Refactor: moved model, attentions definitions and inference code to style_bert_vits2/models/
The code has not yet been cleaned up, just moved.
This commit is contained in:
tsukumi
461
style_bert_vits2/models/attentions.py
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461
style_bert_vits2/models/attentions.py
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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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from style_bert_vits2.models import commons
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class LayerNorm(nn.Module):
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def __init__(self, channels, eps=1e-5):
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super().__init__()
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self.channels = channels
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self.eps = eps
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self.gamma = nn.Parameter(torch.ones(channels))
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self.beta = nn.Parameter(torch.zeros(channels))
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def forward(self, x):
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x = x.transpose(1, -1)
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x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
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return x.transpose(1, -1)
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@torch.jit.script
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
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n_channels_int = n_channels[0]
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in_act = input_a + input_b
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t_act = torch.tanh(in_act[:, :n_channels_int, :])
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
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acts = t_act * s_act
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return acts
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class Encoder(nn.Module):
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def __init__(
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self,
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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=1,
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p_dropout=0.0,
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window_size=4,
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isflow=True,
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**kwargs
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):
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super().__init__()
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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.window_size = window_size
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# if isflow:
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# cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
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# self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
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# self.cond_layer = weight_norm(cond_layer, name='weight')
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# self.gin_channels = 256
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self.cond_layer_idx = self.n_layers
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if "gin_channels" in kwargs:
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self.gin_channels = kwargs["gin_channels"]
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if self.gin_channels != 0:
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self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
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# vits2 says 3rd block, so idx is 2 by default
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self.cond_layer_idx = (
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kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
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)
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# logger.debug(self.gin_channels, self.cond_layer_idx)
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assert (
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self.cond_layer_idx < self.n_layers
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), "cond_layer_idx should be less than n_layers"
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self.drop = nn.Dropout(p_dropout)
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self.attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.attn_layers.append(
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MultiHeadAttention(
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hidden_channels,
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hidden_channels,
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n_heads,
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p_dropout=p_dropout,
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window_size=window_size,
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)
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)
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(
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FFN(
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hidden_channels,
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hidden_channels,
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filter_channels,
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kernel_size,
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p_dropout=p_dropout,
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)
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)
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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def forward(self, x, x_mask, g=None):
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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if i == self.cond_layer_idx and g is not None:
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g = self.spk_emb_linear(g.transpose(1, 2))
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g = g.transpose(1, 2)
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x = x + g
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x = x * x_mask
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y = self.attn_layers[i](x, x, attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = x * x_mask
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return x
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class Decoder(nn.Module):
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def __init__(
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self,
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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=1,
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p_dropout=0.0,
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proximal_bias=False,
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proximal_init=True,
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**kwargs
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):
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super().__init__()
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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.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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self.drop = nn.Dropout(p_dropout)
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self.self_attn_layers = nn.ModuleList()
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self.norm_layers_0 = nn.ModuleList()
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self.encdec_attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.self_attn_layers.append(
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MultiHeadAttention(
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hidden_channels,
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hidden_channels,
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n_heads,
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p_dropout=p_dropout,
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proximal_bias=proximal_bias,
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proximal_init=proximal_init,
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)
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)
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self.norm_layers_0.append(LayerNorm(hidden_channels))
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self.encdec_attn_layers.append(
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MultiHeadAttention(
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hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
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)
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)
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(
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FFN(
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hidden_channels,
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hidden_channels,
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filter_channels,
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kernel_size,
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p_dropout=p_dropout,
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causal=True,
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)
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)
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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def forward(self, x, x_mask, h, h_mask):
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"""
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x: decoder input
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h: encoder output
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"""
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self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
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device=x.device, dtype=x.dtype
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)
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encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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y = self.self_attn_layers[i](x, x, self_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_0[i](x + y)
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y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = x * x_mask
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return x
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class MultiHeadAttention(nn.Module):
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def __init__(
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self,
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channels,
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out_channels,
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n_heads,
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p_dropout=0.0,
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window_size=None,
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heads_share=True,
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block_length=None,
