534 lines
20 KiB
Python
534 lines
20 KiB
Python
import torch
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from torch import nn
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from torch.nn import Conv1d, Conv2d
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from torch.nn import functional as F
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from torch.nn.utils import spectral_norm, weight_norm
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import modules.attentions as attentions
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import modules.commons as commons
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import modules.modules as modules
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import utils
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from modules.commons import get_padding
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from utils import f0_to_coarse
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class ResidualCouplingBlock(nn.Module):
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def __init__(self,
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channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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n_flows=4,
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gin_channels=0,
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share_parameter=False
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):
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super().__init__()
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self.channels = channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.flows = nn.ModuleList()
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self.wn = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=0, gin_channels=gin_channels) if share_parameter else None
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for i in range(n_flows):
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self.flows.append(
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modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
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gin_channels=gin_channels, mean_only=True, wn_sharing_parameter=self.wn))
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self.flows.append(modules.Flip())
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def forward(self, x, x_mask, g=None, reverse=False):
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if not reverse:
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, reverse=reverse)
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else:
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for flow in reversed(self.flows):
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x = flow(x, x_mask, g=g, reverse=reverse)
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return x
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class TransformerCouplingBlock(nn.Module):
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def __init__(self,
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channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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n_flows=4,
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gin_channels=0,
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share_parameter=False
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):
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super().__init__()
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self.channels = channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.flows = nn.ModuleList()
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self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = self.gin_channels) if share_parameter else None
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for i in range(n_flows):
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self.flows.append(
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modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout, filter_channels, mean_only=True, wn_sharing_parameter=self.wn, gin_channels = self.gin_channels))
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self.flows.append(modules.Flip())
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def forward(self, x, x_mask, g=None, reverse=False):
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if not reverse:
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, reverse=reverse)
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else:
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for flow in reversed(self.flows):
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x = flow(x, x_mask, g=g, reverse=reverse)
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return x
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class Encoder(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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gin_channels=0):
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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.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
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self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, x, x_lengths, g=None):
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# print(x.shape,x_lengths.shape)
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
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return z, m, logs, x_mask
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class TextEncoder(nn.Module):
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def __init__(self,
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out_channels,
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hidden_channels,
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kernel_size,
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n_layers,
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gin_channels=0,
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filter_channels=None,
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n_heads=None,
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p_dropout=None):
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super().__init__()
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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self.f0_emb = nn.Embedding(256, hidden_channels)
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self.enc_ = attentions.Encoder(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout)
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def forward(self, x, x_mask, f0=None, noice_scale=1):
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x = x + self.f0_emb(f0).transpose(1, 2)
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x = self.enc_(x * x_mask, x_mask)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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z = (m + torch.randn_like(m) * torch.exp(logs) * noice_scale) * x_mask
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return z, m, logs, x_mask
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class DiscriminatorP(torch.nn.Module):
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
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super(DiscriminatorP, self).__init__()
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self.period = period
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self.use_spectral_norm = use_spectral_norm
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norm_f = weight_norm if use_spectral_norm is False else spectral_norm
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self.convs = nn.ModuleList([
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norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
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])
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
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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, modules.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 DiscriminatorS(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(DiscriminatorS, self).__init__()
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norm_f = weight_norm if use_spectral_norm is False else spectral_norm
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self.convs = nn.ModuleList([
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norm_f(Conv1d(1, 16, 15, 1, padding=7)),
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norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
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norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
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norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
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])
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self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
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def forward(self, x):
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fmap = []
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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, modules.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(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(MultiPeriodDiscriminator, self).__init__()
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periods = [2, 3, 5, 7, 11]
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discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
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discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
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self.discriminators = nn.ModuleList(discs)
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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(y)
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y_d_g, fmap_g = d(y_hat)
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y_d_rs.append(y_d_r)
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y_d_gs.append(y_d_g)
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fmap_rs.append(fmap_r)
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fmap_gs.append(fmap_g)
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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class SpeakerEncoder(torch.nn.Module):
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def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
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super(SpeakerEncoder, self).__init__()
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self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
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self.linear = nn.Linear(model_hidden_size, model_embedding_size)
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self.relu = nn.ReLU()
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def forward(self, mels):
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self.lstm.flatten_parameters()
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_, (hidden, _) = self.lstm(mels)
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embeds_raw = self.relu(self.linear(hidden[-1]))
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return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
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def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
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mel_slices = []
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for i in range(0, total_frames - partial_frames, partial_hop):
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mel_range = torch.arange(i, i + partial_frames)
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mel_slices.append(mel_range)
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return mel_slices
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def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
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mel_len = mel.size(1)
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last_mel = mel[:, -partial_frames:]
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if mel_len > partial_frames:
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mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
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mels = list(mel[:, s] for s in mel_slices)
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mels.append(last_mel)
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mels = torch.stack(tuple(mels), 0).squeeze(1)
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with torch.no_grad():
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partial_embeds = self(mels)
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embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
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# embed = embed / torch.linalg.norm(embed, 2)
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else:
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with torch.no_grad():
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embed = self(last_mel)
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return embed
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class F0Decoder(nn.Module):
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def __init__(self,
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out_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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spk_channels=0):
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super().__init__()
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.spk_channels = spk_channels
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self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
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self.decoder = attentions.FFT(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout)
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self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
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self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1)
