зеркало из https://github.com/mozilla/TTS.git
495 строки
18 KiB
Python
495 строки
18 KiB
Python
# coding: utf-8
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import torch
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from torch import nn
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from .common_layers import Prenet, Attention, Linear
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class BatchNormConv1d(nn.Module):
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r"""A wrapper for Conv1d with BatchNorm. It sets the activation
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function between Conv and BatchNorm layers. BatchNorm layer
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is initialized with the TF default values for momentum and eps.
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Args:
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in_channels: size of each input sample
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out_channels: size of each output samples
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kernel_size: kernel size of conv filters
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stride: stride of conv filters
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padding: padding of conv filters
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activation: activation function set b/w Conv1d and BatchNorm
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Shapes:
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- input: batch x dims
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- output: batch x dims
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"""
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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activation=None):
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super(BatchNormConv1d, self).__init__()
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self.padding = padding
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self.padder = nn.ConstantPad1d(padding, 0)
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self.conv1d = nn.Conv1d(
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in_channels,
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out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=0,
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bias=False)
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# Following tensorflow's default parameters
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self.bn = nn.BatchNorm1d(out_channels, momentum=0.99, eps=1e-3)
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self.activation = activation
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# self.init_layers()
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def init_layers(self):
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if type(self.activation) == torch.nn.ReLU:
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w_gain = 'relu'
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elif type(self.activation) == torch.nn.Tanh:
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w_gain = 'tanh'
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elif self.activation is None:
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w_gain = 'linear'
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else:
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raise RuntimeError('Unknown activation function')
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torch.nn.init.xavier_uniform_(
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self.conv1d.weight, gain=torch.nn.init.calculate_gain(w_gain))
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def forward(self, x):
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x = self.padder(x)
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x = self.conv1d(x)
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x = self.bn(x)
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if self.activation is not None:
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x = self.activation(x)
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return x
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class Highway(nn.Module):
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# TODO: Try GLU layer
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def __init__(self, in_size, out_size):
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super(Highway, self).__init__()
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self.H = nn.Linear(in_size, out_size)
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self.H.bias.data.zero_()
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self.T = nn.Linear(in_size, out_size)
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self.T.bias.data.fill_(-1)
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self.relu = nn.ReLU()
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self.sigmoid = nn.Sigmoid()
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# self.init_layers()
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def init_layers(self):
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torch.nn.init.xavier_uniform_(
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self.H.weight, gain=torch.nn.init.calculate_gain('relu'))
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torch.nn.init.xavier_uniform_(
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self.T.weight, gain=torch.nn.init.calculate_gain('sigmoid'))
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def forward(self, inputs):
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H = self.relu(self.H(inputs))
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T = self.sigmoid(self.T(inputs))
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return H * T + inputs * (1.0 - T)
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class CBHG(nn.Module):
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"""CBHG module: a recurrent neural network composed of:
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- 1-d convolution banks
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- Highway networks + residual connections
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- Bidirectional gated recurrent units
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Args:
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in_features (int): sample size
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K (int): max filter size in conv bank
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projections (list): conv channel sizes for conv projections
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num_highways (int): number of highways layers
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Shapes:
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- input: B x D x T_in
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- output: B x T_in x D*2
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"""
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def __init__(self,
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in_features,
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K=16,
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conv_bank_features=128,
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conv_projections=[128, 128],
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highway_features=128,
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gru_features=128,
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num_highways=4):
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super(CBHG, self).__init__()
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self.in_features = in_features
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self.conv_bank_features = conv_bank_features
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self.highway_features = highway_features
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self.gru_features = gru_features
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self.conv_projections = conv_projections
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self.relu = nn.ReLU()
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# list of conv1d bank with filter size k=1...K
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# TODO: try dilational layers instead
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self.conv1d_banks = nn.ModuleList([
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BatchNormConv1d(in_features,
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conv_bank_features,
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kernel_size=k,
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stride=1,
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padding=[(k - 1) // 2, k // 2],
