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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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"""StyleMelGAN Modules."""
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import copy
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import math
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from typing import Any
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from typing import Dict
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from typing import List
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddlespeech.t2s.models.melgan import MelGANDiscriminator as BaseDiscriminator
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from paddlespeech.t2s.modules.activation import get_activation
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from paddlespeech.t2s.modules.nets_utils import initialize
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from paddlespeech.t2s.modules.pqmf import PQMF
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from paddlespeech.t2s.modules.tade_res_block import TADEResBlock
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class StyleMelGANGenerator(nn.Layer):
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"""Style MelGAN generator module."""
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def __init__(
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self,
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in_channels: int=128,
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aux_channels: int=80,
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channels: int=64,
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out_channels: int=1,
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kernel_size: int=9,
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dilation: int=2,
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bias: bool=True,
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noise_upsample_scales: List[int]=[11, 2, 2, 2],
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noise_upsample_activation: str="leakyrelu",
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noise_upsample_activation_params: Dict[str,
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Any]={"negative_slope": 0.2},
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upsample_scales: List[int]=[2, 2, 2, 2, 2, 2, 2, 2, 1],
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upsample_mode: str="linear",
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gated_function: str="softmax",
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use_weight_norm: bool=True,
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init_type: str="xavier_uniform", ):
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"""Initilize Style MelGAN generator.
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Parameters
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----------
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in_channels : int
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Number of input noise channels.
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aux_channels : int
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Number of auxiliary input channels.
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channels : int
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Number of channels for conv layer.
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out_channels : int
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Number of output channels.
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kernel_size : int
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Kernel size of conv layers.
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dilation : int
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Dilation factor for conv layers.
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bias : bool
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Whether to add bias parameter in convolution layers.
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noise_upsample_scales : list
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List of noise upsampling scales.
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noise_upsample_activation : str
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Activation function module name for noise upsampling.
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noise_upsample_activation_params : dict
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Hyperparameters for the above activation function.
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upsample_scales : list
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List of upsampling scales.
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upsample_mode : str
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Upsampling mode in TADE layer.
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gated_function : str
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Gated function in TADEResBlock ("softmax" or "sigmoid").
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use_weight_norm : bool
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Whether to use weight norm.
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If set to true, it will be applied to all of the conv layers.
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"""
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super().__init__()
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# initialize parameters
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initialize(self, init_type)
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self.in_channels = in_channels
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noise_upsample = []
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in_chs = in_channels
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for noise_upsample_scale in noise_upsample_scales:
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noise_upsample.append(
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nn.Conv1DTranspose(
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in_chs,
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channels,
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noise_upsample_scale * 2,
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stride=noise_upsample_scale,
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padding=noise_upsample_scale // 2 + noise_upsample_scale %
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2,
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output_padding=noise_upsample_scale % 2,
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bias_attr=bias, ))
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noise_upsample.append(
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get_activation(noise_upsample_activation, **
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noise_upsample_activation_params))
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in_chs = channels
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self.noise_upsample = nn.Sequential(*noise_upsample)
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self.noise_upsample_factor = np.prod(noise_upsample_scales)
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self.blocks = nn.LayerList()
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aux_chs = aux_channels
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for upsample_scale in upsample_scales:
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self.blocks.append(
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TADEResBlock(
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in_channels=channels,
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aux_channels=aux_chs,
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kernel_size=kernel_size,
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dilation=dilation,
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bias=bias,
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upsample_factor=upsample_scale,
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upsample_mode=upsample_mode,
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gated_function=gated_function, ), )
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aux_chs = channels
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self.upsample_factor = np.prod(upsample_scales)
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self.output_conv = nn.Sequential(
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nn.Conv1D(
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channels,
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out_channels,
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kernel_size,
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1,
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bias_attr=bias,
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padding=(kernel_size - 1) // 2, ),
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nn.Tanh(), )
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nn.initializer.set_global_initializer(None)
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# apply weight norm
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if use_weight_norm:
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self.apply_weight_norm()
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# reset parameters
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self.reset_parameters()
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def forward(self, c, z=None):
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"""Calculate forward propagation.
