# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from espnet(https://github.com/espnet/espnet)
"""MelGAN Modules."""
from typing import Any
from typing import Dict
from typing import List
import numpy as np
import paddle
from paddle import nn
from paddlespeech.t2s.modules.causal_conv import CausalConv1D
from paddlespeech.t2s.modules.causal_conv import CausalConv1DTranspose
from paddlespeech.t2s.modules.nets_utils import initialize
from paddlespeech.t2s.modules.pqmf import PQMF
from paddlespeech.t2s.modules.residual_stack import ResidualStack
class MelGANGenerator(nn.Layer):
"""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="Pad1D",
pad_params: Dict[str, Any]={"mode": "reflect"},
use_final_nonlinear_activation: bool=True,
use_weight_norm: bool=True,
use_causal_conv: bool=False,
init_type: str="xavier_uniform", ):
"""Initialize MelGANGenerator module.
Parameters
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels,
the number of sub-band is out_channels in multi-band melgan.
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 : Optional[str], optional
Non linear activation in upsample network, by default None
nonlinear_activation_params : Dict[str, Any], optional
Parameters passed to the linear activation in the upsample network,
by default {}
pad : str
Padding function module name before dilated convolution layer.
pad_params : dict
Hyperparameters for padding function.
use_final_nonlinear_activation : paddle.nn.Layer
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.
use_causal_conv : bool
Whether to use causal convolution.
"""
super().__init__()
# check hyper parameters is valid
assert channels >= np.prod(upsample_scales)
assert channels % (2**len(upsample_scales)) == 0
if not use_causal_conv:
assert (kernel_size - 1
) % 2 == 0, "Not support even number kernel size."
# initialize parameters
initialize(self, init_type)
layers = []
if not use_causal_conv:
layers += [
getattr(paddle.nn, pad)((kernel_size - 1) // 2, **pad_params),
nn.Conv1D(in_channels, channels, kernel_size, bias_attr=bias),
]
else:
layers += [
CausalConv1D(
in_channels,
channels,
kernel_size,
bias=bias,
pad=pad,
pad_params=pad_params, ),
]
for i, upsample_scale in enumerate(upsample_scales):
# add upsampling layer
layers += [
getattr(nn, nonlinear_activation)(**nonlinear_activation_params)
]
if not use_causal_conv:
layers += [
nn.Conv1DTranspose(
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_attr=bias, )
]
else:
layers += [
CausalConv1DTranspose(
channels // (2**i),
channels // (2**(i + 1)),
upsample_scale * 2,
stride=upsample_scale,
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,
use_causal_conv=use_causal_conv, )
]
# add final layer
layers += [
getattr(nn, nonlinear_activation)(**nonlinear_activation_params)
]
if not use_causal_conv:
layers += [
getattr(nn, pad)((kernel_size - 1) // 2, **pad_params),
nn.Conv1D(
channels // (2**(i + 1)),
out_channels,
kernel_size,
bias_attr=bias),
]
else:
layers += [
CausalConv1D(
channels // (2**(i + 1)),
out_channels,
kernel_size,
bias=bias,
pad=pad,
pad_params=pad_params, ),
]
if use_final_nonlinear_activation:
layers += [nn.Tanh()]
# define the model as a single function
self.melgan = nn.Sequential(*layers)
nn.initializer.set_global_initializer(None)
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
# reset parameters
self.reset_parameters()
# initialize pqmf for multi-band melgan inference
if out_channels > 1:
self.pqmf = PQMF(subbands=out_channels)
else:
self.pqmf = None
def forward(self, c):
"""Calculate forward propagation.
Parameters
----------
c : Tensor
Input tensor (B, in_channels, T).
Returns
----------
Tensor
Output tensor (B, out_channels, T ** prod(upsample_scales)).
"""
out = self.melgan(c)
return out
def apply_weight_norm(self):
"""Recursively apply weight normalization to all the Convolution layers
in the sublayers.
"""
def _apply_weight_norm(layer):
if isinstance(layer, (nn.Conv1D, nn.Conv2D, nn.Conv1DTranspose)):
nn.utils.weight_norm(layer)
self.apply(_apply_weight_norm)
def remove_weight_norm(self):
"""Recursively remove weight normalization from all the Convolution
layers in the sublayers.
"""
def _remove_weight_norm(layer):
try:
nn.utils.remove_weight_norm(layer)
except ValueError:
pass
self.apply(_remove_weight_norm)
def reset_parameters(self):
"""Reset parameters.
This initialization follows official implementation manner.
https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
"""
# 定义参数为float的正态分布。
dist = paddle.distribution.Normal(loc=0.0, scale=0.02)
def _reset_parameters(m):
if isinstance(m, nn.Conv1D) or isinstance(m, nn.Conv1DTranspose):
w = dist.sample(m.weight.shape)
m.weight.set_value(w)
self.apply(_reset_parameters)
def inference(self, c):
"""Perform inference.
