459 lines
16 KiB
Python
459 lines
16 KiB
Python
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# Copyright (c) 2020, NVIDIA CORPORATION. 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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# MIT License
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#
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# Copyright (c) 2020 Jungil Kong
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# The following functions/classes were based on code from https://github.com/jik876/hifi-gan:
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# ResBlock1, ResBlock2, Generator, DiscriminatorP, DiscriminatorS, MultiScaleDiscriminator,
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# MultiPeriodDiscriminator, init_weights, get_padding
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
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from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
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from nemo.core.classes.common import typecheck
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from nemo.core.classes.module import NeuralModule
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from nemo.core.neural_types.elements import AudioSignal, MelSpectrogramType, VoidType
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from nemo.core.neural_types.neural_type import NeuralType
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LRELU_SLOPE = 0.1
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def init_weights(m, mean=0.0, std=0.01):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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m.weight.data.normal_(mean, std)
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def get_padding(kernel_size, dilation=1):
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return int((kernel_size * dilation - dilation) / 2)
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class ResBlock1(torch.nn.Module):
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__constants__ = ['lrelu_slope']
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def __init__(self, channels, kernel_size, dilation):
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super().__init__()
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self.lrelu_slope = LRELU_SLOPE
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self.convs1 = nn.ModuleList(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2]),
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)
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),
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]
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)
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self.convs1.apply(init_weights)
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self.convs2 = nn.ModuleList(
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[
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weight_norm(
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Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
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),
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weight_norm(
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Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
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),
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weight_norm(
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Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
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),
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]
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)
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self.convs2.apply(init_weights)
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def forward(self, x):
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for c1, c2 in zip(self.convs1, self.convs2):
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xt = F.leaky_relu(x, self.lrelu_slope)
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xt = c1(xt)
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xt = F.leaky_relu(xt, self.lrelu_slope)
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xt = c2(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs1:
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remove_weight_norm(l)
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for l in self.convs2:
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remove_weight_norm(l)
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class ResBlock2(torch.nn.Module):
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__constants__ = ['lrelu_slope']
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def __init__(self, channels, kernel_size, dilation):
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super().__init__()
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self.convs = nn.ModuleList(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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]
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)
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self.convs.apply(init_weights)
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self.lrelu_slope = LRELU_SLOPE
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def forward(self, x):
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for c in self.convs:
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xt = F.leaky_relu(x, self.lrelu_slope)
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xt = c(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs:
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remove_weight_norm(l)
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class Generator(NeuralModule):
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__constants__ = ['lrelu_slope', 'num_kernels', 'num_upsamples']
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def __init__(
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self,
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resblock,
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upsample_rates,
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upsample_kernel_sizes,
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upsample_initial_channel,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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initial_input_size=80,
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apply_weight_init_conv_pre=False,
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):
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super().__init__()
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_rates)
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self.conv_pre = weight_norm(Conv1d(initial_input_size, upsample_initial_channel, 7, 1, padding=3))
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self.lrelu_slope = LRELU_SLOPE
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resblock = ResBlock1 if resblock == 1 else ResBlock2
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
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self.ups.append(
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weight_norm(
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ConvTranspose1d(
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upsample_initial_channel // (2 ** i),
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upsample_initial_channel // (2 ** (i + 1)),
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k,
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u,
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padding=(k - u) // 2,
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)
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)
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)
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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resblock_list = nn.ModuleList()
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ch = upsample_initial_channel // (2 ** (i + 1))
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for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
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resblock_list.append(resblock(ch, k, d))
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self.resblocks.append(resblock_list)
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self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
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self.ups.apply(init_weights)
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self.conv_post.apply(init_weights)
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if apply_weight_init_conv_pre:
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self.conv_pre.apply(init_weights)
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@property
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def input_types(self):
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return {
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"x": NeuralType(('B', 'D', 'T'), MelSpectrogramType()),
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}
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@property
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def output_types(self):
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return {
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"audio": NeuralType(('B', 'S', 'T'), AudioSignal()),
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}
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@typecheck()
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def forward(self, x):
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x = self.conv_pre(x)
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for upsample_layer, resblock_group in zip(self.ups, self.resblocks):
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x = F.leaky_relu(x, self.lrelu_slope)
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x = upsample_layer(x)
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xs = torch.zeros(x.shape, dtype=x.dtype, device=x.device)
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for resblock in resblock_group:
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xs += resblock(x)
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x = xs / self.num_kernels
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x = F.leaky_relu(x)
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x = self.conv_post(x)
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x = torch.tanh(x)
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return x
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def remove_weight_norm(self):
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print('Removing weight norm...')
