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

Reinforcing your learning

The key objectives of the _audio_transform() helper function are selecting a random clip, performing the augmentation using the Audiomentations library function, displaying the WaveForm graph using the _fetch_audio_data() and _draw_audio() helper functions, and showing the audio play button. The key code lines are as follows:

# code snippet, use Pandas to select a random/sample record
p = df.sample(dsize)
# fetch the audio data
data_amp, sam_rate, fname = self._fetch_audio_data(lname)
# do the transformation
xaug = xtransform(data_amp, sample_rate=sam_rate)
# display the Waveform graphs and the audio play button
self._draw_audio(xaug, sam_rate, title + ' Augmented: ' + fname)
display(IPython.display.Audio(xaug, rate=sam_rate))

The full function’s code can be found in the Python Notebook. Pluto will write the Python wrapper functions for audio augmentation in the same order as previously discussed. In particular, they are as follows: