mirror of
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308 lines
No EOL
10 KiB
Python
308 lines
No EOL
10 KiB
Python
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.utils import weight_norm
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import numpy as np
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which_norm = weight_norm
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#################### Definition of basic model components ####################
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#Convolutional layer with 1 frame look-ahead (used for feature PreCondNet)
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class ConvLookahead(nn.Module):
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def __init__(self, in_ch, out_ch, kernel_size, dilation=1, groups=1, bias= False):
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super(ConvLookahead, self).__init__()
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torch.manual_seed(5)
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self.padding_left = (kernel_size - 2) * dilation
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self.padding_right = 1 * dilation
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self.conv = which_norm(nn.Conv1d(in_ch,out_ch,kernel_size,dilation=dilation, groups=groups, bias= bias))
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self.init_weights()
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
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nn.init.orthogonal_(m.weight.data)
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def forward(self, x):
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x = F.pad(x,(self.padding_left, self.padding_right))
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conv_out = self.conv(x)
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return conv_out
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#(modified) GLU Activation layer definition
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class GLU(nn.Module):
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def __init__(self, feat_size):
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super(GLU, self).__init__()
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torch.manual_seed(5)
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self.gate = which_norm(nn.Linear(feat_size, feat_size, bias=False))
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self.init_weights()
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\
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or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
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nn.init.orthogonal_(m.weight.data)
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def forward(self, x):
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out = torch.tanh(x) * torch.sigmoid(self.gate(x))
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return out
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#GRU layer definition
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class ContForwardGRU(nn.Module):
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def __init__(self, input_size, hidden_size, num_layers=1):
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super(ContForwardGRU, self).__init__()
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torch.manual_seed(5)
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self.hidden_size = hidden_size
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self.cont_fc = nn.Sequential(which_norm(nn.Linear(64, self.hidden_size, bias=False)),
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nn.Tanh())
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self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True,\
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bias=False)
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self.nl = GLU(self.hidden_size)
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self.init_weights()
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
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nn.init.orthogonal_(m.weight.data)
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def forward(self, x, x0):
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self.gru.flatten_parameters()
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h0 = self.cont_fc(x0).unsqueeze(0)
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output, h0 = self.gru(x, h0)
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return self.nl(output)
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# Framewise convolution layer definition
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class ContFramewiseConv(torch.nn.Module):
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def __init__(self, frame_len, out_dim, frame_kernel_size=3, act='glu', causal=True):
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super(ContFramewiseConv, self).__init__()
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torch.manual_seed(5)
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self.frame_kernel_size = frame_kernel_size
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self.frame_len = frame_len
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if (causal == True) or (self.frame_kernel_size == 2):
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self.required_pad_left = (self.frame_kernel_size - 1) * self.frame_len
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self.required_pad_right = 0
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self.cont_fc = nn.Sequential(which_norm(nn.Linear(64, self.required_pad_left, bias=False)),
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nn.Tanh()
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)
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else:
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self.required_pad_left = (self.frame_kernel_size - 1)//2 * self.frame_len
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self.required_pad_right = (self.frame_kernel_size - 1)//2 * self.frame_len
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self.fc_input_dim = self.frame_kernel_size * self.frame_len
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self.fc_out_dim = out_dim
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if act=='glu':
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self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)),
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GLU(self.fc_out_dim)
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)
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if act=='tanh':
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self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)),
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nn.Tanh()
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)
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self.init_weights()
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\
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isinstance(m, nn.Embedding):
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nn.init.orthogonal_(m.weight.data)
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def forward(self, x, x0):
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if self.frame_kernel_size == 1:
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return self.fc(x)
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x_flat = x.reshape(x.size(0),1,-1)
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pad = self.cont_fc(x0).view(x0.size(0),1,-1)
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x_flat_padded = torch.cat((pad, x_flat), dim=-1).unsqueeze(2)
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x_flat_padded_unfolded = F.unfold(x_flat_padded,\
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kernel_size= (1,self.fc_input_dim), stride=self.frame_len).permute(0,2,1).contiguous()
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out = self.fc(x_flat_padded_unfolded)
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return out
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# A fully-connected based upsampling layer definition
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class UpsampleFC(nn.Module):
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def __init__(self, in_ch, out_ch, upsample_factor):
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super(UpsampleFC, self).__init__()