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for l in self.ups:
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remove_weight_norm(l)
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for group in self.resblocks:
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for block in group:
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block.remove_weight_norm()
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remove_weight_norm(self.conv_pre)
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remove_weight_norm(self.conv_post)
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class DiscriminatorP(NeuralModule):
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, debug=False):
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super().__init__()
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self.period = period
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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conv_ch = [32, 128, 512, 1024] if not debug else [8, 12, 32, 64]
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self.convs = nn.ModuleList(
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[
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norm_f(Conv2d(1, conv_ch[0], (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(conv_ch[0], conv_ch[1], (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(conv_ch[1], conv_ch[2], (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(conv_ch[2], conv_ch[3], (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(conv_ch[3], conv_ch[3], (kernel_size, 1), 1, padding=(2, 0))),
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]
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)
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self.conv_post = norm_f(Conv2d(conv_ch[3], 1, (3, 1), 1, padding=(1, 0)))
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@property
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def input_types(self):
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return {
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"x": NeuralType(('B', 'S', 'T'), AudioSignal()),
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}
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@property
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def output_types(self):
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return {
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"decision": NeuralType(('B', 'T'), VoidType()),
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"feature_maps": [NeuralType(("B", "C", "H", "W"), VoidType())],
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}
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@typecheck()
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def forward(self, x):
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fmap = []
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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class MultiPeriodDiscriminator(NeuralModule):
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def __init__(self, debug=False):
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super().__init__()
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self.discriminators = nn.ModuleList(
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[
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DiscriminatorP(2, debug=debug),
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DiscriminatorP(3, debug=debug),
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DiscriminatorP(5, debug=debug),
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DiscriminatorP(7, debug=debug),
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DiscriminatorP(11, debug=debug),
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]
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)
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@property
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def input_types(self):
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return {
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"y": NeuralType(('B', 'S', 'T'), AudioSignal()),
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"y_hat": NeuralType(('B', 'S', 'T'), AudioSignal()),
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}
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@property
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def output_types(self):
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return {
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"real_scores": [NeuralType(('B', 'T'), VoidType())],
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"fake_scores": [NeuralType(('B', 'T'), VoidType())],
