676 lines
24 KiB
Python
676 lines
24 KiB
Python
import math
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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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from torch.nn import Conv1d
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from torch.nn.utils import weight_norm, remove_weight_norm
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from utils.util import init_weights, get_padding, convert_pad_shape, convert_pad_shape, subsequent_mask, fused_add_tanh_sigmoid_multiply
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from .common.transforms import piecewise_rational_quadratic_transform
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LRELU_SLOPE = 0.1
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class LayerNorm(nn.Module):
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def __init__(self, channels, eps=1e-5):
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super().__init__()
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self.channels = channels
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self.eps = eps
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self.gamma = nn.Parameter(torch.ones(channels))
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self.beta = nn.Parameter(torch.zeros(channels))
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def forward(self, x):
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x = x.transpose(1, -1)
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x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
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return x.transpose(1, -1)
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class ConvReluNorm(nn.Module):
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def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
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super().__init__()
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self.in_channels = in_channels
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self.hidden_channels = hidden_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.p_dropout = p_dropout
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assert n_layers > 1, "Number of layers should be larger than 0."
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self.conv_layers = nn.ModuleList()
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self.norm_layers = nn.ModuleList()
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self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
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self.norm_layers.append(LayerNorm(hidden_channels))
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self.relu_drop = nn.Sequential(
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nn.ReLU(),
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nn.Dropout(p_dropout))
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for _ in range(n_layers-1):
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self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
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self.norm_layers.append(LayerNorm(hidden_channels))
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self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
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self.proj.weight.data.zero_()
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self.proj.bias.data.zero_()
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def forward(self, x, x_mask):
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x_org = x
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for i in range(self.n_layers):
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x = self.conv_layers[i](x * x_mask)
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x = self.norm_layers[i](x)
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x = self.relu_drop(x)
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x = x_org + self.proj(x)
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return x * x_mask
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class DDSConv(nn.Module):
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"""
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Dilated and Depth-Separable Convolution
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"""
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def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
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super().__init__()
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self.channels = channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.p_dropout = p_dropout
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self.drop = nn.Dropout(p_dropout)
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self.convs_sep = nn.ModuleList()
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self.convs_1x1 = nn.ModuleList()
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self.norms_1 = nn.ModuleList()
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self.norms_2 = nn.ModuleList()
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for i in range(n_layers):
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dilation = kernel_size ** i
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padding = (kernel_size * dilation - dilation) // 2
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self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
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groups=channels, dilation=dilation, padding=padding
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))
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self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
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self.norms_1.append(LayerNorm(channels))
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self.norms_2.append(LayerNorm(channels))
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def forward(self, x, x_mask, g=None):
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if g is not None:
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x = x + g
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for i in range(self.n_layers):
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y = self.convs_sep[i](x * x_mask)
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y = self.norms_1[i](y)
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y = F.gelu(y)
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y = self.convs_1x1[i](y)
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y = self.norms_2[i](y)
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y = F.gelu(y)
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y = self.drop(y)
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x = x + y
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return x * x_mask
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class WN(torch.nn.Module):
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def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
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super(WN, self).__init__()
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assert(kernel_size % 2 == 1)
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self.hidden_channels =hidden_channels
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self.kernel_size = kernel_size,
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.p_dropout = p_dropout
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self.in_layers = torch.nn.ModuleList()
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self.res_skip_layers = torch.nn.ModuleList()
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self.drop = nn.Dropout(p_dropout)
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if gin_channels != 0:
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cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
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self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
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for i in range(n_layers):
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dilation = dilation_rate ** i
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padding = int((kernel_size * dilation - dilation) / 2)
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in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
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dilation=dilation, padding=padding)
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in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
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self.in_layers.append(in_layer)
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# last one is not necessary
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if i < n_layers - 1:
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res_skip_channels = 2 * hidden_channels
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else:
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res_skip_channels = hidden_channels
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res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
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res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
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self.res_skip_layers.append(res_skip_layer)
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def forward(self, x, x_mask, g=None, **kwargs):
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output = torch.zeros_like(x)
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n_channels_tensor = torch.IntTensor([self.hidden_channels])
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if g is not None:
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g = self.cond_layer(g)
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for i in range(self.n_layers):
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x_in = self.in_layers[i](x)
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if g is not None:
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cond_offset = i * 2 * self.hidden_channels
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g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
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else:
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g_l = torch.zeros_like(x_in)
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acts = fused_add_tanh_sigmoid_multiply(
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x_in,
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g_l,
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n_channels_tensor)
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acts = self.drop(acts)
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res_skip_acts = self.res_skip_layers[i](acts)
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if i < self.n_layers - 1:
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res_acts = res_skip_acts[:,:self.hidden_channels,:]
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x = (x + res_acts) * x_mask
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output = output + res_skip_acts[:,self.hidden_channels:,:]
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else:
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output = output + res_skip_acts
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return output * x_mask
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def remove_weight_norm(self):
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if self.gin_channels != 0:
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torch.nn.utils.remove_weight_norm(self.cond_layer)
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for l in self.in_layers:
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torch.nn.utils.remove_weight_norm(l)
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for l in self.res_skip_layers:
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torch.nn.utils.remove_weight_norm(l)
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class ResBlock1(torch.nn.Module):
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def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
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super(ResBlock1, self).__init__()
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self.convs1 = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2])))
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])
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self.convs1.apply(init_weights)
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self.convs2 = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1)))
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])
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self.convs2.apply(init_weights)
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def forward(self, x, x_mask=None):
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for c1, c2 in zip(self.convs1, self.convs2):
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xt = F.leaky_relu(x, LRELU_SLOPE)
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if x_mask is not None:
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xt = xt * x_mask
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xt = c1(xt)
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xt = F.leaky_relu(xt, LRELU_SLOPE)
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if x_mask is not None:
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xt = xt * x_mask
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xt = c2(xt)
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x = xt + x
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if x_mask is not None:
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x = x * x_mask
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return x
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def remove_weight_norm(self):
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for l in self.convs1:
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remove_weight_norm(l)
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for l in self.convs2:
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remove_weight_norm(l)
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class ResBlock2(torch.nn.Module):
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def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
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super(ResBlock2, self).__init__()
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self.convs = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1])))
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])
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self.convs.apply(init_weights)
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def forward(self, x, x_mask=None):
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for c in self.convs:
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xt = F.leaky_relu(x, LRELU_SLOPE)
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if x_mask is not None:
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xt = xt * x_mask
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xt = c(xt)
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x = xt + x
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if x_mask is not None:
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x = x * x_mask
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return x
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def remove_weight_norm(self):
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for l in self.convs:
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remove_weight_norm(l)
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class Log(nn.Module):
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def forward(self, x, x_mask, reverse=False, **kwargs):
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if not reverse:
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y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
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logdet = torch.sum(-y, [1, 2])
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return y, logdet
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else:
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x = torch.exp(x) * x_mask
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return x
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class Flip(nn.Module):
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def forward(self, x, *args, reverse=False, **kwargs):
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x = torch.flip(x, [1])
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if not reverse:
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logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
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return x, logdet
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else:
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return x
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class ElementwiseAffine(nn.Module):
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def __init__(self, channels):
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super().__init__()
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self.channels = channels
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self.m = nn.Parameter(torch.zeros(channels,1))
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self.logs = nn.Parameter(torch.zeros(channels,1))
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def forward(self, x, x_mask, reverse=False, **kwargs):
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if not reverse:
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y = self.m + torch.exp(self.logs) * x
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y = y * x_mask
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logdet = torch.sum(self.logs * x_mask, [1,2])
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return y, logdet
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else:
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x = (x - self.m) * torch.exp(-self.logs) * x_mask
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return x
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class ResidualCouplingLayer(nn.Module):
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def __init__(self,
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channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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p_dropout=0,
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gin_channels=0,
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mean_only=False):
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assert channels % 2 == 0, "channels should be divisible by 2"
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super().__init__()
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self.channels = channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.half_channels = channels // 2
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self.mean_only = mean_only
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self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
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self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
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self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
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self.post.weight.data.zero_()
