mirror of https://github.com/coqui-ai/TTS.git
530 lines
20 KiB
Python
530 lines
20 KiB
Python
import torch
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from torch import nn
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from torch.nn import functional as F
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class Linear(nn.Module):
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"""Linear layer with a specific initialization.
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Args:
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in_features (int): number of channels in the input tensor.
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out_features (int): number of channels in the output tensor.
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bias (bool, optional): enable/disable bias in the layer. Defaults to True.
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init_gain (str, optional): method to compute the gain in the weight initializtion based on the nonlinear activation used afterwards. Defaults to 'linear'.
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"""
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def __init__(self,
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in_features,
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out_features,
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bias=True,
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init_gain='linear'):
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super(Linear, self).__init__()
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self.linear_layer = torch.nn.Linear(
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in_features, out_features, bias=bias)
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self._init_w(init_gain)
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def _init_w(self, init_gain):
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torch.nn.init.xavier_uniform_(
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self.linear_layer.weight,
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gain=torch.nn.init.calculate_gain(init_gain))
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def forward(self, x):
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return self.linear_layer(x)
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class LinearBN(nn.Module):
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"""Linear layer with Batch Normalization.
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x -> linear -> BN -> o
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Args:
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in_features (int): number of channels in the input tensor.
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out_features (int ): number of channels in the output tensor.
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bias (bool, optional): enable/disable bias in the linear layer. Defaults to True.
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init_gain (str, optional): method to set the gain for weight initialization. Defaults to 'linear'.
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"""
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def __init__(self,
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in_features,
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out_features,
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bias=True,
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init_gain='linear'):
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super(LinearBN, self).__init__()
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self.linear_layer = torch.nn.Linear(
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in_features, out_features, bias=bias)
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self.batch_normalization = nn.BatchNorm1d(out_features, momentum=0.1, eps=1e-5)
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self._init_w(init_gain)
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def _init_w(self, init_gain):
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torch.nn.init.xavier_uniform_(
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self.linear_layer.weight,
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gain=torch.nn.init.calculate_gain(init_gain))
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def forward(self, x):
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"""
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Shapes:
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x: [T, B, C] or [B, C]
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"""
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out = self.linear_layer(x)
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if len(out.shape) == 3:
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out = out.permute(1, 2, 0)
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out = self.batch_normalization(out)
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if len(out.shape) == 3:
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out = out.permute(2, 0, 1)
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return out
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class Prenet(nn.Module):
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"""Tacotron specific Prenet with an optional Batch Normalization.
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Note:
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Prenet with BN improves the model performance significantly especially
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if it is enabled after learning a diagonal attention alignment with the original
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prenet. However, if the target dataset is high quality then it also works from
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the start. It is also suggested to disable dropout if BN is in use.
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prenet_type == "original"
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x -> [linear -> ReLU -> Dropout]xN -> o
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prenet_type == "bn"
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x -> [linear -> BN -> ReLU -> Dropout]xN -> o
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Args:
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in_features (int): number of channels in the input tensor and the inner layers.
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prenet_type (str, optional): prenet type "original" or "bn". Defaults to "original".
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prenet_dropout (bool, optional): dropout rate. Defaults to True.
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out_features (list, optional): List of output channels for each prenet block.
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It also defines number of the prenet blocks based on the length of argument list.
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Defaults to [256, 256].
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bias (bool, optional): enable/disable bias in prenet linear layers. Defaults to True.
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"""
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# pylint: disable=dangerous-default-value
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def __init__(self,
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in_features,
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prenet_type="original",
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prenet_dropout=True,
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out_features=[256, 256],
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bias=True):
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super(Prenet, self).__init__()
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self.prenet_type = prenet_type
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self.prenet_dropout = prenet_dropout
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in_features = [in_features] + out_features[:-1]
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if prenet_type == "bn":
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self.linear_layers = nn.ModuleList([
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LinearBN(in_size, out_size, bias=bias)
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for (in_size, out_size) in zip(in_features, out_features)
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])
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elif prenet_type == "original":
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self.linear_layers = nn.ModuleList([
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Linear(in_size, out_size, bias=bias)
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for (in_size, out_size) in zip(in_features, out_features)
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])
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def forward(self, x):
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for linear in self.linear_layers:
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if self.prenet_dropout:
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x = F.dropout(F.relu(linear(x)), p=0.5, training=self.training)
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else:
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x = F.relu(linear(x))
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return x
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####################
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# ATTENTION MODULES
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####################
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class LocationLayer(nn.Module):
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def __init__(self,
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attention_dim,
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attention_n_filters=32,
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attention_kernel_size=31):
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super(LocationLayer, self).__init__()
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self.location_conv1d = nn.Conv1d(
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in_channels=2,
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out_channels=attention_n_filters,
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kernel_size=attention_kernel_size,
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stride=1,
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padding=(attention_kernel_size - 1) // 2,
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bias=False)
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self.location_dense = Linear(
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attention_n_filters, attention_dim, bias=False, init_gain='tanh')
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def forward(self, attention_cat):
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processed_attention = self.location_conv1d(attention_cat)
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processed_attention = self.location_dense(
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processed_attention.transpose(1, 2))
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return processed_attention
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class GravesAttention(nn.Module):
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""" Discretized Graves attention:
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- https://arxiv.org/abs/1910.10288
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- https://arxiv.org/pdf/1906.01083.pdf
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"""
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COEF = 0.3989422917366028 # numpy.sqrt(1/(2*numpy.pi))
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def __init__(self, query_dim, K):
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super(GravesAttention, self).__init__()
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self._mask_value = 1e-8
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self.K = K
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# self.attention_alignment = 0.05
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self.eps = 1e-5
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self.J = None
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self.N_a = nn.Sequential(
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nn.Linear(query_dim, query_dim, bias=True),
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nn.ReLU(),
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nn.Linear(query_dim, 3*K, bias=True))
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self.attention_weights = None
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self.mu_prev = None
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self.init_layers()
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def init_layers(self):
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torch.nn.init.constant_(self.N_a[2].bias[(2*self.K):(3*self.K)], 1.) # bias mean
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torch.nn.init.constant_(self.N_a[2].bias[self.K:(2*self.K)], 10) # bias std
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def init_states(self, inputs):
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if self.J is None or inputs.shape[1]+1 > self.J.shape[-1]:
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self.J = torch.arange(0, inputs.shape[1]+2.0).to(inputs.device) + 0.5
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self.attention_weights = torch.zeros(inputs.shape[0], inputs.shape[1]).to(inputs.device)
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self.mu_prev = torch.zeros(inputs.shape[0], self.K).to(inputs.device)
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# pylint: disable=R0201
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# pylint: disable=unused-argument
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def preprocess_inputs(self, inputs):
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return None
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def forward(self, query, inputs, processed_inputs, mask):
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"""
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shapes:
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query: B x D_attention_rnn
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inputs: B x T_in x D_encoder
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processed_inputs: place_holder
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mask: B x T_in
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"""
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gbk_t = self.N_a(query)
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gbk_t = gbk_t.view(gbk_t.size(0), -1, self.K)
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# attention model parameters
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# each B x K
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g_t = gbk_t[:, 0, :]
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b_t = gbk_t[:, 1, :]
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k_t = gbk_t[:, 2, :]
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# dropout to decorrelate attention heads
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g_t = torch.nn.functional.dropout(g_t, p=0.5, training=self.training)
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# attention GMM parameters
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sig_t = torch.nn.functional.softplus(b_t) + self.eps
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mu_t = self.mu_prev + torch.nn.functional.softplus(k_t)
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g_t = torch.softmax(g_t, dim=-1) + self.eps
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j = self.J[:inputs.size(1)+1]
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# attention weights
