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refactor(freevc): use existing layernorm

This commit is contained in:
Enno Hermann 2024-06-24 13:28:14 +02:00
parent 857cd55ce5
commit 9f80e043e4
1 changed files with 5 additions and 19 deletions
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24
TTS/vc/modules/freevc/modules.py
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@ -6,26 +6,12 @@ from torch.nn.utils.parametrizations import weight_norm
from torch.nn.utils.parametrize import remove_parametrizations from torch.nn.utils.parametrize import remove_parametrizations
import TTS.vc.modules.freevc.commons as commons import TTS.vc.modules.freevc.commons as commons
from TTS.tts.layers.generic.normalization import LayerNorm2
from TTS.vc.modules.freevc.commons import get_padding, init_weights from TTS.vc.modules.freevc.commons import get_padding, init_weights
LRELU_SLOPE = 0.1 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): class ConvReluNorm(nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
super().__init__() super().__init__()
@ -40,11 +26,11 @@ class ConvReluNorm(nn.Module):
self.conv_layers = nn.ModuleList() self.conv_layers = nn.ModuleList()
self.norm_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.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
self.norm_layers.append(LayerNorm(hidden_channels)) self.norm_layers.append(LayerNorm2(hidden_channels))
self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
for _ in range(n_layers - 1): for _ in range(n_layers - 1):
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
self.norm_layers.append(LayerNorm(hidden_channels)) self.norm_layers.append(LayerNorm2(hidden_channels))
self.proj = nn.Conv1d(hidden_channels, out_channels, 1) self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
self.proj.weight.data.zero_() self.proj.weight.data.zero_()
self.proj.bias.data.zero_() self.proj.bias.data.zero_()
@ -83,8 +69,8 @@ class DDSConv(nn.Module):
nn.Conv1d(channels, channels, kernel_size, groups=channels, dilation=dilation, padding=padding) nn.Conv1d(channels, channels, kernel_size, groups=channels, dilation=dilation, padding=padding)
) )
self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
self.norms_1.append(LayerNorm(channels)) self.norms_1.append(LayerNorm2(channels))
self.norms_2.append(LayerNorm(channels)) self.norms_2.append(LayerNorm2(channels))
def forward(self, x, x_mask, g=None): def forward(self, x, x_mask, g=None):
if g is not None: if g is not None: