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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 openvoice import commons
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from openvoice.commons import init_weights, get_padding
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from openvoice.transforms import piecewise_rational_quadratic_transform
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from openvoice.attentions import Encoder
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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__(
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self,
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in_channels,
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hidden_channels,
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out_channels,
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kernel_size,
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n_layers,
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p_dropout,
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):
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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(
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nn.Conv1d(
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in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
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)
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)
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self.norm_layers.append(LayerNorm(hidden_channels))
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self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
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for _ in range(n_layers - 1):
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self.conv_layers.append(
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nn.Conv1d(
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hidden_channels,
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hidden_channels,
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kernel_size,
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padding=kernel_size // 2,
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)
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)
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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=0.0):
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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(
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nn.Conv1d(
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channels,
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channels,
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kernel_size,
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groups=channels,
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dilation=dilation,
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padding=padding,
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)
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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__(
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self,
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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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gin_channels=0,
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p_dropout=0,
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):
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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(
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gin_channels, 2 * hidden_channels * n_layers, 1
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)
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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(
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hidden_channels,
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2 * hidden_channels,
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kernel_size,
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dilation=dilation,
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padding=padding,
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)
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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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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 = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, 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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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2]),
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)
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),
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]
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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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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=1,
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padding=get_padding(kernel_size, 1),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=1,
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padding=get_padding(kernel_size, 1),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=1,
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padding=get_padding(kernel_size, 1),
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)
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),
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]
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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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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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]
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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__(
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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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):
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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(
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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=p_dropout,
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gin_channels=gin_channels,
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)
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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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|
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if not reverse:
|
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|
x1 = m + x1 * torch.exp(logs) * x_mask
|
|
|
x = torch.cat([x0, x1], 1)
|
|
|
logdet = torch.sum(logs, [1, 2])
|
|
|
return x, logdet
|
|
|
else:
|
|
|
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
|
|
x = torch.cat([x0, x1], 1)
|
|
|
return x
|
|
|
|
|
|
|
|
|
class ConvFlow(nn.Module):
|
|
|
def __init__(
|
|
|
self,
|
|
|
in_channels,
|
|
|
filter_channels,
|
|
|
kernel_size,
|
|
|
n_layers,
|
|
|
num_bins=10,
|
|
|
tail_bound=5.0,
|
|
|
):
|
|
|
super().__init__()
|
|
|
self.in_channels = in_channels
|
|
|
self.filter_channels = filter_channels
|
|
|
self.kernel_size = kernel_size
|
|
|
self.n_layers = n_layers
|
|
|
self.num_bins = num_bins
|
|
|
self.tail_bound = tail_bound
|
|
|
self.half_channels = in_channels // 2
|
|
|
|
|
|
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
|
|
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
|
|
|
self.proj = nn.Conv1d(
|
|
|
filter_channels, self.half_channels * (num_bins * 3 - 1), 1
|
|
|
)
|
|
|
self.proj.weight.data.zero_()
|
|
|
self.proj.bias.data.zero_()
|
|
|
|
|
|
def forward(self, x, x_mask, g=None, reverse=False):
|
|
|
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
|
|
h = self.pre(x0)
|
|
|
h = self.convs(h, x_mask, g=g)
|
|
|
h = self.proj(h) * x_mask
|
|
|
|
|
|
b, c, t = x0.shape
|
|
|
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)
|
|
|
|
|
|
unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
|
|
|
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
|
|
|
self.filter_channels
|
|
|
)
|
|
|
unnormalized_derivatives = h[..., 2 * self.num_bins :]
|
|
|
|
|
|
x1, logabsdet = piecewise_rational_quadratic_transform(
|
|
|
x1,
|
|
|
unnormalized_widths,
|
|
|
unnormalized_heights,
|
|
|
unnormalized_derivatives,
|
|
|
inverse=reverse,
|
|
|
tails="linear",
|
|
|
tail_bound=self.tail_bound,
|
|
|
)
|
|
|
|
|
|
x = torch.cat([x0, x1], 1) * x_mask
|
|
|
logdet = torch.sum(logabsdet * x_mask, [1, 2])
|
|
|
if not reverse:
|
|
|
return x, logdet
|
|
|
else:
|
|
|
return x
|
|
|
|
|
|
|
|
|
class TransformerCouplingLayer(nn.Module):
|
|
|
def __init__(
|
|
|
self,
|
|
|
channels,
|
|
|
hidden_channels,
|
|
|
kernel_size,
|
|
|
n_layers,
|
|
|
n_heads,
|
|
|
p_dropout=0,
|
|
|
filter_channels=0,
|
|
|
mean_only=False,
|
|
|
wn_sharing_parameter=None,
|
|
|
gin_channels=0,
|
|
|
):
|
|
|
assert n_layers == 3, n_layers
|
|
|
assert channels % 2 == 0, "channels should be divisible by 2"
|
|
|
super().__init__()
|
|
|
self.channels = channels
|
|
|
self.hidden_channels = hidden_channels
|
|
|
self.kernel_size = kernel_size
|
|
|
self.n_layers = n_layers
|
|
|
self.half_channels = channels // 2
|
|
|
self.mean_only = mean_only
|
|
|
|
|
|
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
|
|
self.enc = (
|
|
|
Encoder(
|
|
|
hidden_channels,
|
|
|
filter_channels,
|
|
|
n_heads,
|
|
|
n_layers,
|
|
|
kernel_size,
|
|
|
p_dropout,
|
|
|
isflow=True,
|
|
|
gin_channels=gin_channels,
|
|
|
)
|
|
|
if wn_sharing_parameter is None
|
|
|
else wn_sharing_parameter
|
|
|
)
|
|
|
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
|
|
self.post.weight.data.zero_()
|
|
|
self.post.bias.data.zero_()
|
|
|
|
|
|
def forward(self, x, x_mask, g=None, reverse=False):
|
|
|
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
|
|
h = self.pre(x0) * x_mask
|
|
|
h = self.enc(h, x_mask, g=g)
|
|
|
stats = self.post(h) * x_mask
|
|
|
if not self.mean_only:
|
|
|
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
|
|
|
else:
|
|
|
m = stats
|
|
|
logs = torch.zeros_like(m)
|
|
|
|
|
|
if not reverse:
|
|
|
x1 = m + x1 * torch.exp(logs) * x_mask
|
|
|
x = torch.cat([x0, x1], 1)
|
|
|
logdet = torch.sum(logs, [1, 2])
|
|
|
return x, logdet
|
|
|
else:
|
|
|
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
|
|
x = torch.cat([x0, x1], 1)
|
|
|
return x
|
|
|
|
|
|
x1, logabsdet = piecewise_rational_quadratic_transform(
|
|
|
x1,
|
|
|
unnormalized_widths,
|
|
|
unnormalized_heights,
|
|
|
unnormalized_derivatives,
|
|
|
inverse=reverse,
|
|
|
tails="linear",
|
|
|
tail_bound=self.tail_bound,
|
|
|
)
|
|
|
|
|
|
x = torch.cat([x0, x1], 1) * x_mask
|
|
|
logdet = torch.sum(logabsdet * x_mask, [1, 2])
|
|
|
if not reverse:
|
|
|
return x, logdet
|
|
|
else:
|
|
|
return x
|
|
|
|