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phi_t = g_t.unsqueeze(-1) * (1 / (1 + torch.sigmoid((mu_t.unsqueeze(-1) - j) / sig_t.unsqueeze(-1))))
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# discritize attention weights
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alpha_t = torch.sum(phi_t, 1)
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alpha_t = alpha_t[:, 1:] - alpha_t[:, :-1]
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alpha_t[alpha_t == 0] = 1e-8
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# apply masking
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if mask is not None:
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alpha_t.data.masked_fill_(~mask, self._mask_value)
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context = torch.bmm(alpha_t.unsqueeze(1), inputs).squeeze(1)
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self.attention_weights = alpha_t
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self.mu_prev = mu_t
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return context
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class OriginalAttention(nn.Module):
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"""Following the methods proposed here:
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- https://arxiv.org/abs/1712.05884
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- https://arxiv.org/abs/1807.06736 + state masking at inference
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- Using sigmoid instead of softmax normalization
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- Attention windowing at inference time
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"""
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# Pylint gets confused by PyTorch conventions here
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#pylint: disable=attribute-defined-outside-init
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def __init__(self, query_dim, embedding_dim, attention_dim,
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location_attention, attention_location_n_filters,
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attention_location_kernel_size, windowing, norm, forward_attn,
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trans_agent, forward_attn_mask):
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super(OriginalAttention, self).__init__()
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self.query_layer = Linear(
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query_dim, attention_dim, bias=False, init_gain='tanh')
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self.inputs_layer = Linear(
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embedding_dim, attention_dim, bias=False, init_gain='tanh')
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self.v = Linear(attention_dim, 1, bias=True)
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if trans_agent:
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self.ta = nn.Linear(
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query_dim + embedding_dim, 1, bias=True)
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if location_attention:
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self.location_layer = LocationLayer(
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attention_dim,
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attention_location_n_filters,
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attention_location_kernel_size,
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)
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self._mask_value = -float("inf")
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self.windowing = windowing
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self.win_idx = None
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self.norm = norm
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self.forward_attn = forward_attn
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self.trans_agent = trans_agent
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self.forward_attn_mask = forward_attn_mask
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self.location_attention = location_attention
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def init_win_idx(self):
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self.win_idx = -1
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self.win_back = 2
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self.win_front = 6
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def init_forward_attn(self, inputs):
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B = inputs.shape[0]
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T = inputs.shape[1]
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self.alpha = torch.cat(
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[torch.ones([B, 1]),
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torch.zeros([B, T])[:, :-1] + 1e-7], dim=1).to(inputs.device)
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self.u = (0.5 * torch.ones([B, 1])).to(inputs.device)
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def init_location_attention(self, inputs):
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B = inputs.size(0)
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T = inputs.size(1)
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self.attention_weights_cum = torch.zeros([B, T], device=inputs.device)
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def init_states(self, inputs):
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B = inputs.size(0)
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T = inputs.size(1)
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self.attention_weights = torch.zeros([B, T], device=inputs.device)
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if self.location_attention:
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self.init_location_attention(inputs)
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if self.forward_attn:
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self.init_forward_attn(inputs)
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if self.windowing:
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self.init_win_idx()
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def preprocess_inputs(self, inputs):
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return self.inputs_layer(inputs)
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def update_location_attention(self, alignments):
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self.attention_weights_cum += alignments
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def get_location_attention(self, query, processed_inputs):
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attention_cat = torch.cat((self.attention_weights.unsqueeze(1),
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self.attention_weights_cum.unsqueeze(1)),
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dim=1)
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processed_query = self.query_layer(query.unsqueeze(1))
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processed_attention_weights = self.location_layer(attention_cat)
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energies = self.v(
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torch.tanh(processed_query + processed_attention_weights +
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processed_inputs))
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energies = energies.squeeze(-1)
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return energies, processed_query
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def get_attention(self, query, processed_inputs):
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processed_query = self.query_layer(query.unsqueeze(1))
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energies = self.v(torch.tanh(processed_query + processed_inputs))
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energies = energies.squeeze(-1)
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return energies, processed_query
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def apply_windowing(self, attention, inputs):
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back_win = self.win_idx - self.win_back
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front_win = self.win_idx + self.win_front
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if back_win > 0:
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attention[:, :back_win] = -float("inf")
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if front_win < inputs.shape[1]:
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attention[:, front_win:] = -float("inf")
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# this is a trick to solve a special problem.
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# but it does not hurt.
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if self.win_idx == -1:
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attention[:, 0] = attention.max()
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# Update the window
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self.win_idx = torch.argmax(attention, 1).long()[0].item()
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return attention
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def apply_forward_attention(self, alignment):
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# forward attention
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fwd_shifted_alpha = F.pad(
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self.alpha[:, :-1].clone().to(alignment.device), (1, 0, 0, 0))
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# compute transition potentials
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alpha = ((1 - self.u) * self.alpha
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+ self.u * fwd_shifted_alpha
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+ 1e-8) * alignment
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# force incremental alignment
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if not self.training and self.forward_attn_mask:
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_, n = fwd_shifted_alpha.max(1)
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val, _ = alpha.max(1)
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for b in range(alignment.shape[0]):
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alpha[b, n[b] + 3:] = 0
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alpha[b, :(
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n[b] - 1
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)] = 0 # ignore all previous states to prevent repetition.
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alpha[b,
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(n[b] - 2
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)] = 0.01 * val[b] # smoothing factor for the prev step
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# renormalize attention weights
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alpha = alpha / alpha.sum(dim=1, keepdim=True)
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return alpha
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def forward(self, query, inputs, processed_inputs, mask):
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"""
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shapes:
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query: B x D_attn_rnn
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inputs: B x T_en x D_en
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processed_inputs:: B x T_en x D_attn
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mask: B x T_en
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"""
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if self.location_attention:
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attention, _ = self.get_location_attention(
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query, processed_inputs)
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else:
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attention, _ = self.get_attention(
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query, processed_inputs)
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# apply masking
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if mask is not None:
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attention.data.masked_fill_(~mask, self._mask_value)
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# apply windowing - only in eval mode
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if not self.training and self.windowing:
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attention = self.apply_windowing(attention, inputs)
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# normalize attention values
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if self.norm == "softmax":
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alignment = torch.softmax(attention, dim=-1)
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elif self.norm == "sigmoid":
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alignment = torch.sigmoid(attention) / torch.sigmoid(
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attention).sum(
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dim=1, keepdim=True)
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else:
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raise ValueError("Unknown value for attention norm type")
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if self.location_attention:
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self.update_location_attention(alignment)
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# apply forward attention if enabled
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if self.forward_attn:
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alignment = self.apply_forward_attention(alignment)
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self.alpha = alignment
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context = torch.bmm(alignment.unsqueeze(1), inputs)
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context = context.squeeze(1)
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self.attention_weights = alignment
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# compute transition agent
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if self.forward_attn and self.trans_agent:
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ta_input = torch.cat([context, query.squeeze(1)], dim=-1)
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self.u = torch.sigmoid(self.ta(ta_input))
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return context
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class MonotonicDynamicConvolutionAttention(nn.Module):
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"""Dynamic convolution attention from
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https://arxiv.org/pdf/1910.10288.pdf
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"""