Parameters
----------
c : Union[Tensor, ndarray]
Input tensor (T, in_channels).
Returns
----------
Tensor
Output tensor (out_channels*T ** prod(upsample_scales), 1).
"""
# pseudo batch
c = c.transpose([1, 0]).unsqueeze(0)
# (B, out_channels, T ** prod(upsample_scales)
out = self.melgan(c)
if self.pqmf is not None:
# (B, 1, out_channels * T ** prod(upsample_scales)
out = self.pqmf(out)
out = out.squeeze(0).transpose([1, 0])
return out
class MelGANDiscriminator(nn.Layer):
"""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="Pad1D",
pad_params: Dict[str, Any]={"mode": "reflect"}, ):
"""Initilize MelGAN discriminator module.
Parameters
----------
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
Hyperparameters for activation function.
pad : str
Padding function module name before dilated convolution layer.
pad_params : dict
Hyperparameters for padding function.
"""
super().__init__()
self.layers = nn.LayerList()
# 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.append(
nn.Sequential(
getattr(nn, pad)((np.prod(kernel_sizes) - 1) // 2, **
pad_params),
nn.Conv1D(
in_channels,
channels,
int(np.prod(kernel_sizes)),
bias_attr=bias),
getattr(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.append(
nn.Sequential(
nn.Conv1D(
in_chs,
out_chs,
kernel_size=downsample_scale * 10 + 1,
stride=downsample_scale,
padding=downsample_scale * 5,
groups=in_chs // 4,
bias_attr=bias, ),
getattr(nn, nonlinear_activation)(
**nonlinear_activation_params), ))
in_chs = out_chs
# add final layers
out_chs = min(in_chs * 2, max_downsample_channels)
self.layers.append(
nn.Sequential(
nn.Conv1D(
in_chs,
out_chs,
kernel_sizes[0],
padding=(kernel_sizes[0] - 1) // 2,
bias_attr=bias, ),
getattr(nn, nonlinear_activation)(
**nonlinear_activation_params), ))
self.layers.append(
nn.Conv1D(
out_chs,
out_channels,
kernel_sizes[1],
padding=(kernel_sizes[1] - 1) // 2,
bias_attr=bias, ), )
def forward(self, x):
"""Calculate forward propagation.
Parameters
----------
x : Tensor
Input noise signal (B, 1, T).
Returns
----------
List
List of output tensors of each layer (for feat_match_loss).
"""
outs = []
for f in self.layers:
x = f(x)
outs += [x]
return outs
class MelGANMultiScaleDiscriminator(nn.Layer):
"""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,
"exclusive": True,
},
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="Pad1D",
pad_params: Dict[str, Any]={"mode": "reflect"},
use_weight_norm: bool=True,
init_type: str="xavier_uniform", ):
"""Initilize MelGAN multi-scale discriminator module.
Parameters
----------
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
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
Hyperparameters for activation function.
pad : str
Padding function module name before dilated convolution layer.
pad_params : dict
Hyperparameters for padding function.
use_causal_conv : bool
Whether to use causal convolution.
"""
super().__init__()
# initialize parameters
initialize(self, init_type)
self.discriminators = nn.LayerList()
# add discriminators
for _ in range(scales):
self.discriminators.append(
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(nn, downsample_pooling)(
**downsample_pooling_params)
nn.initializer.set_global_initializer(None)
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
# reset parameters
self.reset_parameters()
def forward(self, x):
"""Calculate forward propagation.
Parameters
----------
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)]
x = self.pooling(x)
return outs
def apply_weight_norm(self):
"""Recursively apply weight normalization to all the Convolution layers
in the sublayers.
"""
def _apply_weight_norm(layer):
if isinstance(layer, (nn.Conv1D, nn.Conv2D, nn.Conv1DTranspose)):
nn.utils.weight_norm(layer)
self.apply(_apply_weight_norm)
def remove_weight_norm(self):
"""Recursively remove weight normalization from all the Convolution
layers in the sublayers.
"""
def _remove_weight_norm(layer):
try:
nn.utils.remove_weight_norm(layer)
except ValueError:
pass
self.apply(_remove_weight_norm)
def reset_parameters(self):
"""Reset parameters.
This initialization follows official implementation manner.
https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
"""
# 定义参数为float的正态分布。
dist = paddle.distribution.Normal(loc=0.0, scale=0.02)
def _reset_parameters(m):
if isinstance(m, nn.Conv1D) or isinstance(m, nn.Conv1DTranspose):
w = dist.sample(m.weight.shape)
m.weight.set_value(w)
self.apply(_reset_parameters)
class MelGANInference(nn.Layer):
def __init__(self, normalizer, melgan_generator):
super().__init__()
self.normalizer = normalizer
self.melgan_generator = melgan_generator
def forward(self, logmel):
normalized_mel = self.normalizer(logmel)
wav = self.melgan_generator.inference(normalized_mel)
return wav