File size: 9,004 Bytes
bd0655f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
import math
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
def dynamic_batch_collate(batch):
"""
Collates batches dynamically based on the length of sequences within each batch.
This function ensures that each batch contains sequences of similar lengths,
optimizing padding and computational efficiency.
Args:
batch: A list of dictionaries, each containing 'id', 'phoneme_seq_encoded',
'mel_spectrogram', 'mel_length', 'stop_token_targets'.
Returns:
A batch of sequences where sequences are padded to match the longest sequence in the batch.
"""
# Sort the batch by 'mel_length' in descending order for efficient packing
batch.sort(key=lambda x: x['mel_lengths'], reverse=True)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Extract sequences and their lengths
ids = [item['id'] for item in batch]
phoneme_seqs = [item['phoneme_seq_encoded'] for item in batch]
mel_specs = [item['mel_spec'] for item in batch]
#bos_mel_specs = [item['bos_mel_spectrogram'] for item in batch]
#eos_mel_specs = [item['eos_mel_spectrogram'] for item in batch]
mel_lengths = torch.tensor([item['mel_lengths'] for item in batch], device=device)
stop_token_targets = [item['stop_token_targets'] for item in batch]
# Pad phoneme sequences
phoneme_seq_padded = torch.nn.utils.rnn.pad_sequence(phoneme_seqs, batch_first=True, padding_value=0).to(device)
# Find the maximum mel length for padding
max_len = max(mel_lengths).item()
num_mel_bins = 80
mel_specs_padded = torch.zeros((len(mel_specs), num_mel_bins, max_len), device=device)
for i, mel in enumerate(mel_specs):
mel_len = mel.shape[1]
mel_specs_padded[i, :, :mel_len] = mel.to(device)
# # Pad mel spectrograms
# bos_mel_specs_padded = torch.zeros((len(bos_mel_specs), num_mel_bins, max_len), device=device)
# for i, mel in enumerate(bos_mel_specs):
# mel_len = mel.shape[1]
# bos_mel_specs_padded[i, :, :mel_len] = mel.to(device)
#
# eos_mel_specs_padded = torch.zeros((len(eos_mel_specs), num_mel_bins, max_len), device=device)
# for i, mel in enumerate(eos_mel_specs):
# mel_len = mel.shape[1]
# eos_mel_specs_padded[i, :, :mel_len] = mel.to(device)
# Pad stop token targets
stop_token_targets_padded = torch.zeros((len(stop_token_targets), max_len), device=device)
for i, stop in enumerate(stop_token_targets):
stop_len = stop.size(0)
stop_token_targets_padded[i, :stop_len] = stop.to(device)
return ids, phoneme_seq_padded, mel_specs_padded, mel_lengths, stop_token_targets_padded
class EncoderPrenet(torch.nn.Module):
"""
Module for the encoder prenet in the Transformer-based TTS system.
This module consists of several convolutional layers followed by batch normalization,
ReLU activation, and dropout. It then performs a linear projection to the desired dimension.
Parameters:
input_dim (int): Dimension of the input features. Defaults to 512.
hidden_dim (int): Dimension of the hidden layers. Defaults to 512.
num_layers (int): Number of convolutional layers. Defaults to 3.
dropout (float): Dropout probability. Defaults to 0.2.
Inputs:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, input_dim).
Returns:
torch.Tensor: Output tensor of shape (batch_size, seq_len, hidden_dim). """
def __init__(self, input_dim=512, hidden_dim=512, num_layers=3, dropout=0.2):
super().__init__()
# Convolutional layers
conv_layers = []
for _ in range(num_layers):
conv_layers.append(nn.Conv1d(hidden_dim, hidden_dim, kernel_size=3, padding=1))
conv_layers.append(nn.BatchNorm1d(hidden_dim))
conv_layers.append(nn.ReLU())
conv_layers.append(nn.Dropout(dropout))
self.conv_layers = nn.Sequential(*conv_layers)
# Final linear projection
self.projection = nn.Linear(hidden_dim, hidden_dim)
def forward(self, x):
x = x.transpose(1, 2) # Transpose for convolutional layers (Batch, SeqLen, Channels)
x = self.conv_layers(x)
x = x.transpose(1, 2) # Transpose back
x = self.projection(x)
return x
class DecoderPrenet(torch.nn.Module):
"""
Module for the decoder prenet in the Transformer-based TTS system.
This module consists of two fully connected layers followed by ReLU activation,
and performs a linear projection to the desired output dimension.
Parameters:
input_dim (int): Dimension of the input features. Defaults to 80.
hidden_dim (int): Dimension of the hidden layers. Defaults to 256.
output_dim (int): Dimension of the output features. Defaults to 512.
Inputs:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, input_dim).
Returns:
torch.Tensor: Output tensor of shape (batch_size, seq_len, output_dim). """
def __init__(self, input_dim=80, hidden_dim=256, output_dim=512):
super().__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.projection = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = x.transpose(1,2)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.projection(x)
return x
class ScaledPositionalEncoding(nn.Module):
"""
Module for adding scaled positional encoding to input sequences.
Parameters:
d_model (int): Dimensionality of the model. It must match the embedding dimension of the input.
max_len (int): Maximum length of the input sequence. Defaults to 5000.
Inputs:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, embedding_dim).
Returns:
torch.Tensor: Output tensor with scaled positional encoding added, shape (batch_size, seq_len, embedding_dim). """
def __init__(self, d_model, max_len=5000):
super().__init__()
self.d_model = d_model
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe = torch.zeros(max_len, 1, d_model)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
self.scale = nn.Parameter(torch.ones(1))
def forward(self, x):
"""
Adds scaled positional encoding to input tensor x.
Args:
x: Tensor of shape [batch_size, seq_len, embedding_dim]
"""
scaled_pe = self.pe[:x.size(0), :, :] * self.scale
x = x + scaled_pe
return x
class PostNet(torch.nn.Module):
"""
Post-processing network for mel-spectrogram enhancement.
This module consists of multiple convolutional layers with batch normalization and ReLU activation.
It is used to refine the mel-spectrogram output from the decoder.
Parameters:
mel_channels (int): Number of mel channels in the input mel-spectrogram.
postnet_channels (int): Number of channels in the postnet layers.
kernel_size (int): Size of the convolutional kernel.
postnet_layers (int): Number of postnet layers.
Inputs:
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, mel_channels).
Returns:
torch.Tensor: Output tensor with refined mel-spectrogram, shape (batch_size, seq_len, mel_channels). """
def __init__(self, mel_channels, postnet_channels, kernel_size, postnet_layers):
super().__init__()
self.conv_layers = nn.ModuleList()
# First layer
self.conv_layers.append(
nn.Sequential(
nn.Conv1d(mel_channels, postnet_channels, kernel_size, padding=kernel_size // 2),
nn.BatchNorm1d(postnet_channels),
nn.ReLU()
)
)
# Middle layers
for _ in range(1, postnet_layers - 1):
self.conv_layers.append(
nn.Sequential(
nn.Conv1d(postnet_channels, postnet_channels, kernel_size, padding=kernel_size // 2),
nn.BatchNorm1d(postnet_channels),
nn.ReLU()
)
)
# Final layer
self.conv_layers.append(
nn.Sequential(
nn.Conv1d(postnet_channels, mel_channels, kernel_size, padding=kernel_size // 2),
nn.BatchNorm1d(mel_channels)
)
)
def forward(self, x):
x = x.transpose(1, 2)
for conv in self.conv_layers:
x = conv(x)
x = x.transpose(1, 2)
return x
|