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PaddleSpeech/paddlespeech/t2s/models/melgan/style_melgan.py

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add style_melgan
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# 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)
"""StyleMelGAN Modules."""
import copy
import math
from typing import Any
from typing import Dict
from typing import List
import numpy as np
import paddle
import paddle.nn.functional as F
from paddle import nn
from paddlespeech.t2s.models.melgan import MelGANDiscriminator as BaseDiscriminator
from paddlespeech.t2s.modules.activation import get_activation
from paddlespeech.t2s.modules.nets_utils import initialize
from paddlespeech.t2s.modules.pqmf import PQMF
from paddlespeech.t2s.modules.tade_res_block import TADEResBlock
class StyleMelGANGenerator(nn.Layer):
"""Style MelGAN generator module."""
def __init__(
self,
in_channels: int=128,
aux_channels: int=80,
channels: int=64,
out_channels: int=1,
kernel_size: int=9,
dilation: int=2,
bias: bool=True,
noise_upsample_scales: List[int]=[11, 2, 2, 2],
noise_upsample_activation: str="leakyrelu",
noise_upsample_activation_params: Dict[str,
Any]={"negative_slope": 0.2},
upsample_scales: List[int]=[2, 2, 2, 2, 2, 2, 2, 2, 1],
upsample_mode: str="linear",
gated_function: str="softmax",
use_weight_norm: bool=True,
init_type: str="xavier_uniform", ):
"""Initilize Style MelGAN generator.
Parameters
----------
in_channels : int
Number of input noise channels.
aux_channels : int
Number of auxiliary input channels.
channels : int
Number of channels for conv layer.
out_channels : int
Number of output channels.
kernel_size : int
Kernel size of conv layers.
dilation : int
Dilation factor for conv layers.
bias : bool
Whether to add bias parameter in convolution layers.
noise_upsample_scales : list
List of noise upsampling scales.
noise_upsample_activation : str
Activation function module name for noise upsampling.
noise_upsample_activation_params : dict
Hyperparameters for the above activation function.
upsample_scales : list
List of upsampling scales.
upsample_mode : str
Upsampling mode in TADE layer.
gated_function : str
Gated function in TADEResBlock ("softmax" or "sigmoid").
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__()
# initialize parameters
initialize(self, init_type)
self.in_channels = in_channels
noise_upsample = []
in_chs = in_channels
for noise_upsample_scale in noise_upsample_scales:
noise_upsample.append(
nn.Conv1DTranspose(
in_chs,
channels,
noise_upsample_scale * 2,
stride=noise_upsample_scale,
padding=noise_upsample_scale // 2 + noise_upsample_scale %
2,
output_padding=noise_upsample_scale % 2,
bias_attr=bias, ))
noise_upsample.append(
get_activation(noise_upsample_activation, **
noise_upsample_activation_params))
in_chs = channels
self.noise_upsample = nn.Sequential(*noise_upsample)
self.noise_upsample_factor = np.prod(noise_upsample_scales)
self.blocks = nn.LayerList()
aux_chs = aux_channels
for upsample_scale in upsample_scales:
self.blocks.append(
TADEResBlock(
in_channels=channels,
aux_channels=aux_chs,
kernel_size=kernel_size,
dilation=dilation,
bias=bias,
upsample_factor=upsample_scale,
upsample_mode=upsample_mode,
gated_function=gated_function, ), )
aux_chs = channels
self.upsample_factor = np.prod(upsample_scales)
self.output_conv = nn.Sequential(
nn.Conv1D(
channels,
out_channels,
kernel_size,
1,
bias_attr=bias,
padding=(kernel_size - 1) // 2, ),
nn.Tanh(), )
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, c, z=None):
"""Calculate forward propagation.
Parameters
----------
c : Tensor
Auxiliary input tensor (B, channels, T).
z : Tensor
Input noise tensor (B, in_channels, 1).
Returns
----------
Tensor
Output tensor (B, out_channels, T ** prod(upsample_scales)).
"""
# batch_max_steps(24000) == noise_upsample_factor(80) * upsample_factor(300)
if z is None:
z = paddle.randn([paddle.shape(c)[0], self.in_channels, 1])
# (B, in_channels, noise_upsample_factor).
x = self.noise_upsample(z)
for block in self.blocks:
x, c = block(x, c)
x = self.output_conv(x)
return x
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.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:
if layer:
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 : Tensor
Input tensor (T, in_channels).
