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 Data Augmentation

Similar to image and text augmentation, the objective of audio data augmentation is to extend the dataset to gain a higher accuracy forecast or prediction in a generative AI system. Audio augmentation is cost-effective and is a viable option when acquiring additional audio files is expensive or time-consuming.

Writing about audio augmentation methods poses unique challenges. The first is that audio is not visual like images or text. If the format is audiobooks, web pages, or mobile apps, then we play the sound, but the medium is paper. Thus, we must transform the audio signal into a visual representation. The Waveform graph, also known as the time series graph, is a standard method for representing an audio signal. You can listen to the audio in the accompanying Python Notebook.

In this chapter, you will learn how to write Python code to read an audio file and draw a Waveform graph from scratch. Pluto has provided a preview here so that we can discuss...