7 ms·

Deep Voice: Real-Time Neural Text-To-Speech

244 points by PieSquared 10y ago

chikiuso 10y ago
That's great! when will the code / service be available to the public??
mrmaximus 10y ago
Interesting. They are not TTS like we are accustomed to, they are replicating a specific persons voice with TTS. Listen to the ground-truth recordings at the bottom and then the synthesized versions above. "Fake News" is about to get a lot more compelling when you can make anyone say anything as long as you have some previous recordings of their voice.
- modeless 10y ago
  > you can make anyone say anything as long as you have some previous recordings of their voice. That's not what this is doing. They're simply resynthesizing exactly what the person said, in the same voice. It's essentially cheating because they can use the real person's inflection. Generating correct inflection is the hardest part of speech synthesis because doing it perfectly requires a complete understanding of the meaning of the text. The top two are representative of what it sounds like when doing true text to speech. The middle five are just resynthesis of a clip saying the exact same thing. And even in that case, it doesn't always sound good. The fourth one is practically unintelligible. But it's interesting because it demonstrates an upper bound on the quality of the voice synthesis possible with their system given perfect inflection as input. To clarify, this is cool work, the real-time aspect sounds great, and I'm sure it will lead to even more impressive results in the future. But I don't want people to think that all of the clips on this page represent their current text-to-speech quality.
  - mrmaximus 10y ago
    Based upon what little is posted there, I thought they were taking the original recording, then training the model on that recording against the text of the recording... reproducing the recording. I would think next step is to sample enough audio and text to be able to produce new outputs entirely. It should in theory even be able to learn when/where/how to use inflection.
  - mrmaximus 10y ago
    >The top two are representative of what it sounds like when doing true text to speech. The middle five are just resynthesis of a clip saying the exact same thing. Gotcha, now I understand.
  - PieSquared 10y ago
    Thank you for clarifying this! We tried fairly hard to make this clear, because as you say, the hard part is generating inflection and duration that sounds natural. There's still a ton of work left to do in this duration – we're clearly nowhere near being able to generate human-level speech. Our work is meant to make working with TTS easier to deep learning researchers by describing a complete and trainable system that can be trained completely from data, and demonstrate that the neural vocoder substitutes can actually be deployed to streaming production servers. Future work (both by us and hopefully other groups) will make further progress for inflection synthesis!
    - mrmaximus 10y ago
      My "Fake News" comment aside, I think what y'all are doing could be transformational for many reasons. Imagine a scenario where a person loses a loved one, and similar technology is able to allow them to "have conversations" with the deceased as a form of healing and closure. Not to mention, this could add a personal touch to assistant bots that will make them a pleasure to use.
  - phkahler 10y ago
    >> They're simply resynthesizing exactly what the person said, in the same voice. It's essentially cheating because they can use the real person's inflection. Yes, but imagine being able to take sound from one person and inflection from another. If you want to fake someone saying something you don't need to do pure TTS, a human can be used to fake another persons inflections.
- mbrookes 10y ago
  > "Fake News" is about to get a lot more compelling hen you can make anyone say anything as long as you have some previous recordings of their voice. Adobe has already developed that technology: https://arstechnica.co.uk/information-technology/2016/11/adobe-voco-photoshop-for-audio-speech-editing/ https://arstechnica.co.uk/information-technology/2016/11/ado... Now imagine combining it with this: Face2Face: Real-time Face Capture and Reenactment of RGB Videos https://www.youtube.com/watch?v=ohmajJTcpNk https://www.youtube.com/watch?v=ohmajJTcpNk Perhaps using the intonation from the face-actor's voice to guide the speech synthesis.
  - stevenh 10y ago
    I agree and I've upvoted you, but I feel it's worth pointing out that Adobe's claim about their own progress in this field was fake news. https://www.youtube.com/watch?v=I3l4XLZ59iw&t=2m34s https://www.youtube.com/watch?v=I3l4XLZ59iw&t=2m34s "Wife" sounds exactly the same in both places. All they did was copy the exact waveform from one point to another. Nothing is being synthesized. https://www.youtube.com/watch?v=I3l4XLZ59iw&t=3m54s https://www.youtube.com/watch?v=I3l4XLZ59iw&t=3m54s The word "Jordan" is not being synthesized. The speaker was recorded saying "Jordan" beforehand for this insertion demo and they're trying to play it off as though it was synthesized on the fly. This is a scripted performance and Jordan is feigning surprise. https://www.youtube.com/watch?v=I3l4XLZ59iw&t=4m40s https://www.youtube.com/watch?v=I3l4XLZ59iw&t=4m40s The phrase "three times" here was prerecorded. This was a phony demonstration of a nonexistent product. Reporters parroted the claims and none questioned what they witnessed. Adobe falsely took credit and received endless free publicity for a breakthrough they had no hand in by staging this fake demo right on the heels of the genuine interest generated by Google WaveNet. I suppose they're hoping they'll have a real product ready by whatever deadline they've set for themselves. To be clear, I like Adobe and I think it's a cunning move on their part.
    - mbrookes 10y ago
      Thanks for the detailed breakdown. The irony is not lost!
monk_e_boy 10y ago
OK, that went from uncanny valley to flipping amazing. I could picture the person speaking. An old lady. A young woman. It was hard to picture an algorithm in a machine. It's amazing that is all boils down to 1s and 0s and some boolean logic.
- taejavu 10y ago
  You've misunderstood what you're listening to, I suggest reading the post again. The recordings at the bottom are just recordings of an old lady and a young woman.
  - monk_e_boy 10y ago
    Yeah, I understood that. The ones in the middle are generated using their voices. You don't find that amazing?
    - gcr 10y ago
      I mean, it's sort of amazing, but it wasn't completely generated by machine. Those sound clips in the middle were generated by copying the inflections from actual recordings, not generating the inflections from scratch. It sounds like the current system they have sounds like the robotic voices at the very top.
    - visarga 10y ago
      It's not TEXT to speech, it's speech to speech. I think it would be amazing when we have TTS of that quality.
dresaj8 10y ago
does anyone know of good ways to do the opposite, speech to text?
- gauthamsanthosh 10y ago
  Doesn't google api work for you ? I thought it worked perfect
- forthefuture 10y ago
  Depends on how good you're talking. Chrome supports the SpeechRecognition API. https://developer.mozilla.org/en-US/docs/Web/API/SpeechRecognition https://developer.mozilla.org/en-US/docs/Web/API/SpeechRecog...
  - dresaj8 10y ago
    i'm more thinking of ways to programmatically turn long audio files into indexable text.
    - nattaylor 10y ago
      I'm unaware of a ML-based solution, but GCE has an endpoint that _can_ do this, though it is better at short sentences.
    - skykooler 10y ago
      Julius[1] can do this. But the accuracy depends on the language model you are using, and unfortunately the free English language model (VoxForge) is not the best. [1] http://julius.osdn.jp/en_index.php http://julius.osdn.jp/en_index.php
- shanxS 10y ago
  Lex by AWS. Its the same deep learning tech. used as used by Alexa
  - dresaj8 10y ago
hprotagonist 10y ago
how does this stack up against wavenet?
- methou 10y ago
  It's in the abstract. "... For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original ..."[1] [1]: https://arxiv.org/abs/1702.07825 https://arxiv.org/abs/1702.07825
  - Smerity 10y ago
    Disclosure: I'm one of the co-authors of the QRNN paper (James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher) produced by Salesforce Research. There are many interesting advances that Deep Voice paper and implementation make but the part I'm excited by (and which might be transferable to other tasks that use RNNs) is showing that QRNNs are indeed generalizable to speech too - in this case in place of WaveNet. "WaveNet uses transposed convolutions for upsampling and conditioning. We find that our models perform better, train faster, and require fewer parameters if we instead first encode the inputs with a stack of bidirectional quasi-RNN (QRNN) layers (Bradbury et al., 2016) and then perform upsampling by repetition to the desired frequency." QRNNs are a variant of recurrent neural networks. They're up to 16 times faster than even Nvidia's highly optimized cuDNN LSTM implementation and give comparable or better accuracy in many tasks. This is the first time that it has been tried in speech - to see them note the advantages hold across the board (better, faster, smaller) is brilliant! If you're interested in technical details, our blog post[1] provides a broader overview and our paper is available for deeper detail[2]. [1]: https://metamind.io/research/new-neural-network-building-block-allows-faster-and-more-accurate-text-understanding https://metamind.io/research/new-neural-network-building-blo... [2]: https://arxiv.org/abs/1611.01576 https://arxiv.org/abs/1611.01576
computerwizard 10y ago
I have A LOT of pdf's I'd much rather listen to than read. Can't wait for this!
- visarga 10y ago
  I hacked a script on top of PDF.js to make it read the text by TTS while highlighting the words on page. I'm a big fan of having the computer speak to me.
- anotheryou 10y ago
  You better try getting epubs. Ivona Amy us currently the best I know, I use it a lot on my phone.
- kpil 10y ago
  Unless you are doing some manual work at the same time, like ironing or something else that requires very little mental focus, I can't really see why?
PieSquared 10y ago
Hey there! I'm one of the authors of the paper and I'm happy to answer any questions anyone may have! Make sure to check out the paper on arxiv as well.
- state_less 10y ago
  Nice job! The samples sound good. Is there an implementation of this to check out? It seems like you needed to write some custom, low-level code to implement this in real-time. Which libraries did you use to generate the ANNs and do the inferences?
  - PieSquared 10y ago
    We are not currently releasing any code, but hopefully the paper on arxiv is enough to make it easy to reproduce the result. We use TensorFlow for writing and training the model and c++ with a lot of hand optimizations for inference, with assembly kernels written with PeachPy (which is an awesome piece of software!)
    - modeless 10y ago
      I didn't know Baidu uses TensorFlow! Is that the main framework there now or are others in use?
      - PieSquared 10y ago
        Baidu as a company doesn't use TensorFlow (as far as I know). We have our own high-performance and easy to use open source framework called PaddlePaddle [0], which is quite powerful and flexible. However, the Baidu Research Silicon Valley AI Lab (SVAIL) allows researchers and research teams to use whatever frameworks they want to, and we have projects using TensorFlow, Torch, our own SVAIL-internal internal high-performance RNN framework, and PaddlePaddle. Using our own framework sometimes allows us to work on very high-performance implementations of various primitives and techniques that would be harder to do without complete control over the source code. [0] http://www.paddlepaddle.org/ http://www.paddlepaddle.org/
slay2k 10y ago
How soon before you make an API available? In other words, how do I make use of Deep Voice for my own applications?
- PieSquared 10y ago
  Right now, we do not have plans to make an API available. This paper and blog post are mostly meant to describe our techniques to other deep learning researchers and spur innovation in the field. However, we hope that these techniques will be available eventually, and we'll provide more information when that happens.
  - rocky1138 10y ago
    In order to not miss this announcement, do you have a mailing list we could sign up for to notify us when this becomes available? You have a LOT of people interested.
Elv13 10y ago
Semi-related to the Baidu speech research: http://chrislord.net/index.php/2017/02/23/machine-learning-speech-recognition/ http://chrislord.net/index.php/2017/02/23/machine-learning-s... The work is done by Mozilla
bayjingsf 10y ago
Great work!
kayoone 10y ago
if i understand this correctly it's a pretty big achievement on the way to being able to replicate any persons voice in the future given enough audio samples. Amazing. Similarly i have seen lip movement (talking) be replicated using machine learning. Having completely artificial (or even real) identities saying whatever you want them to on video is not that far off i guess (simpler than general AI or even fully self driving cars), which is both amazing and terrifying.
Dowwie 10y ago
Has anyone seen this yet? https://www.youtube.com/watch?v=XfcqBElF0ZI https://www.youtube.com/watch?v=XfcqBElF0ZI So many innovations happening with voice related technology..
whodunser 10y ago
It says they trained on 20 hours of a speech corpus subset. Will larger datasets influence the future of TTS?
100ideas 10y ago
> "We conclude that the main barrier to progress towards natural TTS lies with duration and fundamental frequency prediction, and our systems have not meaningfully progressed past the state of the art in that regard." Who is working on this problem, and how?
- RodolpheO 10y ago
  We're working on this. Here is a very early demo of Julian. Don't be surprised, he sounds like a teenager with a high-pitched voice, recorded in his bedroom, because that's how the sample library was recorded. https://soundcloud.com/komponant/julian-speech-demo https://soundcloud.com/komponant/julian-speech-demo NB the expressions (durations, F0) are manually adjusted, not predicted by a NN. We've built a fully flexible text-to-voice engine, not the brain that goes with it. But we're looking for people with experience in ML to work on this, so feel free to contact us.
m210658 10y ago
very nice paper - one of my colleagues discovered it. I have been trying to understand the details but I do not see how your stacked dilated layers are arranged. "d" is mentioned once but no description given
deleted 10y ago
[deleted]
ymow 10y ago
it's awesome~

