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514 lines
18 KiB
514 lines
18 KiB
# 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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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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from typing import Optional
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import numpy as np
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import paddle
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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 WaveNetResidualBlock as ResidualBlock
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from paddlespeech.t2s.modules.upsample import ConvInUpsampleNet
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class PWGGenerator(nn.Layer):
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"""Wave Generator for Parallel WaveGAN
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Parameters
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----------
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in_channels : int, optional
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Number of channels of the input waveform, by default 1
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out_channels : int, optional
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Number of channels of the output waveform, by default 1
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kernel_size : int, optional
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Kernel size of the residual blocks inside, by default 3
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layers : int, optional
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Number of residual blocks inside, by default 30
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stacks : int, optional
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The number of groups to split the residual blocks into, by default 3
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Within each group, the dilation of the residual block grows
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exponentially.
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residual_channels : int, optional
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Residual channel of the residual blocks, by default 64
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gate_channels : int, optional
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Gate channel of the residual blocks, by default 128
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skip_channels : int, optional
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Skip channel of the residual blocks, by default 64
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aux_channels : int, optional
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Auxiliary channel of the residual blocks, by default 80
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aux_context_window : int, optional
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The context window size of the first convolution applied to the
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auxiliary input, by default 2
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dropout : float, optional
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Dropout of the residual blocks, by default 0.
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bias : bool, optional
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Whether to use bias in residual blocks, by default True
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use_weight_norm : bool, optional
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Whether to use weight norm in all convolutions, by default True
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use_causal_conv : bool, optional
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Whether to use causal padding in the upsample network and residual
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blocks, by default False
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upsample_scales : List[int], optional
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Upsample scales of the upsample network, by default [4, 4, 4, 4]
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nonlinear_activation : Optional[str], optional
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Non linear activation in upsample network, by default None
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nonlinear_activation_params : Dict[str, Any], optional
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Parameters passed to the linear activation in the upsample network,
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by default {}
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interpolate_mode : str, optional
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Interpolation mode of the upsample network, by default "nearest"
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freq_axis_kernel_size : int, optional
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Kernel size along the frequency axis of the upsample network, by default 1
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"""
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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_size: int=3,
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layers: int=30,
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stacks: int=3,
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residual_channels: int=64,
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gate_channels: int=128,
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skip_channels: int=64,
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aux_channels: int=80,
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aux_context_window: int=2,
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dropout: float=0.,
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bias: bool=True,
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use_weight_norm: bool=True,
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use_causal_conv: bool=False,
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upsample_scales: List[int]=[4, 4, 4, 4],
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nonlinear_activation: Optional[str]=None,
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nonlinear_activation_params: Dict[str, Any]={},
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interpolate_mode: str="nearest",
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freq_axis_kernel_size: int=1,
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init_type: str="xavier_uniform", ):
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super().__init__()
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# initialize parameters
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initialize(self, init_type)
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# for compatibility
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if nonlinear_activation:
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nonlinear_activation = nonlinear_activation.lower()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.aux_channels = aux_channels
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self.aux_context_window = aux_context_window
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self.layers = layers
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self.stacks = stacks
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self.kernel_size = kernel_size
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assert layers % stacks == 0
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layers_per_stack = layers // stacks
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self.first_conv = nn.Conv1D(
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in_channels, residual_channels, 1, bias_attr=True)
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self.upsample_net = ConvInUpsampleNet(
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upsample_scales=upsample_scales,
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nonlinear_activation=nonlinear_activation,
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nonlinear_activation_params=nonlinear_activation_params,
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interpolate_mode=interpolate_mode,
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freq_axis_kernel_size=freq_axis_kernel_size,
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aux_channels=aux_channels,
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aux_context_window=aux_context_window,
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use_causal_conv=use_causal_conv)
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self.upsample_factor = np.prod(upsample_scales)
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self.conv_layers = nn.LayerList()
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for layer in range(layers):
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dilation = 2**(layer % layers_per_stack)
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conv = ResidualBlock(
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kernel_size=kernel_size,
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residual_channels=residual_channels,
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gate_channels=gate_channels,
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skip_channels=skip_channels,
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aux_channels=aux_channels,
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dilation=dilation,
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dropout=dropout,
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bias=bias,
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use_causal_conv=use_causal_conv)
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self.conv_layers.append(conv)
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self.last_conv_layers = nn.Sequential(nn.ReLU(),
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nn.Conv1D(
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skip_channels,
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skip_channels,
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1,
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bias_attr=True),
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nn.ReLU(),
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nn.Conv1D(
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skip_channels,
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out_channels,
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1,
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bias_attr=True))
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if use_weight_norm:
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self.apply_weight_norm()
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def forward(self, x, c):
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"""Generate waveform.
