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import os
import sys
import torch
sys.path.append(os.getcwd())
from .commons import fused_add_tanh_sigmoid_multiply
class WaveNet(torch.nn.Module):
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
super(WaveNet, self).__init__()
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 = torch.nn.Dropout(p_dropout)
if gin_channels != 0: self.cond_layer = torch.nn.utils.parametrizations.weight_norm(torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1), name="weight")
dilations = [dilation_rate**i for i in range(n_layers)]
paddings = [(kernel_size * d - d) // 2 for d in dilations]
for i in range(n_layers):
in_layer = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilations[i], padding=paddings[i])
in_layer = torch.nn.utils.parametrizations.weight_norm(in_layer, name="weight")
self.in_layers.append(in_layer)
res_skip_channels = (hidden_channels if i == n_layers - 1 else 2 * hidden_channels)
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
res_skip_layer = torch.nn.utils.parametrizations.weight_norm(res_skip_layer, name="weight")
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)
res_skip_acts = self.res_skip_layers[i](self.drop(fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)))
if i < self.n_layers - 1:
x = (x + (res_skip_acts[:, : self.hidden_channels, :])) * 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) |