I wonder how a hybrid model would sound, where the net generates parameters for a parametric synthesis algorithm (or a common speech codec) instead of samples, to reduce CPU costs.
WaveNet: A Generative Model for Raw Audio
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Re: WaveNet: A Generative Model for Raw Audio
#142So when does the album drop?
In case the above came across as an example of bad sarcasm, I'm very serious. I've a somewhat lazy interest in generative music, and found the snippets in the paper quite appealing. Though, as was mentioned in a previous comment, due to copyright (attribution based on training data sources, blah blah) I might already have an answer. :(
“Is this Hiromi Uehara or WaveNet?”
Re: WaveNet: A Generative Model for Raw Audio
#143Earlier quoted context omitted.
There could be cases where the intonation is dependent on things entirely outside of the book. If say a politician does something in the writing that is far from what we would expect them to do in today's world.
How about we allow annotation of text with prosody cues? Mark the words you want stressed. We already use question and exclamation marks.
I'd love that. Writing is a poor representation of language. It'd be nice to bring it up a notch. Here's a suggestion in a paper I wrote on better second language acquisition. https://www.researchgate.net/publication/261022308_BETTER_SE...
Re: WaveNet: A Generative Model for Raw Audio
#144Earlier quoted context omitted.
Yup, I work in virtual filmmaking and there are tons of way to use this stuff. I give us 10-15 years before it's not possible to trust anything you see or hear that's recorded.
Really? I haven't trusted anything recorded in years.
Speech production is incredibly hard to fake at the moment.
Re: WaveNet: A Generative Model for Raw Audio
#145Here lonely girl looking for fun with the guys http://aruxo4.skhit.in/
Re: WaveNet: A Generative Model for Raw Audio
#146Any suggestions on where to start learning how to implement this? I understand some of the high level concepts (and took an intro AI class years ago - probably not terribly useful), but some of them are very much over my head (e.g. 2.2 Softmax Distributions and 2.3 Gated Activation Units) and some parts of the paper feel somewhat hand-wavy (2.6 Context Stacks). Any pointers would be useful as I attempt to understand…
Best advice is to wait for a version to pop up on github. It's hard to implement such a paper as a beginner.
Agreed there's a lot of breath here, I'm coming from the opposite end with some experience in "manual" concatenative speech synthesis and very little in the ML area, you'd need to be cross disciplined from the get go
Re: WaveNet: A Generative Model for Raw Audio
#147Re: WaveNet: A Generative Model for Raw Audio
#148Re: WaveNet: A Generative Model for Raw Audio
#149Earlier quoted context omitted.
To my Australian English ears, the babbling sounded vaguely Scandinavian.
Indeed. I was surprised by that as well. Sounded like a Dutch speaker with a muffled voice behind a screen.
To my German ears it sounded definitely English, not Dutch, like a very hard to understand dialect.
Re: WaveNet: A Generative Model for Raw Audio
#150Any suggestions on where to start learning how to implement this? I understand some of the high level concepts (and took an intro AI class years ago - probably not terribly useful), but some of them are very much over my head (e.g. 2.2 Softmax Distributions and 2.3 Gated Activation Units) and some parts of the paper feel somewhat hand-wavy (2.6 Context Stacks). Any pointers would be useful as I attempt to understand…
Best advice is to wait for a version to pop up on github. It's hard to implement such a paper as a beginner.
Well, I think since we now have frameworks for doing this kind of stuff (Tensorflow and similar) the barrier of entry is much, much lower. Also the computing power required to build the models can be found in commodity GPUs.
On a hunch I'd say an absolute beginner may be able good results with these tools, just not as quickly as experts on the field who already know how to use the tools properly. That's why I'm going to wait for something to pop up on GitHub, because I have zero practical experience with these things, but I can read these papers comfortably without the need to look up every other term.
There are a number of applications I'd like to throw at deep learning to see how it performs. Most notably I'd like to see how well a deep learning system can extract feature from speckle images. At the moment you have to average out the speckles from ultrasound or OCT images before you can feed it to a feature recognition system. Unfortunately this kind of averaging eliminates certain information you might want to process further down the line.