244 points by PieSquared 10y ago

That's great! when will the code / service be available to the public??

Interesting. They are not TTS like we are accustomed to, they are replicating a specific persons voice with TTS. Listen to the ground-truth recordings at the bottom and then the synthesized versions above. "Fake News" is about to get a lot more compelling when you can make anyone say anything as long as you have some previous recordings of their voice.

modeless 10y ago
> you can make anyone say anything as long as you have some previous recordings of their voice. That's not what this is doing. They're simply resynthesizing exactly what the person said, in the same voice. It's essentially cheating because they can use the real person's inflection. Generating correct inflection is the hardest part of speech synthesis because doing it perfectly requires a complete understanding of the meaning of the text. The top two are representative of what it sounds like when doing true text to speech. The middle five are just resynthesis of a clip saying the exact same thing. And even in that case, it doesn't always sound good. The fourth one is practically unintelligible. But it's interesting because it demonstrates an upper bound on the quality of the voice synthesis possible with their system given perfect inflection as input. To clarify, this is cool work, the real-time aspect sounds great, and I'm sure it will lead to even more impressive results in the future. But I don't want people to think that all of the clips on this page represent their current text-to-speech quality.
- mrmaximus 10y ago
  Based upon what little is posted there, I thought they were taking the original recording, then training the model on that recording against the text of the recording... reproducing the recording. I would think next step is to sample enough audio and text to be able to produce new outputs entirely. It should in theory even be able to learn when/where/how to use inflection.
- mrmaximus 10y ago
  >The top two are representative of what it sounds like when doing true text to speech. The middle five are just resynthesis of a clip saying the exact same thing. Gotcha, now I understand.
- PieSquared 10y ago
  Thank you for clarifying this! We tried fairly hard to make this clear, because as you say, the hard part is generating inflection and duration that sounds natural. There's still a ton of work left to do in this duration – we're clearly nowhere near being able to generate human-level speech. Our work is meant to make working with TTS easier to deep learning researchers by describing a complete and trainable system that can be trained completely from data, and demonstrate that the neural vocoder substitutes can actually be deployed to streaming production servers. Future work (both by us and hopefully other groups) will make further progress for inflection synthesis!
  - mrmaximus 10y ago
    My "Fake News" comment aside, I think what y'all are doing could be transformational for many reasons. Imagine a scenario where a person loses a loved one, and similar technology is able to allow them to "have conversations" with the deceased as a form of healing and closure. Not to mention, this could add a personal touch to assistant bots that will make them a pleasure to use.
- phkahler 10y ago
  >> They're simply resynthesizing exactly what the person said, in the same voice. It's essentially cheating because they can use the real person's inflection. Yes, but imagine being able to take sound from one person and inflection from another. If you want to fake someone saying something you don't need to do pure TTS, a human can be used to fake another persons inflections.
mbrookes 10y ago
> "Fake News" is about to get a lot more compelling hen you can make anyone say anything as long as you have some previous recordings of their voice. Adobe has already developed that technology: https://arstechnica.co.uk/information-technology/2016/11/adobe-voco-photoshop-for-audio-speech-editing/ https://arstechnica.co.uk/information-technology/2016/11/ado... Now imagine combining it with this: Face2Face: Real-time Face Capture and Reenactment of RGB Videos https://www.youtube.com/watch?v=ohmajJTcpNk https://www.youtube.com/watch?v=ohmajJTcpNk Perhaps using the intonation from the face-actor's voice to guide the speech synthesis.
- stevenh 10y ago
  I agree and I've upvoted you, but I feel it's worth pointing out that Adobe's claim about their own progress in this field was fake news. https://www.youtube.com/watch?v=I3l4XLZ59iw&t=2m34s https://www.youtube.com/watch?v=I3l4XLZ59iw&t=2m34s "Wife" sounds exactly the same in both places. All they did was copy the exact waveform from one point to another. Nothing is being synthesized. https://www.youtube.com/watch?v=I3l4XLZ59iw&t=3m54s https://www.youtube.com/watch?v=I3l4XLZ59iw&t=3m54s The word "Jordan" is not being synthesized. The speaker was recorded saying "Jordan" beforehand for this insertion demo and they're trying to play it off as though it was synthesized on the fly. This is a scripted performance and Jordan is feigning surprise. https://www.youtube.com/watch?v=I3l4XLZ59iw&t=4m40s https://www.youtube.com/watch?v=I3l4XLZ59iw&t=4m40s The phrase "three times" here was prerecorded. This was a phony demonstration of a nonexistent product. Reporters parroted the claims and none questioned what they witnessed. Adobe falsely took credit and received endless free publicity for a breakthrough they had no hand in by staging this fake demo right on the heels of the genuine interest generated by Google WaveNet. I suppose they're hoping they'll have a real product ready by whatever deadline they've set for themselves. To be clear, I like Adobe and I think it's a cunning move on their part.
  - mbrookes 10y ago
    Thanks for the detailed breakdown. The irony is not lost!

monk_e_boy 10y ago

OK, that went from uncanny valley to flipping amazing. I could picture the person speaking. An old lady. A young woman. It was hard to picture an algorithm in a machine. It's amazing that is all boils down to 1s and 0s and some boolean logic.

taejavu 10y ago
You've misunderstood what you're listening to, I suggest reading the post again. The recordings at the bottom are just recordings of an old lady and a young woman.
- monk_e_boy 10y ago
  Yeah, I understood that. The ones in the middle are generated using their voices. You don't find that amazing?
  - gcr 10y ago
    I mean, it's sort of amazing, but it wasn't completely generated by machine. Those sound clips in the middle were generated by copying the inflections from actual recordings, not generating the inflections from scratch. It sounds like the current system they have sounds like the robotic voices at the very top.
  - visarga 10y ago
    It's not TEXT to speech, it's speech to speech. I think it would be amazing when we have TTS of that quality.

dresaj8 10y ago

does anyone know of good ways to do the opposite, speech to text?

gauthamsanthosh 10y ago
Doesn't google api work for you ? I thought it worked perfect
forthefuture 10y ago
Depends on how good you're talking. Chrome supports the SpeechRecognition API. https://developer.mozilla.org/en-US/docs/Web/API/SpeechRecognition https://developer.mozilla.org/en-US/docs/Web/API/SpeechRecog...
- dresaj8 10y ago
  i'm more thinking of ways to programmatically turn long audio files into indexable text.
  - nattaylor 10y ago
    I'm unaware of a ML-based solution, but GCE has an endpoint that _can_ do this, though it is better at short sentences.
  - skykooler 10y ago
    Julius[1] can do this. But the accuracy depends on the language model you are using, and unfortunately the free English language model (VoxForge) is not the best. [1] http://julius.osdn.jp/en_index.php http://julius.osdn.jp/en_index.php
shanxS 10y ago
Lex by AWS. Its the same deep learning tech. used as used by Alexa
- dresaj8 10y ago

hprotagonist 10y ago

how does this stack up against wavenet?

methou 10y ago
It's in the abstract. "... For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original ..."[1] [1]: https://arxiv.org/abs/1702.07825 https://arxiv.org/abs/1702.07825
- Smerity 10y ago
  Disclosure: I'm one of the co-authors of the QRNN paper (James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher) produced by Salesforce Research. There are many interesting advances that Deep Voice paper and implementation make but the part I'm excited by (and which might be transferable to other tasks that use RNNs) is showing that QRNNs are indeed generalizable to speech too - in this case in place of WaveNet. "WaveNet uses transposed convolutions for upsampling and conditioning. We find that our models perform better, train faster, and require fewer parameters if we instead first encode the inputs with a stack of bidirectional quasi-RNN (QRNN) layers (Bradbury et al., 2016) and then perform upsampling by repetition to the desired frequency." QRNNs are a variant of recurrent neural networks. They're up to 16 times faster than even Nvidia's highly optimized cuDNN LSTM implementation and give comparable or better accuracy in many tasks. This is the first time that it has been tried in speech - to see them note the advantages hold across the board (better, faster, smaller) is brilliant! If you're interested in technical details, our blog post[1] provides a broader overview and our paper is available for deeper detail[2]. [1]: https://metamind.io/research/new-neural-network-building-block-allows-faster-and-more-accurate-text-understanding https://metamind.io/research/new-neural-network-building-blo... [2]: https://arxiv.org/abs/1611.01576 https://arxiv.org/abs/1611.01576

computerwizard 10y ago

I have A LOT of pdf's I'd much rather listen to than read. Can't wait for this!

visarga 10y ago
I hacked a script on top of PDF.js to make it read the text by TTS while highlighting the words on page. I'm a big fan of having the computer speak to me.
anotheryou 10y ago
You better try getting epubs. Ivona Amy us currently the best I know, I use it a lot on my phone.
kpil 10y ago
Unless you are doing some manual work at the same time, like ironing or something else that requires very little mental focus, I can't really see why?

