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import math |
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from typing import Dict, List, NoReturn, Tuple |
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import matplotlib.pyplot as plt |
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import numpy as np |
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import pytorch_lightning as pl |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.optim as optim |
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from torch.optim.lr_scheduler import LambdaLR |
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from torchlibrosa.stft import ISTFT, STFT, magphase |
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from bytesep.models.pytorch_modules import Base, Subband, act, init_bn, init_layer |
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class ConvBlock(nn.Module): |
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def __init__( |
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self, |
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in_channels: int, |
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out_channels: int, |
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kernel_size: Tuple, |
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activation: str, |
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momentum: float, |
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): |
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r"""Convolutional block.""" |
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super(ConvBlock, self).__init__() |
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self.activation = activation |
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padding = (kernel_size[0] // 2, kernel_size[1] // 2) |
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self.conv1 = nn.Conv2d( |
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in_channels=in_channels, |
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out_channels=out_channels, |
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kernel_size=kernel_size, |
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stride=(1, 1), |
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dilation=(1, 1), |
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padding=padding, |
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bias=False, |
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) |
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self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum) |
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self.conv2 = nn.Conv2d( |
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in_channels=out_channels, |
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out_channels=out_channels, |
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kernel_size=kernel_size, |
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stride=(1, 1), |
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dilation=(1, 1), |
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padding=padding, |
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bias=False, |
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) |
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self.bn2 = nn.BatchNorm2d(out_channels, momentum=momentum) |
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self.init_weights() |
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def init_weights(self) -> NoReturn: |
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r"""Initialize weights.""" |
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init_layer(self.conv1) |
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init_layer(self.conv2) |
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init_bn(self.bn1) |
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init_bn(self.bn2) |
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def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: |
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r"""Forward data into the module. |
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Args: |
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input_tensor: (batch_size, in_feature_maps, time_steps, freq_bins) |
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Returns: |
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output_tensor: (batch_size, out_feature_maps, time_steps, freq_bins) |
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""" |
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x = act(self.bn1(self.conv1(input_tensor)), self.activation) |
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x = act(self.bn2(self.conv2(x)), self.activation) |
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output_tensor = x |
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return output_tensor |
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class EncoderBlock(nn.Module): |
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def __init__( |
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self, |
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in_channels: int, |
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out_channels: int, |
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kernel_size: Tuple, |
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downsample: Tuple, |
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activation: str, |
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momentum: float, |
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): |
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r"""Encoder block.""" |
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super(EncoderBlock, self).__init__() |
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self.conv_block = ConvBlock( |
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in_channels, out_channels, kernel_size, activation, momentum |
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) |
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self.downsample = downsample |
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def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: |
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r"""Forward data into the module. |
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Args: |
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input_tensor: (batch_size, in_feature_maps, time_steps, freq_bins) |
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Returns: |
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encoder_pool: (batch_size, out_feature_maps, downsampled_time_steps, downsampled_freq_bins) |
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encoder: (batch_size, out_feature_maps, time_steps, freq_bins) |
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""" |
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encoder_tensor = self.conv_block(input_tensor) |
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encoder_pool = F.avg_pool2d(encoder_tensor, kernel_size=self.downsample) |
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return encoder_pool, encoder_tensor |
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class DecoderBlock(nn.Module): |
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def __init__( |
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self, |
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in_channels: int, |
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out_channels: int, |
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kernel_size: Tuple, |
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upsample: Tuple, |
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activation: str, |
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momentum: float, |
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): |
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r"""Decoder block.""" |
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super(DecoderBlock, self).__init__() |
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self.kernel_size = kernel_size |
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self.stride = upsample |
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self.activation = activation |
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self.conv1 = torch.nn.ConvTranspose2d( |
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in_channels=in_channels, |
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out_channels=out_channels, |
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kernel_size=self.stride, |
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stride=self.stride, |
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padding=(0, 0), |
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bias=False, |
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dilation=(1, 1), |
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) |
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self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum) |
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self.conv_block2 = ConvBlock( |
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out_channels * 2, out_channels, kernel_size, activation, momentum |
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) |
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self.init_weights() |
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def init_weights(self): |
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r"""Initialize weights.""" |
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init_layer(self.conv1) |
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init_bn(self.bn1) |
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def forward( |
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self, input_tensor: torch.Tensor, concat_tensor: torch.Tensor |
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) -> torch.Tensor: |
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r"""Forward data into the module. |
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Args: |
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torch_tensor: (batch_size, in_feature_maps, downsampled_time_steps, downsampled_freq_bins) |
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concat_tensor: (batch_size, in_feature_maps, time_steps, freq_bins) |
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Returns: |
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output_tensor: (batch_size, out_feature_maps, time_steps, freq_bins) |
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""" |
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x = act(self.bn1(self.conv1(input_tensor)), self.activation) |
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x = torch.cat((x, concat_tensor), dim=1) |
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output_tensor = self.conv_block2(x) |
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return output_tensor |
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class UNet(nn.Module, Base): |
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def __init__(self, input_channels: int, target_sources_num: int): |
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r"""UNet.""" |
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super(UNet, self).__init__() |
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self.input_channels = input_channels |
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self.target_sources_num = target_sources_num |
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window_size = 2048 |
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hop_size = 441 |
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center = True |
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pad_mode = "reflect" |
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window = "hann" |
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activation = "leaky_relu" |
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momentum = 0.01 |
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self.subbands_num = 1 |
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assert ( |
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self.subbands_num == 1 |
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), "Using subbands_num > 1 on spectrogram \ |
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will lead to unexpected performance sometimes. Suggest to use \ |
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subband method on waveform." |
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self.K = 3 |
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self.downsample_ratio = 2 ** 6 |
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self.stft = STFT( |
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n_fft=window_size, |
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hop_length=hop_size, |
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win_length=window_size, |
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window=window, |
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center=center, |
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pad_mode=pad_mode, |
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freeze_parameters=True, |
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) |
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self.istft = ISTFT( |
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n_fft=window_size, |
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hop_length=hop_size, |
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win_length=window_size, |
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window=window, |
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center=center, |
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pad_mode=pad_mode, |
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freeze_parameters=True, |
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) |
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self.bn0 = nn.BatchNorm2d(window_size // 2 + 1, momentum=momentum) |
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self.subband = Subband(subbands_num=self.subbands_num) |
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self.encoder_block1 = EncoderBlock( |
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in_channels=input_channels * self.subbands_num, |
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out_channels=32, |
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kernel_size=(3, 3), |
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downsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.encoder_block2 = EncoderBlock( |
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in_channels=32, |
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out_channels=64, |
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kernel_size=(3, 3), |
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downsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.encoder_block3 = EncoderBlock( |
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in_channels=64, |
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out_channels=128, |
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kernel_size=(3, 3), |
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downsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.encoder_block4 = EncoderBlock( |
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in_channels=128, |
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out_channels=256, |
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kernel_size=(3, 3), |
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downsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.encoder_block5 = EncoderBlock( |
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in_channels=256, |
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out_channels=384, |
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kernel_size=(3, 3), |
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downsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.encoder_block6 = EncoderBlock( |
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in_channels=384, |
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out_channels=384, |
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kernel_size=(3, 3), |
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downsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.conv_block7 = ConvBlock( |
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in_channels=384, |
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out_channels=384, |
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kernel_size=(3, 3), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.decoder_block1 = DecoderBlock( |
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in_channels=384, |
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out_channels=384, |
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kernel_size=(3, 3), |
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upsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.decoder_block2 = DecoderBlock( |
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in_channels=384, |
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out_channels=384, |
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kernel_size=(3, 3), |
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upsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.decoder_block3 = DecoderBlock( |
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in_channels=384, |
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out_channels=256, |
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kernel_size=(3, 3), |
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upsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.decoder_block4 = DecoderBlock( |
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in_channels=256, |
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out_channels=128, |
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kernel_size=(3, 3), |
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upsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.decoder_block5 = DecoderBlock( |
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in_channels=128, |
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out_channels=64, |
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kernel_size=(3, 3), |
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upsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.decoder_block6 = DecoderBlock( |
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in_channels=64, |
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out_channels=32, |
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kernel_size=(3, 3), |
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upsample=(2, 2), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.after_conv_block1 = ConvBlock( |
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in_channels=32, |
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out_channels=32, |
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kernel_size=(3, 3), |
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activation=activation, |
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momentum=momentum, |
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) |
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self.after_conv2 = nn.Conv2d( |
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in_channels=32, |
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out_channels=target_sources_num |
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* input_channels |
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* self.K |
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* self.subbands_num, |
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kernel_size=(1, 1), |
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stride=(1, 1), |
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padding=(0, 0), |
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bias=True, |
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) |
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self.init_weights() |
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def init_weights(self): |
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r"""Initialize weights.""" |
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init_bn(self.bn0) |
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init_layer(self.after_conv2) |
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def feature_maps_to_wav( |
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self, |
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input_tensor: torch.Tensor, |
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sp: torch.Tensor, |
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sin_in: torch.Tensor, |
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cos_in: torch.Tensor, |
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audio_length: int, |
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) -> torch.Tensor: |
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r"""Convert feature maps to waveform. |
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Args: |
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input_tensor: (batch_size, target_sources_num * input_channels * self.K, time_steps, freq_bins) |
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sp: (batch_size, target_sources_num * input_channels, time_steps, freq_bins) |
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sin_in: (batch_size, target_sources_num * input_channels, time_steps, freq_bins) |
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cos_in: (batch_size, target_sources_num * input_channels, time_steps, freq_bins) |
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Outputs: |
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waveform: (batch_size, target_sources_num * input_channels, segment_samples) |
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""" |
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batch_size, _, time_steps, freq_bins = input_tensor.shape |
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x = input_tensor.reshape( |
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batch_size, |
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self.target_sources_num, |
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self.input_channels, |
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self.K, |
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time_steps, |
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freq_bins, |
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) |
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mask_mag = torch.sigmoid(x[:, :, :, 0, :, :]) |
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_mask_real = torch.tanh(x[:, :, :, 1, :, :]) |
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_mask_imag = torch.tanh(x[:, :, :, 2, :, :]) |
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_, mask_cos, mask_sin = magphase(_mask_real, _mask_imag) |
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out_cos = ( |
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cos_in[:, None, :, :, :] * mask_cos - sin_in[:, None, :, :, :] * mask_sin |
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) |
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out_sin = ( |
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sin_in[:, None, :, :, :] * mask_cos + cos_in[:, None, :, :, :] * mask_sin |
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) |
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out_mag = F.relu_(sp[:, None, :, :, :] * mask_mag) |
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out_real = out_mag * out_cos |
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out_imag = out_mag * out_sin |
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shape = ( |
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batch_size * self.target_sources_num * self.input_channels, |
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1, |
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time_steps, |
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freq_bins, |
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) |
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out_real = out_real.reshape(shape) |
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out_imag = out_imag.reshape(shape) |
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x = self.istft(out_real, out_imag, audio_length) |
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waveform = x.reshape( |
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batch_size, self.target_sources_num * self.input_channels, audio_length |
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) |
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return waveform |
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def forward(self, input_dict: Dict) -> Dict: |
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r"""Forward data into the module. |
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Args: |
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input_dict: dict, e.g., { |
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waveform: (batch_size, input_channels, segment_samples), |
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..., |
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} |
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Outputs: |
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output_dict: dict, e.g., { |
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'waveform': (batch_size, input_channels, segment_samples), |
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..., |
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} |
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""" |
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mixtures = input_dict['waveform'] |
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mag, cos_in, sin_in = self.wav_to_spectrogram_phase(mixtures) |
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x = mag.transpose(1, 3) |
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x = self.bn0(x) |
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x = x.transpose(1, 3) |
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origin_len = x.shape[2] |
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pad_len = ( |
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int(np.ceil(x.shape[2] / self.downsample_ratio)) * self.downsample_ratio |
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- origin_len |
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) |
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x = F.pad(x, pad=(0, 0, 0, pad_len)) |
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x = x[..., 0 : x.shape[-1] - 1] |
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if self.subbands_num > 1: |
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x = self.subband.analysis(x) |
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(x1_pool, x1) = self.encoder_block1(x) |
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(x2_pool, x2) = self.encoder_block2(x1_pool) |
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(x3_pool, x3) = self.encoder_block3( |
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x2_pool |
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) |
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(x4_pool, x4) = self.encoder_block4( |
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x3_pool |
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) |
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(x5_pool, x5) = self.encoder_block5( |
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x4_pool |
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) |
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(x6_pool, x6) = self.encoder_block6( |
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x5_pool |
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) |
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x_center = self.conv_block7(x6_pool) |
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x7 = self.decoder_block1(x_center, x6) |
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x8 = self.decoder_block2(x7, x5) |
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x9 = self.decoder_block3(x8, x4) |
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x10 = self.decoder_block4(x9, x3) |
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x11 = self.decoder_block5(x10, x2) |
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x12 = self.decoder_block6(x11, x1) |
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x = self.after_conv_block1(x12) |
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x = self.after_conv2(x) |
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if self.subbands_num > 1: |
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x = self.subband.synthesis(x) |
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x = F.pad(x, pad=(0, 1)) |
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x = x[:, :, 0:origin_len, :] |
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audio_length = mixtures.shape[2] |
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separated_audio = self.feature_maps_to_wav(x, mag, sin_in, cos_in, audio_length) |
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output_dict = {'waveform': separated_audio} |
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return output_dict |
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