tensorflow-wavenet

A few weeks ago, a deep learning researcher at one of the world's leading speech groups told me off-the-record that offline, human-parity speech recognition would be "coming soon" to mobile devices. Not sure s/he realized just how soon that would be. Even though state-of-the-art ASR is really expensive to train, recognition is extremely cheap to run, even on lower-power devices. [1][2] With specialized silicon, you can do this, continuously, for free, on something like a smartwatch. You don't need to open a websocket or call an API running on some beefy server to do this, speech-to-text is now a basic commodity. Fully offline, ubiquitous speech recognition is right around the corner. With human-level speech synthesis [3], speech applications are going to get very interesting, very quickly. [1] http://niclane.org/pubs/deepx_ipsn.pdf [2] ht

I was very impressed with the TTS examples in the original DeepMind article (https://deepmind.com/blog/wavenet-generative-model-raw-audio...). Can someone elaborate on the usefulness of this implementation for Text-to-Speech? I'm keen to experiment with voice synthesis. I want to create dialog, from multiple voice sources, for some characters in a VR application that I'm working on. Perhaps this lib is a better option for TTS: https://github.com/ibab/tensorflow-wavenet I guess I could do with an ELI5 on how I'd approach this with either of these libraries. I'm not familiar with any deep learning frameworks. But I am pretty handy with Python and have implemented SciKit stuff. Also thinking this will give me a reason to try Azure K80 instance vs the AWS GPU instances I've been using for other stuff. That said, is a Tesla K80 the only option

https://github.com/ibab/tensorflow-wavenet - looks like they're starting to show up.