Wavenet/bytenet implementations

emanjavacas: <p>In the roadmap for alpha6, there is a hint to a wavenet/bytenet implementation in the examples section. We are already far past that milestone but did those examples ever make it to the examples repository?</p>
<p>I’d love to see code in that direction. Unfortunately I don’t have time currently to work on my own implementation.</p>

AjayTalati: <p>Hi,</p>
<p>yep that’s a pretty interesting project to work on! I just had a look at the TF code,</p>
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      <a href="https://github.com/paarthneekhara/byteNet-tensorflow" target="_blank" rel="nofollow noopener">GitHub</a>
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<h3><a href="https://github.com/paarthneekhara/byteNet-tensorflow" target="_blank" rel="nofollow noopener">paarthneekhara/byteNet-tensorflow</a></h3>

<p>ByteNet for character-level language modelling. Contribute to paarthneekhara/byteNet-tensorflow development by creating an account on GitHub.</p>


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<p>Doesn’t look to bad?</p>

dhpollack: <p>I’m working on a Wavenet implementation in PyTorch.  I haven’t finished but it’s partially functional now.</p>
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      <a href="https://github.com/dhpollack/fast-wavenet.pytorch" target="_blank" rel="nofollow noopener">GitHub</a>
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<h3><a href="https://github.com/dhpollack/fast-wavenet.pytorch" target="_blank" rel="nofollow noopener">dhpollack/fast-wavenet.pytorch</a></h3>

<p>fast-wavenet.pytorch - A PyTorch implementation of fast-wavenet</p>


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TPU: <p>Check out this WaveNet implementation. It’s pretty neat:</p>
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    <h4><a href="https://github.com/f90/Seq-U-Net/blob/master/raw_audio/wavenet_model.py" target="_blank" rel="noopener nofollow ugc">f90/Seq-U-Net/blob/master/raw_audio/wavenet_model.py</a></h4>
<pre><code class="lang-py">from torch import nn
import torch

class WaveNetModel(nn.Module):
    """
    A Complete Wavenet Model

    Args:
        layers (Int):               Number of layers in each block
        blocks (Int):               Number of wavenet blocks of this model
        dilation_channels (Int):    Number of channels for the dilated convolution
        residual_channels (Int):    Number of channels for the residual connection
        skip_channels (Int):        Number of channels for the skip connections
        classes (Int):              Number of possible values each sample can have
        output_length (Int):        Number of samples that are generated for each input
        kernel_size (Int):          Size of the dilation kernel
        dtype:                      Parameter type of this model

    Shape:
        - Input: :math:`(N, C_{in}, L_{in})`
</code></pre>

  This file has been truncated. <a href="https://github.com/f90/Seq-U-Net/blob/master/raw_audio/wavenet_model.py" target="_blank" rel="noopener nofollow ugc">show original</a>

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