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import torch
from torch.nn import functional as F
class Stretch2d(torch.nn.Module):
def __init__(self, x_scale, y_scale, mode="nearest"):
super().__init__()
self.x_scale = x_scale
self.y_scale = y_scale
self.mode = mode
def forward(self, x):
"""
x (Tensor): Input tensor (B, C, F, T).
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 UpsampleNetwork(torch.nn.Module):
# pylint: disable=dangerous-default-value
def __init__(
self,
upsample_factors,
nonlinear_activation=None,
nonlinear_activation_params={},
interpolate_mode="nearest",
freq_axis_kernel_size=1,
use_causal_conv=False,
):
super().__init__()
self.use_causal_conv = use_causal_conv
self.up_layers = torch.nn.ModuleList()
for scale in upsample_factors:
# 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 = torch.nn.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):
"""
c : (B, C, T_in).
Tensor: (B, C, T_upsample)
"""
c = c.unsqueeze(1) # (B, 1, C, T)
for f in self.up_layers:
c = f(c)
return c.squeeze(1) # (B, C, T')
class ConvUpsample(torch.nn.Module):
# pylint: disable=dangerous-default-value
def __init__(
self,
upsample_factors,
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,
):
super().__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 = torch.nn.Conv1d(aux_channels, aux_channels, kernel_size=kernel_size, bias=False)
self.upsample = UpsampleNetwork(
upsample_factors=upsample_factors,
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):
"""
c : (B, C, T_in).
Tensor: (B, C, T_upsampled),
"""
c_ = self.conv_in(c)
c = c_[:, :, : -self.aux_context_window] if self.use_causal_conv else c_
return self.upsample(c)
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