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proximal_bias=False,
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proximal_init=False,
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):
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super().__init__()
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assert channels % n_heads == 0
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self.channels = channels
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self.out_channels = out_channels
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self.n_heads = n_heads
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self.p_dropout = p_dropout
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self.window_size = window_size
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self.heads_share = heads_share
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self.block_length = block_length
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self.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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self.attn = None
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self.k_channels = channels // n_heads
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self.conv_q = nn.Conv1d(channels, channels, 1)
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self.conv_k = nn.Conv1d(channels, channels, 1)
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self.conv_v = nn.Conv1d(channels, channels, 1)
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self.conv_o = nn.Conv1d(channels, out_channels, 1)
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self.drop = nn.Dropout(p_dropout)
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if window_size is not None:
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n_heads_rel = 1 if heads_share else n_heads
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rel_stddev = self.k_channels**-0.5
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self.emb_rel_k = nn.Parameter(
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torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
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* rel_stddev
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)
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self.emb_rel_v = nn.Parameter(
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torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
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* rel_stddev
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)
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nn.init.xavier_uniform_(self.conv_q.weight)
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nn.init.xavier_uniform_(self.conv_k.weight)
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nn.init.xavier_uniform_(self.conv_v.weight)
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if proximal_init:
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with torch.no_grad():
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self.conv_k.weight.copy_(self.conv_q.weight)
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self.conv_k.bias.copy_(self.conv_q.bias)
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def forward(self, x, c, attn_mask=None):
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q = self.conv_q(x)
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k = self.conv_k(c)
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v = self.conv_v(c)
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x, self.attn = self.attention(q, k, v, mask=attn_mask)
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x = self.conv_o(x)
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return x
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def attention(self, query, key, value, mask=None):
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# reshape [b, d, t] -> [b, n_h, t, d_k]
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b, d, t_s, t_t = (*key.size(), query.size(2))
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query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
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key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
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if self.window_size is not None:
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assert (
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t_s == t_t
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), "Relative attention is only available for self-attention."
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key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
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rel_logits = self._matmul_with_relative_keys(
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query / math.sqrt(self.k_channels), key_relative_embeddings
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)
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scores_local = self._relative_position_to_absolute_position(rel_logits)
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scores = scores + scores_local
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if self.proximal_bias:
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assert t_s == t_t, "Proximal bias is only available for self-attention."
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scores = scores + self._attention_bias_proximal(t_s).to(
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device=scores.device, dtype=scores.dtype
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)
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if mask is not None:
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scores = scores.masked_fill(mask == 0, -1e4)
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if self.block_length is not None:
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assert (
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t_s == t_t
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), "Local attention is only available for self-attention."
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block_mask = (
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torch.ones_like(scores)
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.triu(-self.block_length)
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.tril(self.block_length)
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)
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scores = scores.masked_fill(block_mask == 0, -1e4)
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p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
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p_attn = self.drop(p_attn)
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output = torch.matmul(p_attn, value)
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if self.window_size is not None:
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relative_weights = self._absolute_position_to_relative_position(p_attn)
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value_relative_embeddings = self._get_relative_embeddings(
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self.emb_rel_v, t_s
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)
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output = output + self._matmul_with_relative_values(
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relative_weights, value_relative_embeddings
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)
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output = (
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output.transpose(2, 3).contiguous().view(b, d, t_t)
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) # [b, n_h, t_t, d_k] -> [b, d, t_t]
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return output, p_attn
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def _matmul_with_relative_values(self, x, y):
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"""
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x: [b, h, l, m]
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y: [h or 1, m, d]
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ret: [b, h, l, d]
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"""
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ret = torch.matmul(x, y.unsqueeze(0))
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return ret
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def _matmul_with_relative_keys(self, x, y):
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"""
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x: [b, h, l, d]
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y: [h or 1, m, d]
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ret: [b, h, l, m]
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"""
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ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
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return ret
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def _get_relative_embeddings(self, relative_embeddings, length):
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2 * self.window_size + 1
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# Pad first before slice to avoid using cond ops.
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pad_length = max(length - (self.window_size + 1), 0)
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slice_start_position = max((self.window_size + 1) - length, 0)
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slice_end_position = slice_start_position + 2 * length - 1
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if pad_length > 0:
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padded_relative_embeddings = F.pad(
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relative_embeddings,
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commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
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)
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else:
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padded_relative_embeddings = relative_embeddings
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used_relative_embeddings = padded_relative_embeddings[
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:, slice_start_position:slice_end_position
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]
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return used_relative_embeddings
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def _relative_position_to_absolute_position(self, x):
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"""
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x: [b, h, l, 2*l-1]
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ret: [b, h, l, l]
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"""
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batch, heads, length, _ = x.size()
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# Concat columns of pad to shift from relative to absolute indexing.
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x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
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# Concat extra elements so to add up to shape (len+1, 2*len-1).