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Parameters
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----------
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c : Tensor
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Auxiliary input tensor (B, channels, T).
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z : Tensor
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Input noise tensor (B, in_channels, 1).
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Returns
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----------
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Tensor
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Output tensor (B, out_channels, T ** prod(upsample_scales)).
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"""
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# batch_max_steps(24000) == noise_upsample_factor(80) * upsample_factor(300)
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if z is None:
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z = paddle.randn([paddle.shape(c)[0], self.in_channels, 1])
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# (B, in_channels, noise_upsample_factor).
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x = self.noise_upsample(z)
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for block in self.blocks:
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x, c = block(x, c)
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x = self.output_conv(x)
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return x
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def apply_weight_norm(self):
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"""Recursively apply weight normalization to all the Convolution layers
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in the sublayers.
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"""
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def _apply_weight_norm(layer):
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if isinstance(layer, (nn.Conv1D, nn.Conv1DTranspose)):
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nn.utils.weight_norm(layer)
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self.apply(_apply_weight_norm)
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def remove_weight_norm(self):
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"""Recursively remove weight normalization from all the Convolution
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layers in the sublayers.
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"""
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def _remove_weight_norm(layer):
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try:
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if layer:
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nn.utils.remove_weight_norm(layer)
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except ValueError:
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pass
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self.apply(_remove_weight_norm)
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def reset_parameters(self):
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"""Reset parameters.
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This initialization follows official implementation manner.
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https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
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"""
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# 定义参数为float的正态分布。
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dist = paddle.distribution.Normal(loc=0.0, scale=0.02)
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def _reset_parameters(m):
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if isinstance(m, nn.Conv1D) or isinstance(m, nn.Conv1DTranspose):
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w = dist.sample(m.weight.shape)
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m.weight.set_value(w)
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self.apply(_reset_parameters)
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def inference(self, c):
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"""Perform inference.
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Parameters
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----------
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c : Tensor
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Input tensor (T, in_channels).
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Returns
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----------
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Tensor
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Output tensor (T ** prod(upsample_scales), out_channels).
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"""
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# (1, in_channels, T)
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c = c.transpose([1, 0]).unsqueeze(0)
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c_shape = paddle.shape(c)
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# prepare noise input
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# there is a bug in Paddle int division, we must convert a int tensor to int here
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noise_size = (1, self.in_channels,
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math.ceil(int(c_shape[2]) / self.noise_upsample_factor))
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# (1, in_channels, T/noise_upsample_factor)
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noise = paddle.randn(noise_size)
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# (1, in_channels, T)
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x = self.noise_upsample(noise)
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x_shape = paddle.shape(x)
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total_length = c_shape[2] * self.upsample_factor
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c = F.pad(
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c, (0, x_shape[2] - c_shape[2]), "replicate", data_format="NCL")
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# c.shape[2] == x.shape[2] here
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# (1, in_channels, T*prod(upsample_scales))
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for block in self.blocks:
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x, c = block(x, c)
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x = self.output_conv(x)[..., :total_length]
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return x.squeeze(0).transpose([1, 0])
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# StyleMelGANDiscriminator 不需要 remove weight norm 嘛？
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class StyleMelGANDiscriminator(nn.Layer):
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"""Style MelGAN disciminator module."""
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def __init__(
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self,
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repeats: int=2,
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window_sizes: List[int]=[512, 1024, 2048, 4096],
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pqmf_params: List[List[int]]=[
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[1, None, None, None],
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[2, 62, 0.26700, 9.0],
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[4, 62, 0.14200, 9.0],
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[8, 62, 0.07949, 9.0],
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],
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discriminator_params: Dict[str, Any]={
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"out_channels": 1,
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"kernel_sizes": [5, 3],
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"channels": 16,
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"max_downsample_channels": 512,
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"bias": True,
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"downsample_scales": [4, 4, 4, 1],
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"nonlinear_activation": "leakyrelu",
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"nonlinear_activation_params": {
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"negative_slope": 0.2
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},
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"pad": "Pad1D",
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"pad_params": {
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"mode": "reflect"
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},
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},
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use_weight_norm: bool=True,
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init_type: str="xavier_uniform", ):
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"""Initilize Style MelGAN discriminator.