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def __init__(
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self,
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query_dim,
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embedding_dim, # pylint: disable=unused-argument
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attention_dim,
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static_filter_dim,
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static_kernel_size,
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dynamic_filter_dim,
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dynamic_kernel_size,
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prior_filter_len=11,
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alpha=0.1,
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beta=0.9,
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):
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super().__init__()
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self._mask_value = 1e-8
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self.dynamic_filter_dim = dynamic_filter_dim
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self.dynamic_kernel_size = dynamic_kernel_size
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self.prior_filter_len = prior_filter_len
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self.attention_weights = None
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# setup key and query layers
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self.query_layer = nn.Linear(query_dim, attention_dim)
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self.key_layer = nn.Linear(
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attention_dim, dynamic_filter_dim * dynamic_kernel_size, bias=False
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)
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self.static_filter_conv = nn.Conv1d(
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1,
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static_filter_dim,
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static_kernel_size,
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padding=(static_kernel_size - 1) // 2,
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bias=False,
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)
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self.static_filter_layer = nn.Linear(static_filter_dim, attention_dim, bias=False)
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self.dynamic_filter_layer = nn.Linear(dynamic_filter_dim, attention_dim)
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self.v = nn.Linear(attention_dim, 1, bias=False)
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prior = betabinom.pmf(range(prior_filter_len), prior_filter_len - 1,
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alpha, beta)
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self.register_buffer("prior", torch.FloatTensor(prior).flip(0))
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# pylint: disable=unused-argument
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def forward(self, query, inputs, processed_inputs, mask):
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# compute prior filters
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prior_filter = F.conv1d(
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F.pad(self.attention_weights.unsqueeze(1),
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(self.prior_filter_len - 1, 0)), self.prior.view(1, 1, -1))
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prior_filter = torch.log(prior_filter.clamp_min_(1e-6)).squeeze(1)
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G = self.key_layer(torch.tanh(self.query_layer(query)))
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# compute dynamic filters
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dynamic_filter = F.conv1d(
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self.attention_weights.unsqueeze(0),
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G.view(-1, 1, self.dynamic_kernel_size),
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padding=(self.dynamic_kernel_size - 1) // 2,
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groups=query.size(0),
|
|
)
|
|
dynamic_filter = dynamic_filter.view(query.size(0), self.dynamic_filter_dim, -1).transpose(1, 2)
|
|
# compute static filters
|
|
static_filter = self.static_filter_conv(self.attention_weights.unsqueeze(1)).transpose(1, 2)
|
|
alignment = self.v(
|
|
torch.tanh(
|
|
self.static_filter_layer(static_filter) +
|
|
self.dynamic_filter_layer(dynamic_filter))).squeeze(-1) + prior_filter
|
|
# compute attention weights
|
|
attention_weights = F.softmax(alignment, dim=-1)
|
|
# apply masking
|
|
if mask is not None:
|
|
attention_weights.data.masked_fill_(~mask, self._mask_value)
|
|
self.attention_weights = attention_weights
|
|
# compute context
|
|
context = torch.bmm(attention_weights.unsqueeze(1), inputs).squeeze(1)
|
|
return context
|
|
|
|
def preprocess_inputs(self, inputs): # pylint: disable=no-self-use
|
|
return None
|
|
|
|
def init_states(self, inputs):
|
|
B = inputs.size(0)
|
|
T = inputs.size(1)
|
|
self.attention_weights = torch.zeros([B, T], device=inputs.device)
|
|
self.attention_weights[:, 0] = 1.
|
|
|
|
|
|
def init_attn(attn_type, query_dim, embedding_dim, attention_dim,
|
|
location_attention, attention_location_n_filters,
|
|
attention_location_kernel_size, windowing, norm, forward_attn,
|
|
trans_agent, forward_attn_mask, attn_K):
|
|
if attn_type == "original":
|
|
return OriginalAttention(query_dim, embedding_dim, attention_dim,
|
|
location_attention,
|
|
attention_location_n_filters,
|
|
attention_location_kernel_size, windowing,
|
|
norm, forward_attn, trans_agent,
|
|
forward_attn_mask)
|
|
if attn_type == "graves":
|
|
return GravesAttention(query_dim, attn_K)
|
|
if attn_type == "dynamic_convolution":
|
|
return MonotonicDynamicConvolutionAttention(query_dim,
|
|
embedding_dim,
|
|
attention_dim,
|
|
static_filter_dim=8,
|
|
static_kernel_size=21,
|
|
dynamic_filter_dim=8,
|
|
dynamic_kernel_size=21,
|
|
prior_filter_len=11,
|
|
alpha=0.1,
|
|
beta=0.9)
|
|
|
|
raise RuntimeError(
|
|
" [!] Given Attention Type '{attn_type}' is not exist.") |