Ian Shih

GitHub https://atosystem.github.io/ThemeTransformer/

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The official implementation of Theme Transformer

Related tags

Overview

Theme Transformer

Environment:

To train the model with GPU:

To generate music from theme

Details of the files in this repo

Citation

Comments

data issue

A question about input dimensions

Install requirements fails

Data processing problems

MIDI data problems and training problems

How to generate training pkls and theme files from POP909

Data process in prepareDataPkl.py

Owner

Ian Shih

Code of the lileonardo team for the 2021 Emotion and Theme Recognition in Music task of MediaEval 2021

Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

The implementation of "Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer"

Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"

This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

This is an official implementation for "ResT: An Efficient Transformer for Visual Recognition".

Aggragrating Nested Transformer Official Jax Implementation

Official implementation of Long-Short Transformer in PyTorch.

Official implementation of paper "Query2Label: A Simple Transformer Way to Multi-Label Classification".

Official implementation of the paper Vision Transformer with Progressive Sampling, ICCV 2021.

This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object Tracking with TRansformer.

Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation

Official implementation for ICDAR 2021 paper "Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer"

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