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Do they say how much time is the generation taking? Is this insanely slow to train but extremely fast to do generation?
This can be used to implement seamless voice performance transfer from one speaker to another: 1. Train a WaveNet with the source speaker. 2. Train a second WaveNet with the target speaker. Or for something totally new, train a WaveNet with a bunch of different speakers until you get one you like. This becomes the target WaveNet . 3. Record raw audio from the source speaker. Fun fact: any algorithmic process that "re…
You don't send your speech over the line, instead you send some parameters over the line which are then, at the receiving end, fed into a white(-ish) noise generator to recover the speech.
Edit: not by using a neural net or deep learning, of course.
Wow. I badly want to try this out with music, but I've taken little more than baby steps with neural networks in the past: am I stuck waiting for someone else to reimplement the stuff in the paper? IIRC someone published an OSS implementation of the deep dreaming image synthesis paper fairly quickly...
Re-implementation will be hard, several people (including me) have been working on related architectures, but they have a few extra tricks in WaveNet that seem to make all the difference, on top of what I assume is "monster scale training, tons of data". The core ideas from this can be seen in PixelRNN and PixelCNN, and there are discussions and implementations for the basic concepts of those out there [0][1]. Not to…
This can be used to implement seamless voice performance transfer from one speaker to another: 1. Train a WaveNet with the source speaker. 2. Train a second WaveNet with the target speaker. Or for something totally new, train a WaveNet with a bunch of different speakers until you get one you like. This becomes the target WaveNet . 3. Record raw audio from the source speaker. Fun fact: any algorithmic process that "re…
Another fun fact: this actually happens with (cell) phone calls. You don't send your speech over the line, instead you send some parameters over the line which are then, at the receiving end, fed into a white(-ish) noise generator to recover the speech. Edit: not by using a neural net or deep learning, of course.
So when I get the AI from one place, train it with the voices of hundreds of people from dozens of other sources, and then have it read a book from Project Gutenberg to an mp3... who owns the mechanical rights to that recording?
For the same reason, Google training neural networks with userdata is very legally doubtful – they changed the ToS, but also used data collected before the ToS change for that.
This is incredible. I'd be worried if I were a professional audiobook reader :)
I wouldn't. The results they offer are excellent, but the missing points they need to achieve human level are related to producing the correct intonation, which requires accurate understanding of the material. That is still at least ten years in the future, I expect.
Earlier quoted context omitted.
That would require audio recordings of Abraham Lincoln's voice. Not sure recording technology existed back then.
Audio quality does leave something to be desired. https://vimeo.com/47987691
So when does the album drop?
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. :(
wonder how many gpus are required to hold this model.