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775 lines
28 KiB
775 lines
28 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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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 paddle.nn import functional as F
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class Stretch2D(nn.Layer):
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def __init__(self, w_scale: int, h_scale: int, mode: str="nearest"):
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"""Strech an image (or image-like object) with some interpolation.
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Parameters
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----------
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w_scale : int
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Scalar of width.
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h_scale : int
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Scalar of the height.
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mode : str, optional
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Interpolation mode, modes suppored are "nearest", "bilinear",
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"trilinear", "bicubic", "linear" and "area",by default "nearest"
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For more details about interpolation, see
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`paddle.nn.functional.interpolate <https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/nn/functional/interpolate_en.html>`_.
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"""
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super().__init__()
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self.w_scale = w_scale
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self.h_scale = h_scale
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self.mode = mode
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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, C, H, W)
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Returns
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-------
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Tensor
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Shape (N, C, H', W'), where ``H'=h_scale * H``, ``W'=w_scale * W``.
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The stretched image.
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"""
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out = F.interpolate(
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x, scale_factor=(self.h_scale, self.w_scale), mode=self.mode)
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return out
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class UpsampleNet(nn.Layer):
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"""A Layer to upsample spectrogram by applying consecutive stretch and
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convolutions.
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Parameters
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----------
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upsample_scales : List[int]
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Upsampling factors for each strech.
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nonlinear_activation : Optional[str], optional
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Activation after each convolution, by default None
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nonlinear_activation_params : Dict[str, Any], optional
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Parameters passed to construct the activation, by default {}
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interpolate_mode : str, optional
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Interpolation mode of the strech, by default "nearest"
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freq_axis_kernel_size : int, optional
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Convolution kernel size along the frequency axis, by default 1
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use_causal_conv : bool, optional
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Whether to use causal padding before convolution, by default False
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If True, Causal padding is used along the time axis, i.e. padding
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amount is ``receptive field - 1`` and 0 for before and after,
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respectively.
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If False, "same" padding is used along the time axis.
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"""
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def __init__(self,
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upsample_scales: List[int],
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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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use_causal_conv: bool=False):
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super().__init__()
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self.use_causal_conv = use_causal_conv
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self.up_layers = nn.LayerList()
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for scale in upsample_scales:
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stretch = Stretch2D(scale, 1, interpolate_mode)
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assert freq_axis_kernel_size % 2 == 1
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freq_axis_padding = (freq_axis_kernel_size - 1) // 2
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kernel_size = (freq_axis_kernel_size, scale * 2 + 1)
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if use_causal_conv:
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padding = (freq_axis_padding, scale * 2)
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else:
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padding = (freq_axis_padding, scale)
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conv = nn.Conv2D(
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1, 1, kernel_size, padding=padding, bias_attr=False)
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self.up_layers.extend([stretch, conv])
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if nonlinear_activation is not None:
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nonlinear = getattr(
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nn, nonlinear_activation)(**nonlinear_activation_params)
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self.up_layers.append(nonlinear)
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def forward(self, c):
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"""
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Parameters
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----------
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c : Tensor
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Shape (N, F, T), spectrogram
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Returns
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-------
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Tensor
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Shape (N, F, T'), where ``T' = upsample_factor * T``, upsampled
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spectrogram
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"""
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c = c.unsqueeze(1)
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for f in self.up_layers:
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if self.use_causal_conv and isinstance(f, nn.Conv2D):
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c = f(c)[:, :, :, c.shape[-1]]
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else:
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c = f(c)
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return c.squeeze(1)
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class ConvInUpsampleNet(nn.Layer):
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"""A Layer to upsample spectrogram composed of a convolution and an
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UpsampleNet.
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Parameters
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----------
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upsample_scales : List[int]
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Upsampling factors for each strech.
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nonlinear_activation : Optional[str], optional
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Activation after each convolution, by default None
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nonlinear_activation_params : Dict[str, Any], optional
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Parameters passed to construct the activation, by default {}
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interpolate_mode : str, optional
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Interpolation mode of the strech, by default "nearest"
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freq_axis_kernel_size : int, optional
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Convolution kernel size along the frequency axis, by default 1
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aux_channels : int, optional
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Feature size of the input, by default 80
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aux_context_window : int, optional
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Context window of the first 1D convolution applied to the input. It
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related to the kernel size of the convolution, by default 0
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If use causal convolution, the kernel size is ``window + 1``, else
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the kernel size is ``2 * window + 1``.
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use_causal_conv : bool, optional
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Whether to use causal padding before convolution, by default False
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If True, Causal padding is used along the time axis, i.e. padding
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amount is ``receptive field - 1`` and 0 for before and after,
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respectively.
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If False, "same" padding is used along the time axis.
