mirror of https://github.com/avilliai/Manyana
411 lines
16 KiB
Python
411 lines
16 KiB
Python
import math
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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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from . import commons
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from . import modules
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from . import attentions
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from torch.nn import Conv1d, ConvTranspose1d
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from torch.nn.utils import weight_norm
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from .commons import init_weights
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class StochasticDurationPredictor(nn.Module):
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def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
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super().__init__()
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filter_channels = in_channels # it needs to be removed from future version.
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self.in_channels = in_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.log_flow = modules.Log()
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self.flows = nn.ModuleList()
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self.flows.append(modules.ElementwiseAffine(2))
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for i in range(n_flows):
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self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
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self.flows.append(modules.Flip())
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self.post_pre = nn.Conv1d(1, filter_channels, 1)
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self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
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self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
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self.post_flows = nn.ModuleList()
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self.post_flows.append(modules.ElementwiseAffine(2))
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for i in range(4):
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self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
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self.post_flows.append(modules.Flip())
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self.pre = nn.Conv1d(in_channels, filter_channels, 1)
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self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
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self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
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def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
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x = torch.detach(x)
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x = self.pre(x)
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if g is not None:
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g = torch.detach(g)
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x = x + self.cond(g)
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x = self.convs(x, x_mask)
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x = self.proj(x) * x_mask
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if not reverse:
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flows = self.flows
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assert w is not None
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logdet_tot_q = 0
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h_w = self.post_pre(w)
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h_w = self.post_convs(h_w, x_mask)
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h_w = self.post_proj(h_w) * x_mask
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e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
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z_q = e_q
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for flow in self.post_flows:
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z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
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logdet_tot_q += logdet_q
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z_u, z1 = torch.split(z_q, [1, 1], 1)
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u = torch.sigmoid(z_u) * x_mask
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z0 = (w - u) * x_mask
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logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
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logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
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logdet_tot = 0
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z0, logdet = self.log_flow(z0, x_mask)
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logdet_tot += logdet
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z = torch.cat([z0, z1], 1)
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for flow in flows:
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z, logdet = flow(z, x_mask, g=x, reverse=reverse)
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logdet_tot = logdet_tot + logdet
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nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
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return nll + logq # [b]
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else:
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flows = list(reversed(self.flows))
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flows = flows[:-2] + [flows[-1]] # remove a useless vflow
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z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
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for flow in flows:
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z = flow(z, x_mask, g=x, reverse=reverse)
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z0, z1 = torch.split(z, [1, 1], 1)
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logw = z0
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return logw
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class DurationPredictor(nn.Module):
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def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
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super().__init__()
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self.in_channels = in_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.gin_channels = gin_channels
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self.drop = nn.Dropout(p_dropout)
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self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
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self.norm_1 = modules.LayerNorm(filter_channels)
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self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
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self.norm_2 = modules.LayerNorm(filter_channels)
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self.proj = nn.Conv1d(filter_channels, 1, 1)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, in_channels, 1)
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def forward(self, x, x_mask, g=None):
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x = torch.detach(x)
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if g is not None:
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g = torch.detach(g)
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x = x + self.cond(g)
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x = self.conv_1(x * x_mask)
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x = torch.relu(x)
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x = self.norm_1(x)
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x = self.drop(x)
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x = self.conv_2(x * x_mask)
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x = torch.relu(x)
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x = self.norm_2(x)
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x = self.drop(x)
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x = self.proj(x * x_mask)
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return x * x_mask
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class TextEncoder(nn.Module):
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def __init__(self,
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n_vocab,
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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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emotion_embedding):
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super().__init__()
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self.n_vocab = n_vocab
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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.emotion_embedding = emotion_embedding
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if self.n_vocab != 0:
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self.emb = nn.Embedding(n_vocab, hidden_channels)
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if emotion_embedding:
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self.emo_proj = nn.Linear(1024, hidden_channels)
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nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
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self.encoder = 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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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, x, x_lengths, emotion_embedding=None):
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if self.n_vocab != 0:
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x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
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if emotion_embedding is not None:
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x = x + self.emo_proj(emotion_embedding.unsqueeze(1))
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x = torch.transpose(x, 1, -1) # [b, h, t]
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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.encoder(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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return x, m, logs, x_mask
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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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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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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))
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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 PosteriorEncoder(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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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 Generator(torch.nn.Module):
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def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
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upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
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super(Generator, self).__init__()
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_rates)
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self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
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resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
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self.ups.append(weight_norm(
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ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
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k, u, padding=(k - u) // 2)))
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
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self.resblocks.append(resblock(ch, k, d))
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
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self.ups.apply(init_weights)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
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def forward(self, x, g=None):
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x = self.conv_pre(x)
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if g is not None:
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x = x + self.cond(g)
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for i in range(self.num_upsamples):
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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x = self.ups[i](x)
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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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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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n_vocab,
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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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n_speakers=0,
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gin_channels=0,
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use_sdp=True,
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emotion_embedding=False,
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**kwargs):
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super().__init__()
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self.n_vocab = n_vocab
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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.n_speakers = n_speakers
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self.gin_channels = gin_channels
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self.use_sdp = use_sdp
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self.enc_p = TextEncoder(n_vocab,
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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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emotion_embedding)
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self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
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upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
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self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
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gin_channels=gin_channels)
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self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
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if use_sdp:
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self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
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else:
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self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
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if n_speakers > 1:
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self.emb_g = nn.Embedding(n_speakers, gin_channels)
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def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
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emotion_embedding=None):
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x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
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if self.n_speakers > 0:
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g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
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else:
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g = None
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if self.use_sdp:
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logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
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else:
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logw = self.dp(x, x_mask, g=g)
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w = torch.exp(logw) * x_mask * length_scale
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w_ceil = torch.ceil(w)
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y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
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attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
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attn = commons.generate_path(w_ceil, attn_mask)
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m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
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logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
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2) # [b, t', t], [b, t, d] -> [b, d, t']
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z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
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z = self.flow(z_p, y_mask, g=g, reverse=True)
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o = self.dec((z * y_mask)[:, :, :max_len], g=g)
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return o, attn, y_mask, (z, z_p, m_p, logs_p)
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def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
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assert self.n_speakers > 0, "n_speakers have to be larger than 0."
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g_src = self.emb_g(sid_src).unsqueeze(-1)
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g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
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z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
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z_p = self.flow(z, y_mask, g=g_src)
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z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
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o_hat = self.dec(z_hat * y_mask, g=g_tgt)
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return o_hat, y_mask, (z, z_p, z_hat)
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