pytorch/audio

Data manipulation and transformation for audio signal processing, powered by PyTorch

What it solves

It provides a specialized library for processing audio data specifically for machine learning workflows, bridging the gap between raw audio signals and PyTorch-based neural networks.

How it works

By integrating directly with PyTorch, the library uses PyTorch operations for all computations, allowing for strong GPU acceleration and the use of the automorphism system (autograd) for trainable features. It focuses on processing audio data for ML rather than general signal processing.

Who it’s for

Machine learning engineers and researchers who are building audio and speech processing models using the PyTorch ecosystem.

Highlights

  • Dataloaders for common audio datasets.
  • Audio and speech processing functions, including forced alignment.
  • Common audio transforms such as Spectrogram, MelSpectrogram, MFCC, and Resample.
  • Compliance interfaces to ensure PyTorch code aligns with other libraries like Kaldi.

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