Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Last revision Both sides next revision
autoregressive_network [2018/09/04 02:39]
admin
autoregressive_network [2018/11/03 10:41]
admin
Line 180: Line 180:
 https://​arxiv.org/​pdf/​1802.06901.pdf Deterministic Non-Autoregressive Neural Sequence Modeling https://​arxiv.org/​pdf/​1802.06901.pdf Deterministic Non-Autoregressive Neural Sequence Modeling
 by Iterative Refinement by Iterative Refinement
 +
 +https://​arxiv.org/​abs/​1811.00002v1 WaveGlow: A Flow-based Generative Network for Speech Synthesis
 +
 +WaveGlow combines insights from Glow and WaveNet in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our PyTorch implementation produces audio samples at a rate of more than 500 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. All code will be made publicly available online. https://​nv-adlr.github.io/​WaveGlow
  
 https://​github.com/​ikostrikov/​pytorch-flows A PyTorch implementations of Masked Autoregressive Flow and some other invertible transformations from Glow: Generative Flow with Invertible 1x1 Convolutions and Density estimation using Real NVP. https://​github.com/​ikostrikov/​pytorch-flows A PyTorch implementations of Masked Autoregressive Flow and some other invertible transformations from Glow: Generative Flow with Invertible 1x1 Convolutions and Density estimation using Real NVP.