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Parameters
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----------
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repeats : int
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Number of repititons to apply RWD.
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window_sizes : list
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List of random window sizes.
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pqmf_params : list
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List of list of Parameters for PQMF modules
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discriminator_params : dict
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Parameters for base discriminator module.
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use_weight_nom : bool
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Whether to apply weight normalization.
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"""
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super().__init__()
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# initialize parameters
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initialize(self, init_type)
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# window size check
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assert len(window_sizes) == len(pqmf_params)
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sizes = [ws // p[0] for ws, p in zip(window_sizes, pqmf_params)]
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assert len(window_sizes) == sum([sizes[0] == size for size in sizes])
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self.repeats = repeats
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self.window_sizes = window_sizes
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self.pqmfs = nn.LayerList()
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self.discriminators = nn.LayerList()
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for pqmf_param in pqmf_params:
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d_params = copy.deepcopy(discriminator_params)
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d_params["in_channels"] = pqmf_param[0]
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if pqmf_param[0] == 1:
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self.pqmfs.append(nn.Identity())
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else:
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self.pqmfs.append(PQMF(*pqmf_param))
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self.discriminators.append(BaseDiscriminator(**d_params))
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nn.initializer.set_global_initializer(None)
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# apply weight norm
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if use_weight_norm:
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self.apply_weight_norm()
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# reset parameters
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self.reset_parameters()
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def forward(self, x):
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"""Calculate forward propagation.
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Parameters
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----------
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x : Tensor
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Input tensor (B, 1, T).
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Returns
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----------
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List
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List of discriminator outputs, #items in the list will be
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equal to repeats * #discriminators.
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"""
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outs = []
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for _ in range(self.repeats):
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outs += self._forward(x)
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return outs
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def _forward(self, x):
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outs = []
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for idx, (ws, pqmf, disc) in enumerate(
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zip(self.window_sizes, self.pqmfs, self.discriminators)):
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start_idx = int(np.random.randint(paddle.shape(x)[-1] - ws))
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x_ = x[:, :, start_idx:start_idx + ws]
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if idx == 0:
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# nn.Identity()
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x_ = pqmf(x_)
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else:
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x_ = pqmf.analysis(x_)
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outs += [disc(x_)]
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return outs
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def apply_weight_norm(self):
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"""Recursively apply weight normalization to all the Convolution layers
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in the sublayers.
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"""
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def _apply_weight_norm(layer):
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if isinstance(layer, (nn.Conv1D, nn.Conv1DTranspose)):
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nn.utils.weight_norm(layer)
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self.apply(_apply_weight_norm)
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def remove_weight_norm(self):
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"""Recursively remove weight normalization from all the Convolution
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layers in the sublayers.
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"""
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def _remove_weight_norm(layer):
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try:
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nn.utils.remove_weight_norm(layer)
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except ValueError:
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pass
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self.apply(_remove_weight_norm)
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def reset_parameters(self):
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"""Reset parameters.
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This initialization follows official implementation manner.
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https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
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"""
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# 定义参数为float的正态分布。
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dist = paddle.distribution.Normal(loc=0.0, scale=0.02)
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def _reset_parameters(m):
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if isinstance(m, nn.Conv1D) or isinstance(m, nn.Conv1DTranspose):
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w = dist.sample(m.weight.shape)
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m.weight.set_value(w)
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self.apply(_reset_parameters)
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class StyleMelGANInference(nn.Layer):
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def __init__(self, normalizer, style_melgan_generator):
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super().__init__()
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self.normalizer = normalizer
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self.style_melgan_generator = style_melgan_generator
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def forward(self, logmel):
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normalized_mel = self.normalizer(logmel)
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wav = self.style_melgan_generator.inference(normalized_mel)
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return wav
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