PieSquared 10y ago

Hey there! I'm one of the authors of the paper and I'm happy to answer any questions anyone may have! Make sure to check out the paper on arxiv as well.

state_less 10y ago
Nice job! The samples sound good. Is there an implementation of this to check out? It seems like you needed to write some custom, low-level code to implement this in real-time. Which libraries did you use to generate the ANNs and do the inferences?
- PieSquared 10y ago
  We are not currently releasing any code, but hopefully the paper on arxiv is enough to make it easy to reproduce the result. We use TensorFlow for writing and training the model and c++ with a lot of hand optimizations for inference, with assembly kernels written with PeachPy (which is an awesome piece of software!)
  - modeless 10y ago
    I didn't know Baidu uses TensorFlow! Is that the main framework there now or are others in use?
    - PieSquared 10y ago
      Baidu as a company doesn't use TensorFlow (as far as I know). We have our own high-performance and easy to use open source framework called PaddlePaddle [0], which is quite powerful and flexible. However, the Baidu Research Silicon Valley AI Lab (SVAIL) allows researchers and research teams to use whatever frameworks they want to, and we have projects using TensorFlow, Torch, our own SVAIL-internal internal high-performance RNN framework, and PaddlePaddle. Using our own framework sometimes allows us to work on very high-performance implementations of various primitives and techniques that would be harder to do without complete control over the source code. [0] http://www.paddlepaddle.org/ http://www.paddlepaddle.org/

slay2k 10y ago

How soon before you make an API available? In other words, how do I make use of Deep Voice for my own applications?

PieSquared 10y ago
Right now, we do not have plans to make an API available. This paper and blog post are mostly meant to describe our techniques to other deep learning researchers and spur innovation in the field. However, we hope that these techniques will be available eventually, and we'll provide more information when that happens.
- rocky1138 10y ago
  In order to not miss this announcement, do you have a mailing list we could sign up for to notify us when this becomes available? You have a LOT of people interested.

Elv13 10y ago

Semi-related to the Baidu speech research: http://chrislord.net/index.php/2017/02/23/machine-learning-speech-recognition/ http://chrislord.net/index.php/2017/02/23/machine-learning-s... The work is done by Mozilla

bayjingsf 10y ago

Great work!

kayoone 10y ago

if i understand this correctly it's a pretty big achievement on the way to being able to replicate any persons voice in the future given enough audio samples. Amazing. Similarly i have seen lip movement (talking) be replicated using machine learning. Having completely artificial (or even real) identities saying whatever you want them to on video is not that far off i guess (simpler than general AI or even fully self driving cars), which is both amazing and terrifying.

Dowwie 10y ago

Has anyone seen this yet? https://www.youtube.com/watch?v=XfcqBElF0ZI https://www.youtube.com/watch?v=XfcqBElF0ZI So many innovations happening with voice related technology..

whodunser 10y ago

It says they trained on 20 hours of a speech corpus subset. Will larger datasets influence the future of TTS?

100ideas 10y ago

> "We conclude that the main barrier to progress towards natural TTS lies with duration and fundamental frequency prediction, and our systems have not meaningfully progressed past the state of the art in that regard." Who is working on this problem, and how?

RodolpheO 10y ago
We're working on this. Here is a very early demo of Julian. Don't be surprised, he sounds like a teenager with a high-pitched voice, recorded in his bedroom, because that's how the sample library was recorded. https://soundcloud.com/komponant/julian-speech-demo https://soundcloud.com/komponant/julian-speech-demo NB the expressions (durations, F0) are manually adjusted, not predicted by a NN. We've built a fully flexible text-to-voice engine, not the brain that goes with it. But we're looking for people with experience in ML to work on this, so feel free to contact us.

m210658 10y ago

very nice paper - one of my colleagues discovered it. I have been trying to understand the details but I do not see how your stacked dilated layers are arranged. "d" is mentioned once but no description given

deleted 10y ago

[deleted]

ymow 10y ago

it's awesome~