  The use of the Audio Spectrogram Transformer (AST) model for
gravitational-wave data analysis is investigated. The AST machine-learning
model is a convolution-free classifier that captures long-range global
dependencies through a purely attention-based mechanism. In this paper a model
is applied to a simulated dataset of inspiral gravitational wave signals from
binary neutron star coalescences, built from five distinct, cold equations of
state (EOS) of nuclear matter. From the analysis of the mass dependence of the
tidal deformability parameter for each EOS class it is shown that the AST model
achieves a promising performance in correctly classifying the EOS purely from
the gravitational wave signals, especially when the component masses of the
binary system are in the range $[1,1.5]M_{\odot}$. Furthermore, the
generalization ability of the model is investigated by using gravitational-wave
signals from a new EOS not used during the training of the model, achieving
fairly satisfactory results. Overall, the results, obtained using the
simplified setup of noise-free waveforms, show that the AST model, once
trained, might allow for the instantaneous inference of the cold nuclear matter
EOS directly from the inspiral gravitational-wave signals produced in binary
neutron star coalescences.