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self.post.bias.data.zero_()
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def forward(self, x, x_mask, g=None, reverse=False):
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x0, x1 = torch.split(x, [self.half_channels]*2, 1)
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h = self.pre(x0) * x_mask
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h = self.enc(h, x_mask, g=g)
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stats = self.post(h) * x_mask
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if not self.mean_only:
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m, logs = torch.split(stats, [self.half_channels]*2, 1)
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else:
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m = stats
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logs = torch.zeros_like(m)
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if not reverse:
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x1 = m + x1 * torch.exp(logs) * x_mask
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x = torch.cat([x0, x1], 1)
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logdet = torch.sum(logs, [1,2])
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return x, logdet
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else:
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x1 = (x1 - m) * torch.exp(-logs) * x_mask
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x = torch.cat([x0, x1], 1)
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return x
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class ConvFlow(nn.Module):
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def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
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super().__init__()
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self.in_channels = in_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.num_bins = num_bins
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self.tail_bound = tail_bound
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self.half_channels = in_channels // 2
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self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
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self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
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self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
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self.proj.weight.data.zero_()
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self.proj.bias.data.zero_()
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def forward(self, x, x_mask, g=None, reverse=False):
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x0, x1 = torch.split(x, [self.half_channels]*2, 1)
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h = self.pre(x0)
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h = self.convs(h, x_mask, g=g)
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h = self.proj(h) * x_mask
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b, c, t = x0.shape
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h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
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unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
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unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
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unnormalized_derivatives = h[..., 2 * self.num_bins:]
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x1, logabsdet = piecewise_rational_quadratic_transform(x1,
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unnormalized_widths,
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unnormalized_heights,
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unnormalized_derivatives,
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inverse=reverse,
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tails='linear',
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tail_bound=self.tail_bound
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)
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x = torch.cat([x0, x1], 1) * x_mask
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logdet = torch.sum(logabsdet * x_mask, [1,2])
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if not reverse:
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return x, logdet
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else:
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return x
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class Encoder(nn.Module):
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def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
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super().__init__()
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.window_size = window_size
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self.drop = nn.Dropout(p_dropout)
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self.attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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def forward(self, x, x_mask):
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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y = self.attn_layers[i](x, x, attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = x * x_mask
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return x
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class Decoder(nn.Module):
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def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
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super().__init__()
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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self.drop = nn.Dropout(p_dropout)
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self.self_attn_layers = nn.ModuleList()
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self.norm_layers_0 = nn.ModuleList()
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self.encdec_attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
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self.norm_layers_0.append(LayerNorm(hidden_channels))
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self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
|
|
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
|
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
|
|
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
|
|
|
def forward(self, x, x_mask, h, h_mask):
|
|
"""
|
|
x: decoder input
|
|
h: encoder output
|
|
"""
|
|
self_attn_mask = subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
|
|
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
|
x = x * x_mask
|
|
for i in range(self.n_layers):
|
|
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
|
y = self.drop(y)
|
|
x = self.norm_layers_0[i](x + y)
|
|
|
|
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
|
|
y = self.drop(y)
|
|
x = self.norm_layers_1[i](x + y)
|
|
|
|
y = self.ffn_layers[i](x, x_mask)
|
|
y = self.drop(y)
|
|
x = self.norm_layers_2[i](x + y)
|
|
x = x * x_mask
|
|
return x
|
|
|
|
|
|
class MultiHeadAttention(nn.Module):
|
|
def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
|
|
super().__init__()
|
|
assert channels % n_heads == 0
|
|
|
|
self.channels = channels
|
|
self.out_channels = out_channels
|
|
self.n_heads = n_heads
|
|
self.p_dropout = p_dropout
|
|
self.window_size = window_size
|
|
self.heads_share = heads_share
|
|
self.block_length = block_length
|
|
self.proximal_bias = proximal_bias
|
|
self.proximal_init = proximal_init
|
|
self.attn = None
|
|
|
|
self.k_channels = channels // n_heads
|
|
self.conv_q = nn.Conv1d(channels, channels, 1)
|
|
self.conv_k = nn.Conv1d(channels, channels, 1)
|
|
self.conv_v = nn.Conv1d(channels, channels, 1)
|
|
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
|
self.drop = nn.Dropout(p_dropout)
|
|
|
|
if window_size is not None:
|
|
n_heads_rel = 1 if heads_share else n_heads
|
|
rel_stddev = self.k_channels**-0.5
|
|
self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
|
self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
|
|
|
nn.init.xavier_uniform_(self.conv_q.weight)
|
|
nn.init.xavier_uniform_(self.conv_k.weight)
|
|
nn.init.xavier_uniform_(self.conv_v.weight)
|
|
if proximal_init:
|
|
with torch.no_grad():
|
|
self.conv_k.weight.copy_(self.conv_q.weight)
|
|
self.conv_k.bias.copy_(self.conv_q.bias)
|
|
|
|
def forward(self, x, c, attn_mask=None):
|
|
q = self.conv_q(x)
|
|
k = self.conv_k(c)
|
|
v = self.conv_v(c)
|
|
|
|
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
|
|
|
x = self.conv_o(x)
|
|
return x
|
|
|
|
def attention(self, query, key, value, mask=None):
|
|
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
|
b, d, t_s, t_t = (*key.size(), query.size(2))
|
|
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
|
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
|
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
|
|
|
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
|
if self.window_size is not None:
|
|
assert t_s == t_t, "Relative attention is only available for self-attention."
|
|
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
|
rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
|
|
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
|
scores = scores + scores_local
|
|
if self.proximal_bias:
|
|
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
|
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
|
if mask is not None:
|
|
scores = scores.masked_fill(mask == 0, -1e4)
|
|
if self.block_length is not None:
|
|
assert t_s == t_t, "Local attention is only available for self-attention."
