  Deep learning models have provided extremely successful solutions in most
audio application fields. However, the high accuracy of these models comes at
the expense of a tremendous computation cost. This aspect is almost always
overlooked in evaluating the quality of proposed models. However, models should
not be evaluated without taking into account their complexity. This aspect is
especially critical in audio applications, which heavily relies on specialized
embedded hardware with real-time constraints. In this paper, we build on recent
observations that deep models are highly overparameterized, by studying the
lottery ticket hypothesis on deep generative audio models. This hypothesis
states that extremely efficient small sub-networks exist in deep models and
would provide higher accuracy than larger models if trained in isolation.
However, lottery tickets are found by relying on unstructured masking, which
means that resulting models do not provide any gain in either disk size or
inference time. Instead, we develop here a method aimed at performing
structured trimming. We show that this requires to rely on global selection and
introduce a specific criterion based on mutual information. First, we confirm
the surprising result that smaller models provide higher accuracy than their
large counterparts. We further show that we can remove up to 95% of the model
weights without significant degradation in accuracy. Hence, we can obtain very
light models for generative audio across popular methods such as Wavenet, SING
or DDSP, that are up to 100 times smaller with commensurate accuracy. We study
the theoretical bounds for embedding these models on Raspberry Pi and Arduino,
and show that we can obtain generative models on CPU with equivalent quality as
large GPU models. Finally, we discuss the possibility of implementing deep
generative audio models on embedded platforms.
