mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-09 12:30:38 +08:00
optimize(rvc): move . into layers
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源文雨
224
rvc/layers/encoders.py
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224
rvc/layers/encoders.py
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import math
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from typing import Tuple, Optional
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import torch
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from torch import nn
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from .attentions import MultiHeadAttention, FFN
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from .norms import LayerNorm, WN
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from .utils import sequence_mask
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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: int,
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filter_channels: int,
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n_heads: int,
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n_layers: int,
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kernel_size: int = 1,
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p_dropout: float = 0.0,
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window_size: int = 10,
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):
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super(Encoder, self).__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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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 _ 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 __call__(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
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return super().__call__(x, x_mask)
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def forward(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
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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 attn, norm1, ffn, norm2 in zip(
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self.attn_layers,
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self.norm_layers_1,
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self.ffn_layers,
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self.norm_layers_2,
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):
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y = attn(x, x, attn_mask)
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y = self.drop(y)
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x = norm1(x + y)
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y = ffn(x, x_mask)
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y = self.drop(y)
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x = norm2(x + y)
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x = x * x_mask
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return x
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class TextEncoder(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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hidden_channels: int,
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filter_channels: int,
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n_heads: int,
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n_layers: int,
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kernel_size: int,
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p_dropout: float,
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f0: bool = True,
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):
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super(TextEncoder, self).__init__()
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = float(p_dropout)
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self.emb_phone = nn.Linear(in_channels, hidden_channels)
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self.lrelu = nn.LeakyReLU(0.1, inplace=True)
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if f0 == True:
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self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
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self.encoder = Encoder(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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float(p_dropout),
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)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def __call__(
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self,
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phone: torch.Tensor,
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pitch: torch.Tensor,
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lengths: torch.Tensor,
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# skip_head: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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return super().__call__(
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phone,
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pitch,
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lengths,
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# skip_head=skip_head,
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)
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def forward(
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self,
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phone: torch.Tensor,
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pitch: torch.Tensor,
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lengths: torch.Tensor,
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# skip_head: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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x = self.emb_phone(phone)
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if pitch is not None:
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x += self.emb_pitch(pitch)
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x = x * math.sqrt(self.hidden_channels) # [b, t, h]
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x = self.lrelu(x)
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x = torch.transpose(x, 1, -1) # [b, h, t]
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x_mask = torch.unsqueeze(
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sequence_mask(lengths, x.size(2)),
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1,
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).to(x.dtype)
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x = self.encoder(x * x_mask, x_mask)
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"""
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if skip_head is not None:
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assert isinstance(skip_head, torch.Tensor)
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head = int(skip_head.item())
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x = x[:, :, head:]
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x_mask = x_mask[:, :, head:]
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"""
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stats: torch.Tensor = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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return m, logs, x_mask
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class PosteriorEncoder(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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hidden_channels: int,
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kernel_size: int,
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dilation_rate: int,
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n_layers: int,
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gin_channels=0,
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):
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super(PosteriorEncoder, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
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self.enc = WN(
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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gin_channels=gin_channels,
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)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def __call__(
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self, x: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = None
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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super().__call__(x, x_lengths, g=g)
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def forward(
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self, x: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = None
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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x_mask = torch.unsqueeze(
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sequence_mask(x_lengths, x.size(2)),
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1,
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).to(x.dtype)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
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return z, m, logs, x_mask
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def remove_weight_norm(self):
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self.enc.remove_weight_norm()
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def __prepare_scriptable__(self):
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for hook in self.enc._forward_pre_hooks.values():
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if (
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hook.__module__ == "torch.nn.utils.weight_norm"
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and hook.__class__.__name__ == "WeightNorm"
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):
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torch.nn.utils.remove_weight_norm(self.enc)
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return self
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