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# Copyright (c) 2022 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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# This code is based on https://github.com/jik876/hifi-gan.
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import copy
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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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from typing import Optional
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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.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.residual_block import HiFiGANResidualBlock as ResidualBlock
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class HiFiGANGenerator(nn.Layer):
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"""HiFiGAN generator module."""
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def __init__(
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self,
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in_channels: int=80,
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out_channels: int=1,
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channels: int=512,
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global_channels: int=-1,
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kernel_size: int=7,
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upsample_scales: List[int]=(8, 8, 2, 2),
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upsample_kernel_sizes: List[int]=(16, 16, 4, 4),
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resblock_kernel_sizes: List[int]=(3, 7, 11),
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resblock_dilations: List[List[int]]=[(1, 3, 5), (1, 3, 5),
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(1, 3, 5)],
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use_additional_convs: bool=True,
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bias: bool=True,
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nonlinear_activation: str="leakyrelu",
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nonlinear_activation_params: Dict[str, Any]={"negative_slope": 0.1},
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use_weight_norm: bool=True,
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init_type: str="xavier_uniform", ):
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"""Initialize HiFiGANGenerator module.
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Args:
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in_channels (int): Number of input channels.
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out_channels (int): Number of output channels.
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channels (int): Number of hidden representation channels.
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global_channels (int): Number of global conditioning channels.
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kernel_size (int): Kernel size of initial and final conv layer.
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upsample_scales (list): List of upsampling scales.
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upsample_kernel_sizes (list): List of kernel sizes for upsampling layers.
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resblock_kernel_sizes (list): List of kernel sizes for residual blocks.
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resblock_dilations (list): List of dilation list for residual blocks.
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use_additional_convs (bool): Whether to use additional conv layers in residual blocks.
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bias (bool): Whether to add bias parameter in convolution layers.
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nonlinear_activation (str): Activation function module name.
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nonlinear_activation_params (dict): Hyperparameters for activation function.
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use_weight_norm (bool): 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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# check hyperparameters are valid
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assert kernel_size % 2 == 1, "Kernel size must be odd number."
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assert len(upsample_scales) == len(upsample_kernel_sizes)
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assert len(resblock_dilations) == len(resblock_kernel_sizes)
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# define modules
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self.num_upsamples = len(upsample_kernel_sizes)
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self.num_blocks = len(resblock_kernel_sizes)
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self.input_conv = nn.Conv1D(
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in_channels,
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channels,
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kernel_size,
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1,
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padding=(kernel_size - 1) // 2, )
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self.upsamples = nn.LayerList()
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self.blocks = nn.LayerList()
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for i in range(len(upsample_kernel_sizes)):
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assert upsample_kernel_sizes[i] == 2 * upsample_scales[i]
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self.upsamples.append(
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nn.Sequential(
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get_activation(nonlinear_activation, **
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nonlinear_activation_params),
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nn.Conv1DTranspose(
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channels // (2**i),
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channels // (2**(i + 1)),
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upsample_kernel_sizes[i],
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upsample_scales[i],
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padding=upsample_scales[i] // 2 + upsample_scales[i] %
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2,
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output_padding=upsample_scales[i] % 2, ), ))
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for j in range(len(resblock_kernel_sizes)):
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self.blocks.append(
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ResidualBlock(
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kernel_size=resblock_kernel_sizes[j],
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channels=channels // (2**(i + 1)),
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dilations=resblock_dilations[j],
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bias=bias,
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use_additional_convs=use_additional_convs,
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nonlinear_activation=nonlinear_activation,
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nonlinear_activation_params=nonlinear_activation_params,
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))
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self.output_conv = nn.Sequential(
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nn.LeakyReLU(),
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nn.Conv1D(
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channels // (2**(i + 1)),
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out_channels,
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kernel_size,
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1,
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padding=(kernel_size - 1) // 2, ),
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nn.Tanh(), )
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if global_channels > 0:
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self.global_conv = nn.Conv1D(global_channels, channels, 1)
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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, g: Optional[paddle.Tensor]=None):
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"""Calculate forward propagation.
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Args:
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c (Tensor): Input tensor (B, in_channels, T).
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g (Optional[Tensor]): Global conditioning tensor (B, global_channels, 1).
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Returns:
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Tensor: Output tensor (B, out_channels, T).
