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.

By the end of this tutorial, you’ll understand that:

• TorchAudio processes audio data for deep learning, including tasks like loading datasets and augmenting data with noise.
• You can load audio data in TorchAudio using the torchaudio.load() function, which returns a waveform tensor and sample rate.
• TorchAudio normalizes audio by default during loading, scaling waveform amplitudes between -1.0 and 1.0.
• A spectrogram visually represents the frequency spectrum of an audio signal over time, aiding in frequency analysis.
• You can pad and trim audio in TorchAudio using torch.nn.functional.pad() and sequence slicing for uniform audio lengths.

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.

Take the Quiz: Test your knowledge with our interactive “Use TorchAudio to Prepare Audio Data for Deep Learning” quiz. You’ll receive a score upon completion to help you track your learning progress:

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Interactive Quiz

Use TorchAudio to Prepare Audio Data for Deep Learning

Test your grasp of audio fundamentals and working with TorchAudio in Python! You'll cover loading audio datasets, transforms, and more.

Learn Essential Technical Terms

Before diving into the technical details of audio processing with TorchAudio, take a moment to review some key terms. They’ll help you grasp the basics of working with audio data.

Waveform

A waveform is the visual representation of sound as it travels through air over time. When you speak, sing, or play music, you create vibrations that move through the air as waves. These waves can be captured and displayed as a graph showing how the sound’s pressure changes over time. Here’s an example:

This is a waveform of a 440 Hz wave, plotted over a short duration of 10 milliseconds (ms). This is called a time-domain representation, showing how the wave’s amplitude changes over time. This waveform shows the raw signal as it appears in an audio editor. The ups and downs reflect changes in loudness.

Amplitude

Amplitude is the strength or intensity of a sound wave—in other words, how loud the sound is to the listener. In the previous image, it’s represented by the height of the wave from its center line.

A higher amplitude means a louder sound, while a lower amplitude means a quieter sound. When you adjust the volume on your device, you’re actually changing the amplitude of the audio signal. In digital audio, amplitude is typically measured in decibels (dB) or as a normalized value between -1 and 1.

Frequency

Frequency is how many times a sound wave repeats itself in one second, measured in hertz (Hz). For example, a low bass note is a sound wave that repeats slowly, about 50–100 Hz. In contrast, a high-pitched whistle has a wave that repeats much faster, around 2000–3000 Hz.

In music, different frequencies create different musical notes. For instance, the A₄ note that musicians use to tune their instruments is exactly 440 Hz. Now, if you were to look at the frequency plot of the 440 Hz waveform from before, here’s what you’d see:

This plot displays the signal in the frequency domain, which shows how much of each frequency is present in the sound. The distinct peak at 440 Hz indicates that this is the dominant frequency in the signal, which is exactly what you’d expect from a pure tone. While time-domain plots—like the one you saw earlier—reveal how the sound’s amplitude changes over time, frequency-domain plots help you understand which frequencies make up the sound.

The waveform you just explored was from a 440 Hz wave. You’ll soon see that many examples in audio processing also deal with this mysterious frequency. So, what makes it so special?

Now that you understand frequency and how it relates to sound waves, you might be wondering how computers actually capture and store these waves.

Sampling