|
|
block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
|
|
scores = scores.masked_fill(block_mask == 0, -1e4)
|
|
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
|
p_attn = self.drop(p_attn)
|
|
output = torch.matmul(p_attn, value)
|
|
if self.window_size is not None:
|
|
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
|
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
|
|
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
|
|
output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
|
return output, p_attn
|
|
|
|
def _matmul_with_relative_values(self, x, y):
|
|
"""
|
|
x: [b, h, l, m]
|
|
y: [h or 1, m, d]
|
|
ret: [b, h, l, d]
|
|
"""
|
|
ret = torch.matmul(x, y.unsqueeze(0))
|
|
return ret
|
|
|
|
def _matmul_with_relative_keys(self, x, y):
|
|
"""
|
|
x: [b, h, l, d]
|
|
y: [h or 1, m, d]
|
|
ret: [b, h, l, m]
|
|
"""
|
|
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
|
return ret
|
|
|
|
def _get_relative_embeddings(self, relative_embeddings, length):
|
|
max_relative_position = 2 * self.window_size + 1
|
|
# Pad first before slice to avoid using cond ops.
|
|
pad_length = max(length - (self.window_size + 1), 0)
|
|
slice_start_position = max((self.window_size + 1) - length, 0)
|
|
slice_end_position = slice_start_position + 2 * length - 1
|
|
if pad_length > 0:
|
|
padded_relative_embeddings = F.pad(
|
|
relative_embeddings,
|
|
convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
|
|
else:
|
|
padded_relative_embeddings = relative_embeddings
|
|
used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
|
|
return used_relative_embeddings
|
|
|
|
def _relative_position_to_absolute_position(self, x):
|
|
"""
|
|
x: [b, h, l, 2*l-1]
|
|
ret: [b, h, l, l]
|
|
"""
|
|
batch, heads, length, _ = x.size()
|
|
# Concat columns of pad to shift from relative to absolute indexing.
|
|
x = F.pad(x, convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
|
|
|
|
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
|
x_flat = x.view([batch, heads, length * 2 * length])
|
|
x_flat = F.pad(x_flat, convert_pad_shape([[0,0],[0,0],[0,length-1]]))
|
|
|
|
# Reshape and slice out the padded elements.
|
|
x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
|
|
return x_final
|
|
|
|
def _absolute_position_to_relative_position(self, x):
|
|
"""
|
|
x: [b, h, l, l]
|
|
ret: [b, h, l, 2*l-1]
|
|
"""
|
|
batch, heads, length, _ = x.size()
|
|
# padd along column
|
|
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
|
|
x_flat = x.view([batch, heads, length**2 + length*(length -1)])
|
|
# add 0's in the beginning that will skew the elements after reshape
|
|
x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
|
x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
|
|
return x_final
|
|
|
|
def _attention_bias_proximal(self, length):
|
|
"""Bias for self-attention to encourage attention to close positions.
|
|
Args:
|
|
length: an integer scalar.
|
|
Returns:
|
|
a Tensor with shape [1, 1, length, length]
|
|
"""
|
|
r = torch.arange(length, dtype=torch.float32)
|
|
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
|
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
|
|
|
|
|
class FFN(nn.Module):
|
|
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.filter_channels = filter_channels
|
|
self.kernel_size = kernel_size
|
|
self.p_dropout = p_dropout
|
|
self.activation = activation
|
|
self.causal = causal
|
|
|
|
if causal:
|
|
self.padding = self._causal_padding
|
|
else:
|
|
self.padding = self._same_padding
|
|
|
|
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
|
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
|
self.drop = nn.Dropout(p_dropout)
|
|
|
|
def forward(self, x, x_mask):
|
|
x = self.conv_1(self.padding(x * x_mask))
|
|
if self.activation == "gelu":
|
|
x = x * torch.sigmoid(1.702 * x)
|
|
else:
|
|
x = torch.relu(x)
|
|
x = self.drop(x)
|
|
x = self.conv_2(self.padding(x * x_mask))
|
|
return x * x_mask
|
|
|
|
def _causal_padding(self, x):
|
|
if self.kernel_size == 1:
|
|
return x
|
|
pad_l = self.kernel_size - 1
|
|
pad_r = 0
|
|
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
|
x = F.pad(x, convert_pad_shape(padding))
|
|
return x
|
|
|
|
def _same_padding(self, x):
|
|
if self.kernel_size == 1:
|
|
return x
|
|
pad_l = (self.kernel_size - 1) // 2
|
|
pad_r = self.kernel_size // 2
|
|
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
|
x = F.pad(x, convert_pad_shape(padding))
|
|
return x
|