The dataset used to train Spectro and j-IR-vis was compiled from multiple sources. IR spectra were downloaded from NIST as JDX files and plotted using Matplotlib. The resulting figures were saved in PNG format. For NMR data, we utilized predicted NMR spectral text obtained from NMRium . This predicted NMR data, which simulates the spectral information without requiring experimental measurements, was processed to extract chemical shift information using Selenium WebDriver. For each molecule, the SELFIES and SMILES representations were generated using the SELFIES and RDKit libraries, respectively. All this information was consolidated into a YAML file for ease of access and use in the study. The total dataset comprises 6, 833 molecules, ranging from 1 to 7 functional groups per molecule. We performed an 80-20 split for training and testing both models and all results reported here are based on the test dataset of 1, 366 molecules. For more details regarding the dataset, preprocessing, and tokenization, we refer the reader to the Supplemental Material (SM).