Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Empirical Evaluation of Deep Learning Model Compression Techniques on
  the WaveNet Vocoder   WaveNet is a state-of-the-art text-to-speech vocoder that remains challenging
to deploy due to its autoregressive loop. In this work we focus on ways to
accelerate the original WaveNet architecture directly, as opposed to modifying
the architecture, such that the model can be deployed as part of a scalable
text-to-speech system. We survey a wide variety of model compression techniques
that are amenable to deployment on a range of hardware platforms. In
particular, we compare different model sparsity methods and levels, and seven
widely used precisions as targets for quantization; and are able to achieve
models with a compression ratio of up to 13.84 without loss in audio fidelity
compared to a dense, single-precision floating-point baseline. All techniques
are implemented using existing open source deep learning frameworks and
libraries to encourage their wider adoption.

10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex