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hifigan.py
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# Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""HiFi-GAN Modules.
This code is modified from https://github.com/kan-bayashi/ParallelWaveGAN.
"""
import copy
import logging
from typing import Any, Dict, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
class HiFiGANGenerator(torch.nn.Module):
"""HiFiGAN generator module."""
def __init__(
self,
in_channels: int = 80,
out_channels: int = 1,
channels: int = 512,
global_channels: int = -1,
kernel_size: int = 7,
upsample_scales: List[int] = [8, 8, 2, 2],
upsample_kernel_sizes: List[int] = [16, 16, 4, 4],
resblock_kernel_sizes: List[int] = [3, 7, 11],
resblock_dilations: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
use_additional_convs: bool = True,
bias: bool = True,
nonlinear_activation: str = "LeakyReLU",
nonlinear_activation_params: Dict[str, Any] = {"negative_slope": 0.1},
use_weight_norm: bool = True,
):
"""Initialize HiFiGANGenerator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
channels (int): Number of hidden representation channels.
global_channels (int): Number of global conditioning channels.
kernel_size (int): Kernel size of initial and final conv layer.
upsample_scales (List[int]): List of upsampling scales.
upsample_kernel_sizes (List[int]): List of kernel sizes for upsample layers.
resblock_kernel_sizes (List[int]): List of kernel sizes for residual blocks.
resblock_dilations (List[List[int]]): List of list of dilations for residual
blocks.
use_additional_convs (bool): Whether to use additional conv layers in
residual blocks.
bias (bool): Whether to add bias parameter in convolution layers.
nonlinear_activation (str): Activation function module name.
nonlinear_activation_params (Dict[str, Any]): Hyperparameters for activation
function.
use_weight_norm (bool): Whether to use weight norm. If set to true, it will
be applied to all of the conv layers.
"""
super().__init__()
# check hyperparameters are valid
assert kernel_size % 2 == 1, "Kernel size must be odd number."
assert len(upsample_scales) == len(upsample_kernel_sizes)
assert len(resblock_dilations) == len(resblock_kernel_sizes)
# define modules
self.upsample_factor = int(np.prod(upsample_scales) * out_channels)
self.num_upsamples = len(upsample_kernel_sizes)
self.num_blocks = len(resblock_kernel_sizes)
self.input_conv = torch.nn.Conv1d(
in_channels,
channels,
kernel_size,
1,
padding=(kernel_size - 1) // 2,
)
self.upsamples = torch.nn.ModuleList()
self.blocks = torch.nn.ModuleList()
for i in range(len(upsample_kernel_sizes)):
assert upsample_kernel_sizes[i] == 2 * upsample_scales[i]
self.upsamples += [
torch.nn.Sequential(
getattr(torch.nn, nonlinear_activation)(
**nonlinear_activation_params
),
torch.nn.ConvTranspose1d(
channels // (2**i),
channels // (2 ** (i + 1)),
upsample_kernel_sizes[i],
upsample_scales[i],
padding=upsample_scales[i] // 2 + upsample_scales[i] % 2,
output_padding=upsample_scales[i] % 2,
),
)
]
for j in range(len(resblock_kernel_sizes)):
self.blocks += [
ResidualBlock(
kernel_size=resblock_kernel_sizes[j],
channels=channels // (2 ** (i + 1)),
dilations=resblock_dilations[j],
bias=bias,
use_additional_convs=use_additional_convs,
nonlinear_activation=nonlinear_activation,
nonlinear_activation_params=nonlinear_activation_params,
)
]
self.output_conv = torch.nn.Sequential(
# NOTE(kan-bayashi): follow official implementation but why
# using different slope parameter here? (0.1 vs. 0.01)
torch.nn.LeakyReLU(),
torch.nn.Conv1d(
channels // (2 ** (i + 1)),
out_channels,
kernel_size,
1,
padding=(kernel_size - 1) // 2,
),
torch.nn.Tanh(),
)
if global_channels > 0:
self.global_conv = torch.nn.Conv1d(global_channels, channels, 1)
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
# reset parameters
self.reset_parameters()
def forward(
self, c: torch.Tensor, g: Optional[torch.Tensor] = None
) -> torch.Tensor:
"""Calculate forward propagation.
Args:
c (Tensor): Input tensor (B, in_channels, T).
g (Optional[Tensor]): Global conditioning tensor (B, global_channels, 1).
Returns:
Tensor: Output tensor (B, out_channels, T).
"""
c = self.input_conv(c)
if g is not None:
c = c + self.global_conv(g)
for i in range(self.num_upsamples):
c = self.upsamples[i](c)
cs = 0.0 # initialize
for j in range(self.num_blocks):
cs += self.blocks[i * self.num_blocks + j](c)
c = cs / self.num_blocks
c = self.output_conv(c)
return c
def reset_parameters(self):
"""Reset parameters.
This initialization follows the official implementation manner.
https://github.com/jik876/hifi-gan/blob/master/models.py
"""
def _reset_parameters(m: torch.nn.Module):
if isinstance(m, (torch.nn.Conv1d, torch.nn.ConvTranspose1d)):
m.weight.data.normal_(0.0, 0.01)
logging.debug(f"Reset parameters in {m}.")
self.apply(_reset_parameters)
def remove_weight_norm(self):
"""Remove weight normalization module from all of the layers."""
def _remove_weight_norm(m: torch.nn.Module):
try:
logging.debug(f"Weight norm is removed from {m}.")
torch.nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(_remove_weight_norm)
def apply_weight_norm(self):
"""Apply weight normalization module from all of the layers."""
def _apply_weight_norm(m: torch.nn.Module):
if isinstance(m, torch.nn.Conv1d) or isinstance(
m, torch.nn.ConvTranspose1d
):
torch.nn.utils.weight_norm(m)
logging.debug(f"Weight norm is applied to {m}.")
self.apply(_apply_weight_norm)
def inference(
self, c: torch.Tensor, g: Optional[torch.Tensor] = None
) -> torch.Tensor:
"""Perform inference.
Args:
c (torch.Tensor): Input tensor (T, in_channels).
g (Optional[Tensor]): Global conditioning tensor (global_channels, 1).
Returns:
Tensor: Output tensor (T ** upsample_factor, out_channels).
"""
if g is not None:
g = g.unsqueeze(0)
c = self.forward(c.transpose(1, 0).unsqueeze(0), g=g)
return c.squeeze(0).transpose(1, 0)
class HiFiGANPeriodDiscriminator(torch.nn.Module):
"""HiFiGAN period discriminator module."""
def __init__(
self,
in_channels: int = 1,
out_channels: int = 1,
period: int = 3,
kernel_sizes: List[int] = [5, 3],
channels: int = 32,
downsample_scales: List[int] = [3, 3, 3, 3, 1],
max_downsample_channels: int = 1024,
bias: bool = True,
nonlinear_activation: str = "LeakyReLU",
nonlinear_activation_params: Dict[str, Any] = {"negative_slope": 0.1},
use_weight_norm: bool = True,
use_spectral_norm: bool = False,
):
"""Initialize HiFiGANPeriodDiscriminator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
period (int): Period.
kernel_sizes (list): Kernel sizes of initial conv layers and the final conv
layer.
channels (int): Number of initial channels.
downsample_scales (List[int]): List of downsampling scales.
max_downsample_channels (int): Number of maximum downsampling channels.
use_additional_convs (bool): Whether to use additional conv layers in
residual blocks.
bias (bool): Whether to add bias parameter in convolution layers.
nonlinear_activation (str): Activation function module name.
nonlinear_activation_params (Dict[str, Any]): Hyperparameters for activation
function.
use_weight_norm (bool): Whether to use weight norm.
If set to true, it will be applied to all of the conv layers.
use_spectral_norm (bool): Whether to use spectral norm.
If set to true, it will be applied to all of the conv layers.
"""
super().__init__()
assert len(kernel_sizes) == 2
assert kernel_sizes[0] % 2 == 1, "Kernel size must be odd number."
assert kernel_sizes[1] % 2 == 1, "Kernel size must be odd number."
self.period = period
self.convs = torch.nn.ModuleList()
in_chs = in_channels
out_chs = channels
for downsample_scale in downsample_scales:
self.convs += [
torch.nn.Sequential(
torch.nn.Conv2d(
in_chs,
out_chs,
(kernel_sizes[0], 1),
(downsample_scale, 1),
padding=((kernel_sizes[0] - 1) // 2, 0),
),
getattr(torch.nn, nonlinear_activation)(
**nonlinear_activation_params
),
)
]
in_chs = out_chs
# NOTE(kan-bayashi): Use downsample_scale + 1?
out_chs = min(out_chs * 4, max_downsample_channels)
self.output_conv = torch.nn.Conv2d(
out_chs,
out_channels,
(kernel_sizes[1] - 1, 1),
1,
padding=((kernel_sizes[1] - 1) // 2, 0),
)
if use_weight_norm and use_spectral_norm:
raise ValueError("Either use use_weight_norm or use_spectral_norm.")
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
# apply spectral norm
if use_spectral_norm:
self.apply_spectral_norm()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Calculate forward propagation.
Args:
c (Tensor): Input tensor (B, in_channels, T).
Returns:
list: List of each layer's tensors.
"""
# transform 1d to 2d -> (B, C, T/P, P)
b, c, t = x.shape
if t % self.period != 0:
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t += n_pad
x = x.view(b, c, t // self.period, self.period)
# forward conv
outs = []
for layer in self.convs:
x = layer(x)
outs += [x]
x = self.output_conv(x)
x = torch.flatten(x, 1, -1)
outs += [x]
return outs
def apply_weight_norm(self):
"""Apply weight normalization module from all of the layers."""
def _apply_weight_norm(m: torch.nn.Module):
if isinstance(m, torch.nn.Conv2d):
torch.nn.utils.weight_norm(m)
logging.debug(f"Weight norm is applied to {m}.")
self.apply(_apply_weight_norm)
def apply_spectral_norm(self):
"""Apply spectral normalization module from all of the layers."""
def _apply_spectral_norm(m: torch.nn.Module):
if isinstance(m, torch.nn.Conv2d):
torch.nn.utils.spectral_norm(m)
logging.debug(f"Spectral norm is applied to {m}.")
self.apply(_apply_spectral_norm)
class HiFiGANMultiPeriodDiscriminator(torch.nn.Module):
"""HiFiGAN multi-period discriminator module."""
def __init__(
self,
periods: List[int] = [2, 3, 5, 7, 11],
discriminator_params: Dict[str, Any] = {
"in_channels": 1,
"out_channels": 1,
"kernel_sizes": [5, 3],
"channels": 32,
"downsample_scales": [3, 3, 3, 3, 1],
"max_downsample_channels": 1024,
"bias": True,
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {"negative_slope": 0.1},
"use_weight_norm": True,
"use_spectral_norm": False,
},
):
"""Initialize HiFiGANMultiPeriodDiscriminator module.
Args:
periods (List[int]): List of periods.
discriminator_params (Dict[str, Any]): Parameters for hifi-gan period
discriminator module. The period parameter will be overwritten.
"""
super().__init__()
self.discriminators = torch.nn.ModuleList()
for period in periods:
params = copy.deepcopy(discriminator_params)
params["period"] = period
self.discriminators += [HiFiGANPeriodDiscriminator(**params)]
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Calculate forward propagation.
Args:
x (Tensor): Input noise signal (B, 1, T).
Returns:
List: List of list of each discriminator outputs, which consists of each
layer output tensors.
"""
outs = []
for f in self.discriminators:
outs += [f(x)]
return outs
class HiFiGANScaleDiscriminator(torch.nn.Module):
"""HiFi-GAN scale discriminator module."""
def __init__(
self,
in_channels: int = 1,
out_channels: int = 1,
kernel_sizes: List[int] = [15, 41, 5, 3],
channels: int = 128,
max_downsample_channels: int = 1024,
max_groups: int = 16,
bias: int = True,
downsample_scales: List[int] = [2, 2, 4, 4, 1],
nonlinear_activation: str = "LeakyReLU",
nonlinear_activation_params: Dict[str, Any] = {"negative_slope": 0.1},
use_weight_norm: bool = True,
use_spectral_norm: bool = False,
):
"""Initilize HiFiGAN scale discriminator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_sizes (List[int]): List of four kernel sizes. The first will be used
for the first conv layer, and the second is for downsampling part, and
the remaining two are for the last two output layers.
channels (int): Initial number of channels for conv layer.
max_downsample_channels (int): Maximum number of channels for downsampling
layers.
bias (bool): Whether to add bias parameter in convolution layers.
downsample_scales (List[int]): List of downsampling scales.
nonlinear_activation (str): Activation function module name.
nonlinear_activation_params (Dict[str, Any]): Hyperparameters for activation
function.
use_weight_norm (bool): Whether to use weight norm. If set to true, it will
be applied to all of the conv layers.
use_spectral_norm (bool): Whether to use spectral norm. If set to true, it
will be applied to all of the conv layers.
"""
super().__init__()
self.layers = torch.nn.ModuleList()
# check kernel size is valid
assert len(kernel_sizes) == 4
for ks in kernel_sizes:
assert ks % 2 == 1
# add first layer
self.layers += [
torch.nn.Sequential(
torch.nn.Conv1d(
in_channels,
channels,
# NOTE(kan-bayashi): Use always the same kernel size
kernel_sizes[0],
bias=bias,
padding=(kernel_sizes[0] - 1) // 2,
),
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
)
]
# add downsample layers
in_chs = channels
out_chs = channels
# NOTE(kan-bayashi): Remove hard coding?
groups = 4
for downsample_scale in downsample_scales:
self.layers += [
torch.nn.Sequential(
torch.nn.Conv1d(
in_chs,
out_chs,
kernel_size=kernel_sizes[1],
stride=downsample_scale,
padding=(kernel_sizes[1] - 1) // 2,
groups=groups,
bias=bias,
),
getattr(torch.nn, nonlinear_activation)(
**nonlinear_activation_params
),
)
]
in_chs = out_chs
# NOTE(kan-bayashi): Remove hard coding?
out_chs = min(in_chs * 2, max_downsample_channels)
# NOTE(kan-bayashi): Remove hard coding?
groups = min(groups * 4, max_groups)
# add final layers
out_chs = min(in_chs * 2, max_downsample_channels)
self.layers += [
torch.nn.Sequential(
torch.nn.Conv1d(
in_chs,
out_chs,
kernel_size=kernel_sizes[2],
stride=1,
padding=(kernel_sizes[2] - 1) // 2,
bias=bias,
),
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
)
]
self.layers += [
torch.nn.Conv1d(
out_chs,
out_channels,
kernel_size=kernel_sizes[3],
stride=1,
padding=(kernel_sizes[3] - 1) // 2,
bias=bias,
),
]
if use_weight_norm and use_spectral_norm:
raise ValueError("Either use use_weight_norm or use_spectral_norm.")
# apply weight norm
self.use_weight_norm = use_weight_norm
if use_weight_norm:
self.apply_weight_norm()
# apply spectral norm
self.use_spectral_norm = use_spectral_norm
if use_spectral_norm:
self.apply_spectral_norm()
# backward compatibility
self._register_load_state_dict_pre_hook(self._load_state_dict_pre_hook)
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
"""Calculate forward propagation.
Args:
x (Tensor): Input noise signal (B, 1, T).
Returns:
List[Tensor]: List of output tensors of each layer.