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activation=self.relu) for k in range(1, K + 1)
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])
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# max pooling of conv bank, with padding
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# TODO: try average pooling OR larger kernel size
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out_features = [K * conv_bank_features] + conv_projections[:-1]
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activations = [self.relu] * (len(conv_projections) - 1)
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activations += [None]
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# setup conv1d projection layers
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layer_set = []
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for (in_size, out_size, ac) in zip(out_features, conv_projections,
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activations):
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layer = BatchNormConv1d(in_size,
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out_size,
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kernel_size=3,
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stride=1,
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padding=[1, 1],
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activation=ac)
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layer_set.append(layer)
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self.conv1d_projections = nn.ModuleList(layer_set)
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# setup Highway layers
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if self.highway_features != conv_projections[-1]:
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self.pre_highway = nn.Linear(conv_projections[-1],
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highway_features,
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bias=False)
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self.highways = nn.ModuleList([
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Highway(highway_features, highway_features)
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for _ in range(num_highways)
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])
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# bi-directional GPU layer
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self.gru = nn.GRU(gru_features,
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gru_features,
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1,
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batch_first=True,
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bidirectional=True)
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def forward(self, inputs):
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# (B, in_features, T_in)
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x = inputs
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# (B, hid_features*K, T_in)
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# Concat conv1d bank outputs
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outs = []
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for conv1d in self.conv1d_banks:
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out = conv1d(x)
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outs.append(out)
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x = torch.cat(outs, dim=1)
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assert x.size(1) == self.conv_bank_features * len(self.conv1d_banks)
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for conv1d in self.conv1d_projections:
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x = conv1d(x)
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x += inputs
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x = x.transpose(1, 2)
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if self.highway_features != self.conv_projections[-1]:
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x = self.pre_highway(x)
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# Residual connection
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# TODO: try residual scaling as in Deep Voice 3
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# TODO: try plain residual layers
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for highway in self.highways:
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x = highway(x)
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# (B, T_in, hid_features*2)
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# TODO: replace GRU with convolution as in Deep Voice 3
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self.gru.flatten_parameters()
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outputs, _ = self.gru(x)
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return outputs
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class EncoderCBHG(nn.Module):
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def __init__(self):
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super(EncoderCBHG, self).__init__()
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self.cbhg = CBHG(
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128,
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K=16,
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conv_bank_features=128,
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conv_projections=[128, 128],
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highway_features=128,
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gru_features=128,
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num_highways=4)
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def forward(self, x):
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return self.cbhg(x)
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class Encoder(nn.Module):
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r"""Encapsulate Prenet and CBHG modules for encoder"""
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def __init__(self, in_features):
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super(Encoder, self).__init__()
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self.prenet = Prenet(in_features, out_features=[256, 128])
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self.cbhg = EncoderCBHG()
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def forward(self, inputs):
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r"""
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Args:
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inputs (FloatTensor): embedding features
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Shapes:
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- inputs: batch x time x in_features
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- outputs: batch x time x 128*2
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"""
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# B x T x prenet_dim
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outputs = self.prenet(inputs)
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outputs = self.cbhg(outputs.transpose(1, 2))
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return outputs
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class PostCBHG(nn.Module):
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def __init__(self, mel_dim):
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super(PostCBHG, self).__init__()
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self.cbhg = CBHG(
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mel_dim,
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K=8,
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conv_bank_features=128,
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conv_projections=[256, mel_dim],
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highway_features=128,
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gru_features=128,
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num_highways=4)
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def forward(self, x):
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return self.cbhg(x)
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class Decoder(nn.Module):
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"""Decoder module.
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Args:
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in_features (int): input vector (encoder output) sample size.
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memory_dim (int): memory vector (prev. time-step output) sample size.
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r (int): number of outputs per time step.
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memory_size (int): size of the past window. if <= 0 memory_size = r
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TODO: arguments
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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, in_features, memory_dim, r, memory_size, attn_windowing,
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attn_norm, prenet_type, prenet_dropout, forward_attn,
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trans_agent, forward_attn_mask, location_attn,
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separate_stopnet, speaker_embedding_dim):
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super(Decoder, self).__init__()
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self.r_init = r
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self.r = r
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self.in_features = in_features
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self.max_decoder_steps = 500
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self.use_memory_queue = memory_size > 0
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self.memory_size = memory_size if memory_size > 0 else r
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self.memory_dim = memory_dim
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self.separate_stopnet = separate_stopnet
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self.query_dim = 256
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# memory -> |Prenet| -> processed_memory
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prenet_dim = memory_dim * self.memory_size + speaker_embedding_dim if self.use_memory_queue else memory_dim + speaker_embedding_dim
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self.prenet = Prenet(
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prenet_dim,
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prenet_type,
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prenet_dropout,
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out_features=[256, 128])
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# processed_inputs, processed_memory -> |Attention| -> Attention, attention, RNN_State
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# attention_rnn generates queries for the attention mechanism
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self.attention_rnn = nn.GRUCell(in_features + 128, self.query_dim)
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self.attention = Attention(query_dim=self.query_dim,
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embedding_dim=in_features,
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attention_dim=128,
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location_attention=location_attn,
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attention_location_n_filters=32,
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attention_location_kernel_size=31,
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windowing=attn_windowing,
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norm=attn_norm,
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forward_attn=forward_attn,
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trans_agent=trans_agent,
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forward_attn_mask=forward_attn_mask)
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# (processed_memory | attention context) -> |Linear| -> decoder_RNN_input
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self.project_to_decoder_in = nn.Linear(256 + in_features, 256)
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# decoder_RNN_input -> |RNN| -> RNN_state
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self.decoder_rnns = nn.ModuleList(
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[nn.GRUCell(256, 256) for _ in range(2)])
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# RNN_state -> |Linear| -> mel_spec
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self.proj_to_mel = nn.Linear(256, memory_dim * self.r_init)
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# learn init values instead of zero init.
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self.stopnet = StopNet(256 + memory_dim * self.r_init)
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def set_r(self, new_r):
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self.r = new_r
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def _reshape_memory(self, memory):
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"""
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Reshape the spectrograms for given 'r'
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"""
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# Grouping multiple frames if necessary
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if memory.size(-1) == self.memory_dim:
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memory = memory.view(memory.shape[0], memory.size(1) // self.r, -1)
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# Time first (T_decoder, B, memory_dim)
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memory = memory.transpose(0, 1)
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return memory
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def _init_states(self, inputs):
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"""
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Initialization of decoder states
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"""
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B = inputs.size(0)
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T = inputs.size(1)
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# go frame as zeros matrix
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if self.use_memory_queue:
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self.memory_input = torch.zeros(1, device=inputs.device).repeat(B, self.memory_dim * self.memory_size)
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else:
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self.memory_input = torch.zeros(1, device=inputs.device).repeat(B, self.memory_dim)
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# decoder states
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self.attention_rnn_hidden = torch.zeros(1, device=inputs.device).repeat(B, 256)
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self.decoder_rnn_hiddens = [
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torch.zeros(1, device=inputs.device).repeat(B, 256)
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for idx in range(len(self.decoder_rnns))
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]
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self.context_vec = inputs.data.new(B, self.in_features).zero_()
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# cache attention inputs
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self.processed_inputs = self.attention.inputs_layer(inputs)
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def _parse_outputs(self, outputs, attentions, stop_tokens):
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# Back to batch first
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attentions = torch.stack(attentions).transpose(0, 1)
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stop_tokens = torch.stack(stop_tokens).transpose(0, 1)
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outputs = torch.stack(outputs).transpose(0, 1).contiguous()
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outputs = outputs.view(
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outputs.size(0), -1, self.memory_dim)
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outputs = outputs.transpose(1, 2)
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return outputs, attentions, stop_tokens
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def decode(self, inputs, mask=None):
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# Prenet
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processed_memory = self.prenet(self.memory_input)
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# Attention RNN
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self.attention_rnn_hidden = self.attention_rnn(
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torch.cat((processed_memory, self.context_vec), -1),
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self.attention_rnn_hidden)
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self.context_vec = self.attention(
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self.attention_rnn_hidden, inputs, self.processed_inputs, mask)
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# Concat RNN output and attention context vector
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decoder_input = self.project_to_decoder_in(
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torch.cat((self.attention_rnn_hidden, self.context_vec), -1))
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# Pass through the decoder RNNs
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for idx in range(len(self.decoder_rnns)):
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self.decoder_rnn_hiddens[idx] = self.decoder_rnns[idx](
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decoder_input, self.decoder_rnn_hiddens[idx])