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"real_feature_maps": [[NeuralType(("B", "C", "H", "W"), VoidType())]],
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"fake_feature_maps": [[NeuralType(("B", "C", "H", "W"), VoidType())]],
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}
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@typecheck()
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def forward(self, y, y_hat):
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y_d_rs = []
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y_d_gs = []
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fmap_rs = []
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fmap_gs = []
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for i, d in enumerate(self.discriminators):
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y_d_r, fmap_r = d(x=y)
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y_d_g, fmap_g = d(x=y_hat)
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y_d_rs.append(y_d_r)
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fmap_rs.append(fmap_r)
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y_d_gs.append(y_d_g)
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fmap_gs.append(fmap_g)
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|
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||
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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||
|
|
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||
|
|
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||
|
|
class DiscriminatorS(NeuralModule):
|
||
|
|
def __init__(self, use_spectral_norm=False, debug=False):
|
||
|
|
super().__init__()
|
||
|
|
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||
|
|
conv_ch = [128, 256, 512, 1024] if not debug else [16, 32, 32, 64]
|
||
|
|
self.convs = nn.ModuleList(
|
||
|
|
[
|
||
|
|
norm_f(Conv1d(1, conv_ch[0], 15, 1, padding=7)),
|
||
|
|
norm_f(Conv1d(conv_ch[0], conv_ch[0], 41, 2, groups=4, padding=20)),
|
||
|
|
norm_f(Conv1d(conv_ch[0], conv_ch[1], 41, 2, groups=16, padding=20)),
|
||
|
|
norm_f(Conv1d(conv_ch[1], conv_ch[2], 41, 4, groups=16, padding=20)),
|
||
|
|
norm_f(Conv1d(conv_ch[2], conv_ch[3], 41, 4, groups=16, padding=20)),
|
||
|
|
norm_f(Conv1d(conv_ch[3], conv_ch[3], 41, 1, groups=16, padding=20)),
|
||
|
|
norm_f(Conv1d(conv_ch[3], conv_ch[3], 5, 1, padding=2)),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
self.conv_post = norm_f(Conv1d(conv_ch[3], 1, 3, 1, padding=1))
|
||
|
|
|
||
|
|
@property
|
||
|
|
def input_types(self):
|
||
|
|
return {
|
||
|
|
"x": NeuralType(('B', 'S', 'T'), AudioSignal()),
|
||
|
|
}
|
||
|
|
|
||
|
|
@property
|
||
|
|
def output_types(self):
|
||
|
|
return {
|
||
|
|
"decision": NeuralType(('B', 'T'), VoidType()),
|
||
|
|
"feature_maps": [NeuralType(("B", "C", "T"), VoidType())],
|
||
|
|
}
|
||
|
|
|
||
|
|
@typecheck()
|
||
|
|
def forward(self, x):
|
||
|
|
fmap = []
|
||
|
|
for l in self.convs:
|
||
|
|
x = l(x)
|
||
|
|
x = F.leaky_relu(x, LRELU_SLOPE)
|
||
|
|
fmap.append(x)
|
||
|
|
x = self.conv_post(x)
|
||
|
|
fmap.append(x)
|
||
|
|
x = torch.flatten(x, 1, -1)
|
||
|
|
|
||
|
|
return x, fmap
|
||
|
|
|
||
|
|
|
||
|
|
class MultiScaleDiscriminator(NeuralModule):
|
||
|
|
def __init__(self, debug=False):
|
||
|
|
super().__init__()
|
||
|
|
self.discriminators = nn.ModuleList(
|
||
|
|
[
|
||
|
|
DiscriminatorS(use_spectral_norm=True, debug=debug),
|
||
|
|
DiscriminatorS(debug=debug),
|
||
|
|
DiscriminatorS(debug=debug),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
self.meanpools = nn.ModuleList([AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)])
|
||
|
|
|
||
|
|
@property
|
||
|
|
def input_types(self):
|
||
|
|
return {
|
||
|
|
"y": NeuralType(('B', 'S', 'T'), AudioSignal()),
|
||
|
|
"y_hat": NeuralType(('B', 'S', 'T'), AudioSignal()),
|
||
|
|
}
|
||
|
|
|
||
|
|
@property
|
||
|
|
def output_types(self):
|
||
|
|
return {
|
||
|
|
"real_scores": [NeuralType(('B', 'T'), VoidType())],
|
||
|
|
"fake_scores": [NeuralType(('B', 'T'), VoidType())],
|
||
|
|
"real_feature_maps": [[NeuralType(("B", "C", "T"), VoidType())]],
|
||
|
|
"fake_feature_maps": [[NeuralType(("B", "C", "T"), VoidType())]],
|
||
|
|
}
|
||
|
|
|
||
|
|
@typecheck()
|
||
|
|
def forward(self, y, y_hat):
|
||
|
|
y_d_rs = []
|
||
|
|
y_d_gs = []
|
||
|
|
fmap_rs = []
|
||
|
|
fmap_gs = []
|
||
|
|
for i, d in enumerate(self.discriminators):
|
||
|
|
if i != 0:
|
||
|
|
y = self.meanpools[i - 1](y)
|
||
|
|
y_hat = self.meanpools[i - 1](y_hat)
|
||
|
|
y_d_r, fmap_r = d(x=y)
|
||
|
|
y_d_g, fmap_g = d(x=y_hat)
|
||
|
|
y_d_rs.append(y_d_r)
|
||
|
|
fmap_rs.append(fmap_r)
|
||
|
|
y_d_gs.append(y_d_g)
|
||
|
|
fmap_gs.append(fmap_g)
|
||
|
|
|
||
|
|
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|