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"""
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c = self.input_conv(c)
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if g is not None:
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c = c + self.global_conv(g)
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for i in range(self.num_upsamples):
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c = self.upsamples[i](c)
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# initialize
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cs = 0.0
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for j in range(self.num_blocks):
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cs += self.blocks[i * self.num_blocks + j](c)
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c = cs / self.num_blocks
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c = self.output_conv(c)
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return c
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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/jik876/hifi-gan/blob/master/models.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=0.01)
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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 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.Conv2D, 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 inference(self, c, g: Optional[paddle.Tensor]=None):
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"""Perform inference.
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Args:
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c (Tensor): Input tensor (T, in_channels).
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normalize_before (bool): Whether to perform normalization.
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g (Optional[Tensor]): Global conditioning tensor (global_channels, 1).
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Returns:
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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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if g is not None:
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g = g.unsqueeze(0)
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c = self.forward(c.transpose([1, 0]).unsqueeze(0), g=g)
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return c.squeeze(0).transpose([1, 0])
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class HiFiGANPeriodDiscriminator(nn.Layer):
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"""HiFiGAN period discriminator module."""
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def __init__(
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self,
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in_channels: int=1,
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out_channels: int=1,
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period: int=3,
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kernel_sizes: List[int]=[5, 3],
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channels: int=32,
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downsample_scales: List[int]=[3, 3, 3, 3, 1],
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max_downsample_channels: int=1024,
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bias: bool=True,
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nonlinear_activation: str="leakyrelu",
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nonlinear_activation_params: Dict[str, Any]={"negative_slope": 0.1},
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use_weight_norm: bool=True,
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use_spectral_norm: bool=False,
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init_type: str="xavier_uniform", ):
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"""Initialize HiFiGANPeriodDiscriminator module.
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Args:
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in_channels (int): Number of input channels.
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out_channels (int): Number of output channels.
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period (int): Period.
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kernel_sizes (list): Kernel sizes of initial conv layers and the final conv layer.
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channels (int): Number of initial channels.
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downsample_scales (list): List of downsampling scales.
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max_downsample_channels (int): Number of maximum downsampling channels.
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use_additional_convs (bool): Whether to use additional conv layers in residual blocks.
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bias (bool): Whether to add bias parameter in convolution layers.
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nonlinear_activation (str): Activation function module name.
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nonlinear_activation_params (dict): Hyperparameters for activation function.
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use_weight_norm (bool): 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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use_spectral_norm (bool): Whether to use spectral 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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assert len(kernel_sizes) == 2
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assert kernel_sizes[0] % 2 == 1, "Kernel size must be odd number."
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assert kernel_sizes[1] % 2 == 1, "Kernel size must be odd number."
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self.period = period
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self.convs = nn.LayerList()
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in_chs = in_channels
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out_chs = channels
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for downsample_scale in downsample_scales:
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self.convs.append(
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nn.Sequential(
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nn.Conv2D(
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in_chs,
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out_chs,
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(kernel_sizes[0], 1),
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(downsample_scale, 1),
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padding=((kernel_sizes[0] - 1) // 2, 0), ),
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get_activation(nonlinear_activation, **
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nonlinear_activation_params), ))
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in_chs = out_chs
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# NOTE: Use downsample_scale + 1?
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out_chs = min(out_chs * 4, max_downsample_channels)
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self.output_conv = nn.Conv2D(
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out_chs,
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out_channels,
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(kernel_sizes[1] - 1, 1),
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1,
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padding=((kernel_sizes[1] - 1) // 2, 0), )
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if use_weight_norm and use_spectral_norm:
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raise ValueError("Either use use_weight_norm or use_spectral_norm.")
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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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# apply spectral norm
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if use_spectral_norm:
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self.apply_spectral_norm()
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def forward(self, x):
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"""Calculate forward propagation.
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Args:
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c (Tensor): Input tensor (B, in_channels, T).
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Returns:
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list: List of each layer's tensors.
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"""
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# transform 1d to 2d -> (B, C, T/P, P)
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b, c, t = paddle.shape(x)
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if t % self.period != 0:
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect", data_format="NCL")
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t += n_pad
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x = x.reshape([b, c, t // self.period, self.period])
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# forward conv
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outs = []
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for layer in self.convs:
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x = layer(x)
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outs += [x]
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x = self.output_conv(x)
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x = paddle.flatten(x, 1, -1)
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outs += [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.Conv2D, 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 apply_spectral_norm(self):
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"""Apply spectral normalization module from all of the layers."""
