# Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""MelGAN Modules.
This code is modified from https://github.com/kan-bayashi/ParallelWaveGAN.
"""
import logging
from typing import Any, Dict, List
import numpy as np
import torch
from espnet2.gan_tts.melgan.residual_stack import ResidualStack
[docs]class MelGANGenerator(torch.nn.Module):
"""MelGAN generator module."""
def __init__(
self,
in_channels: int = 80,
out_channels: int = 1,
kernel_size: int = 7,
channels: int = 512,
bias: bool = True,
upsample_scales: List[int] = [8, 8, 2, 2],
stack_kernel_size: int = 3,
stacks: int = 3,
nonlinear_activation: str = "LeakyReLU",
nonlinear_activation_params: Dict[str, Any] = {"negative_slope": 0.2},
pad: str = "ReflectionPad1d",
pad_params: Dict[str, Any] = {},
use_final_nonlinear_activation: bool = True,
use_weight_norm: bool = True,
):
"""Initialize MelGANGenerator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_size (int): Kernel size of initial and final conv layer.
channels (int): Initial number of channels for conv layer.
bias (bool): Whether to add bias parameter in convolution layers.
upsample_scales (List[int]): List of upsampling scales.
stack_kernel_size (int): Kernel size of dilated conv layers in residual
stack.
stacks (int): Number of stacks in a single residual stack.
nonlinear_activation (str): Activation function module name.
nonlinear_activation_params (Dict[str, Any]): Hyperparameters for activation
function.
pad (str): Padding function module name before dilated convolution layer.
pad_params (Dict[str, Any]): Hyperparameters for padding function.
use_final_nonlinear_activation (torch.nn.Module): Activation function for
the final layer.
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 hyper parameters is valid
assert channels >= np.prod(upsample_scales)
assert channels % (2 ** len(upsample_scales)) == 0
assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
# add initial layer
layers = []
layers += [
getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params),
torch.nn.Conv1d(in_channels, channels, kernel_size, bias=bias),
]
self.upsample_factor = int(np.prod(upsample_scales) * out_channels)
for i, upsample_scale in enumerate(upsample_scales):
# add upsampling layer
layers += [
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)
]
layers += [
torch.nn.ConvTranspose1d(
channels // (2**i),
channels // (2 ** (i + 1)),
upsample_scale * 2,
stride=upsample_scale,
padding=upsample_scale // 2 + upsample_scale % 2,
output_padding=upsample_scale % 2,
bias=bias,
)
]
# add residual stack
for j in range(stacks):
layers += [
ResidualStack(
kernel_size=stack_kernel_size,
channels=channels // (2 ** (i + 1)),
dilation=stack_kernel_size**j,
bias=bias,
nonlinear_activation=nonlinear_activation,
nonlinear_activation_params=nonlinear_activation_params,
pad=pad,
pad_params=pad_params,
)
]
# add final layer
layers += [
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)
]
layers += [
getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params),
torch.nn.Conv1d(
channels // (2 ** (i + 1)), out_channels, kernel_size, bias=bias
),
]
if use_final_nonlinear_activation:
layers += [torch.nn.Tanh()]
# define the model as a single function
self.melgan = torch.nn.Sequential(*layers)
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
# reset parameters
self.reset_parameters()
[docs] def forward(self, c: torch.Tensor) -> torch.Tensor:
"""Calculate forward propagation.
Args:
c (Tensor): Input tensor (B, channels, T).
Returns:
Tensor: Output tensor (B, 1, T ** prod(upsample_scales)).
"""
return self.melgan(c)
[docs] 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)
[docs] 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)
[docs] def reset_parameters(self):
"""Reset parameters.
This initialization follows official implementation manner.
https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
"""
def _reset_parameters(m):
if isinstance(m, torch.nn.Conv1d) or isinstance(
m, torch.nn.ConvTranspose1d
):
m.weight.data.normal_(0.0, 0.02)
logging.debug(f"Reset parameters in {m}.")
self.apply(_reset_parameters)
[docs] def inference(self, c: torch.Tensor) -> torch.Tensor:
"""Perform inference.
Args:
c (Tensor): Input tensor (T, in_channels).
Returns:
Tensor: Output tensor (T ** prod(upsample_scales), out_channels).