Returns
----------
Tensor
Output tensor (T ** prod(upsample_scales), out_channels).
"""
# (1, in_channels, T)
c = c.transpose([1, 0]).unsqueeze(0)
c_shape = paddle.shape(c)
# prepare noise input
# there is a bug in Paddle int division, we must convert a int tensor to int here
noise_size = (1, self.in_channels,
math.ceil(int(c_shape[2]) / self.noise_upsample_factor))
# (1, in_channels, T/noise_upsample_factor)
noise = paddle.randn(noise_size)
# (1, in_channels, T)
x = self.noise_upsample(noise)
x_shape = paddle.shape(x)
total_length = c_shape[2] * self.upsample_factor
c = F.pad(
c, (0, x_shape[2] - c_shape[2]), "replicate", data_format="NCL")
# c.shape[2] == x.shape[2] here
# (1, in_channels, T*prod(upsample_scales))
for block in self.blocks:
x, c = block(x, c)
x = self.output_conv(x)[..., :total_length]
return x.squeeze(0).transpose([1, 0])
# StyleMelGANDiscriminator 不需要 remove weight norm 嘛？
class StyleMelGANDiscriminator(nn.Layer):
"""Style MelGAN disciminator module."""
def __init__(
self,
repeats: int=2,
window_sizes: List[int]=[512, 1024, 2048, 4096],
pqmf_params: List[List[int]]=[
[1, None, None, None],
[2, 62, 0.26700, 9.0],
[4, 62, 0.14200, 9.0],
[8, 62, 0.07949, 9.0],
],
discriminator_params: Dict[str, Any]={
"out_channels": 1,
"kernel_sizes": [5, 3],
"channels": 16,
"max_downsample_channels": 512,
"bias": True,
"downsample_scales": [4, 4, 4, 1],
"nonlinear_activation": "leakyrelu",
"nonlinear_activation_params": {
"negative_slope": 0.2
},
"pad": "Pad1D",
"pad_params": {
"mode": "reflect"
},
},
use_weight_norm: bool=True,
init_type: str="xavier_uniform", ):
"""Initilize Style MelGAN discriminator.
Parameters
----------
repeats : int
Number of repititons to apply RWD.
window_sizes : list
List of random window sizes.
pqmf_params : list
List of list of Parameters for PQMF modules
discriminator_params : dict
Parameters for base discriminator module.
use_weight_nom : bool
Whether to apply weight normalization.
"""
super().__init__()
# initialize parameters
initialize(self, init_type)
# window size check
assert len(window_sizes) == len(pqmf_params)
sizes = [ws // p[0] for ws, p in zip(window_sizes, pqmf_params)]
assert len(window_sizes) == sum([sizes[0] == size for size in sizes])
self.repeats = repeats
self.window_sizes = window_sizes
self.pqmfs = nn.LayerList()
self.discriminators = nn.LayerList()
for pqmf_param in pqmf_params:
d_params = copy.deepcopy(discriminator_params)
d_params["in_channels"] = pqmf_param[0]
if pqmf_param[0] == 1:
self.pqmfs.append(nn.Identity())
else:
self.pqmfs.append(PQMF(*pqmf_param))
self.discriminators.append(BaseDiscriminator(**d_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 tensor (B, 1, T).
Returns
----------
List
List of discriminator outputs, #items in the list will be
equal to repeats * #discriminators.
"""
outs = []
for _ in range(self.repeats):
outs += self._forward(x)
return outs
def _forward(self, x):
outs = []
for idx, (ws, pqmf, disc) in enumerate(
zip(self.window_sizes, self.pqmfs, self.discriminators)):
start_idx = int(np.random.randint(paddle.shape(x)[-1] - ws))
x_ = x[:, :, start_idx:start_idx + ws]
if idx == 0:
# nn.Identity()
x_ = pqmf(x_)
else:
x_ = pqmf.analysis(x_)
outs += [disc(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.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 StyleMelGANInference(nn.Layer):
def __init__(self, normalizer, style_melgan_generator):
super().__init__()
self.normalizer = normalizer
self.style_melgan_generator = style_melgan_generator
def forward(self, logmel):
normalized_mel = self.normalizer(logmel)
wav = self.style_melgan_generator.inference(normalized_mel)
return wav