def __init__(self):
super(LocationLayer, self).__init__()
kernel_size = 31
padding = int(((kernel_size - 1) / 2))
self.location_conv = ConvNorm(2, 32,
kernel_size=kernel_size,
padding=padding,
bias=False, stride=1, dilation=1)
self.location_dense = LinearNorm(32, 128,
bias=False, w_init_gain='tanh')
def __init__(self, attention_n_filters, attention_kernel_size, attention_dim): super(LocationLayer, self).__init__() padding = int((attention_kernel_size - 1) / 2) self.location_conv = ConvNorm(2, attention_n_filters, kernel_size=attention_kernel_size, padding=padding, bias=False, stride=1, dilation=1) self.location_dense = LinearNorm(attention_n_filters, attention_dim, bias=False, w_init_gain='tanh')
def __init__(self):
super(Postnet, self).__init__()
self.convolutions = nn.ModuleList()
self.convolutions.append(
nn.Sequential(
ConvNorm(hparams.n_mel_channels,
hparams.postnet_embedding_dim,
kernel_size=hparams.postnet_kernel_size,
stride=1,
padding=int((hparams.postnet_kernel_size - 1) / 2),
dilation=1,
w_init_gain='tanh'),
nn.BatchNorm1d(hparams.postnet_embedding_dim)))
for i in range(1, hparams.postnet_n_convolutions - 1):
self.convolutions.append(
nn.Sequential(
ConvNorm(hparams.postnet_embedding_dim,
hparams.postnet_embedding_dim,
kernel_size=hparams.postnet_kernel_size,
stride=1,
padding=int(
(hparams.postnet_kernel_size - 1) / 2),
dilation=1,
w_init_gain='tanh'),
nn.BatchNorm1d(hparams.postnet_embedding_dim)))
self.convolutions.append(
nn.Sequential(
ConvNorm(hparams.postnet_embedding_dim,
hparams.n_mel_channels,
kernel_size=hparams.postnet_kernel_size,
stride=1,
padding=int((hparams.postnet_kernel_size - 1) / 2),
dilation=1,
w_init_gain='linear'),
nn.BatchNorm1d(hparams.n_mel_channels)))
def __init__(self):
super(PostNet, self).__init__()
kernel_size = 5
padding = int((kernel_size - 1) / 2)
self.convolutions = nn.ModuleList()
self.convolutions.append(
nn.Sequential(
ConvNorm(80,
512,
kernel_size=kernel_size,
stride=1,
padding=padding,
dilation=1,
w_init_gain='tanh'), nn.BatchNorm1d(512)))
for i in range(3):
self.convolutions.append(
nn.Sequential(
ConvNorm(512,
512,
kernel_size=kernel_size,
padding=padding,
stride=1,
dilation=1,
w_init_gain='tanh'), nn.BatchNorm1d(512)))
self.convolutions.append(
nn.Sequential(
ConvNorm(512,
80,
kernel_size=kernel_size,
padding=padding,
stride=1,
dilation=1), nn.BatchNorm1d(80)))
def __init__(self): super(Encoder, self).__init__() convolutions = [] for _ in range(hps.encoder_n_convolutions): conv_layer = nn.Sequential( ConvNorm(hps.encoder_embedding_dim, hps.encoder_embedding_dim, kernel_size=hps.encoder_kernel_size, stride=1, padding=int((hps.encoder_kernel_size - 1) / 2), dilation=1, w_init_gain='relu'), nn.BatchNorm1d(hps.encoder_embedding_dim)) convolutions.append(conv_layer) self.convolutions = nn.ModuleList(convolutions) self.lstm = nn.LSTM(hps.encoder_embedding_dim, int(hps.encoder_embedding_dim / 2), 1, batch_first=True, bidirectional=True)