# Morris-Frank

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## WaveNet from scratch

Today we are building a WaveNet from scratch. The WaveNet is an autoregressive, generative and deep model for audio signals.

Machine Learning

This guide is geared towards readers with a background in modern deep learning.

### The problem

Lets say we want to generate sound signals. This might happen in different settings. We might have music notes + a instrument and want to generate their sound $$w(\mathrm{note}, \mathrm{instrument}) = \mathrm{sound}$$, or we have a text and want to generate the corresponding speech $$w(\mathrm{text}) = \mathrm{sound}$$ or maybe we already have a sound signal, but it is noisy and we want to remove the noise $$w(\mathrm{noisy\ sound}) = \mathrm{sound}$$.

For all of these we need to find a model $$w(\cdot)$$ which can generate high-quality semantic sounds.

Sound is just a 1-dimensional temporal signal. Therefore the same methods can be used for other similar signals like EEG or financial data. I will ignore those areas here but you can find cool applications in other literature.

### Using Waveforms

Sounds are vibrations of air, increase and decrease of air pressure over time. As such sound, measured at one given point, is a 1D-signal over time. Stereo sound recordings (over more than that) are multiple such signals, from different spatial positions. When we are working with sound digitally we need an approximation of those recorded analog signals. The simplest digitalization is Pulse-code modulation (PCM). In PCM we take samples from the analog signal and discretize them at fixed same-length intervals. E.g. with 8kHz 16bit PCM that means that we take a sample from the vibration every $$\frac{1}{8000}\mathrm{sec}$$ and choose the closest of $$2^{16}=65536$$ bins/amplitude values. Below we have a small excerpt of a sound file encoded with 16kHz 16bit. As we have here a sample length of 100 at a sample rate of 16kHz this sample is only 6.25 ms long. Directly we see the problem in working with music in the time-domain: Interesting information is at completely different temporal scales. On the low level, we have the characteristics of different notes, e.g. a piano has frequencies between around 30 Hz to 4 kHz, on top of you could expect modulating effects (e.g a voice vibrato is around 6Hz), growing larger in the temporal dependencies, a short rhythm in the range of seconds to the structure of songs/works at multiple minutes. How can we capture all those scales with one model?

The general idea of the WaveNet  to have a causal generation process. This means that each predicted output value is only based on information of previous input values, given an ordering. Now the idea of this causal autoregressive process comes from previous works by the same authors (and other) PixelRNN , and incorporating convolutions into the same process in PixelCNN . In those they also used the same principle to generate images, quite successfully. They took this approach to sound, as sound, in contrast to images, actually has a natural 1D-ordering.

For the WaveNet the input is assumed to be as long as the output that we want to generate. For now let us assume we have a sound to sound translation task, so that the input and output are of the same type (Mono-audio).

### Dilated Convolutions

Do make the dilated convolutions we are doing the dilation and the convolution separately. Looking back to the animation above, we see that for the dilation in the first hidden layer the convolution we kind of have two dense convolutions (with the same kernel), for the even and the odd elements. In the implementation we can achieve that by splitting up the input along its time axis and transposing as such that these blocks go into the batch-size dimension.

In PyTorch we always have the dimensions ordering $$\mathrm{batch\_size} \times \mathrm{channels} \times \mathrm{length}$$.

So for an input of the size $$4\times 1\times 128$$ with a dilation of $$2$$ we end up with $$8\times 1\times 64$$. Or a direct example ($$2\times 1\times 4$$):

\begin{align}x &= \begin{bmatrix}0 & 1 & 2 & 3\\A & B & C & D\end{bmatrix}\\&\Rightarrow\\dilate(x, 2) &= \begin{bmatrix}0 & 2\\A & C\\1 & 3\\B & D\end{bmatrix}\end{align}

Of course now we have the difficulty that our batch dimension is cluttered with all blocks from the different samples in the mini-batch. Therefore we always need to keep track of the dilation we are having right now, so that we can reverse it. In code this gives us:

def dilate(x: torch.Tensor, new: int, old: int = 1) -> torch.Tensor:
"""
:param x: The input Tensor
:param new: The new dilation we want
:param old: The dilation x already has
"""
[N, C, L] = x.shape  # N == Batch size × old
if (dilation := new / old) == 1:
return x
L, N = int(L / dilation), int(N * dilation)
x = x.permute(1, 2, 0)
x = torch.reshape(x, [C, L, N])
x = x.permute(2, 0, 1)
return x.contiguous()


Now that we dilated we can apply a normal 1-dim convolution on the new Tensor which will go along the time axis and thus have a dilated receptive field. For the animations given above we would dilate three times each with a factor of two ($$2, 4, 8$$).

As a sidenote: This is different from the dilated (à trous) convolution as implemented in PyTorch’s nn.Conv1d itself.

### Gates, Residuals and Skips

The different hidden layers in the model have differently sized receptive fields (play around with the animation to see this). We introduced the problem of time-domain modeling as a problem of different temporal scales. Now these different layers, so the assumption, will look at features at different scales, combining the information from more low-level features. As the inference process needs the information from all those levels, the model employs skip-connections. Instead of taking the output of the last layer the actual model output is the sum of projections out of all the layers.

Next we see that not all information flowing upwards from the low-level to the high level is useful information to keep, especially with the big temporal receptive field the flow of information needs to be regulated. Here he authors take the idea of gated convolutions as it is known from e.g. LSTM . For each hidden layer we have have two convolutions followed by a sigmoid $$\sigma$$ and a tanh, respectively. The sigmoid gives a scaling in $$[0, 1]$$ and is acting as the gate (the value is the amount of allowed information to flow). The tanh gives a scaling of $$[-1,1]$$ and is acting as the feature magnitude. Their outputs are just multiplied, to apply the gating. Keep in mind though that this does not necessarily accurately predict the trained behavior, but it has shown better training performance in comparable settings.

Further all hidden layers are constructed as residuals, the to the convolution is added back to the output of the convolutions. Residual learning ensures that the zero-centered initialization of the weights constructs an identity mapping, meaning if the layer does not learn anything it also does not degrade the inference in any way . For deeper models this has shown to considerably improves training speed.

To summarize the construction of one hidden layer: The output of the previous layer gets dilated, we keep this as the reference, goes through the gated convolution giving the residual. The residual is then added to the skip-connection flow and the reference as the input for the next hidden layer.

In a picture:

Why the $$1\times 1$$ convolutions after the residual? The width (channels) of the residual, skip and reference might be different. Therefore we need to learn a mapping channels to channels, which is precisely a $$1\times 1$$ convolution.

Setting the forward pass of one hidden layer in code we will get something along:

dilated = dilate(feat, new=new_dilation, old=old_dilation)

filters = torch.sigmoid(filter_conv(dilated))
gates = torch.tanh(gate_conv(dilated))
residual = filters * gates

feat = dilated + feat_conv(residual)
skip = skip + skip_conv(residual)


### Putting it together

The previous section defines the flow for one layer (as in the dilated layer in the animation from the beginning). We want to put multiple such layers together to get the large dilation that we want. As in the original work we use multiple blocks where one block is as in our visualization. So if we want to compute the compound receptive field size of the complete model we have:

$\mathrm{receptivefield} = n_{\mathrm{blocks}} \cdot \prod_{i=0}^{n_{\mathrm{layers}}} \mathrm{dilation}_i$

$\mathrm{receptivefield} = n_{\mathrm{blocks}} \cdot 2^{n_{\mathrm{layers}}}$

With the second one for the specific case that we only always dilate with 2.

Now lets write the final WaveNet model and lets start with the __init__:

class WaveNet(nn.Module):
def __init__(self, n_blocks: int = 3, n_layers: int = 10,
feat_width: int = 32, residual_width: int = 32,
skip_width: int = 32, kernel_size: int = 3,
bias: bool = True):
super(WaveNet, self).__init__()
self.n_blocks, self.n_layers = n_blocks, n_layers
self.bias = bias

self.filter_conv = self._conv_list(feat_width, residual_width,
kernel_size)
self.gate_conv = self._conv_list(feat_width, residual_width,
kernel_size)
self.skip_conv = self._conv_list(residual_width, skip_width, 1)
self.feat_conv = self._conv_list(residual_width, feat_width, 1)

self.dilations = [2**l for l in range(1, n_layers+1)]


With a helper function to generate all the list of convolutions:

    def _conv_list(self, in_channels: int, out_channels: int,
kernel_size: int) -> nn.ModuleList:
module_list = []
for _ in range(self.n_blocks * self.n_layers):
module_list.append(nn.Conv1d(in_channels, out_channels, kernel_size,
bias=self.bias))
return nn.ModuleList(module_list)


And the forward pass:

    def forward(self, x: torch.Tensor) -> torch.Tensor:
for i in product(self.n_blocks * self.n_layers):
l = i % self.n_blocks
dilated = dilate(feat, new=self.dilations[l], old=self.dilations[l-1])

f = torch.sigmoid(self.filter_conv[i](dilated))
g = torch.tanh(self.gate_conv[i](dilated))
residual = f * g

feat = dilated + self.feat_conv[i](residual)
skip = skip + self.skip_conv[i](residual)


1. WaveNet: A Generative Model for Raw Audio
A. van den Oord et al., Sep. 2016
arXiv:1609.03499
2. Pixel Recurrent Neural Networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu, Aug. 2016
arXiv:1601.06759
3. Conditional Image Generation with PixelCNN Decoders
A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, and K. Kavukcuoglu, Jun. 2016
arXiv:1606.05328
4. Fast Wavenet Generation Algorithm
T. L. Paine et al., Nov. 2016
arxiv:1611.09482
5. Long Short-Term Memory
S. Hochreiter and J. Schmidhuber, Neural Computation, vol. 9, no. 8, Nov. 1997.
6. Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun, Dec. 2015
arXiv:1512.03385