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https://github.com/RVC-Boss/GPT-SoVITS.git
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Add Vits Onnx Module
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Ναρουσέ·μ·γιουμεμί·Χινακάννα
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GPT_SoVITS/module/attentions_onnx.py
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365
GPT_SoVITS/module/attentions_onnx.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 module import commons
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from module.modules import LayerNorm
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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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logging.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 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, _ = (*key.size(), query.size(2))
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query = query.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3)
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key = key.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3)
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value = value.view(b, self.n_heads, self.k_channels, -1).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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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 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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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)
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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, -1)
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)
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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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max_relative_position = 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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# padd 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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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)
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else:
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x = torch.relu(x)
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x = self.drop(x)
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x = self.conv_2(self.padding(x * x_mask))
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return x * x_mask
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def _causal_padding(self, x):
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if self.kernel_size == 1:
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return x
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pad_l = self.kernel_size - 1
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pad_r = 0
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padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(x, commons.convert_pad_shape(padding))
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return x
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def _same_padding(self, x):
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if self.kernel_size == 1:
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return x
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pad_l = (self.kernel_size - 1) // 2
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pad_r = self.kernel_size // 2
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padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(x, commons.convert_pad_shape(padding))
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return x
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920
GPT_SoVITS/module/models_onnx.py
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920
GPT_SoVITS/module/models_onnx.py
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@ -0,0 +1,920 @@
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import copy
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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 module import commons
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from module import modules
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from module import attentions_onnx as attentions
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from module.commons import init_weights, get_padding
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from module.mrte_model import MRTE
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from module.quantize import ResidualVectorQuantizer
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from text import symbols
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from torch.cuda.amp import autocast
