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Running
on
Zero
| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from torch.nn.utils import weight_norm | |
| # This code is adopted from MelGAN under the MIT License | |
| # https://github.com/descriptinc/melgan-neurips | |
| def weights_init(m): | |
| classname = m.__class__.__name__ | |
| if classname.find("Conv") != -1: | |
| m.weight.data.normal_(0.0, 0.02) | |
| elif classname.find("BatchNorm2d") != -1: | |
| m.weight.data.normal_(1.0, 0.02) | |
| m.bias.data.fill_(0) | |
| def WNConv1d(*args, **kwargs): | |
| return weight_norm(nn.Conv1d(*args, **kwargs)) | |
| def WNConvTranspose1d(*args, **kwargs): | |
| return weight_norm(nn.ConvTranspose1d(*args, **kwargs)) | |
| class ResnetBlock(nn.Module): | |
| def __init__(self, dim, dilation=1): | |
| super().__init__() | |
| self.block = nn.Sequential( | |
| nn.LeakyReLU(0.2), | |
| nn.ReflectionPad1d(dilation), | |
| WNConv1d(dim, dim, kernel_size=3, dilation=dilation), | |
| nn.LeakyReLU(0.2), | |
| WNConv1d(dim, dim, kernel_size=1), | |
| ) | |
| self.shortcut = WNConv1d(dim, dim, kernel_size=1) | |
| def forward(self, x): | |
| return self.shortcut(x) + self.block(x) | |
| class MelGAN(nn.Module): | |
| def __init__(self, cfg): | |
| super().__init__() | |
| self.cfg = cfg | |
| self.hop_length = np.prod(self.cfg.model.melgan.ratios) | |
| mult = int(2 ** len(self.cfg.model.melgan.ratios)) | |
| model = [ | |
| nn.ReflectionPad1d(3), | |
| WNConv1d( | |
| self.cfg.preprocess.n_mel, | |
| mult * self.cfg.model.melgan.ngf, | |
| kernel_size=7, | |
| padding=0, | |
| ), | |
| ] | |
| # Upsample to raw audio scale | |
| for i, r in enumerate(self.cfg.model.melgan.ratios): | |
| model += [ | |
| nn.LeakyReLU(0.2), | |
| WNConvTranspose1d( | |
| mult * self.cfg.model.melgan.ngf, | |
| mult * self.cfg.model.melgan.ngf // 2, | |
| kernel_size=r * 2, | |
| stride=r, | |
| padding=r // 2 + r % 2, | |
| output_padding=r % 2, | |
| ), | |
| ] | |
| for j in range(self.cfg.model.melgan.n_residual_layers): | |
| model += [ | |
| ResnetBlock(mult * self.cfg.model.melgan.ngf // 2, dilation=3**j) | |
| ] | |
| mult //= 2 | |
| model += [ | |
| nn.LeakyReLU(0.2), | |
| nn.ReflectionPad1d(3), | |
| WNConv1d(self.cfg.model.melgan.ngf, 1, kernel_size=7, padding=0), | |
| nn.Tanh(), | |
| ] | |
| self.model = nn.Sequential(*model) | |
| self.apply(weights_init) | |
| def forward(self, x): | |
| return self.model(x) | |