  TorchAudio is an open-source audio and speech processing library built for
PyTorch. It aims to accelerate the research and development of audio and speech
technologies by providing well-designed, easy-to-use, and performant PyTorch
components. Its contributors routinely engage with users to understand their
needs and fulfill them by developing impactful features. Here, we survey
TorchAudio's development principles and contents and highlight key features we
include in its latest version (2.1): self-supervised learning pre-trained
pipelines and training recipes, high-performance CTC decoders, speech
recognition models and training recipes, advanced media I/O capabilities, and
tools for performing forced alignment, multi-channel speech enhancement, and
reference-less speech assessment. For a selection of these features, through
empirical studies, we demonstrate their efficacy and show that they achieve
competitive or state-of-the-art performance.