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x_flat = x.view([batch, heads, length * 2 * length])
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x_flat = F.pad(
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x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
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)
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# Reshape and slice out the padded elements.
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x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
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:, :, :length, length - 1 :
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]
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return x_final
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def _absolute_position_to_relative_position(self, x):
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"""
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x: [b, h, l, l]
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ret: [b, h, l, 2*l-1]
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"""
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batch, heads, length, _ = x.size()
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# pad along column
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x = F.pad(
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x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
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)
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x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
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# add 0's in the beginning that will skew the elements after reshape
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x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
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x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
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return x_final
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def _attention_bias_proximal(self, length):
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"""Bias for self-attention to encourage attention to close positions.
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Args:
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length: an integer scalar.
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Returns:
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a Tensor with shape [1, 1, length, length]
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"""
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r = torch.arange(length, dtype=torch.float32)
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diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
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return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
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class FFN(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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filter_channels,
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kernel_size,
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p_dropout=0.0,
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activation=None,
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causal=False,
|
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):
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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.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.activation = activation
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self.causal = causal
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if causal:
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self.padding = self._causal_padding
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else:
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self.padding = self._same_padding
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self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
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self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
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||||
self.drop = nn.Dropout(p_dropout)
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||||
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||||
def forward(self, x, x_mask):
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x = self.conv_1(self.padding(x * x_mask))
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if self.activation == "gelu":
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x = x * torch.sigmoid(1.702 * x)
|
||||
else:
|
||||
x = torch.relu(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(self.padding(x * x_mask))
|
||||
return x * x_mask
|
||||
|
||||
def _causal_padding(self, x):
|
||||
if self.kernel_size == 1:
|
||||
return x
|
||||
pad_l = self.kernel_size - 1
|
||||
pad_r = 0
|
||||
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
||||
x = F.pad(x, commons.convert_pad_shape(padding))
|
||||
return x
|
||||
|
||||
def _same_padding(self, x):
|
||||
if self.kernel_size == 1:
|
||||
return x
|
||||
pad_l = (self.kernel_size - 1) // 2
|
||||
pad_r = self.kernel_size // 2
|
||||
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
||||
x = F.pad(x, commons.convert_pad_shape(padding))
|
||||
return x
|
||||
314
style_bert_vits2/models/infer.py
Normal file
314
style_bert_vits2/models/infer.py
Normal file
@@ -0,0 +1,314 @@
|
||||
import torch
|
||||
|
||||
import utils
|
||||
from text import cleaned_text_to_sequence, get_bert
|
||||
from text.cleaner import clean_text
|
||||
from style_bert_vits2.logging import logger
|
||||
from style_bert_vits2.models import commons
|
||||
from style_bert_vits2.models.models import SynthesizerTrn
|
||||
from style_bert_vits2.models.models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra
|
||||
from style_bert_vits2.text_processing.symbols import SYMBOLS
|
||||
|
||||
|
||||
class InvalidToneError(ValueError):
|
||||
pass
|
||||
|
||||
|
||||
def get_net_g(model_path: str, version: str, device: str, hps):
|
||||
if version.endswith("JP-Extra"):
|
||||
logger.info("Using JP-Extra model")
|
||||
net_g = SynthesizerTrnJPExtra(
|
||||
len(SYMBOLS),
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
n_speakers=hps.data.n_speakers,
|
||||
**hps.model,
|
||||
).to(device)
|
||||
else:
|
||||
logger.info("Using normal model")
|
||||
net_g = SynthesizerTrn(
|
||||
len(SYMBOLS),
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
n_speakers=hps.data.n_speakers,
|
||||
**hps.model,
|
||||
).to(device)
|
||||
net_g.state_dict()
|
||||
_ = net_g.eval()
|
||||
if model_path.endswith(".pth") or model_path.endswith(".pt"):
|
||||
_ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
|
||||
elif model_path.endswith(".safetensors"):
|
||||
_ = utils.load_safetensors(model_path, net_g, True)
|
||||
else:
|
||||
raise ValueError(f"Unknown model format: {model_path}")
|
||||
return net_g
|
||||
|
||||
|
||||
def get_text(
|
||||
text,
|
||||
language_str,
|
||||
hps,
|
||||
device,
|
||||
assist_text=None,
|
||||
assist_text_weight=0.7,
|
||||
given_tone=None,
|
||||
):
|
||||
use_jp_extra = hps.version.endswith("JP-Extra")
|
||||
# 推論のときにのみ呼び出されるので、raise_yomi_errorはFalseに設定
|
||||
norm_text, phone, tone, word2ph = clean_text(
|
||||
text, language_str, use_jp_extra, raise_yomi_error=False
|
||||
)
|
||||
if given_tone is not None:
|
||||
if len(given_tone) != len(phone):
|
||||
raise InvalidToneError(
|
||||
f"Length of given_tone ({len(given_tone)}) != length of phone ({len(phone)})"
|
||||
)
|
||||
tone = given_tone
|
||||
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
|
||||
|
||||
if hps.data.add_blank:
|
||||
phone = commons.intersperse(phone, 0)
|
||||
tone = commons.intersperse(tone, 0)
|
||||
language = commons.intersperse(language, 0)
|
||||
for i in range(len(word2ph)):
|
||||
word2ph[i] = word2ph[i] * 2
|
||||
word2ph[0] += 1
|
||||
bert_ori = get_bert(
|
||||
norm_text,
|
||||
word2ph,
|
||||
language_str,
|
||||
device,
|
||||
assist_text,
|
||||
assist_text_weight,
|
||||
)
|
||||
del word2ph
|
||||
assert bert_ori.shape[-1] == len(phone), phone
|
||||
|
||||
if language_str == "ZH":
|
||||
bert = bert_ori
|
||||
ja_bert = torch.zeros(1024, len(phone))
|
||||
en_bert = torch.zeros(1024, len(phone))
|
||||
elif language_str == "JP":
|
||||
bert = torch.zeros(1024, len(phone))
|
||||
ja_bert = bert_ori
|
||||
en_bert = torch.zeros(1024, len(phone))
|
||||
elif language_str == "EN":
|
||||
bert = torch.zeros(1024, len(phone))
|
||||
ja_bert = torch.zeros(1024, len(phone))
|
||||
en_bert = bert_ori
|
||||
else:
|
||||
raise ValueError("language_str should be ZH, JP or EN")
|
||||
|
||||
assert bert.shape[-1] == len(
|
||||
phone
|
||||
), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
|
||||
|
||||
phone = torch.LongTensor(phone)
|
||||
tone = torch.LongTensor(tone)
|
||||
language = torch.LongTensor(language)
|
||||
return bert, ja_bert, en_bert, phone, tone, language
|
||||
|
||||
|
||||
def infer(
|
||||
text,
|
||||
style_vec,
|
||||
sdp_ratio,
|
||||
noise_scale,
|
||||
noise_scale_w,
|
||||
length_scale,
|
||||
sid: int, # In the original Bert-VITS2, its speaker_name: str, but here it's id
|
||||
language,
|
||||
hps,
|
||||
net_g,
|
||||
device,
|
||||
skip_start=False,
|
||||
skip_end=False,
|
||||
assist_text=None,
|
||||
assist_text_weight=0.7,
|
||||
given_tone=None,
|
||||
):
|
||||
is_jp_extra = hps.version.endswith("JP-Extra")
|
||||
bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
|
||||
text,
|
||||
language,
|
||||
hps,
|
||||
device,
|
||||
assist_text=assist_text,
|
||||
assist_text_weight=assist_text_weight,
|
||||
given_tone=given_tone,
|
||||
)
|
||||
if skip_start:
|
||||
phones = phones[3:]
|
||||
tones = tones[3:]
|
||||
lang_ids = lang_ids[3:]
|
||||
bert = bert[:, 3:]
|
||||
ja_bert = ja_bert[:, 3:]
|
||||
en_bert = en_bert[:, 3:]
|
||||
if skip_end:
|
||||
phones = phones[:-2]
|
||||
tones = tones[:-2]
|
||||
lang_ids = lang_ids[:-2]
|
||||
bert = bert[:, :-2]
|
||||
ja_bert = ja_bert[:, :-2]
|
||||
en_bert = en_bert[:, :-2]
|
||||
with torch.no_grad():
|
||||
x_tst = phones.to(device).unsqueeze(0)
|
||||
tones = tones.to(device).unsqueeze(0)
|
||||
lang_ids = lang_ids.to(device).unsqueeze(0)
|
||||
bert = bert.to(device).unsqueeze(0)
|
||||
ja_bert = ja_bert.to(device).unsqueeze(0)
|
||||
en_bert = en_bert.to(device).unsqueeze(0)
|
||||
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
|
||||
style_vec = torch.from_numpy(style_vec).to(device).unsqueeze(0)
|
||||
del phones
|
||||
sid_tensor = torch.LongTensor([sid]).to(device)
|
||||
if is_jp_extra:
|
||||
output = net_g.infer(
|
||||
x_tst,
|
||||
x_tst_lengths,
|
||||
sid_tensor,
|
||||
tones,
|
||||
lang_ids,
|
||||
ja_bert,
|
||||
style_vec=style_vec,
|
||||
sdp_ratio=sdp_ratio,
|
||||
noise_scale=noise_scale,
|
||||
noise_scale_w=noise_scale_w,
|
||||
length_scale=length_scale,
|
||||
)
|
||||
else:
|
||||
output = net_g.infer(
|
||||
x_tst,
|
||||
x_tst_lengths,
|
||||
sid_tensor,
|
||||
tones,
|
||||
lang_ids,
|
||||
bert,
|
||||
ja_bert,
|
||||
en_bert,
|
||||
style_vec=style_vec,
|
||||
sdp_ratio=sdp_ratio,
|
||||
noise_scale=noise_scale,
|
||||
noise_scale_w=noise_scale_w,
|
||||
length_scale=length_scale,
|
||||
)
|
||||
audio = output[0][0, 0].data.cpu().float().numpy()
|
||||
del (
|
||||
x_tst,
|
||||
tones,
|
||||
lang_ids,
|
||||
bert,
|
||||
x_tst_lengths,
|
||||
sid_tensor,
|
||||
ja_bert,
|
||||
en_bert,
|
||||
style_vec,
|
||||
) # , emo
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
return audio
|
||||
|
||||
|
||||
def infer_multilang(
|
||||
text,
|
||||
style_vec,
|
||||
sdp_ratio,
|
||||
noise_scale,
|
||||
noise_scale_w,
|
||||
length_scale,
|
||||
sid,
|
||||
language,
|
||||
hps,
|
||||
net_g,
|
||||
device,
|
||||
skip_start=False,
|
||||
skip_end=False,
|
||||
):
|
||||
bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], []
|
||||
# emo = get_emo_(reference_audio, emotion, sid)
|
||||
# if isinstance(reference_audio, np.ndarray):
|
||||
# emo = get_clap_audio_feature(reference_audio, device)
|
||||
# else:
|
||||
# emo = get_clap_text_feature(emotion, device)
|
||||
# emo = torch.squeeze(emo, dim=1)
|
||||
for idx, (txt, lang) in enumerate(zip(text, language)):
|
||||
_skip_start = (idx != 0) or (skip_start and idx == 0)
|
||||
_skip_end = (idx != len(language) - 1) or skip_end
|
||||
(
|
||||
temp_bert,
|
||||
temp_ja_bert,
|
||||
temp_en_bert,
|
||||
temp_phones,
|
||||
temp_tones,
|
||||
temp_lang_ids,
|
||||
) = get_text(txt, lang, hps, device)
|
||||
if _skip_start:
|
||||
temp_bert = temp_bert[:, 3:]
|
||||
temp_ja_bert = temp_ja_bert[:, 3:]
|
||||
temp_en_bert = temp_en_bert[:, 3:]
|
||||
temp_phones = temp_phones[3:]
|
||||
temp_tones = temp_tones[3:]
|
||||
temp_lang_ids = temp_lang_ids[3:]
|
||||
if _skip_end:
|
||||
temp_bert = temp_bert[:, :-2]
|
||||
temp_ja_bert = temp_ja_bert[:, :-2]
|
||||
temp_en_bert = temp_en_bert[:, :-2]
|
||||
temp_phones = temp_phones[:-2]
|
||||
temp_tones = temp_tones[:-2]
|
||||
temp_lang_ids = temp_lang_ids[:-2]
|
||||
bert.append(temp_bert)
|
||||
ja_bert.append(temp_ja_bert)
|
||||
en_bert.append(temp_en_bert)
|
||||
phones.append(temp_phones)
|
||||
tones.append(temp_tones)
|
||||
lang_ids.append(temp_lang_ids)
|
||||
bert = torch.concatenate(bert, dim=1)
|
||||
ja_bert = torch.concatenate(ja_bert, dim=1)
|
||||
en_bert = torch.concatenate(en_bert, dim=1)
|
||||
phones = torch.concatenate(phones, dim=0)
|
||||
tones = torch.concatenate(tones, dim=0)
|
||||
lang_ids = torch.concatenate(lang_ids, dim=0)
|
||||
with torch.no_grad():
|
||||
x_tst = phones.to(device).unsqueeze(0)
|
||||
tones = tones.to(device).unsqueeze(0)
|
||||
lang_ids = lang_ids.to(device).unsqueeze(0)
|
||||
bert = bert.to(device).unsqueeze(0)
|
||||
ja_bert = ja_bert.to(device).unsqueeze(0)
|
||||
en_bert = en_bert.to(device).unsqueeze(0)
|
||||
# emo = emo.to(device).unsqueeze(0)
|
||||
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
|
||||
del phones
|
||||
speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
|
||||
audio = (
|
||||
net_g.infer(
|
||||
x_tst,
|
||||
x_tst_lengths,
|
||||
speakers,
|
||||
tones,
|
||||
lang_ids,
|
||||
bert,
|
||||
ja_bert,
|
||||
en_bert,
|
||||
style_vec=style_vec,
|
||||
sdp_ratio=sdp_ratio,
|
||||
noise_scale=noise_scale,
|
||||
noise_scale_w=noise_scale_w,
|
||||
length_scale=length_scale,
|
||||
)[0][0, 0]
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
)
|
||||
del (
|
||||
x_tst,
|
||||
tones,
|
||||
lang_ids,
|
||||
bert,
|
||||
x_tst_lengths,
|
||||
speakers,
|
||||
ja_bert,
|
||||
en_bert,
|
||||
) # , emo
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
return audio
|
||||
1023
style_bert_vits2/models/models.py
Normal file
1023
style_bert_vits2/models/models.py
Normal file
File diff suppressed because it is too large
Load Diff
1069
style_bert_vits2/models/models_jp_extra.py
Normal file
1069
style_bert_vits2/models/models_jp_extra.py
Normal file
File diff suppressed because it is too large
Load Diff
579
style_bert_vits2/models/modules.py
Normal file
579
style_bert_vits2/models/modules.py
Normal file
@@ -0,0 +1,579 @@
|
||||
import math
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import Conv1d
|
||||
from torch.nn import functional as F
|
||||
from torch.nn.utils import remove_weight_norm, weight_norm
|
||||
from transforms import piecewise_rational_quadratic_transform
|
||||
|
||||
from style_bert_vits2.models import commons
|
||||
from style_bert_vits2.models.attentions import Encoder
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.gamma = nn.Parameter(torch.ones(channels))
|
||||
self.beta = nn.Parameter(torch.zeros(channels))
|
||||
|
||||
def forward(self, x):
|
||||
x = x.transpose(1, -1)
|
||||
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
||||
return x.transpose(1, -1)
|
||||
|
||||
|
||||
class ConvReluNorm(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
hidden_channels,
|
||||
out_channels,
|
||||
kernel_size,
|
||||
n_layers,
|
||||
p_dropout,
|
||||
):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
assert n_layers > 1, "Number of layers should be larger than 0."
|
||||
|
||||
self.conv_layers = nn.ModuleList()
|
||||
self.norm_layers = nn.ModuleList()
|
||||
self.conv_layers.append(
|
||||
nn.Conv1d(
|
||||
in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
|
||||
)
|
||||
)
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
|
||||
for _ in range(n_layers - 1):
|
||||
self.conv_layers.append(
|
||||
nn.Conv1d(
|
||||
hidden_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
padding=kernel_size // 2,
|
||||
)
|
||||
)
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
self.proj.weight.data.zero_()
|
||||
self.proj.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x_org = x
|
||||
for i in range(self.n_layers):
|
||||
x = self.conv_layers[i](x * x_mask)
|
||||
x = self.norm_layers[i](x)
|
||||
x = self.relu_drop(x)
|
||||
x = x_org + self.proj(x)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class DDSConv(nn.Module):
|
||||
"""
|
||||
Dialted and Depth-Separable Convolution
|
||||
"""
|
||||
|
||||
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.convs_sep = nn.ModuleList()
|
||||
self.convs_1x1 = nn.ModuleList()
|
||||
self.norms_1 = nn.ModuleList()
|
||||
self.norms_2 = nn.ModuleList()
|
||||
for i in range(n_layers):
|
||||
dilation = kernel_size**i
|
||||
padding = (kernel_size * dilation - dilation) // 2
|
||||
self.convs_sep.append(
|
||||
nn.Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
groups=channels,
|
||||
dilation=dilation,
|
||||
padding=padding,
|
||||
)
|
||||
)
|
||||
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
||||
self.norms_1.append(LayerNorm(channels))
|
||||
self.norms_2.append(LayerNorm(channels))
|
||||
|
||||
def forward(self, x, x_mask, g=None):
|
||||
if g is not None:
|
||||
x = x + g
|
||||
for i in range(self.n_layers):
|
||||
y = self.convs_sep[i](x * x_mask)
|
||||
y = self.norms_1[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.convs_1x1[i](y)
|
||||
y = self.norms_2[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.drop(y)
|
||||
x = x + y
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class WN(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0,
|
||||
p_dropout=0,
|
||||
):
|
||||
super(WN, self).__init__()
|
||||
assert kernel_size % 2 == 1
|
||||
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.p_dropout = p_dropout
|
||||
|
||||
self.in_layers = torch.nn.ModuleList()
|
||||
self.res_skip_layers = torch.nn.ModuleList()
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
if gin_channels != 0:
|
||||
cond_layer = torch.nn.Conv1d(
|
||||
gin_channels, 2 * hidden_channels * n_layers, 1
|
||||
)
|
||||
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
|
||||
|
||||
for i in range(n_layers):
|
||||
dilation = dilation_rate**i
|
||||
padding = int((kernel_size * dilation - dilation) / 2)
|
||||
in_layer = torch.nn.Conv1d(
|
||||
hidden_channels,
|
||||
2 * hidden_channels,
|
||||
kernel_size,
|
||||
dilation=dilation,
|
||||
padding=padding,
|
||||
)
|
||||
in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
|
||||
self.in_layers.append(in_layer)
|
||||
|
||||
# last one is not necessary
|
||||
if i < n_layers - 1:
|
||||
res_skip_channels = 2 * hidden_channels
|
||||
else:
|
||||
res_skip_channels = hidden_channels
|
||||
|
||||
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
||||
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
|
||||
self.res_skip_layers.append(res_skip_layer)
|
||||
|
||||
def forward(self, x, x_mask, g=None, **kwargs):
|
||||
output = torch.zeros_like(x)
|
||||
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
||||
|
||||
if g is not None:
|
||||
g = self.cond_layer(g)
|
||||
|
||||
for i in range(self.n_layers):
|
||||
x_in = self.in_layers[i](x)
|
||||
if g is not None:
|
||||
cond_offset = i * 2 * self.hidden_channels
|
||||
g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
|
||||
else:
|
||||
g_l = torch.zeros_like(x_in)
|
||||
|
||||
acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
|
||||
acts = self.drop(acts)
|
||||
|
||||
res_skip_acts = self.res_skip_layers[i](acts)
|
||||
if i < self.n_layers - 1:
|
||||
res_acts = res_skip_acts[:, : self.hidden_channels, :]
|
||||
x = (x + res_acts) * x_mask
|
||||
output = output + res_skip_acts[:, self.hidden_channels :, :]
|
||||
else:
|
||||
output = output + res_skip_acts
|
||||
return output * x_mask
|
||||
|
||||
def remove_weight_norm(self):
|
||||
if self.gin_channels != 0:
|
||||
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
||||
for l in self.in_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
for l in self.res_skip_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock1(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
||||
super(ResBlock1, self).__init__()
|
||||
self.convs1 = nn.ModuleList(
|
||||
[
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[0],
|
||||
padding=commons.get_padding(kernel_size, dilation[0]),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[1],
|
||||
padding=commons.get_padding(kernel_size, dilation[1]),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[2],
|
||||
padding=commons.get_padding(kernel_size, dilation[2]),
|
||||
)
|
||||
),
|
||||
]
|
||||
)
|
||||
self.convs1.apply(commons.init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList(
|
||||
[
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=commons.get_padding(kernel_size, 1),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=commons.get_padding(kernel_size, 1),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=commons.get_padding(kernel_size, 1),
|
||||
)
|
||||
),
|
||||
]
|
||||
)
|
||||
self.convs2.apply(commons.init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c1, c2 in zip(self.convs1, self.convs2):
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c1(xt)
|
||||
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock2(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
||||
super(ResBlock2, self).__init__()
|
||||
self.convs = nn.ModuleList(
|
||||
[
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[0],
|
||||
padding=commons.get_padding(kernel_size, dilation[0]),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[1],
|
||||
padding=commons.get_padding(kernel_size, dilation[1]),
|
||||
)
|
||||
),
|
||||
]
|
||||
)
|
||||
self.convs.apply(commons.init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c in self.convs:
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class Log(nn.Module):
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
||||
logdet = torch.sum(-y, [1, 2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = torch.exp(x) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class Flip(nn.Module):
|
||||
def forward(self, x, *args, reverse=False, **kwargs):
|
||||
x = torch.flip(x, [1])
|
||||
if not reverse:
|
||||
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
||||
return x, logdet
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
class ElementwiseAffine(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.m = nn.Parameter(torch.zeros(channels, 1))
|
||||
self.logs = nn.Parameter(torch.zeros(channels, 1))
|
||||
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = self.m + torch.exp(self.logs) * x
|
||||
y = y * x_mask
|
||||
logdet = torch.sum(self.logs * x_mask, [1, 2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class ResidualCouplingLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
p_dropout=0,
|
||||
gin_channels=0,
|
||||
mean_only=False,
|
||||
):
|
||||
assert channels % 2 == 0, "channels should be divisible by 2"
|
||||
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.half_channels = channels // 2
|
||||
self.mean_only = mean_only
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
||||
self.enc = WN(
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
p_dropout=p_dropout,
|
||||
gin_channels=gin_channels,
|
||||
)
|
||||
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
||||
self.post.weight.data.zero_()
|
||||
self.post.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
||||
h = self.pre(x0) * x_mask
|
||||
h = self.enc(h, x_mask, g=g)
|
||||
stats = self.post(h) * x_mask
|
||||
if not self.mean_only:
|
||||
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
|
||||
else:
|
||||
m = stats
|
||||
logs = torch.zeros_like(m)
|
||||
|
||||
if not reverse:
|
||||
x1 = m + x1 * torch.exp(logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
logdet = torch.sum(logs, [1, 2])
|
||||
return x, logdet
|
||||
else:
|
||||
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
return x
|
||||
|
||||
|
||||
class ConvFlow(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
filter_channels,
|
||||
kernel_size,
|
||||
n_layers,
|
||||
num_bins=10,
|
||||
tail_bound=5.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.num_bins = num_bins
|
||||
self.tail_bound = tail_bound
|
||||
self.half_channels = in_channels // 2
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
||||
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
|
||||
self.proj = nn.Conv1d(
|
||||
filter_channels, self.half_channels * (num_bins * 3 - 1), 1
|
||||
)
|
||||
self.proj.weight.data.zero_()
|
||||
self.proj.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
||||
h = self.pre(x0)
|
||||
h = self.convs(h, x_mask, g=g)
|
||||
h = self.proj(h) * x_mask
|
||||
|
||||
b, c, t = x0.shape
|
||||
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
||||
|
||||
unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
|
||||
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
|
||||
self.filter_channels
|
||||
)
|
||||
unnormalized_derivatives = h[..., 2 * self.num_bins :]
|
||||
|
||||
x1, logabsdet = piecewise_rational_quadratic_transform(
|
||||
x1,
|
||||
unnormalized_widths,
|
||||
unnormalized_heights,
|
||||
unnormalized_derivatives,
|
||||
inverse=reverse,
|
||||
tails="linear",
|
||||
tail_bound=self.tail_bound,
|
||||
)
|
||||
|
||||
x = torch.cat([x0, x1], 1) * x_mask
|
||||
logdet = torch.sum(logabsdet * x_mask, [1, 2])
|
||||
if not reverse:
|
||||
return x, logdet
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
class TransformerCouplingLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
n_layers,
|
||||
n_heads,
|
||||
p_dropout=0,
|
||||
filter_channels=0,
|
||||
mean_only=False,
|
||||
wn_sharing_parameter=None,
|
||||
gin_channels=0,
|
||||
):
|
||||
assert channels % 2 == 0, "channels should be divisible by 2"
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.half_channels = channels // 2
|
||||
self.mean_only = mean_only
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
||||
self.enc = (
|
||||
Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
isflow=True,
|
||||
gin_channels=gin_channels,
|
||||
)
|
||||
if wn_sharing_parameter is None
|
||||
else wn_sharing_parameter
|
||||
)
|
||||
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
||||
self.post.weight.data.zero_()
|
||||
self.post.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
||||
h = self.pre(x0) * x_mask
|
||||
h = self.enc(h, x_mask, g=g)
|
||||
stats = self.post(h) * x_mask
|
||||
if not self.mean_only:
|
||||
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
|
||||
else:
|
||||
m = stats
|
||||
logs = torch.zeros_like(m)
|
||||
|
||||
if not reverse:
|
||||
x1 = m + x1 * torch.exp(logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
logdet = torch.sum(logs, [1, 2])
|
||||
return x, logdet
|
||||
else:
|
||||
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
return x
|
||||
Reference in New Issue
Block a user