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d78f08c | 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 | # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
def get_padding_2d(kernel_size, dilation=(1, 1)):
"""
Calculate the padding size for a 2D convolutional layer.
Args:
- kernel_size (tuple): Size of the convolutional kernel (height, width).
- dilation (tuple, optional): Dilation rate of the convolution (height, width). Defaults to (1, 1).
Returns:
- tuple: Calculated padding size (height, width).
"""
return (int((kernel_size[0] * dilation[0] - dilation[0]) / 2),
int((kernel_size[1] * dilation[1] - dilation[1]) / 2))
class SPConvTranspose2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, r=1):
super(SPConvTranspose2d, self).__init__()
self.pad1 = nn.ConstantPad2d((1, 1, 0, 0), value=0.)
self.out_channels = out_channels
self.conv = nn.Conv2d(in_channels, out_channels * r, kernel_size=kernel_size, stride=(1, 1))
self.r = r
def forward(self, x):
x = self.pad1(x)
out = self.conv(x)
batch_size, nchannels, H, W = out.shape
out = out.view((batch_size, self.r, nchannels // self.r, H, W))
out = out.permute(0, 2, 3, 4, 1)
out = out.contiguous().view((batch_size, nchannels // self.r, H, -1))
return out
class DenseBlock(nn.Module):
"""
DenseBlock module consisting of multiple convolutional layers with dilation.
"""
def __init__(self, cfg, kernel_size=(3, 3), depth=4):
super(DenseBlock, self).__init__()
self.cfg = cfg
self.depth = depth
self.dense_block = nn.ModuleList()
self.hid_feature = cfg['model_cfg']['hid_feature']
for i in range(depth):
dil = 2 ** i
dense_conv = nn.Sequential(
nn.Conv2d(self.hid_feature * (i + 1), self.hid_feature, kernel_size,
dilation=(dil, 1), padding=get_padding_2d(kernel_size, (dil, 1))),
nn.InstanceNorm2d(self.hid_feature, affine=True),
nn.PReLU(self.hid_feature)
)
self.dense_block.append(dense_conv)
def forward(self, x):
skip = x
for i in range(self.depth):
x = self.dense_block[i](skip)
skip = torch.cat([x, skip], dim=1)
return x
class DenseEncoder(nn.Module):
"""
DenseEncoder module consisting of initial convolution, dense block, and a final convolution.
"""
def __init__(self, cfg):
super(DenseEncoder, self).__init__()
self.cfg = cfg
self.input_channel = cfg['model_cfg']['input_channel']
self.hid_feature = cfg['model_cfg']['hid_feature']
self.dense_conv_1 = nn.Sequential(
nn.Conv2d(self.input_channel, self.hid_feature, (1, 1)),
nn.InstanceNorm2d(self.hid_feature, affine=True),
nn.PReLU(self.hid_feature)
)
self.dense_block = DenseBlock(cfg, depth=4)
self.dense_conv_2 = nn.Sequential(
nn.Conv2d(self.hid_feature, self.hid_feature, (1, 3), stride=(4, 2)),
nn.InstanceNorm2d(self.hid_feature, affine=True),
nn.PReLU(self.hid_feature)
)
def forward(self, x):
x = self.dense_conv_1(x) # [batch, hid_feature, time, freq]
x = self.dense_block(x) # [batch, hid_feature, time, freq]
x = self.dense_conv_2(x) # [batch, hid_feature, time, freq//2]
return x
class MagDecoder(nn.Module):
"""
MagDecoder module for decoding magnitude information.
"""
def __init__(self, cfg):
super(MagDecoder, self).__init__()
self.dense_block = DenseBlock(cfg, depth=4)
self.hid_feature = cfg['model_cfg']['hid_feature']
self.output_channel = cfg['model_cfg']['output_channel']
self.n_fft = cfg['stft_cfg']['n_fft']
self.beta = cfg['model_cfg']['beta']
self.up_conv1 = nn.Sequential(
SPConvTranspose2d(self.hid_feature, self.hid_feature, (1, 3), 2),
nn.InstanceNorm2d(self.hid_feature, affine=True),
nn.PReLU(self.hid_feature)
)
self.up_conv2 = nn.Sequential(
SPConvTranspose2d(self.hid_feature, self.hid_feature, (1, 3), 4),
nn.InstanceNorm2d(self.hid_feature, affine=True),
nn.PReLU(self.hid_feature)
)
self.final_conv = nn.Conv2d(self.hid_feature, self.output_channel, (1, 1))
def forward(self, x):
x = self.dense_block(x)
x = self.up_conv1(x)
x = self.up_conv2(x.permute(0,1,3,2)).permute(0,1,3,2)
x = self.final_conv(x)
return x
class PhaseDecoder(nn.Module):
"""
PhaseDecoder module for decoding phase information.
"""
def __init__(self, cfg):
super(PhaseDecoder, self).__init__()
self.dense_block = DenseBlock(cfg, depth=4)
self.hid_feature = cfg['model_cfg']['hid_feature']
self.output_channel = cfg['model_cfg']['output_channel']
self.up_conv1 = nn.Sequential(
SPConvTranspose2d(self.hid_feature, self.hid_feature, (1, 3), 2),
nn.InstanceNorm2d(self.hid_feature, affine=True),
nn.PReLU(self.hid_feature)
)
self.up_conv2 = nn.Sequential(
SPConvTranspose2d(self.hid_feature, self.hid_feature, (1, 3), 4),
nn.InstanceNorm2d(self.hid_feature, affine=True),
nn.PReLU(self.hid_feature)
)
self.phase_conv_r = nn.Conv2d(self.hid_feature, self.output_channel, (1, 1))
self.phase_conv_i = nn.Conv2d(self.hid_feature, self.output_channel, (1, 1))
def forward(self, x):
x = self.dense_block(x)
x = self.up_conv1(x)
x = self.up_conv2(x.permute(0,1,3,2)).permute(0,1,3,2)
x_r = self.phase_conv_r(x)
x_i = self.phase_conv_i(x)
x = torch.atan2(x_i, x_r)
return x |