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def _apply_spectral_norm(m):
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if isinstance(m, nn.Conv2D):
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nn.utils.spectral_norm(m)
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self.apply(_apply_spectral_norm)
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class HiFiGANMultiPeriodDiscriminator(nn.Layer):
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"""HiFiGAN multi-period discriminator module."""
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def __init__(
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self,
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periods: List[int]=[2, 3, 5, 7, 11],
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discriminator_params: Dict[str, Any]={
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"in_channels": 1,
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"out_channels": 1,
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"kernel_sizes": [5, 3],
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"channels": 32,
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"downsample_scales": [3, 3, 3, 3, 1],
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"max_downsample_channels": 1024,
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"bias": True,
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"nonlinear_activation": "leakyrelu",
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"nonlinear_activation_params": {
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"negative_slope": 0.1
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},
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"use_weight_norm": True,
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"use_spectral_norm": False,
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},
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init_type: str="xavier_uniform", ):
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"""Initialize HiFiGANMultiPeriodDiscriminator module.
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Args:
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periods (list): List of periods.
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discriminator_params (dict): Parameters for hifi-gan period discriminator module.
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The period parameter will be overwritten.
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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.discriminators = nn.LayerList()
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for period in periods:
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params = copy.deepcopy(discriminator_params)
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params["period"] = period
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self.discriminators.append(HiFiGANPeriodDiscriminator(**params))
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def forward(self, x):
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"""Calculate forward propagation.
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Args:
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x (Tensor): Input noise signal (B, 1, T).
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Returns:
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List: List of list of each discriminator outputs, which consists of each layer output tensors.
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"""
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outs = []
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for f in self.discriminators:
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outs += [f(x)]
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return outs
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class HiFiGANScaleDiscriminator(nn.Layer):
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"""HiFi-GAN scale discriminator module."""
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def __init__(
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self,
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in_channels: int=1,
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out_channels: int=1,
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kernel_sizes: List[int]=[15, 41, 5, 3],
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channels: int=128,
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max_downsample_channels: int=1024,
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max_groups: int=16,
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bias: bool=True,
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downsample_scales: List[int]=[2, 2, 4, 4, 1],
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nonlinear_activation: str="leakyrelu",
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nonlinear_activation_params: Dict[str, Any]={"negative_slope": 0.1},
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use_weight_norm: bool=True,
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use_spectral_norm: bool=False,
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init_type: str="xavier_uniform", ):
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"""Initilize HiFiGAN scale discriminator module.
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Args:
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in_channels (int): Number of input channels.
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out_channels (int): Number of output channels.
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kernel_sizes (list): List of four kernel sizes. The first will be used for the first conv layer,
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and the second is for downsampling part, and the remaining two are for output layers.
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channels (int): Initial number of channels for conv layer.
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max_downsample_channels (int): Maximum number of channels for downsampling layers.
|
|
|
bias (bool): Whether to add bias parameter in convolution layers.
|
|
|
downsample_scales (list): List of downsampling scales.
|
|
|
nonlinear_activation (str): Activation function module name.
|
|
|
nonlinear_activation_params (dict): 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__()
|
|
|
|
|
|
# initialize parameters
|
|
|
initialize(self, init_type)
|
|
|
|
|
|
self.layers = nn.LayerList()
|
|
|
|
|
|
# check kernel size is valid
|
|
|
assert len(kernel_sizes) == 4
|
|
|
for ks in kernel_sizes:
|
|
|
assert ks % 2 == 1
|
|
|
|
|
|
# add first layer
|
|
|
self.layers.append(
|
|
|
nn.Sequential(
|
|
|
nn.Conv1D(
|
|
|
in_channels,
|
|
|
channels,
|
|
|
# NOTE: Use always the same kernel size
|
|
|
kernel_sizes[0],
|
|
|
bias_attr=bias,
|
|
|
padding=(kernel_sizes[0] - 1) // 2, ),
|
|
|
get_activation(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.append(
|
|
|
nn.Sequential(
|
|
|
nn.Conv1D(
|
|
|
in_chs,
|
|
|
out_chs,
|
|
|
kernel_size=kernel_sizes[1],
|
|
|
stride=downsample_scale,
|
|
|
padding=(kernel_sizes[1] - 1) // 2,
|
|
|
groups=groups,
|
|
|
bias_attr=bias, ),
|
|
|
get_activation(nonlinear_activation, **
|
|
|
nonlinear_activation_params), ))