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Parameters
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----------
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x : Tensor
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Shape (N, C_in, T), The input waveform.
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c : Tensor
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Shape (N, C_aux, T'). The auxiliary input (e.g. spectrogram). It
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is upsampled to match the time resolution of the input.
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Returns
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-------
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Tensor
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Shape (N, C_out, T), the generated waveform.
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"""
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c = self.upsample_net(c)
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assert c.shape[-1] == x.shape[-1]
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x = self.first_conv(x)
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skips = 0
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for f in self.conv_layers:
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x, s = f(x, c)
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skips += s
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skips *= math.sqrt(1.0 / len(self.conv_layers))
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x = self.last_conv_layers(skips)
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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.Conv2D)):
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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=None):
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"""Waveform generation. This function is used for single instance
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inference.
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Parameters
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----------
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c : Tensor, optional
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Shape (T', C_aux), the auxiliary input, by default None
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x : Tensor, optional
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Shape (T, C_in), the noise waveform, by default None
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If not provided, a sample is drawn from a gaussian distribution.
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Returns
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-------
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Tensor
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Shape (T, C_out), the generated waveform
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"""
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# when to static, can not input x, see https://github.com/PaddlePaddle/Parakeet/pull/132/files
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x = paddle.randn(
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[1, self.in_channels, paddle.shape(c)[0] * self.upsample_factor])
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c = paddle.transpose(c, [1, 0]).unsqueeze(0) # pseudo batch
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c = nn.Pad1D(self.aux_context_window, mode='replicate')(c)
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out = self(x, c).squeeze(0).transpose([1, 0])
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return out
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class PWGDiscriminator(nn.Layer):
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"""A convolutional discriminator for audio.
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Parameters
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----------
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in_channels : int, optional
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Number of channels of the input audio, by default 1
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out_channels : int, optional
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Output feature size, by default 1
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kernel_size : int, optional
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Kernel size of convolutional sublayers, by default 3
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layers : int, optional
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Number of layers, by default 10
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conv_channels : int, optional
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Feature size of the convolutional sublayers, by default 64
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dilation_factor : int, optional
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The factor with which dilation of each convolutional sublayers grows
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exponentially if it is greater than 1, else the dilation of each
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convolutional sublayers grows linearly, by default 1
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nonlinear_activation : str, optional
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The activation after each convolutional sublayer, by default "leakyrelu"
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nonlinear_activation_params : Dict[str, Any], optional
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The parameters passed to the activation's initializer, by default
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{"negative_slope": 0.2}
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bias : bool, optional
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Whether to use bias in convolutional sublayers, by default True
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use_weight_norm : bool, optional
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Whether to use weight normalization at all convolutional sublayers,
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by default True
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"""
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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_size: int=3,
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layers: int=10,
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conv_channels: int=64,
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dilation_factor: int=1,
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nonlinear_activation: str="leakyrelu",
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nonlinear_activation_params: Dict[str, Any]={"negative_slope": 0.2},
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bias: bool=True,
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use_weight_norm: bool=True,
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init_type: str="xavier_uniform", ):
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super().__init__()
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# initialize parameters
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initialize(self, init_type)
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# for compatibility
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if nonlinear_activation:
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nonlinear_activation = nonlinear_activation.lower()
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assert kernel_size % 2 == 1
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assert dilation_factor > 0
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conv_layers = []
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conv_in_channels = in_channels
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for i in range(layers - 1):
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if i == 0:
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dilation = 1
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else:
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dilation = i if dilation_factor == 1 else dilation_factor**i
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conv_in_channels = conv_channels
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padding = (kernel_size - 1) // 2 * dilation
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conv_layer = nn.Conv1D(
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conv_in_channels,
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conv_channels,
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kernel_size,
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padding=padding,
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dilation=dilation,
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bias_attr=bias)
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nonlinear = get_activation(nonlinear_activation,
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**nonlinear_activation_params)
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conv_layers.append(conv_layer)
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conv_layers.append(nonlinear)
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padding = (kernel_size - 1) // 2
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last_conv = nn.Conv1D(
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conv_in_channels,
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out_channels,
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kernel_size,
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padding=padding,
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bias_attr=bias)
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conv_layers.append(last_conv)
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self.conv_layers = nn.Sequential(*conv_layers)
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if use_weight_norm:
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self.apply_weight_norm()
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def forward(self, x):
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"""
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Parameters
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----------
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x : Tensor
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Shape (N, in_channels, num_samples), the input audio.