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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 # it needs to be removed from future version.
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self.in_channels = in_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.log_flow = modules.Log()
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self.flows = nn.ModuleList()
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self.flows.append(modules.ElementwiseAffine(2))
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for i in range(n_flows):
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self.flows.append(
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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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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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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:
|
||||
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
||||
|
||||
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
||||
x = torch.detach(x)
|
||||
x = self.pre(x)
|
||||
if g is not None:
|
||||
g = torch.detach(g)
|
||||
x = x + self.cond(g)
|
||||
x = self.convs(x, x_mask)
|
||||
x = self.proj(x) * x_mask
|
||||
|
||||
if not reverse:
|
||||
flows = self.flows
|
||||
assert w is not None
|
||||
|
||||
logdet_tot_q = 0
|
||||
h_w = self.post_pre(w)
|
||||
h_w = self.post_convs(h_w, x_mask)
|
||||
h_w = self.post_proj(h_w) * x_mask
|
||||
e_q = (
|
||||
torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
|
||||
* x_mask
|
||||
)
|
||||
z_q = e_q
|
||||
for flow in self.post_flows:
|
||||
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
||||
logdet_tot_q += logdet_q
|
||||
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
||||
u = torch.sigmoid(z_u) * x_mask
|
||||
z0 = (w - u) * x_mask
|
||||
logdet_tot_q += torch.sum(
|
||||
(F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
|
||||
)
|
||||
logq = (
|
||||
torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
|
||||
- logdet_tot_q
|
||||
)
|
||||
|
||||
logdet_tot = 0
|
||||
z0, logdet = self.log_flow(z0, x_mask)
|
||||
logdet_tot += logdet
|
||||
z = torch.cat([z0, z1], 1)
|
||||
for flow in flows:
|
||||
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
||||
logdet_tot = logdet_tot + logdet
|
||||
nll = (
|
||||
torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
|
||||
- logdet_tot
|
||||
)
|
||||
return nll + logq # [b]
|
||||
else:
|
||||
flows = list(reversed(self.flows))
|
||||
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
||||
z = (
|
||||
torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
|
||||
* noise_scale
|
||||
)
|
||||
for flow in flows:
|
||||
z = flow(z, x_mask, g=x, reverse=reverse)
|
||||
z0, z1 = torch.split(z, [1, 1], 1)
|
||||
logw = z0
|
||||
return logw
|
||||
|
||||
|
||||
class DurationPredictor(nn.Module):
|
||||
def __init__(
|
||||
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.conv_1 = nn.Conv1d(
|
||||
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
||||
)
|
||||
self.norm_1 = modules.LayerNorm(filter_channels)
|
||||
self.conv_2 = nn.Conv1d(
|
||||
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
||||
)
|
||||
self.norm_2 = modules.LayerNorm(filter_channels)
|
||||
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
||||
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
||||
|
||||
def forward(self, x, x_mask, g=None):
|
||||
x = torch.detach(x)
|
||||
if g is not None:
|
||||
g = torch.detach(g)
|
||||
x = x + self.cond(g)
|
||||
x = self.conv_1(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_1(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_2(x)
|
||||
x = self.drop(x)
|
||||
x = self.proj(x * x_mask)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
latent_channels=192,
|
||||
):
|
||||
super().__init__()
|
||||
self.out_channels = out_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.latent_channels = latent_channels
|
||||
|
||||
self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
|
||||
|
||||
self.encoder_ssl = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers // 2,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
)
|
||||
|
||||
self.encoder_text = attentions.Encoder(
|
||||
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
|
||||
)
|
||||
self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
|
||||
|
||||
self.mrte = MRTE()
|
||||
|
||||
self.encoder2 = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers // 2,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
)
|
||||
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, y, text, ge):
|
||||
y_mask = torch.ones_like(y[:1,:1,:])
|
||||
|
||||
y = self.ssl_proj(y * y_mask) * y_mask
|
||||
y = self.encoder_ssl(y * y_mask, y_mask)
|
||||
|
||||
