Various scripts for machine learning
function for Libri speech audio pcm data base that creates dict: keys - speaker label, value - path to flac file then creates callable class; during calling instance estimate MFCC using librosa lib and does some other minor preprocessing steps like: convert to mono, resampling to 16 kHz, trmming then using multiprocessing lib start process-based parallelism
function creates PCA algorithm using theoretical description
Jupyter notobook shows how to use ensamble methods like Bagging (BaggingClassifier), Boosting (GradientBoostingClassifier, AdaBoostClassifier, XGBoost), Stacking (used all above and Logistic Regression or SVM as a meta models) and Blending (used all above and Logistic Regression as a meta model). In example data from Kaggle were used.