Alexander Hendorf: Speech Synthesis with Tacotron2 and PyTorch | PyData Amsterdam 2019

Computer generated speech has existed for a while, parameters being painfully engineered by hand. Deep Learning models can be efficient at learning inherent features of data - how well does this work out for audio?

There are different DL-models as WaveNet, SampleRNN and Tacotron2. After a quick overview, I'm going to focus on Tacotron2 - how it works, it's benefits and how to implement it with PyTorch.

With Tacotron2 we make no assumption what features should be passed to the vocoder. All there is required are audio-snippets and corresponding text. Non-English language audio datasets are hard to get. I had to generate my own dataset. This talk will also cover how I have created my own dataset in a semi-automatic efficiently with tools like audiotok and methods as Speaker diarisation.

The talks will feature synthesised speech audio demos. I will also cover some failures and reason about them.

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