metadata

license: mit
pipeline_tag: text-generation
widget:
  - text: ''
tags:
  - music

TunesFormer

Model description

TunesFormer is a Transformer-based melody generation system trained on 285,449 melodies with musical forms (represented by control codes), where all scores are represented in ABC notation. It was introduced in the paper TunesFormer: Forming Tunes with Control Codes by Wu et al. The code is released in this repository, and the dataset is released in huggingface.

By utilizing specific symbols commonly found in ABC notation to indicate section boundaries, TunesFormer can understand and generate melodies with given musical forms based on control codes. The checkpoint released here is TunesFormer-GP (Global Placement), where all the control codes are placed at the beginning of the ABC notation.

Intended uses & limitations

You can use this model for melody generation conditioned on musical forms. All scores generated by this model can be written on one stave (for vocal solo or instrumental solo) in standard classical notation, and are in a variety of styles, e.g., blues, classical, folk, jazz, pop, and world music. The generated tunes are in ABC notation, and can be converted to sheet music or audio using this website, or this software.

TunesFormer supports the generation of up to 8 sections, and up to 32 bars per section. In addition, although TunesFormer mostly generates music correctly according to the control codes, due to the random nature of sampling, the musical structure generated by the model occasionally does not match that specified by the control codes when more than 6 sections are generated, or when more than 17 bars are generated for a single section. For more information, please check our paper.

How to use

Install dependencies for the code released in this repository:

torch                        1.9.1+cu111
samplings                    0.1.7
transformers                 4.18.0

Set the control_codes and prompt in the script run_inference.py for conditional music generation.

control_codes = "[SECS_3][BARS_4][SIM_6][BARS_4][SIM_10][SIM_6][BARS_4]"
prompt = """L:1/4
M:4/4
K:C
"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 ||"""

For TunesFormer, the input is a concatenation of control_codes and prompt. Both control_codes and prompt are optional. However, if you need to set the prompt, you must set the control codes.

Run the script run_inference.py. When running a script for the first time, the downloaded files will be cached for future reuse.

python run_inference.py -num_tunes 3 -max_length 1024 -top_p 0.9 -temperature 1.0 -seed 1

Enjoy tunes in the folder output_tunes! If you want to convert these ABC tunes to sheet music or audio, please refer to Intended uses & limitations.

X:1
L:1/4
M:4/4
K:C
"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 ||"C" G G"F" A A |"G" G G"C" E2 | 
"G" F F"C" E E |"G" D D"C" C2 ||"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 |]

X:2
L:1/4
M:4/4
K:C
"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 ||"C" E E"F" F F |"C" G G"F" A2 | 
"G7" F F"C" E E |"G" D D"C" C2 ||"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 |]

X:3
L:1/4
M:4/4
K:C
"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 ||"C" G G"F" A A |"C" G G"F" F2 | 
"C" E E"G" D D |"G" D D"C" C2 ||"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 |]

Usage

optional arguments:
  -h, --help            show this help message and exit
  -num_tunes NUM_TUNES  the number of independently computed returned tunes
  -max_length MAX_LENGTH
                        integer to define the maximum length in tokens of each
                        tune
  -top_p TOP_P          float to define the tokens that are within the sample
                        operation of text generation
  -temperature TEMPERATURE
                        the temperature of the sampling operation
  -seed SEED            seed for randomstate

BibTeX entry and citation info

@misc{https://doi.org/10.48550/arxiv.2301.02884,
  doi = {10.48550/ARXIV.2301.02884},
  
  url = {https://arxiv.org/abs/2301.02884},
  
  author = {Wu, Shangda and Sun, Maosong},
  
  keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
  
  title = {TunesFormer: Forming Tunes with Control Codes},
  
  publisher = {arXiv},
  
  year = {2023},
  
  copyright = {Creative Commons Attribution 4.0 International}
}