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Returns
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-------
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Tensor
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Shape (N, out_channels, num_samples), the predicted logits.
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"""
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return self.conv_layers(x)
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def apply_weight_norm(self):
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def _apply_weight_norm(layer):
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if isinstance(layer, (nn.Conv1D, nn.Conv2D)):
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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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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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class ResidualPWGDiscriminator(nn.Layer):
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"""A wavenet-style discriminator for audio.
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Parameters
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----------
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in_channels : int, optional
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Number of channels of the input audio, by default 1
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out_channels : int, optional
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Output feature size, by default 1
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kernel_size : int, optional
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Kernel size of residual blocks, by default 3
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layers : int, optional
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Number of residual blocks, by default 30
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stacks : int, optional
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Number of groups of residual blocks, within which the dilation
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of each residual blocks grows exponentially, by default 3
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residual_channels : int, optional
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Residual channels of residual blocks, by default 64
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gate_channels : int, optional
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Gate channels of residual blocks, by default 128
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skip_channels : int, optional
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Skip channels of residual blocks, by default 64
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dropout : float, optional
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Dropout probability of residual blocks, by default 0.
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bias : bool, optional
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Whether to use bias in residual blocks, by default True
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use_weight_norm : bool, optional
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Whether to use weight normalization in all convolutional layers,
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by default True
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use_causal_conv : bool, optional
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Whether to use causal convolution in residual blocks, by default False
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nonlinear_activation : str, optional
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Activation after convolutions other than those in residual blocks,
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by default "leakyrelu"
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nonlinear_activation_params : Dict[str, Any], optional
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Parameters to pass to the activation, by default {"negative_slope": 0.2}
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"""
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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_size: int=3,
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layers: int=30,
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stacks: int=3,
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residual_channels: int=64,
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gate_channels: int=128,
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skip_channels: int=64,
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dropout: float=0.,
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bias: bool=True,
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use_weight_norm: bool=True,
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use_causal_conv: bool=False,
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nonlinear_activation: str="leakyrelu",
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nonlinear_activation_params: Dict[str, Any]={"negative_slope": 0.2},
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init_type: str="xavier_uniform", ):
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super().__init__()
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# initialize parameters
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initialize(self, init_type)
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# for compatibility
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if nonlinear_activation:
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nonlinear_activation = nonlinear_activation.lower()
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assert kernel_size % 2 == 1
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.layers = layers
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self.stacks = stacks
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self.kernel_size = kernel_size
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assert layers % stacks == 0
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layers_per_stack = layers // stacks
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self.first_conv = nn.Sequential(
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nn.Conv1D(in_channels, residual_channels, 1, bias_attr=True),
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get_activation(nonlinear_activation, **nonlinear_activation_params))
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self.conv_layers = nn.LayerList()
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for layer in range(layers):
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dilation = 2**(layer % layers_per_stack)
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conv = ResidualBlock(
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kernel_size=kernel_size,
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residual_channels=residual_channels,
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gate_channels=gate_channels,
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skip_channels=skip_channels,
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aux_channels=None, # no auxiliary input
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dropout=dropout,
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dilation=dilation,
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bias=bias,
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use_causal_conv=use_causal_conv)
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self.conv_layers.append(conv)
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self.last_conv_layers = nn.Sequential(
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get_activation(nonlinear_activation, **nonlinear_activation_params),
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nn.Conv1D(skip_channels, skip_channels, 1, bias_attr=True),
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get_activation(nonlinear_activation, **nonlinear_activation_params),
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nn.Conv1D(skip_channels, out_channels, 1, bias_attr=True))
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if use_weight_norm:
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self.apply_weight_norm()
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def forward(self, x):
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"""
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Parameters
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----------
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x : Tensor
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Shape (N, in_channels, num_samples), the input audio.
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Returns
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-------
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Tensor
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Shape (N, out_channels, num_samples), the predicted logits.
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"""
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x = self.first_conv(x)
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skip = 0
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for f in self.conv_layers:
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x, h = f(x, None)
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skip += h
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skip *= math.sqrt(1 / len(self.conv_layers))
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x = skip
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x = self.last_conv_layers(x)
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return x
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def apply_weight_norm(self):
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def _apply_weight_norm(layer):
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if isinstance(layer, (nn.Conv1D, nn.Conv2D)):
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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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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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class PWGInference(nn.Layer):
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def __init__(self, normalizer, pwg_generator):
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super().__init__()
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self.normalizer = normalizer
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self.pwg_generator = pwg_generator
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def forward(self, logmel):
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normalized_mel = self.normalizer(logmel)
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wav = self.pwg_generator.inference(normalized_mel)
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return wav
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