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?
LibriVox
WaveNet: A Generative Model for Raw Audio
91–100 of 153 posts
Re: WaveNet: A Generative Model for Raw Audio
#92So 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?
LibriVox
Re: WaveNet: A Generative Model for Raw Audio
#93I'm guessing DeepMind has already done this (or is already doing), but conditioning on a video is the obvious next step. It would be incredibly interesting to see how accurate it can get generating the audio for a movie. Though I imagine for really great results they'll need to mix in an adversarial network.
Re: WaveNet: A Generative Model for Raw Audio
#94Earlier quoted context omitted.
Every single person who had rights on the sources for audio you used. 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.
>Every single person who had rights on the sources for audio you used. What if my 'AI' was a human who learned to speak by being trained with the voices of hundreds of people from dozens of other sources? What's the difference? Those waters seem muddy. I think that'd be an interesting copyright case, don't think it's self evident.
So if I remix just 200 songs together, the result is not copyright protected anymore?
Re: WaveNet: A Generative Model for Raw Audio
#95This is incredible. I'd be worried if I were a professional audiobook reader :)
I worked for Audible for five years, and this exact conversation was had often in my division (ACX.com - Audible's "Audiobook Creation Exchange".)
Audible brought ACX together in order to bolster its catalog. The company-wide initiative was called PTTM ('pedal to the metal') and ACX was Audible's secret weapon to gain an enormous competitive foothold over the rest of the audiobook industry. Because we paid amateurs dirt-cheap rates to record horrible, self-published crap (to which Amazon, Audible's parent company had the exclusive rights), Audible was able to bolster its numbers substantially in a short period of time.
The dirty not-so-secret behind this strategy was: nobody bought these particular audiobooks. These audio titles were not really made to be "purchased," but rather to bulk up Audible's bottom line. We knew that the ACX titles were not popular, because the amateur narrators' acting talents and audio production skills were remarkably subpar.
Neural nets may be able to narrow the gap between the pros and the lowest-common-denominator to the point where they can become the next "ACX," but frankly, it won't matter to audiobook listeners, because audiobook listeners don't buy "ACX" audiobooks. Books, even in audio form, are a major intellectual and temporal commitment (not to mention -- they tend to be pricey.) Customers will always want to buy the human-narrated version of a book - the professional production of a book. If that stops being offered, Audible will anger a lot of customers and I think Bezos has better shit to worry about than his puny audiobooks subsidiary.
Despite that, user-generated content is a secret weapon that a lot of websites wield effectively - including HN - but this is beginning to shed its effectiveness. Indeed, the next generation of cost-slashing-while-polluting-the-quality-of-your-catalog will belong to the neural nets. They may be able to get better sales than ACX titles do today with AI-generated audio content, but the actors are going nowhere.
Re: WaveNet: A Generative Model for Raw Audio
#96How much data does a model take up? I wonder if this would work for compression? Train a model on a corpus of audio, then store the audio as text that turns back into a close approximation of that audio. (Optionally store deltas for egregious differences.)
It would be a slow (but very efficient information-wise - only have to send text which itself can be compressed!) decompression process with current models / hardware due to sequential relationships in generation. I am sure people will start trying to speed this up, as it could be a game changer in that space with a fast enough implementation. Google also has a lot of great engineers with direct motivation to get it…
We've known for decades that neural networks are really good at image and video compression. But as far as I know, this has never been used in practice, because the compression and decompression times are ridiculous. I imagine this would be even more true for audio.
Re: WaveNet: A Generative Model for Raw Audio
#97Earlier quoted context omitted.
>Every single person who had rights on the sources for audio you used. What if my 'AI' was a human who learned to speak by being trained with the voices of hundreds of people from dozens of other sources? What's the difference? Those waters seem muddy. I think that'd be an interesting copyright case, don't think it's self evident.
So if I remix just 200 songs together, the result is not copyright protected anymore?
No its not like remixing. Its more like listening to 200 songs and then writing one that sounds just like them.
More like turning the songs into series of numbers (say 44100 of these numbers per second) and then using an AI to predict which number comes next to make a song that sounds something like the 200. The result is not possible without ingesting the 200 songs but the 200 songs are not "contained" in the net and then sampled to produce the result like stitching together a recoding from other recordings by copying little bits.
The hairs split too fine at the bottom for our current legal system to really handle. That's why its interesting.
Re: WaveNet: A Generative Model for Raw Audio
#98This is amazing. And it's not even a GAN. Presumably a GAN version of this would be even more natural — or maybe they tried that and it didn't work so they didn't put it in the paper?
Definitely the death knell for biometric word lists.
Re: WaveNet: A Generative Model for Raw Audio
#99So 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?
> who owns the mechanical rights to that recording?
The monkey who shot the picture. https://en.wikipedia.org/wiki/Monkey_selfie
Re: WaveNet: A Generative Model for Raw Audio
#100And when you think of all those Hollywood SF movies where the robot could reason and act quite well but in a tin-voice. How wrong they got it. We can simulate high quality voices but we can't have our reasoning, walking robots.