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"""
Code taken from https://github.com/tuanh123789/AdaSpeech/blob/main/model/adaspeech_modules.py
By https://github.com/tuanh123789
No license specified
Implemented as outlined in AdaSpeech https://arxiv.org/pdf/2103.00993.pdf
Used in this toolkit similar to how it is done in AdaSpeech 4 https://arxiv.org/pdf/2204.00436.pdf
"""
import torch
from torch import nn
class ConditionalLayerNorm(nn.Module):
def __init__(self,
hidden_dim,
speaker_embedding_dim,
dim=-1):
super(ConditionalLayerNorm, self).__init__()
self.dim = dim
if isinstance(hidden_dim, int):
self.normal_shape = hidden_dim
self.speaker_embedding_dim = speaker_embedding_dim
self.W_scale = nn.Sequential(nn.Linear(self.speaker_embedding_dim, self.normal_shape),
nn.Tanh(),
nn.Linear(self.normal_shape, self.normal_shape))
self.W_bias = nn.Sequential(nn.Linear(self.speaker_embedding_dim, self.normal_shape),
nn.Tanh(),
nn.Linear(self.normal_shape, self.normal_shape))
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.constant_(self.W_scale[0].weight, 0.0)
torch.nn.init.constant_(self.W_scale[2].weight, 0.0)
torch.nn.init.constant_(self.W_scale[0].bias, 1.0)
torch.nn.init.constant_(self.W_scale[2].bias, 1.0)
torch.nn.init.constant_(self.W_bias[0].weight, 0.0)
torch.nn.init.constant_(self.W_bias[2].weight, 0.0)
torch.nn.init.constant_(self.W_bias[0].bias, 0.0)
torch.nn.init.constant_(self.W_bias[2].bias, 0.0)
def forward(self, x, speaker_embedding):
if self.dim != -1:
x = x.transpose(-1, self.dim)
mean = x.mean(dim=-1, keepdim=True)
var = ((x - mean) ** 2).mean(dim=-1, keepdim=True)
scale = self.W_scale(speaker_embedding)
bias = self.W_bias(speaker_embedding)
y = scale.unsqueeze(1) * ((x - mean) / var) + bias.unsqueeze(1)
if self.dim != -1:
y = y.transpose(-1, self.dim)
return y
class SequentialWrappableConditionalLayerNorm(nn.Module):
def __init__(self,
hidden_dim,
speaker_embedding_dim):
super(SequentialWrappableConditionalLayerNorm, self).__init__()
if isinstance(hidden_dim, int):
self.normal_shape = hidden_dim
self.speaker_embedding_dim = speaker_embedding_dim
self.W_scale = nn.Sequential(nn.Linear(self.speaker_embedding_dim, self.normal_shape),
nn.Tanh(),
nn.Linear(self.normal_shape, self.normal_shape))
self.W_bias = nn.Sequential(nn.Linear(self.speaker_embedding_dim, self.normal_shape),
nn.Tanh(),
nn.Linear(self.normal_shape, self.normal_shape))
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.constant_(self.W_scale[0].weight, 0.0)
torch.nn.init.constant_(self.W_scale[2].weight, 0.0)
torch.nn.init.constant_(self.W_scale[0].bias, 1.0)
torch.nn.init.constant_(self.W_scale[2].bias, 1.0)
torch.nn.init.constant_(self.W_bias[0].weight, 0.0)
torch.nn.init.constant_(self.W_bias[2].weight, 0.0)
torch.nn.init.constant_(self.W_bias[0].bias, 0.0)
torch.nn.init.constant_(self.W_bias[2].bias, 0.0)
def forward(self, packed_input):
x, speaker_embedding = packed_input
mean = x.mean(dim=-1, keepdim=True)
var = ((x - mean) ** 2).mean(dim=-1, keepdim=True)
scale = self.W_scale(speaker_embedding)
bias = self.W_bias(speaker_embedding)
y = scale.unsqueeze(1) * ((x - mean) / var) + bias.unsqueeze(1)
return y
class AdaIN1d(nn.Module):
"""
MIT Licensed
Copyright (c) 2022 Aaron (Yinghao) Li
https://github.com/yl4579/StyleTTS/blob/main/models.py
"""
def __init__(self, style_dim, num_features):
super().__init__()
self.norm = nn.InstanceNorm1d(num_features, affine=False)
self.fc = nn.Linear(style_dim, num_features * 2)
def forward(self, x, s):
s = torch.nn.functional.normalize(s)
h = self.fc(s)
h = h.view(h.size(0), h.size(1), 1)
gamma, beta = torch.chunk(h, chunks=2, dim=1)
return (1 + gamma.transpose(1, 2)) * self.norm(x.transpose(1, 2)).transpose(1, 2) + beta.transpose(1, 2)
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