Visualizing Audio Pipelines with Streamlit
Examine the effect of augmentations in your browser
Published in
3 min readJun 13, 2021
When working with image data, practitioners often use augmentations. Augmentations are techniques that artificially and randomly alter the data to increase diversity. Applying such transformations to the training data makes the model more robust. For image data, frequently used candidates are rotating, resizing, or blurring. The effects of the transformations are easy to see and comprehend. Even multiple augmentations can be grasped quickly, as the following example shows:
