c8261b2ccc
* Reformat
* rewrite _get_name_params
* Add workflow for automatic formatting
* Revert "Add workflow for automatic formatting"
This reverts commit 9111c5dbc1.
* revert Retrieval_based_Voice_Conversion_WebUI.ipynb
---------
Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
850 lines
29 KiB
Python
850 lines
29 KiB
Python
import math, pdb, os
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from time import time as ttime
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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 infer_pack import modules
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from infer_pack import attentions
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from infer_pack import commons
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from infer_pack.commons import init_weights, get_padding
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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 infer_pack.commons import init_weights
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import numpy as np
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from infer_pack import commons
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class TextEncoder256(nn.Module):
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def __init__(
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self,
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out_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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f0=True,
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):
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super().__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 = p_dropout
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self.emb_phone = nn.Linear(256, 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 = attentions.Encoder(
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hidden_channels, filter_channels, n_heads, n_layers, kernel_size, 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 forward(self, phone, pitch, lengths):
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if pitch == None:
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x = self.emb_phone(phone)
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else:
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x = self.emb_phone(phone) + 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(commons.sequence_mask(lengths, x.size(2)), 1).to(
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x.dtype
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)
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x = self.encoder(x * x_mask, x_mask)
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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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class TextEncoder256Sim(nn.Module):
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def __init__(
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self,
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out_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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f0=True,
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):
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super().__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 = p_dropout
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self.emb_phone = nn.Linear(256, 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 = attentions.Encoder(
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hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
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)
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self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
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def forward(self, phone, pitch, lengths):
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if pitch == None:
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x = self.emb_phone(phone)
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else:
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x = self.emb_phone(phone) + 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(commons.sequence_mask(lengths, x.size(2)), 1).to(
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x.dtype
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)
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x = self.encoder(x * x_mask, x_mask)
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x = self.proj(x) * x_mask
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return x, x_mask
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class ResidualCouplingBlock(nn.Module):
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def __init__(
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self,
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channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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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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self.channels = channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.flows = nn.ModuleList()
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for i in range(n_flows):
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self.flows.append(
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modules.ResidualCouplingLayer(
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channels,
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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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mean_only=True,
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)
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)
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self.flows.append(modules.Flip())
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def forward(self, x, x_mask, g=None, reverse=False):
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if not reverse:
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, reverse=reverse)
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else:
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for flow in reversed(self.flows):
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x = flow(x, x_mask, g=g, reverse=reverse)
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return x
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def remove_weight_norm(self):
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for i in range(self.n_flows):
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self.flows[i * 2].remove_weight_norm()
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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,
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out_channels,
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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=0,
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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.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 = modules.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 forward(self, x, x_lengths, g=None):
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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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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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class Generator(torch.nn.Module):
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def __init__(
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self,
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initial_channel,
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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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gin_channels=0,
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):
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super(Generator, self).__init__()
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_rates)
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self.conv_pre = Conv1d(
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initial_channel, upsample_initial_channel, 7, 1, padding=3
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)
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resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
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self.ups.append(
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weight_norm(
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ConvTranspose1d(
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upsample_initial_channel // (2**i),
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upsample_initial_channel // (2 ** (i + 1)),
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k,
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u,
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padding=(k - u) // 2,
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)
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)
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)
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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for j, (k, d) in enumerate(
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zip(resblock_kernel_sizes, resblock_dilation_sizes)
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):
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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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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
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def forward(self, x, g=None):
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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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for i in range(self.num_upsamples):