text_mask = torch.ones_like(text).to(y.dtype).unsqueeze(0)
|
||||
|
||||
text = self.text_embedding(text).transpose(1, 2)
|
||||
text = self.encoder_text(text * text_mask, text_mask)
|
||||
y = self.mrte(y, y_mask, text, text_mask, ge)
|
||||
|
||||
y = self.encoder2(y * y_mask, y_mask)
|
||||
|
||||
stats = self.proj(y) * y_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
return y, m, logs, y_mask
|
||||
|
||||
def extract_latent(self, x):
|
||||
x = self.ssl_proj(x)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(x)
|
||||
return codes.transpose(0, 1)
|
||||
|
||||
def decode_latent(self, codes, y_mask, refer, refer_mask, ge):
|
||||
quantized = self.quantizer.decode(codes)
|
||||
|
||||
y = self.vq_proj(quantized) * y_mask
|
||||
y = self.encoder_ssl(y * y_mask, y_mask)
|
||||
|
||||
y = self.mrte(y, y_mask, refer, refer_mask, ge)
|
||||
|
||||
y = self.encoder2(y * y_mask, y_mask)
|
||||
|
||||
stats = self.proj(y) * y_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
return y, m, logs, y_mask, quantized
|
||||
|
||||
|
||||
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(
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=gin_channels,
|
||||
mean_only=True,
|
||||
)
|
||||
)
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
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__(
|
||||
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):
|
||||
if g != None:
|
||||
g = g.detach()
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
|
||||
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 WNEncoder(nn.Module):
|
||||
def __init__(
|
||||
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, 1)
|
||||
self.norm = modules.LayerNorm(out_channels)
|
||||
|
||||
def forward(self, x, x_lengths, g=None):
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
|
||||
x.dtype
|
||||
)
|
||||
x = self.pre(x) * x_mask
|
||||
x = self.enc(x, x_mask, g=g)
|
||||
out = self.proj(x) * x_mask
|
||||
out = self.norm(out)
|
||||
return out
|
||||
|
||||
|
||||
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 l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.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 == 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 = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
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 l in self.convs:
|
||||
x = l(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 == 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 l in self.convs:
|
||||
x = l(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)
|
||||
# self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
|
||||
|
||||
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):
|
||||
N = inputs.size(0)
|
||||
out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
|
||||
for conv in self.convs:
|
||||
out = conv(out)
|
||||
# out = wn(out)
|
||||
out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
|
||||
|
||||
out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
|
||||
T = out.size(1)
|
||||
N = out.size(0)
|
||||
out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
|
||||
|
||||
self.gru.flatten_parameters()
|
||||
memory, out = self.gru(out) # out --- [1, N, 128]
|
||||
|
||||
return self.proj(out.squeeze(0)).unsqueeze(-1)
|
||||
|
||||
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 Quantizer_module(torch.nn.Module):
|
||||
def __init__(self, n_e, e_dim):
|
||||
super(Quantizer_module, self).__init__()
|
||||
self.embedding = nn.Embedding(n_e, e_dim)
|
||||
self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e)
|
||||
|
||||
def forward(self, x):
|
||||
d = (
|
||||
torch.sum(x**2, 1, keepdim=True)
|
||||
+ torch.sum(self.embedding.weight**2, 1)
|
||||
- 2 * torch.matmul(x, self.embedding.weight.T)
|
||||
)
|
||||
min_indicies = torch.argmin(d, 1)
|
||||
z_q = self.embedding(min_indicies)
|
||||
return z_q, min_indicies
|
||||
|
||||
|
||||
class Quantizer(torch.nn.Module):
|
||||
def __init__(self, embed_dim=512, n_code_groups=4, n_codes=160):
|
||||
super(Quantizer, self).__init__()
|
||||
assert embed_dim % n_code_groups == 0
|
||||
self.quantizer_modules = nn.ModuleList(
|
||||
[
|
||||
Quantizer_module(n_codes, embed_dim // n_code_groups)
|
||||
for _ in range(n_code_groups)
|
||||
]
|
||||
)
|
||||
self.n_code_groups = n_code_groups
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
def forward(self, xin):
|
||||
# B, C, T
|
||||
B, C, T = xin.shape
|
||||
xin = xin.transpose(1, 2)
|
||||
x = xin.reshape(-1, self.embed_dim)
|
||||
x = torch.split(x, self.embed_dim // self.n_code_groups, dim=-1)
|
||||
min_indicies = []
|
||||
z_q = []
|
||||
for _x, m in zip(x, self.quantizer_modules):
|
||||
_z_q, _min_indicies = m(_x)
|
||||
z_q.append(_z_q)
|
||||
min_indicies.append(_min_indicies) # B * T,
|
||||
z_q = torch.cat(z_q, -1).reshape(xin.shape)
|
||||
loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean(
|
||||
(z_q - xin.detach()) ** 2
|
||||
)
|
||||
z_q = xin + (z_q - xin).detach()
|
||||
z_q = z_q.transpose(1, 2)
|
||||
codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups)
|
||||
return z_q, loss, codes.transpose(1, 2)
|
||||
|
||||
def embed(self, x):
|
||||
# idx: N, 4, T
|
||||
x = x.transpose(1, 2)
|
||||
x = torch.split(x, 1, 2)
|
||||
ret = []
|
||||
for q, embed in zip(x, self.quantizer_modules):
|
||||
q = embed.embedding(q.squeeze(-1))
|
||||
ret.append(q)
|
||||
ret = torch.cat(ret, -1)
|
||||
return ret.transpose(1, 2) # N, C, T
|
||||
|
||||
|
||||
class CodePredictor(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
n_q=8,
|
||||
dims=1024,
|
||||
ssl_dim=768,
|
||||
):
|
||||
super().__init__()
|
||||
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.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1)
|
||||
self.ref_enc = modules.MelStyleEncoder(
|
||||
ssl_dim, style_vector_dim=hidden_channels
|
||||
)
|
||||
|
||||
self.encoder = attentions.Encoder(
|
||||
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
|
||||
)
|
||||
|
||||
self.out_proj = nn.Conv1d(hidden_channels, (n_q - 1) * dims, 1)
|
||||
self.n_q = n_q
|
||||
self.dims = dims
|
||||
|
||||
def forward(self, x, x_mask, refer, codes, infer=False):
|
||||
x = x.detach()
|
||||
x = self.vq_proj(x * x_mask) * x_mask
|
||||
g = self.ref_enc(refer, x_mask)
|
||||
x = x + g
|
||||
x = self.encoder(x * x_mask, x_mask)
|
||||
x = self.out_proj(x * x_mask) * x_mask
|
||||
logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose(
|
||||
2, 3
|
||||
)
|
||||
target = codes[1:].transpose(0, 1)
|
||||
if not infer:
|
||||
logits = logits.reshape(-1, self.dims)
|
||||
target = target.reshape(-1)
|
||||
loss = torch.nn.functional.cross_entropy(logits, target)
|
||||
return loss
|
||||
else:
|
||||
_, top10_preds = torch.topk(logits, 10, dim=-1)
|
||||
correct_top10 = torch.any(top10_preds == target.unsqueeze(-1), dim=-1)
|
||||
top3_acc = 100 * torch.mean(correct_top10.float()).detach().cpu().item()
|
||||
|
||||
print("Top-10 Accuracy:", top3_acc, "%")
|
||||
|
||||
pred_codes = torch.argmax(logits, dim=-1)
|
||||
acc = 100 * torch.mean((pred_codes == target).float()).detach().cpu().item()
|
||||
print("Top-1 Accuracy:", acc, "%")
|
||||
|
||||
return pred_codes.transpose(0, 1)
|
||||
|
||||
|
||||
class SynthesizerTrn(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
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=0,
|
||||
gin_channels=0,
|
||||
use_sdp=True,
|
||||
semantic_frame_rate=None,
|
||||
freeze_quantizer=None,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__()
|
||||
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.use_sdp = use_sdp
|
||||
self.enc_p = TextEncoder(
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
)
|
||||
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,
|
||||
)
|
||||
self.flow = ResidualCouplingBlock(
|
||||
inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
|
||||
)
|
||||
|
||||
self.ref_enc = modules.MelStyleEncoder(
|
||||
spec_channels, style_vector_dim=gin_channels
|
||||
)
|
||||
|
||||
ssl_dim = 768
|
||||
self.ssl_dim = ssl_dim
|
||||
assert semantic_frame_rate in ["25hz", "50hz"]
|
||||
self.semantic_frame_rate = semantic_frame_rate
|
||||
if semantic_frame_rate == "25hz":
|
||||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
|
||||
else:
|
||||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
|
||||
|
||||
self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024)
|
||||
if freeze_quantizer:
|
||||
self.ssl_proj.requires_grad_(False)
|
||||
self.quantizer.requires_grad_(False)
|
||||
# self.enc_p.text_embedding.requires_grad_(False)
|
||||
# self.enc_p.encoder_text.requires_grad_(False)
|
||||
# self.enc_p.mrte.requires_grad_(False)
|
||||
|
||||
def forward(self, codes, text, refer):
|
||||
refer_mask = torch.ones_like(refer[:1,:1,:])
|
||||
ge = self.ref_enc(refer * refer_mask, refer_mask)
|
||||
|
||||
y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
|
||||
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
|
||||
|
||||
quantized = self.quantizer.decode(codes)
|
||||
if self.semantic_frame_rate == "25hz":
|
||||
dquantized = torch.cat([quantized, quantized]).permute(1, 2, 0)
|
||||
quantized = dquantized.contiguous().view(1, self.ssl_dim, -1)
|
||||
|
||||
x, m_p, logs_p, y_mask = self.enc_p(
|
||||
quantized, text, ge
|
||||
)
|
||||
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p)
|
||||
|
||||
z = self.flow(z_p, y_mask, g=ge, reverse=True)
|
||||
|
||||
o = self.dec((z * y_mask)[:, :, :], g=ge)
|
||||
return o
|
||||
|
||||
def extract_latent(self, x):
|
||||
ssl = self.ssl_proj(x)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
|
||||
return codes.transpose(0, 1)
|
||||
Loading…
x
Reference in New Issue
Block a user