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Zero
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# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
# Adapted by Florian Lux 2023
from abc import ABC
import torch
from Modules.GeneralLayers.ConditionalLayerNorm import AdaIN1d
from Modules.GeneralLayers.ConditionalLayerNorm import ConditionalLayerNorm
from Modules.GeneralLayers.LayerNorm import LayerNorm
from Utility.utils import integrate_with_utt_embed
class VariancePredictor(torch.nn.Module, ABC):
"""
Variance predictor module.
This is a module of variance predictor described in `FastSpeech 2:
Fast and High-Quality End-to-End Text to Speech`_.
.. _`FastSpeech 2: Fast and High-Quality End-to-End Text to Speech`:
https://arxiv.org/abs/2006.04558
"""
def __init__(self,
idim,
n_layers=2,
n_chans=384,
kernel_size=3,
bias=True,
dropout_rate=0.5,
utt_embed_dim=None,
embedding_integration="AdaIN"):
"""
Initialize duration predictor module.
Args:
idim (int): Input dimension.
n_layers (int, optional): Number of convolutional layers.
n_chans (int, optional): Number of channels of convolutional layers.
kernel_size (int, optional): Kernel size of convolutional layers.
dropout_rate (float, optional): Dropout rate.
"""
super().__init__()
self.conv = torch.nn.ModuleList()
self.dropouts = torch.nn.ModuleList()
self.norms = torch.nn.ModuleList()
self.embedding_projections = torch.nn.ModuleList()
self.utt_embed_dim = utt_embed_dim
self.use_conditional_layernorm_embedding_integration = embedding_integration in ["AdaIN", "ConditionalLayerNorm"]
for idx in range(n_layers):
if utt_embed_dim is not None:
if embedding_integration == "AdaIN":
self.embedding_projections += [AdaIN1d(style_dim=utt_embed_dim, num_features=idim)]
elif embedding_integration == "ConditionalLayerNorm":
self.embedding_projections += [ConditionalLayerNorm(speaker_embedding_dim=utt_embed_dim, hidden_dim=idim)]
else:
self.embedding_projections += [torch.nn.Linear(utt_embed_dim + idim, idim)]
else:
self.embedding_projections += [lambda x: x]
in_chans = idim if idx == 0 else n_chans
self.conv += [torch.nn.Sequential(torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias, ),
torch.nn.ReLU())]
self.norms += [LayerNorm(n_chans, dim=1)]
self.dropouts += [torch.nn.Dropout(dropout_rate)]
self.linear = torch.nn.Linear(n_chans, 1)
def forward(self, xs, padding_mask=None, utt_embed=None):
"""
Calculate forward propagation.
Args:
xs (Tensor): Batch of input sequences (B, Tmax, idim).
padding_mask (ByteTensor, optional):
Batch of masks indicating padded part (B, Tmax).
Returns:
Tensor: Batch of predicted sequences (B, Tmax, 1).
"""
xs = xs.transpose(1, -1) # (B, idim, Tmax)
for f, c, d, p in zip(self.conv, self.norms, self.dropouts, self.embedding_projections):
xs = f(xs) # (B, C, Tmax)
if self.utt_embed_dim is not None:
xs = integrate_with_utt_embed(hs=xs.transpose(1, 2), utt_embeddings=utt_embed, projection=p, embedding_training=self.use_conditional_layernorm_embedding_integration).transpose(1, 2)
xs = c(xs)
xs = d(xs)
xs = self.linear(xs.transpose(1, 2)) # (B, Tmax, 1)
if padding_mask is not None:
xs = xs.masked_fill(padding_mask, 0.0)
return xs
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