mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-08 20:10:44 +08:00
optimize(rvc): move commons to rvc.utils
- remove redundant attentions_onnx - shrink models_onnx - add some type note to rvc.utils
This commit is contained in:
源文雨
@@ -1,17 +1,18 @@
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import math
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import logging
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from typing import Optional
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from typing import Optional, Tuple, List
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from rvc import utils
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logger = logging.getLogger(__name__)
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
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from torch.nn import Conv1d, Conv2d, ConvTranspose1d
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from torch.nn import functional as F
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from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
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from infer.lib.infer_pack import attentions, commons, modules
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from infer.lib.infer_pack.commons import get_padding, init_weights
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from infer.lib.infer_pack import attentions, modules
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from rvc.utils import get_padding, call_weight_data_normal_if_Conv
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has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
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@@ -51,13 +52,25 @@ class TextEncoder(nn.Module):
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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, pitch, 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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):
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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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if pitch is None:
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x = self.emb_phone(phone)
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else:
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@@ -65,15 +78,19 @@ class TextEncoder(nn.Module):
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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(commons.sequence_mask(lengths, x.size(2)), 1).to(
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x.dtype
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)
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x_mask = torch.unsqueeze(
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utils.sequence_mask(
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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 = 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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@@ -125,7 +142,7 @@ class ResidualCouplingBlock(nn.Module):
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, reverse=reverse)
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else:
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for flow in self.flows[::-1]:
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for flow in reversed(self.flows):
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x, _ = flow.forward(x, x_mask, g=g, reverse=reverse)
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return x
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@@ -175,12 +192,19 @@ class PosteriorEncoder(nn.Module):
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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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):
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
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x.dtype
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)
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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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utils.sequence_mask(
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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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@@ -244,7 +268,7 @@ class Generator(torch.nn.Module):
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self.resblocks.append(resblock(ch, k, d))
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
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self.ups.apply(init_weights)
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self.ups.apply(call_weight_data_normal_if_Conv)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
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@@ -253,13 +277,15 @@ class Generator(torch.nn.Module):
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self,
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x: torch.Tensor,
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g: Optional[torch.Tensor] = None,
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n_res: Optional[torch.Tensor] = None,
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# n_res: Optional[torch.Tensor] = None,
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):
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"""
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if n_res is not None:
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assert isinstance(n_res, torch.Tensor)
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n = int(n_res.item())
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if n != x.shape[-1]:
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x = F.interpolate(x, size=n, mode="linear")
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"""
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x = self.conv_pre(x)
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if g is not None:
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x = x + self.cond(g)
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@@ -529,7 +555,7 @@ class GeneratorNSF(torch.nn.Module):
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self.resblocks.append(resblock(ch, k, d))
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
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self.ups.apply(init_weights)
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self.ups.apply(call_weight_data_normal_if_Conv)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
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@@ -543,10 +569,11 @@ class GeneratorNSF(torch.nn.Module):
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x,
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f0,
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g: Optional[torch.Tensor] = None,
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n_res: Optional[torch.Tensor] = None,
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# n_res: Optional[torch.Tensor] = None,
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):
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har_source, noi_source, uv = self.m_source(f0, self.upp)
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har_source = har_source.transpose(1, 2)
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"""
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if n_res is not None:
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assert isinstance(n_res, torch.Tensor)
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n = int(n_res.item())
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@@ -554,6 +581,7 @@ class GeneratorNSF(torch.nn.Module):
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har_source = F.interpolate(har_source, size=n * self.upp, mode="linear")
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if n != x.shape[-1]:
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x = F.interpolate(x, size=n, mode="linear")
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"""
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x = self.conv_pre(x)
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if g is not None:
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x = x + self.cond(g)
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@@ -611,39 +639,35 @@ class GeneratorNSF(torch.nn.Module):
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return self
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sr2sr = {
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"32k": 32000,
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"40k": 40000,
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"48k": 48000,
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}
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class SynthesizerTrnMs256NSFsid(nn.Module):
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def __init__(
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self,
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spec_channels,
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segment_size,
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inter_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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resblock,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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upsample_rates,
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upsample_initial_channel,
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upsample_kernel_sizes,
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spk_embed_dim,
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gin_channels,
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sr,
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segment_size: int,
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inter_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: int,
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resblock: str,
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resblock_kernel_sizes: List[int],
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resblock_dilation_sizes: List[List[int]],
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upsample_rates: List[int],
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upsample_initial_channel: int,
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upsample_kernel_sizes: List[int],
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spk_embed_dim: int,
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gin_channels: int,
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sr: str | int,
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**kwargs
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):
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super(SynthesizerTrnMs256NSFsid, self).__init__()
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if isinstance(sr, str):
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sr = sr2sr[sr]
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if isinstance(sr, str): sr = {
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"32k": 32000,
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"40k": 40000,
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"48k": 48000,
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}[sr]
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self.spec_channels = spec_channels
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self.inter_channels = inter_channels
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self.hidden_channels = hidden_channels
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@@ -752,11 +776,11 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
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m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
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z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
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z_p = self.flow(z, y_mask, g=g)
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z_slice, ids_slice = commons.rand_slice_segments(
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z_slice, ids_slice = utils.rand_slice_segments(
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z, y_lengths, self.segment_size
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)
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# print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
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pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
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pitchf = utils.slice_on_last_dim(pitchf, ids_slice, self.segment_size)
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# print(-2,pitchf.shape,z_slice.shape)
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o = self.dec(z_slice, pitchf, g=g)
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return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
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@@ -771,7 +795,7 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
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sid: torch.Tensor,
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skip_head: Optional[torch.Tensor] = None,
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return_length: Optional[torch.Tensor] = None,
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return_length2: Optional[torch.Tensor] = None,
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# return_length2: Optional[torch.Tensor] = None,
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):
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g = self.emb_g(sid).unsqueeze(-1)
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if skip_head is not None and return_length is not None:
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@@ -791,7 +815,10 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
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m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
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z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec(z * x_mask, nsff0, g=g, n_res=return_length2)
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o = self.dec(
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z * x_mask, nsff0, g=g,
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# n_res=return_length2,
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)
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return o, x_mask, (z, z_p, m_p, logs_p)
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@@ -973,7 +1000,7 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
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m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
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z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
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z_p = self.flow(z, y_mask, g=g)
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z_slice, ids_slice = commons.rand_slice_segments(
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z_slice, ids_slice = utils.rand_slice_segments(
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z, y_lengths, self.segment_size
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)
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o = self.dec(z_slice, g=g)
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@@ -987,7 +1014,7 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
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sid: torch.Tensor,
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skip_head: Optional[torch.Tensor] = None,
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return_length: Optional[torch.Tensor] = None,
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return_length2: Optional[torch.Tensor] = None,
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#return_length2: Optional[torch.Tensor] = None,
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):
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g = self.emb_g(sid).unsqueeze(-1)
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if skip_head is not None and return_length is not None:
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@@ -1006,7 +1033,10 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
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m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
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z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec(z * x_mask, g=g, n_res=return_length2)
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o = self.dec(
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z * x_mask, g=g,
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# n_res=return_length2
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)
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return o, x_mask, (z, z_p, m_p, logs_p)
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