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"""
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def __init__(self,
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upsample_scales: List[int],
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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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aux_channels: int=80,
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aux_context_window: int=0,
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use_causal_conv: bool=False):
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super().__init__()
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self.aux_context_window = aux_context_window
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self.use_causal_conv = use_causal_conv and aux_context_window > 0
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kernel_size = aux_context_window + 1 if use_causal_conv else 2 * aux_context_window + 1
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self.conv_in = nn.Conv1D(
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aux_channels,
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aux_channels,
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kernel_size=kernel_size,
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bias_attr=False)
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self.upsample = UpsampleNet(
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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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use_causal_conv=use_causal_conv)
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def forward(self, c):
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"""
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Parameters
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----------
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c : Tensor
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Shape (N, F, T), spectrogram
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Returns
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-------
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Tensors
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Shape (N, F, T'), where ``T' = upsample_factor * T``, upsampled
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spectrogram
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"""
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c_ = self.conv_in(c)
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c = c_[:, :, :-self.aux_context_window] if self.use_causal_conv else c_
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return self.upsample(c)
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class ResidualBlock(nn.Layer):
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"""A gated activation unit composed of an 1D convolution, a gated tanh
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unit and parametric redidual and skip connections. For more details,
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refer to `WaveNet: A Generative Model for Raw Audio <https://arxiv.org/abs/1609.03499>`_.
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Parameters
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----------
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kernel_size : int, optional
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Kernel size of the 1D convolution, by default 3
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residual_channels : int, optional
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Feature size of the resiaudl output(and also the input), by default 64
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gate_channels : int, optional
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Output feature size of the 1D convolution, by default 128
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skip_channels : int, optional
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Feature size of the skip output, by default 64
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aux_channels : int, optional
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Feature size of the auxiliary input (e.g. spectrogram), by default 80
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dropout : float, optional
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Probability of the dropout before the 1D convolution, by default 0.
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dilation : int, optional
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Dilation of the 1D convolution, by default 1
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bias : bool, optional
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Whether to use bias in the 1D convolution, by default True
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use_causal_conv : bool, optional
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Whether to use causal padding for the 1D convolution, by default False
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"""
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def __init__(self,
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kernel_size: 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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dropout: float=0.,
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dilation: int=1,
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bias: bool=True,
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use_causal_conv: bool=False):
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super().__init__()
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self.dropout = dropout
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if use_causal_conv:
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padding = (kernel_size - 1) * dilation
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else:
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assert kernel_size % 2 == 1
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padding = (kernel_size - 1) // 2 * dilation
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self.use_causal_conv = use_causal_conv
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self.conv = nn.Conv1D(
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residual_channels,
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gate_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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if aux_channels is not None:
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self.conv1x1_aux = nn.Conv1D(
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aux_channels, gate_channels, kernel_size=1, bias_attr=False)
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else:
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self.conv1x1_aux = None
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gate_out_channels = gate_channels // 2
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self.conv1x1_out = nn.Conv1D(
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gate_out_channels, residual_channels, kernel_size=1, bias_attr=bias)
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self.conv1x1_skip = nn.Conv1D(
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gate_out_channels, skip_channels, kernel_size=1, bias_attr=bias)
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def forward(self, x, c):
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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, C_res, T), the input features.
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c : Tensor
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Shape (N, C_aux, T), the auxiliary input.
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Returns
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-------
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res : Tensor
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Shape (N, C_res, T), the residual output, which is used as the
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input of the next ResidualBlock in a stack of ResidualBlocks.
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skip : Tensor
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Shape (N, C_skip, T), the skip output, which is collected among
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each layer in a stack of ResidualBlocks.
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"""
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x_input = x
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x = F.dropout(x, self.dropout, training=self.training)
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x = self.conv(x)
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x = x[:, :, x_input.shape[-1]] if self.use_causal_conv else x
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if c is not None:
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c = self.conv1x1_aux(c)
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x += c
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a, b = paddle.chunk(x, 2, axis=1)
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x = paddle.tanh(a) * F.sigmoid(b)
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skip = self.conv1x1_skip(x)
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res = (self.conv1x1_out(x) + x_input) * math.sqrt(0.5)
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return res, skip
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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__(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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super().__init__()
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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
|
|
x = paddle.randn(
|
|
[1, self.in_channels, paddle.shape(c)[0] * self.upsample_factor])
|
|
c = paddle.transpose(c, [1, 0]).unsqueeze(0) # pseudo batch
|
|
c = nn.Pad1D(self.aux_context_window, mode='replicate')(c)
|
|
out = self(x, c).squeeze(0).transpose([1, 0])
|
|
return out
|
|
|
|
|
|
class PWGDiscriminator(nn.Layer):
|
|
"""A convolutional discriminator for audio.
