  Essentia is a reference open-source C++/Python library for audio and music
analysis. In this work, we present a set of algorithms that employ TensorFlow
in Essentia, allow predictions with pre-trained deep learning models, and are
designed to offer flexibility of use, easy extensibility, and real-time
inference. To show the potential of this new interface with TensorFlow, we
provide a number of pre-trained state-of-the-art music tagging and
classification CNN models. We run an extensive evaluation of the developed
models. In particular, we assess the generalization capabilities in a
cross-collection evaluation utilizing both external tag datasets as well as
manual annotations tailored to the taxonomies of our models.
