Update README.md

README.md

@@ -10,5 +10,40 @@ library_name: transformers
 ---
 
 # tasty-musicgen-small
-tasty-musicgen-small is a [musicgen-small](https://huggingface.co/facebook/musicgen-stereo-melody-large) fine-tuned on a patched version of the Taste & Affect Music Database.
-It generates music that's supposed to induce gustatory synesthesia perceptions. It generates mono audio in 32khz.
+
+[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
+[![arXiv](https://img.shields.io/badge/arXiv-2503.02823-b31b1b.svg)](https://arxiv.org/abs/2503.02823)
+
+tasty-musicgen-small is a [musicgen-small](https://huggingface.co/facebook/musicgen-small) fine-tuned on a [patched version](https://github.com/matteospanio/taste-music-dataset) of the [Taste & Affect Music Database](https://osf.io/2cqa5/).
+It generates music that's supposed to induce gustatory synesthesia perceptions based on multimodal research. It generates mono audio in 32khz.
+
+## How to use
+
+Here is a showcase on how to use the model with the transformer library, it is also possible to make inference with the audiocraft library, for a detailed explanation we suggest to read the [official MusicGEN guide](https://huggingface.co/docs/transformers/main/model_doc/musicgen) by Hugging Face
+
+```python
+from transformers import pipeline
+import scipy
+
+synthesiser = pipeline("text-to-audio", "csc-unipd/tasty-musicgen-small")
+
+music = synthesiser("sweet music for fine restaurents", forward_params={"do_sample": True})
+
+scipy.io.wavfile.write("musicgen_out.wav", rate=music["sampling_rate"], data=music["audio"])
+```
+
+## Citation
+
+If you use this model, 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}, 
+}
+```