modules/wavenet.py

import math
import torch
from torch import nn
from torch.nn import functional as F

from modules.encodec import SConv1d

from . import commons
LRELU_SLOPE = 0.1

class LayerNorm(nn.Module):
    def __init__(self, channels, eps=1e-5):
        super().__init__()
        self.channels = channels
        self.eps = eps

        self.gamma = nn.Parameter(torch.ones(channels))
        self.beta = nn.Parameter(torch.zeros(channels))

    def forward(self, x):
        x = x.transpose(1, -1)
        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
        return x.transpose(1, -1)


class ConvReluNorm(nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
        super().__init__()
        self.in_channels = in_channels
        self.hidden_channels = hidden_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.n_layers = n_layers
        self.p_dropout = p_dropout
        assert n_layers > 1, "Number of layers should be larger than 0."

        self.conv_layers = nn.ModuleList()
        self.norm_layers = nn.ModuleList()
        self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
        self.norm_layers.append(LayerNorm(hidden_channels))
        self.relu_drop = nn.Sequential(
            nn.ReLU(),
            nn.Dropout(p_dropout))
        for _ in range(n_layers - 1):
            self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
            self.norm_layers.append(LayerNorm(hidden_channels))
        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
        self.proj.weight.data.zero_()
        self.proj.bias.data.zero_()

    def forward(self, x, x_mask):
        x_org = x
        for i in range(self.n_layers):
            x = self.conv_layers[i](x * x_mask)
            x = self.norm_layers[i](x)
            x = self.relu_drop(x)
        x = x_org + self.proj(x)
        return x * x_mask


class DDSConv(nn.Module):
    """
    Dialted and Depth-Separable Convolution
    """

    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
        super().__init__()
        self.channels = channels
        self.kernel_size = kernel_size
        self.n_layers = n_layers
        self.p_dropout = p_dropout

        self.drop = nn.Dropout(p_dropout)
        self.convs_sep = nn.ModuleList()
        self.convs_1x1 = nn.ModuleList()
        self.norms_1 = nn.ModuleList()
        self.norms_2 = nn.ModuleList()
        for i in range(n_layers):
            dilation = kernel_size ** i
            padding = (kernel_size * dilation - dilation) // 2
            self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
                                            groups=channels, dilation=dilation, padding=padding
                                            ))
            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
            self.norms_1.append(LayerNorm(channels))
            self.norms_2.append(LayerNorm(channels))

    def forward(self, x, x_mask, g=None):
        if g is not None:
            x = x + g
        for i in range(self.n_layers):
            y = self.convs_sep[i](x * x_mask)
            y = self.norms_1[i](y)
            y = F.gelu(y)
            y = self.convs_1x1[i](y)
            y = self.norms_2[i](y)
            y = F.gelu(y)
            y = self.drop(y)
            x = x + y
        return x * x_mask


class WN(torch.nn.Module):
    def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0, causal=False):
        super(WN, self).__init__()
        conv1d_type = SConv1d
        assert (kernel_size % 2 == 1)
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size,
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.gin_channels = gin_channels
        self.p_dropout = p_dropout

        self.in_layers = torch.nn.ModuleList()
        self.res_skip_layers = torch.nn.ModuleList()
        self.drop = nn.Dropout(p_dropout)

        if gin_channels != 0:
            self.cond_layer = conv1d_type(gin_channels, 2 * hidden_channels * n_layers, 1, norm='weight_norm')

        for i in range(n_layers):
            dilation = dilation_rate ** i
            padding = int((kernel_size * dilation - dilation) / 2)
            in_layer = conv1d_type(hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation,
                                   padding=padding, norm='weight_norm', causal=causal)
            self.in_layers.append(in_layer)

            # last one is not necessary
            if i < n_layers - 1:
                res_skip_channels = 2 * hidden_channels
            else:
                res_skip_channels = hidden_channels

            res_skip_layer = conv1d_type(hidden_channels, res_skip_channels, 1, norm='weight_norm', causal=causal)
            self.res_skip_layers.append(res_skip_layer)

    def forward(self, x, x_mask, g=None, **kwargs):
        output = torch.zeros_like(x)
        n_channels_tensor = torch.IntTensor([self.hidden_channels])

        if g is not None:
            g = self.cond_layer(g)

        for i in range(self.n_layers):
            x_in = self.in_layers[i](x)
            if g is not None:
                cond_offset = i * 2 * self.hidden_channels
                g_l = g[:, cond_offset:cond_offset + 2 * self.hidden_channels, :]
            else:
                g_l = torch.zeros_like(x_in)

            acts = commons.fused_add_tanh_sigmoid_multiply(
                x_in,
                g_l,
                n_channels_tensor)
            acts = self.drop(acts)

            res_skip_acts = self.res_skip_layers[i](acts)
            if i < self.n_layers - 1:
                res_acts = res_skip_acts[:, :self.hidden_channels, :]
                x = (x + res_acts) * x_mask
                output = output + res_skip_acts[:, self.hidden_channels:, :]
            else:
                output = output + res_skip_acts
        return output * x_mask

    def remove_weight_norm(self):
        if self.gin_channels != 0:
            torch.nn.utils.remove_weight_norm(self.cond_layer)
        for l in self.in_layers:
            torch.nn.utils.remove_weight_norm(l)
        for l in self.res_skip_layers:
            torch.nn.utils.remove_weight_norm(l)