  Measuring quality and intelligibility of a speech signal is usually a
critical step in development of speech processing systems. To enable this, a
variety of metrics to measure quality and intelligibility under different
assumptions have been developed. Through this paper, we introduce tools and a
set of models to estimate such known metrics using deep neural networks. These
models are made available in the well-established TorchAudio library, the core
audio and speech processing library within the PyTorch deep learning framework.
We refer to it as TorchAudio-Squim, TorchAudio-Speech QUality and
Intelligibility Measures. More specifically, in the current version of
TorchAudio-squim, we establish and release models for estimating PESQ, STOI and
SI-SDR among objective metrics and MOS among subjective metrics. We develop a
novel approach for objective metric estimation and use a recently developed
approach for subjective metric estimation. These models operate in a
``reference-less" manner, that is they do not require the corresponding clean
speech as reference for speech assessment. Given the unavailability of clean
speech and the effortful process of subjective evaluation in real-world
situations, such easy-to-use tools would greatly benefit speech processing
research and development.
