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TorchAudio is an open-source audio and speech processing library built forPyTorch. It aims to accelerate the research and development of audio and speechtechnologies by providing well-designed, easy-to-use, and performant PyTorchcomponents. Its contributors routinely engage with users to understand theirneeds and fulfill them by developing impactful features. Here, we surveyTorchAudio's development principles and contents and highlight key features weinclude in its latest version (2.1): self-supervised learning pre-trainedpipelines and training recipes, high-performance CTC decoders, speechrecognition models and training recipes, advanced media I/O capabilities, andtools for performing forced alignment, multi-channel speech enhancement, andreference-less speech assessment. For a selection of these features, throughempirical studies, we demonstrate their efficacy and show that they achievecompetitive or state-of-the-art performance.

TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch