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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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|
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import commons |
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import modules |
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import attentions_onnx |
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|
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from torch.nn import Conv1d, ConvTranspose1d, Conv2d |
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm |
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from commons import init_weights, get_padding |
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from text import symbols, num_tones, num_languages |
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class DurationDiscriminator(nn.Module): |
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def __init__( |
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self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 |
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): |
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super().__init__() |
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|
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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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|
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self.drop = nn.Dropout(p_dropout) |
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self.conv_1 = nn.Conv1d( |
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in_channels, filter_channels, kernel_size, padding=kernel_size // 2 |
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) |
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self.norm_1 = modules.LayerNorm(filter_channels) |
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self.conv_2 = nn.Conv1d( |
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filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 |
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) |
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self.norm_2 = modules.LayerNorm(filter_channels) |
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self.dur_proj = nn.Conv1d(1, filter_channels, 1) |
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|
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self.pre_out_conv_1 = nn.Conv1d( |
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2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 |
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) |
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self.pre_out_norm_1 = modules.LayerNorm(filter_channels) |
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self.pre_out_conv_2 = nn.Conv1d( |
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filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 |
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) |
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self.pre_out_norm_2 = modules.LayerNorm(filter_channels) |
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|
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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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|
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self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid()) |
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|
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def forward_probability(self, x, x_mask, dur, g=None): |
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dur = self.dur_proj(dur) |
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x = torch.cat([x, dur], dim=1) |
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x = self.pre_out_conv_1(x * x_mask) |
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x = torch.relu(x) |
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x = self.pre_out_norm_1(x) |
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x = self.drop(x) |
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x = self.pre_out_conv_2(x * x_mask) |
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x = torch.relu(x) |
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x = self.pre_out_norm_2(x) |
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x = self.drop(x) |
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x = x * x_mask |
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x = x.transpose(1, 2) |
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output_prob = self.output_layer(x) |
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return output_prob |
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|
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def forward(self, x, x_mask, dur_r, dur_hat, 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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|
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output_probs = [] |
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for dur in [dur_r, dur_hat]: |
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output_prob = self.forward_probability(x, x_mask, dur, g) |
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output_probs.append(output_prob) |
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return output_probs |
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class TransformerCouplingBlock(nn.Module): |
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def __init__( |
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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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|
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self.flows = nn.ModuleList() |
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|
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self.wn = ( |
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attentions_onnx.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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isflow=True, |
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gin_channels=self.gin_channels, |
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) |
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if share_parameter |
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else None |
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) |
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|
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for i in range(n_flows): |
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self.flows.append( |
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modules.TransformerCouplingLayer( |
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channels, |
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hidden_channels, |
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kernel_size, |
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n_layers, |
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n_heads, |
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p_dropout, |
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filter_channels, |
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mean_only=True, |
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wn_sharing_parameter=self.wn, |
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gin_channels=self.gin_channels, |
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) |
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) |
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self.flows.append(modules.Flip()) |
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|
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def forward(self, x, x_mask, g=None, reverse=True): |
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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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|
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class StochasticDurationPredictor(nn.Module): |
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def __init__( |
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self, |
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in_channels, |
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filter_channels, |
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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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): |
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super().__init__() |
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filter_channels = in_channels |
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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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|
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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( |
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modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3) |
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) |
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self.flows.append(modules.Flip()) |
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|
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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( |
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filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout |
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) |
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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( |
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modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3) |
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) |
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self.post_flows.append(modules.Flip()) |
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|
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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( |
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filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout |
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) |
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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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|
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def forward(self, x, x_mask, z, g=None): |
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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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|
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flows = list(reversed(self.flows)) |
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flows = flows[:-2] + [flows[-1]] |
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for flow in flows: |
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z = flow(z, x_mask, g=x, reverse=True) |
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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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|
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class DurationPredictor(nn.Module): |
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def __init__( |
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self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 |
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): |
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super().__init__() |
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|
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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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|
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self.drop = nn.Dropout(p_dropout) |
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self.conv_1 = nn.Conv1d( |
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in_channels, filter_channels, kernel_size, padding=kernel_size // 2 |
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) |
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self.norm_1 = modules.LayerNorm(filter_channels) |
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self.conv_2 = nn.Conv1d( |
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filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 |
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) |
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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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|
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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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|
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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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|
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class TextEncoder(nn.Module): |
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def __init__( |
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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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gin_channels=0, |
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): |
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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.gin_channels = gin_channels |
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self.emb = nn.Embedding(len(symbols), hidden_channels) |
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nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) |
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self.tone_emb = nn.Embedding(num_tones, hidden_channels) |
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nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5) |
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self.language_emb = nn.Embedding(num_languages, hidden_channels) |
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nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5) |
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self.bert_proj = nn.Conv1d(1024, hidden_channels, 1) |
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self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1) |
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self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1) |
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|
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self.encoder = attentions_onnx.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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gin_channels=self.gin_channels, |
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) |
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) |
|
|
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def forward(self, x, x_lengths, tone, language, bert, ja_bert, en_bert, g=None): |
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x_mask = torch.ones_like(x).unsqueeze(0) |
|
bert_emb = self.bert_proj(bert.transpose(0, 1).unsqueeze(0)).transpose(1, 2) |
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ja_bert_emb = self.ja_bert_proj(ja_bert.transpose(0, 1).unsqueeze(0)).transpose( |
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1, 2 |
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) |
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en_bert_emb = self.en_bert_proj(en_bert.transpose(0, 1).unsqueeze(0)).transpose( |
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1, 2 |
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) |
|
x = ( |
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self.emb(x) |
|
+ self.tone_emb(tone) |
|
+ self.language_emb(language) |
|
+ bert_emb |
|
+ ja_bert_emb |
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+ en_bert_emb |
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) * math.sqrt( |
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self.hidden_channels |
|
) |
|
x = torch.transpose(x, 1, -1) |
|
x_mask = x_mask.to(x.dtype) |
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|
|
x = self.encoder(x * x_mask, x_mask, g=g) |
|
stats = self.proj(x) * x_mask |
|
|
|
m, logs = torch.split(stats, self.out_channels, dim=1) |
|
return x, m, logs, x_mask |
|
|
|
|
|
class ResidualCouplingBlock(nn.Module): |
|
def __init__( |
|
self, |
|
channels, |
|
hidden_channels, |
|
kernel_size, |
|
dilation_rate, |
|
n_layers, |
|
n_flows=4, |
|
gin_channels=0, |
|
): |
|
super().__init__() |
|
self.channels = channels |
|
self.hidden_channels = hidden_channels |
|
self.kernel_size = kernel_size |
|
self.dilation_rate = dilation_rate |
|
self.n_layers = n_layers |
|
self.n_flows = n_flows |
|
self.gin_channels = gin_channels |
|
|
|
self.flows = nn.ModuleList() |
|
for i in range(n_flows): |
|
self.flows.append( |
|
modules.ResidualCouplingLayer( |
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channels, |
|
hidden_channels, |
|
kernel_size, |
|
dilation_rate, |
|
n_layers, |
|
gin_channels=gin_channels, |
|
mean_only=True, |
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) |
|
) |
|
self.flows.append(modules.Flip()) |
|
|
|
def forward(self, x, x_mask, g=None, reverse=True): |
|
if not reverse: |
|
for flow in self.flows: |
|
x, _ = flow(x, x_mask, g=g, reverse=reverse) |
|
else: |
|
for flow in reversed(self.flows): |
|
x = flow(x, x_mask, g=g, reverse=reverse) |
|
return x |
|
|
|
|
|
class PosteriorEncoder(nn.Module): |
|
def __init__( |
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self, |
|
in_channels, |
|
out_channels, |
|
hidden_channels, |
|
kernel_size, |
|
dilation_rate, |
|
n_layers, |
|
gin_channels=0, |
|
): |
|
super().__init__() |
|
self.in_channels = in_channels |
|
self.out_channels = out_channels |
|
self.hidden_channels = hidden_channels |
|
self.kernel_size = kernel_size |
|
self.dilation_rate = dilation_rate |
|
self.n_layers = n_layers |
|
self.gin_channels = gin_channels |
|
|
|
self.pre = nn.Conv1d(in_channels, hidden_channels, 1) |
|
self.enc = modules.WN( |
|
hidden_channels, |
|
kernel_size, |
|
dilation_rate, |
|
n_layers, |
|
gin_channels=gin_channels, |
|
) |
|
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) |
|
|
|
def forward(self, x, x_lengths, g=None): |
|
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( |
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x.dtype |
|
) |
|
x = self.pre(x) * x_mask |
|
x = self.enc(x, x_mask, g=g) |
|
stats = self.proj(x) * x_mask |
|
m, logs = torch.split(stats, self.out_channels, dim=1) |
|
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask |
|
return z, m, logs, x_mask |
|
|
|
|
|
class Generator(torch.nn.Module): |
|
def __init__( |
|
self, |
|
initial_channel, |
|
resblock, |
|
resblock_kernel_sizes, |
|
resblock_dilation_sizes, |
|
upsample_rates, |
|
upsample_initial_channel, |
|
upsample_kernel_sizes, |
|
gin_channels=0, |
|
): |
|
super(Generator, self).__init__() |
|
self.num_kernels = len(resblock_kernel_sizes) |
|
self.num_upsamples = len(upsample_rates) |
|
self.conv_pre = Conv1d( |
|
initial_channel, upsample_initial_channel, 7, 1, padding=3 |
|
) |
|
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 |
|
|
|
self.ups = nn.ModuleList() |
|
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): |
|
self.ups.append( |
|
weight_norm( |
|
ConvTranspose1d( |
|
upsample_initial_channel // (2**i), |
|
upsample_initial_channel // (2 ** (i + 1)), |
|
k, |
|
u, |
|
padding=(k - u) // 2, |
|
) |
|
) |
|
) |
|
|
|
self.resblocks = nn.ModuleList() |
|
for i in range(len(self.ups)): |
|
ch = upsample_initial_channel // (2 ** (i + 1)) |
|
for j, (k, d) in enumerate( |
|
zip(resblock_kernel_sizes, resblock_dilation_sizes) |
|
): |
|
self.resblocks.append(resblock(ch, k, d)) |
|
|
|
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) |
|
self.ups.apply(init_weights) |
|
|
|
if gin_channels != 0: |
|
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) |
|
|
|
def forward(self, x, g=None): |
|
x = self.conv_pre(x) |
|
if g is not None: |
|
x = x + self.cond(g) |
|
|
|
for i in range(self.num_upsamples): |
|
x = F.leaky_relu(x, modules.LRELU_SLOPE) |
|
x = self.ups[i](x) |
|
xs = None |
|
for j in range(self.num_kernels): |
|
if xs is None: |
|
xs = self.resblocks[i * self.num_kernels + j](x) |
|
else: |
|
xs += self.resblocks[i * self.num_kernels + j](x) |
|
x = xs / self.num_kernels |
|
x = F.leaky_relu(x) |
|
x = self.conv_post(x) |
|
x = torch.tanh(x) |
|
|
|
return x |
|
|
|
def remove_weight_norm(self): |
|
print("Removing weight norm...") |
|
for layer in self.ups: |
|
remove_weight_norm(layer) |
|
for layer in self.resblocks: |
|
layer.remove_weight_norm() |
|
|
|
|
|
class DiscriminatorP(torch.nn.Module): |
|
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): |
|
super(DiscriminatorP, self).__init__() |
|
self.period = period |
|
self.use_spectral_norm = use_spectral_norm |
|
norm_f = weight_norm if use_spectral_norm is False else spectral_norm |
|
self.convs = nn.ModuleList( |
|
[ |
|
norm_f( |
|
Conv2d( |
|
1, |
|
32, |
|
(kernel_size, 1), |
|
(stride, 1), |
|
padding=(get_padding(kernel_size, 1), 0), |
|
) |
|
), |
|
norm_f( |
|
Conv2d( |
|
32, |
|
128, |
|
(kernel_size, 1), |
|
(stride, 1), |
|
padding=(get_padding(kernel_size, 1), 0), |
|
) |
|
), |
|
norm_f( |
|
Conv2d( |
|
128, |
|
512, |
|
(kernel_size, 1), |
|
(stride, 1), |
|
padding=(get_padding(kernel_size, 1), 0), |
|
) |
|
), |
|
norm_f( |
|
Conv2d( |
|
512, |
|
1024, |
|
(kernel_size, 1), |
|
(stride, 1), |
|
padding=(get_padding(kernel_size, 1), 0), |
|
) |
|
), |
|
norm_f( |
|
Conv2d( |
|
1024, |
|
1024, |
|
(kernel_size, 1), |
|
1, |
|
padding=(get_padding(kernel_size, 1), 0), |
|
) |
|
), |
|
] |
|
) |
|
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) |
|
|
|
def forward(self, x): |
|
fmap = [] |
|
|
|
|
|
b, c, t = x.shape |
|
if t % self.period != 0: |
|
n_pad = self.period - (t % self.period) |
|
x = F.pad(x, (0, n_pad), "reflect") |
|
t = t + n_pad |
|
x = x.view(b, c, t // self.period, self.period) |
|
|
|
for layer in self.convs: |
|
x = layer(x) |
|
x = F.leaky_relu(x, modules.LRELU_SLOPE) |
|
fmap.append(x) |
|
x = self.conv_post(x) |
|
fmap.append(x) |
|
x = torch.flatten(x, 1, -1) |
|
|
|
return x, fmap |
|
|
|
|
|
class DiscriminatorS(torch.nn.Module): |
|
def __init__(self, use_spectral_norm=False): |
|
super(DiscriminatorS, self).__init__() |
|
norm_f = weight_norm if use_spectral_norm is False else spectral_norm |
|
self.convs = nn.ModuleList( |
|
[ |
|
norm_f(Conv1d(1, 16, 15, 1, padding=7)), |
|
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), |
|
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), |
|
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), |
|
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), |
|
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), |
|
] |
|
) |
|
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) |
|
|
|
def forward(self, x): |
|
fmap = [] |
|
|
|
for layer in self.convs: |
|
x = layer(x) |
|
x = F.leaky_relu(x, modules.LRELU_SLOPE) |
|
fmap.append(x) |
|
x = self.conv_post(x) |
|
fmap.append(x) |
|
x = torch.flatten(x, 1, -1) |
|
|
|
return x, fmap |
|
|
|
|
|
class MultiPeriodDiscriminator(torch.nn.Module): |
|
def __init__(self, use_spectral_norm=False): |
|
super(MultiPeriodDiscriminator, self).__init__() |
|
periods = [2, 3, 5, 7, 11] |
|
|
|
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] |
|
discs = discs + [ |
|
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods |
|
] |
|
self.discriminators = nn.ModuleList(discs) |
|
|
|
def forward(self, y, y_hat): |
|
y_d_rs = [] |
|
y_d_gs = [] |
|
fmap_rs = [] |
|
fmap_gs = [] |
|
for i, d in enumerate(self.discriminators): |
|
y_d_r, fmap_r = d(y) |
|
y_d_g, fmap_g = d(y_hat) |
|
y_d_rs.append(y_d_r) |
|
y_d_gs.append(y_d_g) |
|
fmap_rs.append(fmap_r) |
|
fmap_gs.append(fmap_g) |
|
|
|
return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
|
|
|
|
|
class ReferenceEncoder(nn.Module): |
|
""" |
|
inputs --- [N, Ty/r, n_mels*r] mels |
|
outputs --- [N, ref_enc_gru_size] |
|
""" |
|
|
|
def __init__(self, spec_channels, gin_channels=0): |
|
super().__init__() |
|
self.spec_channels = spec_channels |
|
ref_enc_filters = [32, 32, 64, 64, 128, 128] |
|
K = len(ref_enc_filters) |
|
filters = [1] + ref_enc_filters |
|
convs = [ |
|
weight_norm( |
|
nn.Conv2d( |
|
in_channels=filters[i], |
|
out_channels=filters[i + 1], |
|
kernel_size=(3, 3), |
|
stride=(2, 2), |
|
padding=(1, 1), |
|
) |
|
) |
|
for i in range(K) |
|
] |
|
self.convs = nn.ModuleList(convs) |
|
|
|
|
|
out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K) |
|
self.gru = nn.GRU( |
|
input_size=ref_enc_filters[-1] * out_channels, |
|
hidden_size=256 // 2, |
|
batch_first=True, |
|
) |
|
self.proj = nn.Linear(128, gin_channels) |
|
|
|
def forward(self, inputs, mask=None): |
|
N = inputs.size(0) |
|
out = inputs.view(N, 1, -1, self.spec_channels) |
|
for conv in self.convs: |
|
out = conv(out) |
|
|
|
out = F.relu(out) |
|
|
|
out = out.transpose(1, 2) |
|
T = out.size(1) |
|
N = out.size(0) |
|
out = out.contiguous().view(N, T, -1) |
|
|
|
self.gru.flatten_parameters() |
|
memory, out = self.gru(out) |
|
|
|
return self.proj(out.squeeze(0)) |
|
|
|
def calculate_channels(self, L, kernel_size, stride, pad, n_convs): |
|
for i in range(n_convs): |
|
L = (L - kernel_size + 2 * pad) // stride + 1 |
|
return L |
|
|
|
|
|
class SynthesizerTrn(nn.Module): |
|
""" |
|
Synthesizer for Training |
|
""" |
|
|
|
def __init__( |
|
self, |
|
n_vocab, |
|
spec_channels, |
|
segment_size, |
|
inter_channels, |
|
hidden_channels, |
|
filter_channels, |
|
n_heads, |
|
n_layers, |
|
kernel_size, |
|
p_dropout, |
|
resblock, |
|
resblock_kernel_sizes, |
|
resblock_dilation_sizes, |
|
upsample_rates, |
|
upsample_initial_channel, |
|
upsample_kernel_sizes, |
|
n_speakers=256, |
|
gin_channels=256, |
|
use_sdp=True, |
|
n_flow_layer=4, |
|
n_layers_trans_flow=4, |
|
flow_share_parameter=False, |
|
use_transformer_flow=True, |
|
**kwargs, |
|
): |
|
super().__init__() |
|
self.n_vocab = n_vocab |
|
self.spec_channels = spec_channels |
|
self.inter_channels = inter_channels |
|
self.hidden_channels = hidden_channels |
|
self.filter_channels = filter_channels |
|
self.n_heads = n_heads |
|
self.n_layers = n_layers |
|
self.kernel_size = kernel_size |
|
self.p_dropout = p_dropout |
|
self.resblock = resblock |
|
self.resblock_kernel_sizes = resblock_kernel_sizes |
|
self.resblock_dilation_sizes = resblock_dilation_sizes |
|
self.upsample_rates = upsample_rates |
|
self.upsample_initial_channel = upsample_initial_channel |
|
self.upsample_kernel_sizes = upsample_kernel_sizes |
|
self.segment_size = segment_size |
|
self.n_speakers = n_speakers |
|
self.gin_channels = gin_channels |
|
self.n_layers_trans_flow = n_layers_trans_flow |
|
self.use_spk_conditioned_encoder = kwargs.get( |
|
"use_spk_conditioned_encoder", True |
|
) |
|
self.use_sdp = use_sdp |
|
self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False) |
|
self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01) |
|
self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6) |
|
self.current_mas_noise_scale = self.mas_noise_scale_initial |
|
if self.use_spk_conditioned_encoder and gin_channels > 0: |
|
self.enc_gin_channels = gin_channels |
|
self.enc_p = TextEncoder( |
|
n_vocab, |
|
inter_channels, |
|
hidden_channels, |
|
filter_channels, |
|
n_heads, |
|
n_layers, |
|
kernel_size, |
|
p_dropout, |
|
gin_channels=self.enc_gin_channels, |
|
) |
|
self.dec = Generator( |
|
inter_channels, |
|
resblock, |
|
resblock_kernel_sizes, |
|
resblock_dilation_sizes, |
|
upsample_rates, |
|
upsample_initial_channel, |
|
upsample_kernel_sizes, |
|
gin_channels=gin_channels, |
|
) |
|
self.enc_q = PosteriorEncoder( |
|
spec_channels, |
|
inter_channels, |
|
hidden_channels, |
|
5, |
|
1, |
|
16, |
|
gin_channels=gin_channels, |
|
) |
|
if use_transformer_flow: |
|
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, |
|
) |
|
else: |
|
self.flow = ResidualCouplingBlock( |
|
inter_channels, |
|
hidden_channels, |
|
5, |
|
1, |
|
n_flow_layer, |
|
gin_channels=gin_channels, |
|
) |
|
self.sdp = StochasticDurationPredictor( |
|
hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels |
|
) |
|
self.dp = DurationPredictor( |
|
hidden_channels, 256, 3, 0.5, gin_channels=gin_channels |
|
) |
|
|
|
if n_speakers >= 1: |
|
self.emb_g = nn.Embedding(n_speakers, gin_channels) |
|
else: |
|
self.ref_enc = ReferenceEncoder(spec_channels, gin_channels) |
|
|
|
def export_onnx( |
|
self, |
|
path, |
|
max_len=None, |
|
sdp_ratio=0, |
|
y=None, |
|
): |
|
noise_scale = 0.667 |
|
length_scale = 1 |
|
noise_scale_w = 0.8 |
|
x = torch.LongTensor( |
|
[ |
|
0, |
|
97, |
|
0, |
|
8, |
|
0, |
|
78, |
|
0, |
|
8, |
|
0, |
|
76, |
|
0, |
|
37, |
|
0, |
|
40, |
|
0, |
|
97, |
|
0, |
|
8, |
|
0, |
|
23, |
|
0, |
|
8, |
|
0, |
|
74, |
|
0, |
|
26, |
|
0, |
|
104, |
|
0, |
|
] |
|
).unsqueeze(0) |
|
tone = torch.zeros_like(x) |
|
language = torch.zeros_like(x) |
|
x_lengths = torch.LongTensor([x.shape[1]]) |
|
sid = torch.LongTensor([0]) |
|
bert = torch.randn(size=(x.shape[1], 1024)) |
|
ja_bert = torch.randn(size=(x.shape[1], 1024)) |
|
en_bert = torch.randn(size=(x.shape[1], 1024)) |
|
|
|
if self.n_speakers > 0: |
|
g = self.emb_g(sid).unsqueeze(-1) |
|
torch.onnx.export( |
|
self.emb_g, |
|
(sid), |
|
f"onnx/{path}/{path}_emb.onnx", |
|
input_names=["sid"], |
|
output_names=["g"], |
|
verbose=True, |
|
) |
|
else: |
|
g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1) |
|
|
|
torch.onnx.export( |
|
self.enc_p, |
|
(x, x_lengths, tone, language, bert, ja_bert, en_bert, g), |
|
f"onnx/{path}/{path}_enc_p.onnx", |
|
input_names=[ |
|
"x", |
|
"x_lengths", |
|
"t", |
|
"language", |
|
"bert_0", |
|
"bert_1", |
|
"bert_2", |
|
"g", |
|
], |
|
output_names=["xout", "m_p", "logs_p", "x_mask"], |
|
dynamic_axes={ |
|
"x": [0, 1], |
|
"t": [0, 1], |
|
"language": [0, 1], |
|
"bert_0": [0], |
|
"bert_1": [0], |
|
"bert_2": [0], |
|
"xout": [0, 2], |
|
"m_p": [0, 2], |
|
"logs_p": [0, 2], |
|
"x_mask": [0, 2], |
|
}, |
|
verbose=True, |
|
opset_version=16, |
|
) |
|
x, m_p, logs_p, x_mask = self.enc_p( |
|
x, x_lengths, tone, language, bert, ja_bert, en_bert, g=g |
|
) |
|
zinput = ( |
|
torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) |
|
* noise_scale_w |
|
) |
|
torch.onnx.export( |
|
self.sdp, |
|
(x, x_mask, zinput, g), |
|
f"onnx/{path}/{path}_sdp.onnx", |
|
input_names=["x", "x_mask", "zin", "g"], |
|
output_names=["logw"], |
|
dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]}, |
|
verbose=True, |
|
) |
|
torch.onnx.export( |
|
self.dp, |
|
(x, x_mask, g), |
|
f"onnx/{path}/{path}_dp.onnx", |
|
input_names=["x", "x_mask", "g"], |
|
output_names=["logw"], |
|
dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]}, |
|
verbose=True, |
|
) |
|
logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp( |
|
x, x_mask, g=g |
|
) * (1 - sdp_ratio) |
|
w = torch.exp(logw) * x_mask * length_scale |
|
w_ceil = torch.ceil(w) |
|
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() |
|
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to( |
|
x_mask.dtype |
|
) |
|
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) |
|
attn = commons.generate_path(w_ceil, attn_mask) |
|
|
|
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose( |
|
1, 2 |
|
) |
|
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose( |
|
1, 2 |
|
) |
|
|
|
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale |
|
torch.onnx.export( |
|
self.flow, |
|
(z_p, y_mask, g), |
|
f"onnx/{path}/{path}_flow.onnx", |
|
input_names=["z_p", "y_mask", "g"], |
|
output_names=["z"], |
|
dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]}, |
|
verbose=True, |
|
) |
|
|
|
z = self.flow(z_p, y_mask, g=g, reverse=True) |
|
z_in = (z * y_mask)[:, :, :max_len] |
|
|
|
torch.onnx.export( |
|
self.dec, |
|
(z_in, g), |
|
f"onnx/{path}/{path}_dec.onnx", |
|
input_names=["z_in", "g"], |
|
output_names=["o"], |
|
dynamic_axes={"z_in": [0, 2], "o": [0, 2]}, |
|
verbose=True, |
|
) |
|
o = self.dec((z * y_mask)[:, :, :max_len], g=g) |
|
|
