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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright (c) 2021 Kazuhiro KOBAYASHI <root.4mac@gmail.com>
#
# Distributed under terms of the MIT license.
"""
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvLayers(nn.Module):
in_channels = 80
conv_channels = 256
out_channels = 222
kernel_size = 3
dilation_size = 1
group_size = 8
n_conv_layers = 3
use_causal = False
conv_type = "original"
def __init__(self, **kwargs):
super().__init__()
for k, v in kwargs.items():
if k not in self.__class__.__dict__.keys():
raise ValueError(f"{k} not in arguments {self.__class__}.")
setattr(self, k, v)
if self.conv_type == "ddsconv":
self.conv_layers = self.ddsconv()
elif self.conv_type == "original":
self.conv_layers = self.original_conv()
else:
raise ValueError(f"Unsupported conv_type: {self.conv_type}")
def forward(self, x):
"""
x: (B, T, in_channels)
y: (B, T, out_channels)
"""
return self.conv_layers(x)
def original_conv(self):
modules = []
modules += [
Conv1d(
self.in_channels,
self.conv_channels,
self.kernel_size,
self.dilation_size,
1,
self.use_causal,
),
nn.ReLU(),
]
for i in range(self.n_conv_layers):
modules += [
Conv1d(
self.conv_channels,
self.conv_channels,
self.kernel_size,
self.dilation_size,
self.group_size,
self.use_causal,
),
nn.ReLU(),
]
modules += [
Conv1d(
self.conv_channels,
self.out_channels,
self.kernel_size,
self.dilation_size,
1,
self.use_causal,
),
]
return nn.Sequential(*modules)
def ddsconv(self):
modules = []
modules += [
Conv1d(
in_channels=self.in_channels,
out_channels=self.conv_channels,
kernel_size=1,
dilation_size=1,
group_size=1,
use_causal=self.use_causal,
)
]
for i in range(self.n_conv_layers):
if self.dilation_size == 1:
dilation_size = self.kernel_size ** i
else:
dilation_size = self.dilation_size ** i
modules += [
DepthSeparableConv1d(
channels=self.conv_channels,
kernel_size=self.kernel_size,
dilation_size=dilation_size,
use_causal=self.use_causal,
)
]
modules += [
Conv1d(
in_channels=self.conv_channels,
out_channels=self.out_channels,
kernel_size=1,
dilation_size=1,
group_size=1,
use_causal=self.use_causal,
)
]
return nn.Sequential(*modules)
def remove_weight_norm(self):
def _remove_weight_norm(m):
try:
torch.nn.utils.remove_weight_norm(m)
except ValueError:
return
self.apply(_remove_weight_norm)
class Conv1d(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=3,
dilation_size=1,
group_size=1,
use_causal=False,
):
super().__init__()
self.use_causal = use_causal
self.kernel_size = kernel_size
self.padding = (kernel_size - 1) * dilation_size
self.conv1d = nn.Conv1d(
in_channels,
out_channels,
kernel_size,
padding=self.padding,
dilation=dilation_size,
groups=group_size,
)
nn.init.kaiming_normal_(self.conv1d.weight)
def forward(self, x):
"""
x: (B, T, D)
y: (B, T, D)
"""
x = x.transpose(1, 2)
y = self.conv1d(x)
# NOTE(k2kobayashi): kernel_size=1 does not discard padding
if self.kernel_size > 1 and self.conv1d.padding != (0,):
if self.use_causal:
y = y[..., : -self.padding]
else:
y = y[..., self.padding // 2 : -self.padding // 2]
return y.transpose(1, 2)
class DepthSeparableConv1d(nn.Module):
def __init__(self, channels, kernel_size, dilation_size, use_causal=False):
super().__init__()
sep_conv = Conv1d(
channels,
channels,
kernel_size,
dilation_size,
group_size=channels,
use_causal=use_causal,
)
conv1d = Conv1d(
channels,
channels,
kernel_size=1,
dilation_size=1,
group_size=1,
use_causal=use_causal,
)
ln1 = nn.LayerNorm(channels)
ln2 = nn.LayerNorm(channels)
gelu1 = nn.GELU()
gelu2 = nn.GELU()
modules = [sep_conv, ln1, gelu1, conv1d, ln2, gelu2]
self.layers = nn.Sequential(*modules)
def forward(self, x):
y = self.layers(x)
return x + y
class DFTLayer(nn.Module):
def __init__(self, n_fft=1024):
super().__init__()
self.n_fft = n_fft
wsin, wcos = self._generate_fourier_kernels(n_fft=n_fft)
self.register_buffer(
"wsin", torch.tensor(wsin, dtype=torch.float), persistent=False
)
self.register_buffer(
"wcos", torch.tensor(wcos, dtype=torch.float), persistent=False
)
@staticmethod
def _generate_fourier_kernels(n_fft, window="hann"):
freq_bins = n_fft
s = np.arange(0, n_fft, 1.0)
wsin = np.empty((freq_bins, 1, n_fft))
wcos = np.empty((freq_bins, 1, n_fft))
for k in range(freq_bins):
wsin[k, 0, :] = np.sin(2 * np.pi * k * s / n_fft)
wcos[k, 0, :] = np.cos(2 * np.pi * k * s / n_fft)
return wsin.astype(np.float32), wcos.astype(np.float32)
def forward(self, x, imag=None, inverse=False):
if not inverse:
return self.dft(x)
else:
return self.idft(x, imag)
def dft(self, real):
real = real.transpose(0, 1)
imag = F.conv1d(real, self.wsin, stride=self.n_fft + 1).permute(2, 0, 1)
real = F.conv1d(real, self.wcos, stride=self.n_fft + 1).permute(2, 0, 1)
return real, -imag
def idft(self, real, imag):
real = real.transpose(0, 1)
imag = imag.transpose(0, 1)
a1 = F.conv1d(real, self.wcos, stride=self.n_fft + 1)
a2 = F.conv1d(real, self.wsin, stride=self.n_fft + 1)
b1 = F.conv1d(imag, self.wcos, stride=self.n_fft + 1)
b2 = F.conv1d(imag, self.wsin, stride=self.n_fft + 1)
imag = a2 + b1
real = a1 - b2
return (
(real / self.n_fft).permute(2, 0, 1),
(imag / self.n_fft).permute(2, 0, 1),
)
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