Audio Classification using FastAI and On-the-Fly Frequency Transforms

While deep learning models are able to help tackle many different types of problems, image classification is the most prevalent example for courses and frameworks, often acting as the "hello, world" introduction. FastAI is a high-level library built on top of PyTorch that makes it extremely easy to get started classifying images, with an example showing how train an accurate model in only four lines of code. With the new v1 release of the library, an API called data_block allows users a flexible way to simplify the data loading process. After competing in the Freesound General-Purpose Audio Tagging Kaggle competition over the summer, I decided to repurpose some of my code to take advantage of fastai's benefits for audio classification as well. This article will give a quick introduction to working with audio files in Python, give some background around creating spectrogram images, and then show how to leverage pretrained image models without actually having to generate images beforehand.