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Advanced Convolutional Neural Networks
This model has been deployed in production at Google, and is currently being used
to serve Google Assistant queries in real time to millions of users. At the annual
I/O developer conference in May 2018, it was announced that new Google Assistant
voices were available thanks to WaveNet.
Two implementations of WaveNet models for TensorFlow are currently available.
One is the original implementation of DeepMind's WaveNet (original WaveNet
version is available at https://github.com/ibab/tensorflow-wavenet), and the
other is called Magenta NSynth. NSynth (Magenta is available at https://magenta.
tensorflow.org/nsynth) is an evolution of WaveNet recently released by the
Google Brain group, which, instead of being causal, aims at seeing the entire context
of the input chunk. The neural network is truly complex, as depicted in following
diagram, but for the sake of this introductory discussion it is sufficient to know
that the network learns how to reproduce its input by using an approach based
on reducing the error during the encoding/decoding phases:
If you are interested in understanding more, I would suggest having a look at the
online Colab notebook where you can play with models generated with NSynth
(NSynth Colab is available at https://colab.research.google.com/notebooks/
magenta/nsynth/nsynth.ipynb).
MuseNet is a very recent and impressive audio generation tool developed by
OpenAI. MuseNet uses a sparse transformer to train a 72-layer network with 24
attention heads. Transformers will be discussed in Chapter 9, Autoencoders, but for
now all we need to know is that they are deep neural networks that are very good at
predicting what comes next in a sequence—whether text, images, or sound.
In Transformers, every output element is connected to every input element, and the
weightings between them are dynamically calculated according to a process called
attention.
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