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import torch |
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import numpy as np |
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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 Conv1d, ConvTranspose1d |
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm |
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from modules.neural_source_filter import * |
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from modules.vocoder_blocks import * |
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LRELU_SLOPE = 0.1 |
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class ResBlock1(nn.Module): |
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def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)): |
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super(ResBlock1, self).__init__() |
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self.cfg = cfg |
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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( |
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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=1, |
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padding=get_padding(kernel_size, 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=1, |
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padding=get_padding(kernel_size, 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=1, |
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padding=get_padding(kernel_size, 1), |
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) |
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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, LRELU_SLOPE) |
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xt = c1(xt) |
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xt = F.leaky_relu(xt, 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(nn.Module): |
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def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3)): |
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super(ResBlock1, self).__init__() |
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self.cfg = cfg |
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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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def forward(self, x): |
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for c in self.convs: |
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xt = F.leaky_relu(x, 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 SourceModuleHnNSF(nn.Module): |
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def __init__( |
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self, fs, harmonic_num=0, amp=0.1, noise_std=0.003, voiced_threshold=0 |
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): |
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super(SourceModuleHnNSF, self).__init__() |
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self.amp = amp |
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self.noise_std = noise_std |
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self.l_sin_gen = SineGen(fs, harmonic_num, amp, noise_std, voiced_threshold) |
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self.l_linear = nn.Linear(harmonic_num + 1, 1) |
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self.l_tanh = nn.Tanh() |
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def forward(self, x, upp): |
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sine_wavs, uv, _ = self.l_sin_gen(x, upp) |
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sine_merge = self.l_tanh(self.l_linear(sine_wavs)) |
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return sine_merge |
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class NSFHiFiGAN(nn.Module): |
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def __init__(self, cfg): |
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super(NSFHiFiGAN, self).__init__() |
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self.cfg = cfg |
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self.num_kernels = len(self.cfg.model.nsfhifigan.resblock_kernel_sizes) |
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self.num_upsamples = len(self.cfg.model.nsfhifigan.upsample_rates) |
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self.m_source = SourceModuleHnNSF( |
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fs=self.cfg.preprocess.sample_rate, |
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harmonic_num=self.cfg.model.nsfhifigan.harmonic_num, |
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) |
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self.noise_convs = nn.ModuleList() |
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self.conv_pre = weight_norm( |
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Conv1d( |
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self.cfg.preprocess.n_mel, |
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self.cfg.model.nsfhifigan.upsample_initial_channel, |
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7, |
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1, |
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padding=3, |
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) |
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) |
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resblock = ResBlock1 if self.cfg.model.nsfhifigan.resblock == "1" else ResBlock2 |
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self.ups = nn.ModuleList() |
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for i, (u, k) in enumerate( |
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zip( |
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self.cfg.model.nsfhifigan.upsample_rates, |
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self.cfg.model.nsfhifigan.upsample_kernel_sizes, |
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) |
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): |
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c_cur = self.cfg.model.nsfhifigan.upsample_initial_channel // (2 ** (i + 1)) |
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self.ups.append( |
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weight_norm( |
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ConvTranspose1d( |
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self.cfg.model.nsfhifigan.upsample_initial_channel // (2**i), |
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self.cfg.model.nsfhifigan.upsample_initial_channel |
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// (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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if i + 1 < len(self.cfg.model.nsfhifigan.upsample_rates): |
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stride_f0 = int( |
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np.prod(self.cfg.model.nsfhifigan.upsample_rates[i + 1 :]) |
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) |
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self.noise_convs.append( |
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Conv1d( |
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1, |
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c_cur, |
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kernel_size=stride_f0 * 2, |
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stride=stride_f0, |
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padding=stride_f0 // 2, |
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) |
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) |
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else: |
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self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) |
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self.resblocks = nn.ModuleList() |
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ch = self.cfg.model.nsfhifigan.upsample_initial_channel |
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for i in range(len(self.ups)): |
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ch //= 2 |
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for j, (k, d) in enumerate( |
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zip( |
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self.cfg.model.nsfhifigan.resblock_kernel_sizes, |
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self.cfg.model.nsfhifigan.resblock_dilation_sizes, |
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) |
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): |
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self.resblocks.append(resblock(cfg, ch, k, d)) |
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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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self.upp = int(np.prod(self.cfg.model.nsfhifigan.upsample_rates)) |
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def forward(self, x, f0): |
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har_source = self.m_source(f0, self.upp).transpose(1, 2) |
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x = self.conv_pre(x) |
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for i in range(self.num_upsamples): |
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x = F.leaky_relu(x, LRELU_SLOPE) |
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x = self.ups[i](x) |
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x_source = self.noise_convs[i](har_source) |
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length = min(x.shape[-1], x_source.shape[-1]) |
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x = x[:, :, :length] |
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x_source = x[:, :, :length] |
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x = x + x_source |
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xs = None |
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for j in range(self.num_kernels): |
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if xs is None: |
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xs = self.resblocks[i * self.num_kernels + j](x) |
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else: |
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xs += self.resblocks[i * self.num_kernels + j](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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