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torch.manual_seed(5)
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self.in_ch = in_ch
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self.out_ch = out_ch
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self.upsample_factor = upsample_factor
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self.fc = nn.Linear(in_ch, out_ch * upsample_factor, bias=False)
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self.nl = nn.Tanh()
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self.init_weights()
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or\
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isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
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nn.init.orthogonal_(m.weight.data)
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def forward(self, x):
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batch_size = x.size(0)
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x = x.permute(0, 2, 1)
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x = self.nl(self.fc(x))
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x = x.reshape((batch_size, -1, self.out_ch))
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x = x.permute(0, 2, 1)
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return x
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########################### The complete model definition #################################
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class FWGAN400ContLarge(nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(5)
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self.bfcc_with_corr_upsampler = UpsampleFC(19,80,4)
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self.feat_in_conv1 = ConvLookahead(160,256,kernel_size=5)
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self.feat_in_nl1 = GLU(256)
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self.cont_net = nn.Sequential(which_norm(nn.Linear(321, 160, bias=False)),
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nn.Tanh(),
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which_norm(nn.Linear(160, 160, bias=False)),
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nn.Tanh(),
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which_norm(nn.Linear(160, 80, bias=False)),
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nn.Tanh(),
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which_norm(nn.Linear(80, 80, bias=False)),
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nn.Tanh(),
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which_norm(nn.Linear(80, 64, bias=False)),
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nn.Tanh(),
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which_norm(nn.Linear(64, 64, bias=False)),
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nn.Tanh())
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self.rnn = ContForwardGRU(256,256)
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self.fwc1 = ContFramewiseConv(256, 256)
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self.fwc2 = ContFramewiseConv(256, 128)
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self.fwc3 = ContFramewiseConv(128, 128)
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self.fwc4 = ContFramewiseConv(128, 64)
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self.fwc5 = ContFramewiseConv(64, 64)
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self.fwc6 = ContFramewiseConv(64, 40)
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self.fwc7 = ContFramewiseConv(40, 40)
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self.init_weights()
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self.count_parameters()
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\
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isinstance(m, nn.Embedding):
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nn.init.orthogonal_(m.weight.data)
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def count_parameters(self):
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num_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
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print(f"Total number of {self.__class__.__name__} network parameters = {num_params}\n")
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def create_phase_signals(self, periods):
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batch_size = periods.size(0)
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progression = torch.arange(1, 160 + 1, dtype=periods.dtype, device=periods.device).view((1, -1))
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progression = torch.repeat_interleave(progression, batch_size, 0)
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phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1)
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chunks = []
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for sframe in range(periods.size(1)):
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f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1)
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chunk_sin = torch.sin(f * progression + phase0)
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chunk_sin = chunk_sin.reshape(chunk_sin.size(0),-1,40)
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chunk_cos = torch.cos(f * progression + phase0)
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chunk_cos = chunk_cos.reshape(chunk_cos.size(0),-1,40)
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chunk = torch.cat((chunk_sin, chunk_cos), dim = -1)
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phase0 = phase0 + 160 * f
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chunks.append(chunk)
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phase_signals = torch.cat(chunks, dim=1)
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return phase_signals
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def gain_multiply(self, x, c0):
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gain = 10**(0.5*c0/np.sqrt(18.0))
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gain = torch.repeat_interleave(gain, 160, dim=-1)
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gain = gain.reshape(gain.size(0),1,-1).squeeze(1)
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return x * gain
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def forward(self, pitch_period, bfcc_with_corr, x0):
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norm_x0 = torch.norm(x0,2, dim=-1, keepdim=True)
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x0 = x0 / torch.sqrt((1e-8) + norm_x0**2)
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x0 = torch.cat((torch.log(norm_x0 + 1e-7), x0), dim=-1)
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p_embed = self.create_phase_signals(pitch_period).permute(0, 2, 1).contiguous()
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envelope = self.bfcc_with_corr_upsampler(bfcc_with_corr.permute(0,2,1).contiguous())
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feat_in = torch.cat((p_embed , envelope), dim=1)
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wav_latent1 = self.feat_in_nl1(self.feat_in_conv1(feat_in).permute(0,2,1).contiguous())
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cont_latent = self.cont_net(x0)
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rnn_out = self.rnn(wav_latent1, cont_latent)
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fwc1_out = self.fwc1(rnn_out, cont_latent)
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fwc2_out = self.fwc2(fwc1_out, cont_latent)
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fwc3_out = self.fwc3(fwc2_out, cont_latent)
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fwc4_out = self.fwc4(fwc3_out, cont_latent)
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fwc5_out = self.fwc5(fwc4_out, cont_latent)
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fwc6_out = self.fwc6(fwc5_out, cont_latent)
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fwc7_out = self.fwc7(fwc6_out, cont_latent)
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waveform = fwc7_out.reshape(fwc7_out.size(0),1,-1).squeeze(1)
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waveform = self.gain_multiply(waveform,bfcc_with_corr[:,:,:1])
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return waveform |