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self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
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def forward(self, x, norm_f0, x_mask, spk_emb=None):
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x = torch.detach(x)
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if (spk_emb is not None):
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x = x + self.cond(spk_emb)
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x += self.f0_prenet(norm_f0)
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x = self.prenet(x) * x_mask
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x = self.decoder(x * x_mask, x_mask)
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x = self.proj(x) * x_mask
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return x
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class SynthesizerTrn(nn.Module):
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"""
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Synthesizer for Training
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"""
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def __init__(self,
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spec_channels,
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segment_size,
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inter_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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resblock,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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upsample_rates,
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upsample_initial_channel,
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upsample_kernel_sizes,
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gin_channels,
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ssl_dim,
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n_speakers,
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sampling_rate=44100,
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vol_embedding=False,
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vocoder_name = "nsf-hifigan",
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use_depthwise_conv = False,
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use_automatic_f0_prediction = True,
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flow_share_parameter = False,
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n_flow_layer = 4,
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n_layers_trans_flow = 3,
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use_transformer_flow = False,
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**kwargs):
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super().__init__()
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self.spec_channels = spec_channels
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self.inter_channels = inter_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.resblock = resblock
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self.resblock_kernel_sizes = resblock_kernel_sizes
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self.resblock_dilation_sizes = resblock_dilation_sizes
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self.upsample_rates = upsample_rates
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self.upsample_initial_channel = upsample_initial_channel
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self.upsample_kernel_sizes = upsample_kernel_sizes
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self.segment_size = segment_size
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self.gin_channels = gin_channels
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self.ssl_dim = ssl_dim
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self.vol_embedding = vol_embedding
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self.emb_g = nn.Embedding(n_speakers, gin_channels)
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self.use_depthwise_conv = use_depthwise_conv
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self.use_automatic_f0_prediction = use_automatic_f0_prediction
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self.n_layers_trans_flow = n_layers_trans_flow
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if vol_embedding:
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self.emb_vol = nn.Linear(1, hidden_channels)
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self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
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self.enc_p = TextEncoder(
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inter_channels,
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hidden_channels,
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filter_channels=filter_channels,
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n_heads=n_heads,
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n_layers=n_layers,
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kernel_size=kernel_size,
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p_dropout=p_dropout
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)
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hps = {
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"sampling_rate": sampling_rate,
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"inter_channels": inter_channels,
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"resblock": resblock,
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"resblock_kernel_sizes": resblock_kernel_sizes,
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"resblock_dilation_sizes": resblock_dilation_sizes,
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"upsample_rates": upsample_rates,
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"upsample_initial_channel": upsample_initial_channel,
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"upsample_kernel_sizes": upsample_kernel_sizes,
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"gin_channels": gin_channels,
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"use_depthwise_conv":use_depthwise_conv
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}
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modules.set_Conv1dModel(self.use_depthwise_conv)
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if vocoder_name == "nsf-hifigan":
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from vdecoder.hifigan.models import Generator
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self.dec = Generator(h=hps)
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elif vocoder_name == "nsf-snake-hifigan":
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from vdecoder.hifiganwithsnake.models import Generator
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self.dec = Generator(h=hps)
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else:
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print("[?] Unkown vocoder: use default(nsf-hifigan)")
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from vdecoder.hifigan.models import Generator
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self.dec = Generator(h=hps)
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self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
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if use_transformer_flow:
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self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads, n_layers_trans_flow, 5, p_dropout, n_flow_layer, gin_channels=gin_channels, share_parameter= flow_share_parameter)
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else:
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self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels, share_parameter= flow_share_parameter)
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if self.use_automatic_f0_prediction:
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self.f0_decoder = F0Decoder(
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1,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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spk_channels=gin_channels
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)
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self.emb_uv = nn.Embedding(2, hidden_channels)
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self.character_mix = False
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def EnableCharacterMix(self, n_speakers_map, device):
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self.speaker_map = torch.zeros((n_speakers_map, 1, 1, self.gin_channels)).to(device)
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for i in range(n_speakers_map):
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self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]).to(device))
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self.speaker_map = self.speaker_map.unsqueeze(0).to(device)
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self.character_mix = True
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def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None, vol = None):
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g = self.emb_g(g).transpose(1,2)
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# vol proj
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vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol is not None and self.vol_embedding else 0
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# ssl prenet
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x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
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x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2) + vol
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# f0 predict
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if self.use_automatic_f0_prediction:
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lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
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norm_lf0 = utils.normalize_f0(lf0, x_mask, uv)
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pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
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else:
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lf0 = 0
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norm_lf0 = 0
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pred_lf0 = 0
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# encoder
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z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0))
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z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
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# flow
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z_p = self.flow(z, spec_mask, g=g)
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z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
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# nsf decoder
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o = self.dec(z_slice, g=g, f0=pitch_slice)
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return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0
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@torch.no_grad()
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def infer(self, c, f0, uv, g=None, noice_scale=0.35, seed=52468, predict_f0=False, vol = None):
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if c.device == torch.device("cuda"):
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torch.cuda.manual_seed_all(seed)
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else:
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torch.manual_seed(seed)
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c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
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if self.character_mix and len(g) > 1: # [N, S] * [S, B, 1, H]
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g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1]
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g = g * self.speaker_map # [N, S, B, 1, H]
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g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
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g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N]
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else:
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if g.dim() == 1:
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g = g.unsqueeze(0)
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g = self.emb_g(g).transpose(1, 2)
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x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
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# vol proj
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|
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vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol is not None and self.vol_embedding else 0
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|
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x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2) + vol
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|
|
|
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|
if self.use_automatic_f0_prediction and predict_f0:
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|
lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
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|
norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
|
|
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
|
|
f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
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|
|
|
z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale)
|
|
z = self.flow(z_p, c_mask, g=g, reverse=True)
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|
o = self.dec(z * c_mask, g=g, f0=f0)
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|
return o,f0
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