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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x = self.ups[i](x)
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xs = None
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for j in range(self.num_kernels):
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if xs is None:
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xs = self.resblocks[i * self.num_kernels + j](x)
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else:
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xs += self.resblocks[i * self.num_kernels + j](x)
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x = xs / self.num_kernels
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x = F.leaky_relu(x)
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x = self.conv_post(x)
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x = torch.tanh(x)
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return x
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def remove_weight_norm(self):
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for l in self.ups:
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remove_weight_norm(l)
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for l in self.resblocks:
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l.remove_weight_norm()
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class SineGen(torch.nn.Module):
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"""Definition of sine generator
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SineGen(samp_rate, harmonic_num = 0,
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sine_amp = 0.1, noise_std = 0.003,
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voiced_threshold = 0,
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flag_for_pulse=False)
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samp_rate: sampling rate in Hz
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harmonic_num: number of harmonic overtones (default 0)
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sine_amp: amplitude of sine-wavefrom (default 0.1)
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noise_std: std of Gaussian noise (default 0.003)
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voiced_thoreshold: F0 threshold for U/V classification (default 0)
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flag_for_pulse: this SinGen is used inside PulseGen (default False)
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Note: when flag_for_pulse is True, the first time step of a voiced
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segment is always sin(np.pi) or cos(0)
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"""
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def __init__(
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self,
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samp_rate,
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harmonic_num=0,
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sine_amp=0.1,
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noise_std=0.003,
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voiced_threshold=0,
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flag_for_pulse=False,
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):
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super(SineGen, self).__init__()
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self.sine_amp = sine_amp
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self.noise_std = noise_std
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self.harmonic_num = harmonic_num
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self.dim = self.harmonic_num + 1
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self.sampling_rate = samp_rate
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self.voiced_threshold = voiced_threshold
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def _f02uv(self, f0):
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# generate uv signal
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uv = torch.ones_like(f0)
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uv = uv * (f0 > self.voiced_threshold)
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return uv
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def forward(self, f0, upp):
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"""sine_tensor, uv = forward(f0)
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input F0: tensor(batchsize=1, length, dim=1)
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f0 for unvoiced steps should be 0
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output sine_tensor: tensor(batchsize=1, length, dim)
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output uv: tensor(batchsize=1, length, 1)
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"""
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with torch.no_grad():
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f0 = f0[:, None].transpose(1, 2)
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f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
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# fundamental component
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f0_buf[:, :, 0] = f0[:, :, 0]
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for idx in np.arange(self.harmonic_num):
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f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
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idx + 2
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) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
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rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
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rand_ini = torch.rand(
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f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
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)
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rand_ini[:, 0] = 0
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rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
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tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
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tmp_over_one *= upp
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tmp_over_one = F.interpolate(
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tmp_over_one.transpose(2, 1),
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scale_factor=upp,
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mode="linear",
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align_corners=True,
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).transpose(2, 1)
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rad_values = F.interpolate(
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rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
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).transpose(
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2, 1
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) #######
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tmp_over_one %= 1
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tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
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cumsum_shift = torch.zeros_like(rad_values)
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cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
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sine_waves = torch.sin(
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torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
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)
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sine_waves = sine_waves * self.sine_amp
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uv = self._f02uv(f0)
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uv = F.interpolate(
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uv.transpose(2, 1), scale_factor=upp, mode="nearest"
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).transpose(2, 1)
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noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
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noise = noise_amp * torch.randn_like(sine_waves)
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sine_waves = sine_waves * uv + noise
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return sine_waves, uv, noise
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class SourceModuleHnNSF(torch.nn.Module):
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"""SourceModule for hn-nsf
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SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
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add_noise_std=0.003, voiced_threshod=0)
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sampling_rate: sampling_rate in Hz
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harmonic_num: number of harmonic above F0 (default: 0)
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sine_amp: amplitude of sine source signal (default: 0.1)
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add_noise_std: std of additive Gaussian noise (default: 0.003)
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note that amplitude of noise in unvoiced is decided
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by sine_amp
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voiced_threshold: threhold to set U/V given F0 (default: 0)
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Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
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F0_sampled (batchsize, length, 1)
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Sine_source (batchsize, length, 1)
