WaveNet implementation in Keras
github.com
WaveNet implementation in Keras
1–10 of 17 posts
Re: WaveNet implementation in Keras
#2The most interesting thing here is the note at the bottom regarding computational cost: "A recent macbook pro reaches about 5 samples per second."
This shows how far this model is from realtime usage. However I'm sure Deepmind researchers are already looking into how to make this blockbased or some other optimization strategy.
Re: WaveNet implementation in Keras
#3The most interesting thing here is the note at the bottom regarding computational cost: "A recent macbook pro reaches about 5 samples per second." This shows how far this model is from realtime usage. However I'm sure Deepmind researchers are already looking into how to make this blockbased or some other optimization strategy.
And I should add that this was measured using a downsized model (just two blocks of dilated convolutions and a sampling rate of 4khz). Deepmind's paper does not report how many stacks are used to generate the samples, but I assume it's quite a bit more.
Re: WaveNet implementation in Keras
#4The most interesting thing here is the note at the bottom regarding computational cost: "A recent macbook pro reaches about 5 samples per second." This shows how far this model is from realtime usage. However I'm sure Deepmind researchers are already looking into how to make this blockbased or some other optimization strategy.
And I should add that this was measured using a downsized model (just two blocks of dilated convolutions and a sampling rate of 4khz). Deepmind's paper does not report how many stacks are used to generate the samples, but I assume it's quite a bit more.
They (deepmind) reported it took 90 minutes of processing to generate 1s of speech via tweet. Hopefully this comes down in the future.
Re: WaveNet implementation in Keras
#5Earlier quoted context omitted.
And I should add that this was measured using a downsized model (just two blocks of dilated convolutions and a sampling rate of 4khz). Deepmind's paper does not report how many stacks are used to generate the samples, but I assume it's quite a bit more.
They (deepmind) reported it took 90 minutes of processing to generate 1s of speech via tweet. Hopefully this comes down in the future.
Do you have a link?
This implementation says: “A Tesla K80 needs around ~4 minutes for generating a second of audio at a sampling rate of 4000hz”, which is significantly faster.
Re: WaveNet implementation in Keras
#6Earlier quoted context omitted.
They (deepmind) reported it took 90 minutes of processing to generate 1s of speech via tweet. Hopefully this comes down in the future.
Do you have a link? This implementation says: “A Tesla K80 needs around ~4 minutes for generating a second of audio at a sampling rate of 4000hz”, which is significantly faster.
90 minutes for 1s of audio was reported by someone from Google on twitter, but the tweet has been deleted.
I've clarified in the readme that my measurements are for a much lighter/smaller model than Deepmind's :).
Re: WaveNet implementation in Keras
#7I 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 for WaveNet? I'm guessing I could run it on other GPU's but had read that memory might be an issue on some cards. If so what the lowest card I can run it on and will one of the AWS GPU instances suffice? I also have a GTX 970 at home, but I'm guessing that won't cut it.
Re: WaveNet implementation in Keras
#8I 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 opt…
To my understanding - WaveNet itself is quite resource heavy to run see this thread: https://news.ycombinator.com/item?id=12501204
I'm looking forward to seeing faster implementations in the future, playing around with this looks like a lot of fun.
Re: WaveNet implementation in Keras
#9I 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 opt…
To my understanding - WaveNet itself is quite resource heavy to run see this thread: https://news.ycombinator.com/item?id=12501204 I'm looking forward to seeing faster implementations in the future, playing around with this looks like a lot of fun.
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Re: WaveNet implementation in Keras
#10Tangentially related, but was not aware of the python package sacred that they used. Seems pretty useful for organizing data science runs.