taste-music-dataset
A multimodal dataset shaped to fine tune MusicGEN
Science Score: 67.0%
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Repository
A multimodal dataset shaped to fine tune MusicGEN
Basic Info
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Metadata Files
README.md
taste-music-dataset
This is the repository to make a fine-tuning dataset for MusicGEN, a model for music generation by Facebook Research.
This dataset has been released under the project A Multimodal Symphony: Integrating Taste and Sound through Generative AI, and is an attachment to the paper A Multimodal Symphony: Integrating Taste and Sound through Generative AI by Matteo Spanio, Massimiliano Zampini, Antonio Rodà and Franco Pierucci.
How to use
- Clone this repository
- Run
make datato download the dataset and generate the fine-tuning dataset
At the end of the process you should have a folder named data with the following structure:
data
├── dataset
├── eval
├── metadata_1.xlsx
├── metadata_2.xlsx
└── train
Move to your MusicGEN folder and copy the dataset to make some finetuning, if you cloned the audiocraft repo you can run the following commands from the audiocraft folder:
bash
mv /path/to/taste-music-dataset/data/dataset ./dataset/finetune
mv /path/to/taste-music-dataset/data/train ./egs/train
mv /path/to/taste-music-dataset/data/eval ./egs/eval
Citation
If you use this code or the data in your research, please cite the following article:
@misc{spanio2025multimodalsymphonyintegratingtaste,
title={A Multimodal Symphony: Integrating Taste and Sound through Generative AI},
author={Matteo Spanio and Massimiliano Zampini and Antonio Rodà and Franco Pierucci},
year={2025},
eprint={2503.02823},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2503.02823},
}
Acknowledgements
We thank the authors of the Taste & Affect Music Database[^1], a set of 100 musical stimuli suitable for crossmodal and affective research, on which this dataset is based.
License
This dataset is distributed under the CC BY 4.0 license.
[^1]: Guedes, D., Prada, M., Garrido, M. V., & Lamy, E. (2022, November 24). The Taste & Affect Music Database. Retrieved from osf.io/2cqa5
Owner
- Name: Matteo
- Login: matteospanio
- Kind: user
- Location: Padua, Italy
- Company: CSC @ Unipd
- Website: matteospanio.github.io
- Repositories: 62
- Profile: https://github.com/matteospanio
Ph.D. student @CSCPadova. I'm a linux and python lover. I also have a conservatory degree in clarinet.
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: Taste music dataset
message: >-
If you use this dataset, please cite it using the metadata
from this file.
type: dataset
authors:
- given-names: Matteo
name-particle: M
family-names: Spanio
email: spanio@dei.unipd.it
affiliation: University of Padova
orcid: 'https://orcid.org/0000-0002-2436-7208'
repository-code: 'https://github.com/matteospanio/taste-music-dataset'
url: 'https://osf.io/2cqa5/'
keywords:
- dataset
- Crossmodal correspondences
- multimodality
- music
- taste
- Synesthesia
license: CC-BY-4.0
identifiers:
- type: doi
value: 10.5281/zenodo.14879129
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Dependencies
- audioread ==3.0.1
- certifi ==2024.7.4
- cffi ==1.16.0
- charset-normalizer ==3.3.2
- decorator ==5.1.1
- et-xmlfile ==1.1.0
- idna ==3.7
- joblib ==1.4.2
- lazy_loader ==0.4
- librosa ==0.10.2.post1
- llvmlite ==0.43.0
- msgpack ==1.0.8
- mutagen ==1.47.0
- numba ==0.60.0
- numpy ==1.26.4
- openpyxl ==3.1.5
- packaging ==24.1
- pandas ==2.2.1
- platformdirs ==4.2.2
- pooch ==1.8.2
- pycparser ==2.22
- python-dateutil ==2.9.0.post0
- pytz ==2024.1
- requests ==2.32.3
- scikit-learn ==1.5.1
- scipy ==1.14.0
- six ==1.16.0
- soundfile ==0.12.1
- soxr ==0.3.7
- threadpoolctl ==3.5.0
- typing_extensions ==4.12.2
- tzdata ==2024.1
- urllib3 ==2.2.2