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noise_source (batchsize, length 1)
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uv (batchsize, length, 1)
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"""
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def __init__(
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self,
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sampling_rate,
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harmonic_num=0,
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sine_amp=0.1,
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add_noise_std=0.003,
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voiced_threshod=0,
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is_half=True,
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):
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super(SourceModuleHnNSF, self).__init__()
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self.sine_amp = sine_amp
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self.noise_std = add_noise_std
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self.is_half = is_half
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# to produce sine waveforms
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self.l_sin_gen = SineGen(
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sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
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)
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# to merge source harmonics into a single excitation
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self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
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self.l_tanh = torch.nn.Tanh()
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def forward(self, x, upp=None):
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sine_wavs, uv, _ = self.l_sin_gen(x, upp)
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if self.is_half:
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sine_wavs = sine_wavs.half()
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sine_merge = self.l_tanh(self.l_linear(sine_wavs))
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return sine_merge, None, None # noise, uv
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class GeneratorNSF(torch.nn.Module):
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def __init__(
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self,
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initial_channel,
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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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gin_channels,
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sr,
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is_half=False,
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):
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super(GeneratorNSF, self).__init__()
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_rates)
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self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
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self.m_source = SourceModuleHnNSF(
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sampling_rate=sr, harmonic_num=0, is_half=is_half
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)
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self.noise_convs = nn.ModuleList()
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self.conv_pre = Conv1d(
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initial_channel, upsample_initial_channel, 7, 1, padding=3
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)
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resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
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c_cur = upsample_initial_channel // (2 ** (i + 1))
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self.ups.append(
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weight_norm(
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ConvTranspose1d(
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upsample_initial_channel // (2**i),
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upsample_initial_channel // (2 ** (i + 1)),
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k,
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u,
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padding=(k - u) // 2,
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)
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)
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)
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if i + 1 < len(upsample_rates):
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stride_f0 = np.prod(upsample_rates[i + 1 :])
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self.noise_convs.append(
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Conv1d(
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1,
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c_cur,
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kernel_size=stride_f0 * 2,
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stride=stride_f0,
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padding=stride_f0 // 2,
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)
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)
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else:
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self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
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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)
|
||
|
||
self.upp = np.prod(upsample_rates)
|
||
|
||
def forward(self, x, f0, g=None):
|
||
har_source, noi_source, uv = self.m_source(f0, self.upp)
|
||
har_source = har_source.transpose(1, 2)
|
||
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)
|
||
x_source = self.noise_convs[i](har_source)
|
||
x = x + x_source
|
||
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):
|
||
for l in self.ups:
|
||
remove_weight_norm(l)
|
||
for l in self.resblocks:
|
||
l.remove_weight_norm()
|
||
|
||
|
||
sr2sr = {
|
||
"32k": 32000,
|
||
"40k": 40000,
|
||
"48k": 48000,
|
||
}
|
||
|
||
|
||
class SynthesizerTrnMs256NSFsid(nn.Module):
|
||
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,
|
||
spk_embed_dim,
|
||
gin_channels,
|
||
sr,
|
||
**kwargs
|
||
):
|
||
super().__init__()
|
||
if type(sr) == type("strr"):
|
||
sr = sr2sr[sr]
|
||
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.gin_channels = gin_channels
|
||
# self.hop_length = hop_length#
|
||
self.spk_embed_dim = spk_embed_dim
|
||
self.enc_p = TextEncoder256(
|
||
inter_channels,
|
||
hidden_channels,
|
||
filter_channels,
|
||
n_heads,
|
||
n_layers,
|
||
kernel_size,
|
||
p_dropout,
|
||
)
|
||
self.dec = GeneratorNSF(
|
||
inter_channels,
|
||
resblock,
|
||
resblock_kernel_sizes,
|
||
resblock_dilation_sizes,
|
||
upsample_rates,
|
||
upsample_initial_channel,
|
||
upsample_kernel_sizes,
|
||
gin_channels=gin_channels,
|
||
sr=sr,
|
||
is_half=kwargs["is_half"],
|
||
)
|
||
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, 3, gin_channels=gin_channels
|
||
)
|
||
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
|
||
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
|
||
|
||
def remove_weight_norm(self):
|
||
self.dec.remove_weight_norm()
|
||
self.flow.remove_weight_norm()
|
||
self.enc_q.remove_weight_norm()
|
||
|
||
def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
|
||
g = self.emb_g(sid).unsqueeze(-1)
|
||
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
|
||
z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
|
||
z = self.flow(z_p, x_mask, g=g, reverse=True)
|
||
o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
|
||
return o
|
||
|
||
|
||
class SynthesizerTrnMs256NSFsid_sim(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,
|
||
spk_embed_dim,
|
||
# hop_length,
|
||
gin_channels=0,
|
||
use_sdp=True,
|
||
**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.gin_channels = gin_channels
|
||
# self.hop_length = hop_length#
|
||
self.spk_embed_dim = spk_embed_dim
|
||
self.enc_p = TextEncoder256Sim(
|
||
inter_channels,
|
||
hidden_channels,
|
||
filter_channels,
|
||
n_heads,
|
||
n_layers,
|
||
kernel_size,
|
||
p_dropout,
|
||
)
|
||
self.dec = GeneratorNSF(
|
||
inter_channels,
|
||
resblock,
|
||
resblock_kernel_sizes,
|
||
resblock_dilation_sizes,
|
||
upsample_rates,
|
||
upsample_initial_channel,
|
||
upsample_kernel_sizes,
|
||
gin_channels=gin_channels,
|
||
is_half=kwargs["is_half"],
|
||
)
|
||
|
||
self.flow = ResidualCouplingBlock(
|
||
inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
|
||
)
|
||
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
|
||
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
|
||
|
||
def remove_weight_norm(self):
|
||
self.dec.remove_weight_norm()
|
||
self.flow.remove_weight_norm()
|
||
self.enc_q.remove_weight_norm()
|
||
|
||
def forward(
|
||
self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
|
||
): # y是spec不需要了现在
|
||
g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
|
||
x, x_mask = self.enc_p(phone, pitch, phone_lengths)
|
||
x = self.flow(x, x_mask, g=g, reverse=True)
|
||
o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
|
||
return o
|
||
|
||
|
||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||
def __init__(self, use_spectral_norm=False):
|
||
super(MultiPeriodDiscriminator, self).__init__()
|
||
periods = [2, 3, 5, 7, 11, 17]
|
||
# periods = [3, 5, 7, 11, 17, 23, 37]
|
||
|
||
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)
|
||
# for j in range(len(fmap_r)):
|
||
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
|
||
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 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 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
|