|
|
|
|
Parameters
|
|
----------
|
|
in_channels : int, optional
|
|
Number of channels of the input audio, by default 1
|
|
out_channels : int, optional
|
|
Output feature size, by default 1
|
|
kernel_size : int, optional
|
|
Kernel size of convolutional sublayers, by default 3
|
|
layers : int, optional
|
|
Number of layers, by default 10
|
|
conv_channels : int, optional
|
|
Feature size of the convolutional sublayers, by default 64
|
|
dilation_factor : int, optional
|
|
The factor with which dilation of each convolutional sublayers grows
|
|
exponentially if it is greater than 1, else the dilation of each
|
|
convolutional sublayers grows linearly, by default 1
|
|
nonlinear_activation : str, optional
|
|
The activation after each convolutional sublayer, by default "LeakyReLU"
|
|
nonlinear_activation_params : Dict[str, Any], optional
|
|
The parameters passed to the activation's initializer, by default
|
|
{"negative_slope": 0.2}
|
|
bias : bool, optional
|
|
Whether to use bias in convolutional sublayers, by default True
|
|
use_weight_norm : bool, optional
|
|
Whether to use weight normalization at all convolutional sublayers,
|
|
by default True
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels: int=1,
|
|
out_channels: int=1,
|
|
kernel_size: int=3,
|
|
layers: int=10,
|
|
conv_channels: int=64,
|
|
dilation_factor: int=1,
|
|
nonlinear_activation: str="LeakyReLU",
|
|
nonlinear_activation_params: Dict[str, Any]={"negative_slope": 0.2},
|
|
bias: bool=True,
|
|
use_weight_norm: bool=True):
|
|
super().__init__()
|
|
assert kernel_size % 2 == 1
|
|
assert dilation_factor > 0
|
|
conv_layers = []
|
|
conv_in_channels = in_channels
|
|
for i in range(layers - 1):
|
|
if i == 0:
|
|
dilation = 1
|
|
else:
|
|
dilation = i if dilation_factor == 1 else dilation_factor**i
|
|
conv_in_channels = conv_channels
|
|
padding = (kernel_size - 1) // 2 * dilation
|
|
conv_layer = nn.Conv1D(
|
|
conv_in_channels,
|
|
conv_channels,
|
|
kernel_size,
|
|
padding=padding,
|
|
dilation=dilation,
|
|
bias_attr=bias)
|
|
nonlinear = getattr(
|
|
nn, nonlinear_activation)(**nonlinear_activation_params)
|
|
conv_layers.append(conv_layer)
|
|
conv_layers.append(nonlinear)
|
|
padding = (kernel_size - 1) // 2
|
|
last_conv = nn.Conv1D(
|
|
conv_in_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
padding=padding,
|
|
bias_attr=bias)
|
|
conv_layers.append(last_conv)
|
|
self.conv_layers = nn.Sequential(*conv_layers)
|
|
|
|
if use_weight_norm:
|
|
self.apply_weight_norm()
|
|
|
|
def forward(self, x):
|
|
"""
|
|
Parameters
|
|
----------
|
|
x : Tensor
|
|
Shape (N, in_channels, num_samples), the input audio.
|
|
|
|
Returns
|
|
-------
|
|
Tensor
|
|
Shape (N, out_channels, num_samples), the predicted logits.
|
|
"""
|
|
return self.conv_layers(x)
|
|
|
|
def apply_weight_norm(self):
|
|
def _apply_weight_norm(layer):
|
|
if isinstance(layer, (nn.Conv1D, nn.Conv2D)):
|
|
nn.utils.weight_norm(layer)
|
|
|
|
self.apply(_apply_weight_norm)
|
|
|
|
def remove_weight_norm(self):
|
|
def _remove_weight_norm(layer):
|
|
try:
|
|
nn.utils.remove_weight_norm(layer)
|
|
except ValueError:
|
|
pass
|
|
|
|
self.apply(_remove_weight_norm)
|
|
|
|
|
|
class ResidualPWGDiscriminator(nn.Layer):
|
|
"""A wavenet-style discriminator for audio.
|
|
|
|
Parameters
|
|
----------
|
|
in_channels : int, optional
|
|
Number of channels of the input audio, by default 1
|
|
out_channels : int, optional
|
|
Output feature size, by default 1
|
|
kernel_size : int, optional
|
|
Kernel size of residual blocks, by default 3
|
|
layers : int, optional
|
|
Number of residual blocks, by default 30
|
|
stacks : int, optional
|
|
Number of groups of residual blocks, within which the dilation
|
|
of each residual blocks grows exponentially, by default 3
|
|
residual_channels : int, optional
|
|
Residual channels of residual blocks, by default 64
|
|
gate_channels : int, optional
|
|
Gate channels of residual blocks, by default 128
|
|
skip_channels : int, optional
|
|
Skip channels of residual blocks, by default 64
|
|
dropout : float, optional
|
|
Dropout probability of residual blocks, by default 0.
