Book Image

Data Augmentation with Python

By : Duc Haba
Book Image

Data Augmentation with Python

By: Duc Haba

Overview of this book

Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset. The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You’ll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you’ll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges. By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques.
Table of Contents (17 chapters)
Part 1: Data Augmentation
Part 2: Image Augmentation
Part 3: Text Augmentation
Part 4: Audio Data Augmentation
Part 5: Tabular Data Augmentation

Audio augmentation libraries

There are many commercial and open source audio data augmentation libraries. In this chapter, we will focus on open source libraries available on GitHub. Some libraries are more robust than others, and some focus on a particular subject, such as human speech. Pluto will write wrapper functions using the libraries provided to do the heavy lifting; thus, you can select more than one library in your project. If a library is implemented in the CPU, it may not be suitable for dynamic data augmenting during the ML training cycle because it will slow down the process. Instead, choose a library that can run on the GPU. Choose a robust and easy-to-implement library to learn new audio augmentation techniques or output the augmented data on local or cloud disk space.

The well-known open source libraries for audio augmentation are as follows:

  • Librosa is an open source Python library for music and audio analysis. It was made available in 2015 and has long...