Use TorchAudio to Prepare Audio Data for Deep Learning

Ever wondered how machine learning models process audio data? How do you handle different audio lengths, convert sound frequencies into learnable patterns, and make sure your model is robust? This tutorial will show you how to handle audio data using TorchAudio, a PyTorch-based toolkit.

You’ll work with real speech data to learn essential techniques like converting waveforms to spectrograms, standardizing audio lengths, and adding controlled noise to build machine and deep learning models.

Dive into the tutorial to explore these concepts and learn how they can be applied to prepare audio data for deep learning tasks using TorchAudio.