367 lines
14 KiB
Python
367 lines
14 KiB
Python
import torch
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from torch import nn
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from torch.nn import functional as F
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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 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, z=None):
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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 + z * torch.exp(logs)) * x_mask
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return z, m, logs, x_mask
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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.predict_f0 = False
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self.speaker_map = []
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self.export_mix = False
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def export_chara_mix(self, speakers_mix):
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self.speaker_map = torch.zeros((len(speakers_mix), 1, 1, self.gin_channels))
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i = 0
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for key in speakers_mix.keys():
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spkidx = speakers_mix[key]
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self.speaker_map[i] = self.emb_g(torch.LongTensor([[spkidx]]))
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i = i + 1
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self.speaker_map = self.speaker_map.unsqueeze(0)
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self.export_mix = True
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def forward(self, c, f0, mel2ph, uv, noise=None, g=None, vol = None):
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decoder_inp = F.pad(c, [0, 0, 1, 0])
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mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]])
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c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H]
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if self.export_mix: # [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(torch.ones_like(f0), 1).to(c.dtype)
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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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x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2) + vol
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if self.use_automatic_f0_prediction and self.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)
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pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
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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), z=noise)
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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
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