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"""
Taken from ESPNet
"""
import torch
class PostNet(torch.nn.Module):
"""
From Tacotron2
Postnet module for Spectrogram prediction network.
This is a module of Postnet in Spectrogram prediction network,
which described in `Natural TTS Synthesis by
Conditioning WaveNet on Mel Spectrogram Predictions`_.
The Postnet refines the predicted
Mel-filterbank of the decoder,
which helps to compensate the detail sturcture of spectrogram.
.. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
https://arxiv.org/abs/1712.05884
"""
def __init__(self, idim, odim, n_layers=5, n_chans=512, n_filts=5, dropout_rate=0.5, use_batch_norm=True):
"""
Initialize postnet module.
Args:
idim (int): Dimension of the inputs.
odim (int): Dimension of the outputs.
n_layers (int, optional): The number of layers.
n_filts (int, optional): The number of filter size.
n_units (int, optional): The number of filter channels.
use_batch_norm (bool, optional): Whether to use batch normalization..
dropout_rate (float, optional): Dropout rate..
"""
super(PostNet, self).__init__()
self.postnet = torch.nn.ModuleList()
for layer in range(n_layers - 1):
ichans = odim if layer == 0 else n_chans
ochans = odim if layer == n_layers - 1 else n_chans
if use_batch_norm:
self.postnet += [torch.nn.Sequential(torch.nn.Conv1d(ichans, ochans, n_filts, stride=1, padding=(n_filts - 1) // 2, bias=False, ),
torch.nn.GroupNorm(num_groups=32, num_channels=ochans), torch.nn.Tanh(),
torch.nn.Dropout(dropout_rate), )]
else:
self.postnet += [
torch.nn.Sequential(torch.nn.Conv1d(ichans, ochans, n_filts, stride=1, padding=(n_filts - 1) // 2, bias=False, ), torch.nn.Tanh(),
torch.nn.Dropout(dropout_rate), )]
ichans = n_chans if n_layers != 1 else odim
if use_batch_norm:
self.postnet += [torch.nn.Sequential(torch.nn.Conv1d(ichans, odim, n_filts, stride=1, padding=(n_filts - 1) // 2, bias=False, ),
torch.nn.GroupNorm(num_groups=20, num_channels=odim),
torch.nn.Dropout(dropout_rate), )]
else:
self.postnet += [torch.nn.Sequential(torch.nn.Conv1d(ichans, odim, n_filts, stride=1, padding=(n_filts - 1) // 2, bias=False, ),
torch.nn.Dropout(dropout_rate), )]
def forward(self, xs):
"""
Calculate forward propagation.
Args:
xs (Tensor): Batch of the sequences of padded input tensors (B, idim, Tmax).
Returns:
Tensor: Batch of padded output tensor. (B, odim, Tmax).
"""
for i in range(len(self.postnet)):
xs = self.postnet[i](xs)
return xs
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