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| # -*- coding: utf-8 -*- | |
| """Upsampling module. | |
| This code is modified from https://github.com/r9y9/wavenet_vocoder. | |
| """ | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from . import Conv1d | |
| class Stretch2d(torch.nn.Module): | |
| """Stretch2d module.""" | |
| def __init__(self, x_scale, y_scale, mode="nearest"): | |
| """Initialize Stretch2d module. | |
| Args: | |
| x_scale (int): X scaling factor (Time axis in spectrogram). | |
| y_scale (int): Y scaling factor (Frequency axis in spectrogram). | |
| mode (str): Interpolation mode. | |
| """ | |
| super(Stretch2d, self).__init__() | |
| self.x_scale = x_scale | |
| self.y_scale = y_scale | |
| self.mode = mode | |
| def forward(self, x): | |
| """Calculate forward propagation. | |
| Args: | |
| x (Tensor): Input tensor (B, C, F, T). | |
| Returns: | |
| Tensor: Interpolated tensor (B, C, F * y_scale, T * x_scale), | |
| """ | |
| return F.interpolate( | |
| x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode) | |
| class Conv2d(torch.nn.Conv2d): | |
| """Conv2d module with customized initialization.""" | |
| def __init__(self, *args, **kwargs): | |
| """Initialize Conv2d module.""" | |
| super(Conv2d, self).__init__(*args, **kwargs) | |
| def reset_parameters(self): | |
| """Reset parameters.""" | |
| self.weight.data.fill_(1. / np.prod(self.kernel_size)) | |
| if self.bias is not None: | |
| torch.nn.init.constant_(self.bias, 0.0) | |
| class UpsampleNetwork(torch.nn.Module): | |
| """Upsampling network module.""" | |
| def __init__(self, | |
| upsample_scales, | |
| nonlinear_activation=None, | |
| nonlinear_activation_params={}, | |
| interpolate_mode="nearest", | |
| freq_axis_kernel_size=1, | |
| use_causal_conv=False, | |
| ): | |
| """Initialize upsampling network module. | |
| Args: | |
| upsample_scales (list): List of upsampling scales. | |
| nonlinear_activation (str): Activation function name. | |
| nonlinear_activation_params (dict): Arguments for specified activation function. | |
| interpolate_mode (str): Interpolation mode. | |
| freq_axis_kernel_size (int): Kernel size in the direction of frequency axis. | |
| """ | |
| super(UpsampleNetwork, self).__init__() | |
| self.use_causal_conv = use_causal_conv | |
| self.up_layers = torch.nn.ModuleList() | |
| for scale in upsample_scales: | |
| # interpolation layer | |
| stretch = Stretch2d(scale, 1, interpolate_mode) | |
| self.up_layers += [stretch] | |
| # conv layer | |
| assert (freq_axis_kernel_size - 1) % 2 == 0, "Not support even number freq axis kernel size." | |
| freq_axis_padding = (freq_axis_kernel_size - 1) // 2 | |
| kernel_size = (freq_axis_kernel_size, scale * 2 + 1) | |
| if use_causal_conv: | |
| padding = (freq_axis_padding, scale * 2) | |
| else: | |
| padding = (freq_axis_padding, scale) | |
| conv = Conv2d(1, 1, kernel_size=kernel_size, padding=padding, bias=False) | |
| self.up_layers += [conv] | |
| # nonlinear | |
| if nonlinear_activation is not None: | |
| nonlinear = getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params) | |
| self.up_layers += [nonlinear] | |
| def forward(self, c): | |
| """Calculate forward propagation. | |
| Args: | |
| c : Input tensor (B, C, T). | |
| Returns: | |
| Tensor: Upsampled tensor (B, C, T'), where T' = T * prod(upsample_scales). | |
| """ | |
| c = c.unsqueeze(1) # (B, 1, C, T) | |
| for f in self.up_layers: | |
| if self.use_causal_conv and isinstance(f, Conv2d): | |
| c = f(c)[..., :c.size(-1)] | |
| else: | |
| c = f(c) | |
| return c.squeeze(1) # (B, C, T') | |
| class ConvInUpsampleNetwork(torch.nn.Module): | |
| """Convolution + upsampling network module.""" | |
| def __init__(self, | |
| upsample_scales, | |
| nonlinear_activation=None, | |
| nonlinear_activation_params={}, | |
| interpolate_mode="nearest", | |
| freq_axis_kernel_size=1, | |
| aux_channels=80, | |
| aux_context_window=0, | |
| use_causal_conv=False | |
| ): | |
| """Initialize convolution + upsampling network module. | |
| Args: | |
| upsample_scales (list): List of upsampling scales. | |
| nonlinear_activation (str): Activation function name. | |
| nonlinear_activation_params (dict): Arguments for specified activation function. | |
| mode (str): Interpolation mode. | |
| freq_axis_kernel_size (int): Kernel size in the direction of frequency axis. | |
| aux_channels (int): Number of channels of pre-convolutional layer. | |
| aux_context_window (int): Context window size of the pre-convolutional layer. | |
| use_causal_conv (bool): Whether to use causal structure. | |
| """ | |
| super(ConvInUpsampleNetwork, self).__init__() | |
| self.aux_context_window = aux_context_window | |
| self.use_causal_conv = use_causal_conv and aux_context_window > 0 | |
| # To capture wide-context information in conditional features | |
| kernel_size = aux_context_window + 1 if use_causal_conv else 2 * aux_context_window + 1 | |
| # NOTE(kan-bayashi): Here do not use padding because the input is already padded | |
| self.conv_in = Conv1d(aux_channels, aux_channels, kernel_size=kernel_size, bias=False) | |
| self.upsample = UpsampleNetwork( | |
| upsample_scales=upsample_scales, | |
| nonlinear_activation=nonlinear_activation, | |
| nonlinear_activation_params=nonlinear_activation_params, | |
| interpolate_mode=interpolate_mode, | |
| freq_axis_kernel_size=freq_axis_kernel_size, | |
| use_causal_conv=use_causal_conv, | |
| ) | |
| def forward(self, c): | |
| """Calculate forward propagation. | |
| Args: | |
| c : Input tensor (B, C, T'). | |
| Returns: | |
| Tensor: Upsampled tensor (B, C, T), | |
| where T = (T' - aux_context_window * 2) * prod(upsample_scales). | |
| Note: | |
| The length of inputs considers the context window size. | |
| """ | |
| c_ = self.conv_in(c) | |
| c = c_[:, :, :-self.aux_context_window] if self.use_causal_conv else c_ | |
| return self.upsample(c) | |