Experimentally solving the structure of a peptide or a protein by techniques like X-ray crystallography or nuclear magnetic resonance is time-consuming and costly, making it challenging to have known structures for the vast number of available sequences . This issue is even more pronounced in peptides, as their short length and high flexibility make it difficult to obtain stable structures experimentally. As a result, various computational methods have been developed to estimate peptide/protein structures, from the propensities of specific amino acids to form secondary structures to the predictions of secondary structures and tertiary structures . Basic methods are inaccurate but easy to use, while advanced methods have high accuracy but require significant computational resources. We need a fast and reliable approach to estimate the structural diversity of medium-large training datasets used prior building ML models. Secondary structure predictors offer a moderate cost and good performance, and are often used as a preliminary step before predicting the tridimensional structure. They are particularly handy for analysing the structural landscape of medium-large peptide/protein databases.