"""
c = self.melgan(c.transpose(1, 0).unsqueeze(0))
return c.squeeze(0).transpose(1, 0)
[docs]class MelGANDiscriminator(torch.nn.Module):
"""MelGAN discriminator module."""
def __init__(
self,
in_channels: int = 1,
out_channels: int = 1,
kernel_sizes: List[int] = [5, 3],
channels: int = 16,
max_downsample_channels: int = 1024,
bias: bool = True,
downsample_scales: List[int] = [4, 4, 4, 4],
nonlinear_activation: str = "LeakyReLU",
nonlinear_activation_params: Dict[str, Any] = {"negative_slope": 0.2},
pad: str = "ReflectionPad1d",
pad_params: Dict[str, Any] = {},
):
"""Initilize MelGANDiscriminator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_sizes (List[int]): List of two kernel sizes. The prod will be used
for the first conv layer, and the first and the second kernel sizes
will be used for the last two layers. For example if kernel_sizes =
[5, 3], the first layer kernel size will be 5 * 3 = 15, the last two
layers' kernel size will be 5 and 3, respectively.
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.
pad (str): Padding function module name before dilated convolution layer.
pad_params (Dict[str, Any]): Hyperparameters for padding function.
"""
super().__init__()
self.layers = torch.nn.ModuleList()
# check kernel size is valid
assert len(kernel_sizes) == 2
assert kernel_sizes[0] % 2 == 1
assert kernel_sizes[1] % 2 == 1
# add first layer
self.layers += [
torch.nn.Sequential(
getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params),
torch.nn.Conv1d(
in_channels, channels, np.prod(kernel_sizes), bias=bias
),
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
)
]
# add downsample layers
in_chs = channels
for downsample_scale in downsample_scales:
out_chs = min(in_chs * downsample_scale, max_downsample_channels)
self.layers += [
torch.nn.Sequential(
torch.nn.Conv1d(
in_chs,
out_chs,
kernel_size=downsample_scale * 10 + 1,
stride=downsample_scale,
padding=downsample_scale * 5,
groups=in_chs // 4,
bias=bias,
),
getattr(torch.nn, nonlinear_activation)(
**nonlinear_activation_params
),
)
]
in_chs = out_chs
# 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_sizes[0],
padding=(kernel_sizes[0] - 1) // 2,
bias=bias,
),
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
)
]
self.layers += [
torch.nn.Conv1d(
out_chs,
out_channels,
kernel_sizes[1],
padding=(kernel_sizes[1] - 1) // 2,
bias=bias,
),
]
[docs] 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
[docs]class MelGANMultiScaleDiscriminator(torch.nn.Module):
"""MelGAN multi-scale discriminator module."""
def __init__(
self,
in_channels: int = 1,
out_channels: int = 1,
scales: int = 3,
downsample_pooling: str = "AvgPool1d",
# follow the official implementation setting
downsample_pooling_params: Dict[str, Any] = {
"kernel_size": 4,
"stride": 2,
"padding": 1,
"count_include_pad": False,
},
kernel_sizes: List[int] = [5, 3],
channels: int = 16,
max_downsample_channels: int = 1024,
bias: bool = True,
downsample_scales: List[int] = [4, 4, 4, 4],
nonlinear_activation: str = "LeakyReLU",
nonlinear_activation_params: Dict[str, Any] = {"negative_slope": 0.2},
pad: str = "ReflectionPad1d",
pad_params: Dict[str, Any] = {},
use_weight_norm: bool = True,
):
"""Initilize MelGANMultiScaleDiscriminator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
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.
kernel_sizes (List[int]): List of two kernel sizes. The sum will be used
for the first conv layer, and the first and the second kernel sizes
will be used for the last two 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.
pad (str): Padding function module name before dilated convolution layer.
pad_params (Dict[str, Any]): Hyperparameters for padding function.
use_weight_norm (bool): Whether to use weight norm.
"""
super().__init__()
self.discriminators = torch.nn.ModuleList()
# add discriminators
for _ in range(scales):
self.discriminators += [
MelGANDiscriminator(
in_channels=in_channels,
out_channels=out_channels,
kernel_sizes=kernel_sizes,
channels=channels,
max_downsample_channels=max_downsample_channels,
bias=bias,
downsample_scales=downsample_scales,
nonlinear_activation=nonlinear_activation,
nonlinear_activation_params=nonlinear_activation_params,
pad=pad,
pad_params=pad_params,
)
]
self.pooling = getattr(torch.nn, downsample_pooling)(
**downsample_pooling_params
)
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
# reset parameters
self.reset_parameters()
[docs] 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.
"""
outs = []
for f in self.discriminators:
outs += [f(x)]
x = self.pooling(x)
return outs
[docs] 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)
[docs] 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)
[docs] def reset_parameters(self):
"""Reset parameters.
This initialization follows official implementation manner.
https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
"""
def _reset_parameters(m: torch.nn.Module):
if isinstance(m, torch.nn.Conv1d) or isinstance(
m, torch.nn.ConvTranspose1d
):
m.weight.data.normal_(0.0, 0.02)
logging.debug(f"Reset parameters in {m}.")
self.apply(_reset_parameters)