### Update 10/07/2018 ## MAJOR CHANGES COMING IN SOON, inlcuding pytorch implementation and better structure Tensorflow Speech Recognition Challenge https://www.kaggle.com/c/tensorflow-speech-recognition-challenge Folders : images: audio clips -> spectrogram images im_train: -> images -> resize to 28x28 results: results in graphs papers: some useful papers test_pics : ignore (spectrograms of test audio clips) Deprecated : old GCP files. Ignore Files : complete.py -> code with two CNN models and adversarial training ReadMe -> this Some files were used for preprocessing on older data but maybe useful for other projects ignore these: CNN_code_for_resized_data.py dataset.py downsizing.py <- recursively resize all images in a folder ds.py <- tried an iterator pp.py <- audio to image conversion. recursively converts all audio clips in a folder to corresponding spectrograms speech_recog.py <- ignore GCP-SR.py <-- for local usage in google cloud platform Models: Shallow CNN: CNN similar to AlexNet. Two fc layers at the end, dropout enabled/disabled. Deeper CNN: wide : added more layers to the CNN, removed dropout wider : increased number of filters For Results and Talks: ML_final.pdf ML_talk.pdf