|
|
|
in_chs = out_chs
|
|
|
# NOTE: Remove hard coding?
|
|
|
out_chs = min(in_chs * 2, max_downsample_channels)
|
|
|
# NOTE: Remove hard coding?
|
|
|
groups = min(groups * 4, max_groups)
|
|
|
|
|
|
# add final layers
|
|
|
out_chs = min(in_chs * 2, max_downsample_channels)
|
|
|
self.layers.append(
|
|
|
nn.Sequential(
|
|
|
nn.Conv1D(
|
|
|
in_chs,
|
|
|
out_chs,
|
|
|
kernel_size=kernel_sizes[2],
|
|
|
stride=1,
|
|
|
padding=(kernel_sizes[2] - 1) // 2,
|
|
|
bias_attr=bias, ),
|
|
|
get_activation(nonlinear_activation, **
|
|
|
nonlinear_activation_params), ))
|
|
|
self.layers.append(
|
|
|
nn.Conv1D(
|
|
|
out_chs,
|
|
|
out_channels,
|
|
|
kernel_size=kernel_sizes[3],
|
|
|
stride=1,
|
|
|
padding=(kernel_sizes[3] - 1) // 2,
|
|
|
bias_attr=bias, ), )
|
|
|
|
|
|
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):
|
|
|
"""Calculate forward propagation.
|
|
|
|
|
|
Args:
|
|
|
x (Tensor): Input noise signal (B, 1, T).
|
|
|
Returns:
|
|
|
List: 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):
|
|
|
"""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 apply_spectral_norm(self):
|
|
|
"""Apply spectral normalization module from all of the layers."""
|
|
|
|
|
|
def _apply_spectral_norm(m):
|
|
|
if isinstance(m, nn.Conv2D):
|
|
|
nn.utils.spectral_norm(m)
|
|
|
|
|
|
self.apply(_apply_spectral_norm)
|
|
|
|
|
|
|
|
|
class HiFiGANMultiScaleDiscriminator(nn.Layer):
|
|
|
"""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,
|
|
|
init_type: str="xavier_uniform", ):
|
|
|
"""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): Parameters for the above pooling module.
|
|
|
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.
|
|
|
"""
|
|
|
super().__init__()
|
|
|
|
|
|
# initialize parameters
|
|
|
initialize(self, init_type)
|
|
|
|
|
|
self.discriminators = nn.LayerList()
|
|
|
|
|
|
# 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.append(HiFiGANScaleDiscriminator(**params))
|
|
|
self.pooling = getattr(nn, downsample_pooling)(
|
|
|
**downsample_pooling_params)
|
|
|
|
|
|
def forward(self, x):
|
|
|
"""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)]
|
|
|
x = self.pooling(x)
|
|
|
|
|
|
return outs
|
|
|
|
|
|
|
|
|
class HiFiGANMultiScaleMultiPeriodDiscriminator(nn.Layer):
|
|
|
"""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,
|
|
|
},
|
|
|
init_type: str="xavier_uniform", ):
|
|
|
"""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__()
|
|
|
|
|
|
# initialize parameters
|
|
|
initialize(self, init_type)
|
|
|
|
|
|
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):
|
|
|
"""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.
|
|
|
Multi scale and multi period ones are concatenated.
|
|
|
"""
|
|
|
msd_outs = self.msd(x)
|
|
|
mpd_outs = self.mpd(x)
|
|
|
return msd_outs + mpd_outs
|
|
|
|
|
|
|
|
|
class HiFiGANInference(nn.Layer):
|
|
|
def __init__(self, normalizer, hifigan_generator):
|
|
|
super().__init__()
|
|
|
self.normalizer = normalizer
|
|
|
self.hifigan_generator = hifigan_generator
|
|
|
|
|
|
def forward(self, logmel):
|
|
|
normalized_mel = self.normalizer(logmel)
|
|
|
wav = self.hifigan_generator.inference(normalized_mel)
|
|
|
return wav
|