|
|
bias : bool, optional
|
|
Whether to use bias in residual blocks, by default True
|
|
use_weight_norm : bool, optional
|
|
Whether to use weight normalization in all convolutional layers,
|
|
by default True
|
|
use_causal_conv : bool, optional
|
|
Whether to use causal convolution in residual blocks, by default False
|
|
nonlinear_activation : str, optional
|
|
Activation after convolutions other than those in residual blocks,
|
|
by default "LeakyReLU"
|
|
nonlinear_activation_params : Dict[str, Any], optional
|
|
Parameters to pass to the activation, by default {"negative_slope": 0.2}
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels: int=1,
|
|
out_channels: int=1,
|
|
kernel_size: int=3,
|
|
layers: int=30,
|
|
stacks: int=3,
|
|
residual_channels: int=64,
|
|
gate_channels: int=128,
|
|
skip_channels: int=64,
|
|
dropout: float=0.,
|
|
bias: bool=True,
|
|
use_weight_norm: bool=True,
|
|
use_causal_conv: bool=False,
|
|
nonlinear_activation: str="LeakyReLU",
|
|
nonlinear_activation_params: Dict[
|
|
str, Any]={"negative_slope": 0.2}):
|
|
super().__init__()
|
|
assert kernel_size % 2 == 1
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.layers = layers
|
|
self.stacks = stacks
|
|
self.kernel_size = kernel_size
|
|
|
|
assert layers % stacks == 0
|
|
layers_per_stack = layers // stacks
|
|
|
|
self.first_conv = nn.Sequential(
|
|
nn.Conv1D(in_channels, residual_channels, 1, bias_attr=True),
|
|
getattr(nn, nonlinear_activation)(**nonlinear_activation_params))
|
|
|
|
self.conv_layers = nn.LayerList()
|
|
for layer in range(layers):
|
|
dilation = 2**(layer % layers_per_stack)
|
|
conv = ResidualBlock(
|
|
kernel_size=kernel_size,
|
|
residual_channels=residual_channels,
|
|
gate_channels=gate_channels,
|
|
skip_channels=skip_channels,
|
|
aux_channels=None, # no auxiliary input
|
|
dropout=dropout,
|
|
dilation=dilation,
|
|
bias=bias,
|
|
use_causal_conv=use_causal_conv)
|
|
self.conv_layers.append(conv)
|
|
|
|
self.last_conv_layers = nn.Sequential(
|
|
getattr(nn, nonlinear_activation)(**nonlinear_activation_params),
|
|
nn.Conv1D(skip_channels, skip_channels, 1, bias_attr=True),
|
|
getattr(nn, nonlinear_activation)(**nonlinear_activation_params),
|
|
nn.Conv1D(skip_channels, out_channels, 1, bias_attr=True))
|
|
|
|
if use_weight_norm:
|
|
self.apply_weight_norm()
|
|
|
|
def forward(self, x):
|
|
"""
|
|
Parameters
|
|
----------
|
|
x : Tensor
|
|
Shape (N, in_channels, num_samples), the input audio.
|
|
|
|
Returns
|
|
-------
|
|
Tensor
|
|
Shape (N, out_channels, num_samples), the predicted logits.
|
|
"""
|
|
x = self.first_conv(x)
|
|
skip = 0
|
|
for f in self.conv_layers:
|
|
x, h = f(x, None)
|
|
skip += h
|
|
skip *= math.sqrt(1 / len(self.conv_layers))
|
|
|
|
x = skip
|
|
x = self.last_conv_layers(x)
|
|
return x
|
|
|
|
def apply_weight_norm(self):
|
|
def _apply_weight_norm(layer):
|
|
if isinstance(layer, (nn.Conv1D, nn.Conv2D)):
|
|
nn.utils.weight_norm(layer)
|
|
|
|
self.apply(_apply_weight_norm)
|
|
|
|
def remove_weight_norm(self):
|
|
def _remove_weight_norm(layer):
|
|
try:
|
|
nn.utils.remove_weight_norm(layer)
|
|
except ValueError:
|
|
pass
|
|
|
|
self.apply(_remove_weight_norm)
|
|
|
|
|
|
class PWGInference(nn.Layer):
|
|
def __init__(self, normalizer, pwg_generator):
|
|
super().__init__()
|
|
self.normalizer = normalizer
|
|
self.pwg_generator = pwg_generator
|
|
|
|
def forward(self, logmel):
|
|
normalized_mel = self.normalizer(logmel)
|
|
wav = self.pwg_generator.inference(normalized_mel)
|
|
return wav
|