"""
outs = []
for f in self.layers:
x = f(x)
outs += [x]
return outs
def apply_weight_norm(self):
"""Apply weight normalization module from all of the layers."""
def _apply_weight_norm(m: torch.nn.Module):
if isinstance(m, torch.nn.Conv1d):
torch.nn.utils.weight_norm(m)
logging.debug(f"Weight norm is applied to {m}.")
self.apply(_apply_weight_norm)
def apply_spectral_norm(self):
"""Apply spectral normalization module from all of the layers."""
def _apply_spectral_norm(m: torch.nn.Module):
if isinstance(m, torch.nn.Conv1d):
torch.nn.utils.spectral_norm(m)
logging.debug(f"Spectral norm is applied to {m}.")
self.apply(_apply_spectral_norm)
def remove_weight_norm(self):
"""Remove weight normalization module from all of the layers."""
def _remove_weight_norm(m):
try:
logging.debug(f"Weight norm is removed from {m}.")
torch.nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(_remove_weight_norm)
def remove_spectral_norm(self):
"""Remove spectral normalization module from all of the layers."""
def _remove_spectral_norm(m):
try:
logging.debug(f"Spectral norm is removed from {m}.")
torch.nn.utils.remove_spectral_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(_remove_spectral_norm)
def _load_state_dict_pre_hook(
self,
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
"""Fix the compatibility of weight / spectral normalization issue.
Some pretrained models are trained with configs that use weight / spectral
normalization, but actually, the norm is not applied. This causes the mismatch
of the parameters with configs. To solve this issue, when parameter mismatch
happens in loading pretrained model, we remove the norm from the current model.
See also:
- https://github.com/espnet/espnet/pull/5240
- https://github.com/kan-bayashi/ParallelWaveGAN/pull/409
"""
current_module_keys = [x for x in state_dict.keys() if x.startswith(prefix)]
if self.use_weight_norm and not any(
["weight_g" in k for k in current_module_keys]
):
logging.warning(
"It seems weight norm is not applied in the pretrained model but the"
" current model uses it. To keep the compatibility, we remove the norm"
" from the current model. This may causes training error due to the"
" parameter mismatch when finetuning. To avoid this issue, please"
" change the following parameters in config to false:\n"
" - discriminator_params.follow_official_norm\n"
" - discriminator_params.scale_discriminator_params.use_weight_norm\n"
" - discriminator_params.scale_discriminator_params.use_spectral_norm\n"
" See also: https://github.com/espnet/espnet/pull/5240"
)
self.remove_weight_norm()
self.use_weight_norm = False
if self.use_spectral_norm and not any(
["weight_u" in k for k in current_module_keys]
):
logging.warning(
"It seems spectral norm is not applied in the pretrained model but the"
" current model uses it. To keep the compatibility, we remove the norm"
" from the current model. This may causes training error due to the"
" parameter mismatch when finetuning. To avoid this issue, please"
" change the following parameters in config to false:\n"
" - discriminator_params.follow_official_norm\n"
" - discriminator_params.scale_discriminator_params.use_weight_norm\n"
" - discriminator_params.scale_discriminator_params.use_spectral_norm\n"
" See also: https://github.com/espnet/espnet/pull/5240"
)
self.remove_spectral_norm()
self.use_spectral_norm = False
class HiFiGANMultiScaleDiscriminator(torch.nn.Module):
"""HiFi-GAN multi-scale discriminator module."""
def __init__(
self,
scales: int = 3,
downsample_pooling: str = "AvgPool1d",
# follow the official implementation setting
downsample_pooling_params: Dict[str, Any] = {
"kernel_size": 4,
"stride": 2,
"padding": 2,
},
discriminator_params: Dict[str, Any] = {
"in_channels": 1,
"out_channels": 1,
"kernel_sizes": [15, 41, 5, 3],
"channels": 128,
"max_downsample_channels": 1024,
"max_groups": 16,
"bias": True,
"downsample_scales": [2, 2, 4, 4, 1],
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {"negative_slope": 0.1},
},
follow_official_norm: bool = False,
):
"""Initilize HiFiGAN multi-scale discriminator module.
Args:
scales (int): Number of multi-scales.
downsample_pooling (str): Pooling module name for downsampling of the
inputs.
downsample_pooling_params (Dict[str, Any]): Parameters for the above pooling
module.
discriminator_params (Dict[str, Any]): Parameters for hifi-gan scale
discriminator module.
follow_official_norm (bool): Whether to follow the norm setting of the
official implementaion. The first discriminator uses spectral norm
and the other discriminators use weight norm.
"""
super().__init__()
self.discriminators = torch.nn.ModuleList()
# add discriminators
for i in range(scales):
params = copy.deepcopy(discriminator_params)
if follow_official_norm:
if i == 0:
params["use_weight_norm"] = False
params["use_spectral_norm"] = True
else:
params["use_weight_norm"] = True
params["use_spectral_norm"] = False
self.discriminators += [HiFiGANScaleDiscriminator(**params)]
self.pooling = None
if scales > 1:
self.pooling = getattr(torch.nn, downsample_pooling)(
**downsample_pooling_params
)
def forward(self, x: torch.Tensor) -> List[List[torch.Tensor]]:
"""Calculate forward propagation.
Args:
x (Tensor): Input noise signal (B, 1, T).
Returns:
List[List[torch.Tensor]]: List of list of each discriminator outputs,
which consists of eachlayer output tensors.
"""
outs = []
for f in self.discriminators:
outs += [f(x)]
if self.pooling is not None:
x = self.pooling(x)
return outs
class HiFiGANMultiScaleMultiPeriodDiscriminator(torch.nn.Module):
"""HiFi-GAN multi-scale + multi-period discriminator module."""
def __init__(
self,
# Multi-scale discriminator related
scales: int = 3,
scale_downsample_pooling: str = "AvgPool1d",
scale_downsample_pooling_params: Dict[str, Any] = {
"kernel_size": 4,
"stride": 2,
"padding": 2,
},
scale_discriminator_params: Dict[str, Any] = {
"in_channels": 1,
"out_channels": 1,
"kernel_sizes": [15, 41, 5, 3],
"channels": 128,
"max_downsample_channels": 1024,
"max_groups": 16,
"bias": True,
"downsample_scales": [2, 2, 4, 4, 1],
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {"negative_slope": 0.1},
},
follow_official_norm: bool = True,
# Multi-period discriminator related
periods: List[int] = [2, 3, 5, 7, 11],
period_discriminator_params: Dict[str, Any] = {
"in_channels": 1,
"out_channels": 1,
"kernel_sizes": [5, 3],
"channels": 32,
"downsample_scales": [3, 3, 3, 3, 1],
"max_downsample_channels": 1024,
"bias": True,
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {"negative_slope": 0.1},
"use_weight_norm": True,
"use_spectral_norm": False,
},
):
"""Initilize HiFiGAN multi-scale + multi-period discriminator module.
Args:
scales (int): Number of multi-scales.
scale_downsample_pooling (str): Pooling module name for downsampling of the
inputs.
scale_downsample_pooling_params (dict): Parameters for the above pooling
module.
scale_discriminator_params (dict): Parameters for hifi-gan scale
discriminator module.
follow_official_norm (bool): Whether to follow the norm setting of the
official implementaion. The first discriminator uses spectral norm and
the other discriminators use weight norm.
periods (list): List of periods.
period_discriminator_params (dict): Parameters for hifi-gan period
discriminator module. The period parameter will be overwritten.
"""
super().__init__()
self.msd = HiFiGANMultiScaleDiscriminator(
scales=scales,
downsample_pooling=scale_downsample_pooling,
downsample_pooling_params=scale_downsample_pooling_params,
discriminator_params=scale_discriminator_params,
follow_official_norm=follow_official_norm,
)
self.mpd = HiFiGANMultiPeriodDiscriminator(
periods=periods,
discriminator_params=period_discriminator_params,
)
def forward(self, x: torch.Tensor) -> List[List[torch.Tensor]]:
"""Calculate forward propagation.
Args:
x (Tensor): Input noise signal (B, 1, T).
Returns:
List[List[Tensor]]: List of list of each discriminator outputs,
which consists of each layer output tensors. Multi scale and
multi period ones are concatenated.
"""
msd_outs = self.msd(x)
mpd_outs = self.mpd(x)
return msd_outs + mpd_outs