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# Residual connection
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decoder_input = self.decoder_rnn_hiddens[idx] + decoder_input
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decoder_output = decoder_input
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# predict mel vectors from decoder vectors
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output = self.proj_to_mel(decoder_output)
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# output = torch.sigmoid(output)
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# predict stop token
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stopnet_input = torch.cat([decoder_output, output], -1)
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if self.separate_stopnet:
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stop_token = self.stopnet(stopnet_input.detach())
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else:
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stop_token = self.stopnet(stopnet_input)
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output = output[:, : self.r * self.memory_dim]
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return output, stop_token, self.attention.attention_weights
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def _update_memory_input(self, new_memory):
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if self.use_memory_queue:
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if self.memory_size > self.r:
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# memory queue size is larger than number of frames per decoder iter
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self.memory_input = torch.cat([
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new_memory, self.memory_input[:, :(
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self.memory_size - self.r) * self.memory_dim].clone()
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], dim=-1)
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else:
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# memory queue size smaller than number of frames per decoder iter
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self.memory_input = new_memory[:, :self.memory_size * self.memory_dim]
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else:
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# use only the last frame prediction
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# assert new_memory.shape[-1] == self.r * self.memory_dim
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self.memory_input = new_memory[:, self.memory_dim * (self.r - 1):]
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def forward(self, inputs, memory, mask, speaker_embeddings=None):
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"""
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Args:
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inputs: Encoder outputs.
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memory: Decoder memory (autoregression. If None (at eval-time),
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decoder outputs are used as decoder inputs. If None, it uses the last
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output as the input.
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mask: Attention mask for sequence padding.
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Shapes:
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- inputs: batch x time x encoder_out_dim
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- memory: batch x #mel_specs x mel_spec_dim
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"""
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# Run greedy decoding if memory is None
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memory = self._reshape_memory(memory)
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outputs = []
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attentions = []
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stop_tokens = []
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t = 0
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self._init_states(inputs)
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self.attention.init_states(inputs)
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while len(outputs) < memory.size(0):
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if t > 0:
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new_memory = memory[t - 1]
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self._update_memory_input(new_memory)
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if speaker_embeddings is not None:
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self.memory_input = torch.cat([self.memory_input, speaker_embeddings], dim=-1)
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output, stop_token, attention = self.decode(inputs, mask)
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outputs += [output]
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attentions += [attention]
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stop_tokens += [stop_token.squeeze(1)]
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t += 1
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return self._parse_outputs(outputs, attentions, stop_tokens)
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def inference(self, inputs, speaker_embeddings=None):
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"""
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Args:
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inputs: encoder outputs.
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speaker_embeddings: speaker vectors.
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Shapes:
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- inputs: batch x time x encoder_out_dim
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- speaker_embeddings: batch x embed_dim
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"""
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outputs = []
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attentions = []
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stop_tokens = []
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t = 0
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self._init_states(inputs)
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self.attention.init_win_idx()
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self.attention.init_states(inputs)
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while True:
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if t > 0:
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new_memory = outputs[-1]
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self._update_memory_input(new_memory)
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if speaker_embeddings is not None:
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self.memory_input = torch.cat([self.memory_input, speaker_embeddings], dim=-1)
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output, stop_token, attention = self.decode(inputs, None)
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stop_token = torch.sigmoid(stop_token.data)
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outputs += [output]
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attentions += [attention]
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stop_tokens += [stop_token]
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t += 1
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if t > inputs.shape[1] / 4 and (stop_token > 0.6
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or attention[:, -1].item() > 0.6):
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break
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elif t > self.max_decoder_steps:
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print(" | > Decoder stopped with 'max_decoder_steps")
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break
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return self._parse_outputs(outputs, attentions, stop_tokens)
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class StopNet(nn.Module):
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r"""
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Args:
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in_features (int): feature dimension of input.
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"""
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def __init__(self, in_features):
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super(StopNet, self).__init__()
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self.dropout = nn.Dropout(0.1)
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self.linear = nn.Linear(in_features, 1)
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torch.nn.init.xavier_uniform_(
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self.linear.weight, gain=torch.nn.init.calculate_gain('linear'))
|
|
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def forward(self, inputs):
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outputs = self.dropout(inputs)
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outputs = self.linear(outputs)
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|
return outputs
|