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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 lib.infer_pack import modules |
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from lib.infer_pack import attentions |
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from lib.infer_pack import commons |
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from lib.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 lib.infer_pack.commons import init_weights |
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import numpy as np |
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from lib.infer_pack import commons |
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|
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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) |
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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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|
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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) |
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x = self.lrelu(x) |
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x = torch.transpose(x, 1, -1) |
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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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|
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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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|
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class TextEncoder768(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(768, 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) |
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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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|
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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) |
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x = self.lrelu(x) |
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x = torch.transpose(x, 1, -1) |
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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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|
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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 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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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def remove_weight_norm(self): |
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self.enc.remove_weight_norm() |
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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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return x |
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|
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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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|
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|
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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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|
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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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|
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def _f02uv(self, f0): |
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|
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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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|
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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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|
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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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) |
|
rad_values = (f0_buf / self.sampling_rate) % 1 |
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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 |
|
tmp_over_one = torch.cumsum(rad_values, 1) |
|
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) |
|
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 |
|
tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 |
|
cumsum_shift = torch.zeros_like(rad_values) |
|
cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 |
|
sine_waves = torch.sin( |
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torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi |
|
) |
|
sine_waves = sine_waves * self.sine_amp |
|
uv = self._f02uv(f0) |
|
uv = F.interpolate( |
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uv.transpose(2, 1), scale_factor=upp, mode="nearest" |
|
).transpose(2, 1) |
|
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 |
|
noise = noise_amp * torch.randn_like(sine_waves) |
|
sine_waves = sine_waves * uv + noise |
|
return sine_waves, uv, noise |
|
|
|
|
|
class SourceModuleHnNSF(torch.nn.Module): |
|
"""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) |
|
sampling_rate: sampling_rate in Hz |
|
harmonic_num: number of harmonic above F0 (default: 0) |
|
sine_amp: amplitude of sine source signal (default: 0.1) |
|
add_noise_std: std of additive Gaussian noise (default: 0.003) |
|
note that amplitude of noise in unvoiced is decided |
|
by sine_amp |
|
voiced_threshold: threhold to set U/V given F0 (default: 0) |
|
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) |
|
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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""" |
|
|
|
def __init__( |
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self, |
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sampling_rate, |
|
harmonic_num=0, |
|
sine_amp=0.1, |
|
add_noise_std=0.003, |
|
voiced_threshod=0, |
|
is_half=True, |
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): |
|
super(SourceModuleHnNSF, self).__init__() |
|
|
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self.sine_amp = sine_amp |
|
self.noise_std = add_noise_std |
|
self.is_half = is_half |
|
|
|
self.l_sin_gen = SineGen( |
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sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod |
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) |
|
|
|
|
|
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) |
|
self.l_tanh = torch.nn.Tanh() |
|
|
|
def forward(self, x, upp=None): |
|
sine_wavs, uv, _ = self.l_sin_gen(x, upp) |
|
if self.is_half: |
|
sine_wavs = sine_wavs.half() |
|
sine_merge = self.l_tanh(self.l_linear(sine_wavs)) |
|
return sine_merge, None, None |
|
|
|
|
|
class GeneratorNSF(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, |
|
sr, |
|
is_half=False, |
|
): |
|
super(GeneratorNSF, self).__init__() |
|
self.num_kernels = len(resblock_kernel_sizes) |
|
self.num_upsamples = len(upsample_rates) |
|
|
|
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates)) |
|
self.m_source = SourceModuleHnNSF( |
|
sampling_rate=sr, harmonic_num=0, is_half=is_half |
|
) |
|
self.noise_convs = nn.ModuleList() |
|
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)): |
|
c_cur = upsample_initial_channel // (2 ** (i + 1)) |
|
self.ups.append( |
|
weight_norm( |
|
ConvTranspose1d( |
|
upsample_initial_channel // (2**i), |
|
upsample_initial_channel // (2 ** (i + 1)), |
|
k, |
|
u, |
|
padding=(k - u) // 2, |
|
) |
|
) |
|
) |
|
if i + 1 < len(upsample_rates): |
|
stride_f0 = np.prod(upsample_rates[i + 1 :]) |
|
self.noise_convs.append( |
|
Conv1d( |
|
1, |
|
c_cur, |
|
kernel_size=stride_f0 * 2, |
|
stride=stride_f0, |
|
padding=stride_f0 // 2, |
|
) |
|
) |
|
else: |
|
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) |
|
|
|
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.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, pitchf, y, y_lengths, ds |
|
): |
|
|
|
g = self.emb_g(ds).unsqueeze(-1) |
|
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) |
|
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) |
|
z_p = self.flow(z, y_mask, g=g) |
|
z_slice, ids_slice = commons.rand_slice_segments( |
|
z, y_lengths, self.segment_size |
|
) |
|
|
|
pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) |
|
|
|
o = self.dec(z_slice, pitchf, g=g) |
|
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
|
|
|
def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=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) * torch.randn_like(m_p) * 0.66666) * x_mask |
|
if rate: |
|
head = int(z_p.shape[2] * rate) |
|
z_p = z_p[:, :, -head:] |
|
x_mask = x_mask[:, :, -head:] |
|
nsff0 = nsff0[:, -head:] |
|
z = self.flow(z_p, x_mask, g=g, reverse=True) |
|
o = self.dec(z * x_mask, nsff0, g=g) |
|
return o, x_mask, (z, z_p, m_p, logs_p) |
|
|
|
|
|
class SynthesizerTrnMs768NSFsid(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.spk_embed_dim = spk_embed_dim |
|
self.enc_p = TextEncoder768( |
|
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, pitchf, y, y_lengths, ds |
|
): |
|
|
|
g = self.emb_g(ds).unsqueeze(-1) |
|
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) |
|
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) |
|
z_p = self.flow(z, y_mask, g=g) |
|
z_slice, ids_slice = commons.rand_slice_segments( |
|
z, y_lengths, self.segment_size |
|
) |
|
|
|
pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) |
|
|
|
o = self.dec(z_slice, pitchf, g=g) |
|
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
|
|
|
def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=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) * torch.randn_like(m_p) * 0.66666) * x_mask |
|
if rate: |
|
head = int(z_p.shape[2] * rate) |
|
z_p = z_p[:, :, -head:] |
|
x_mask = x_mask[:, :, -head:] |
|
nsff0 = nsff0[:, -head:] |
|
z = self.flow(z_p, x_mask, g=g, reverse=True) |
|
o = self.dec(z * x_mask, nsff0, g=g) |
|
return o, x_mask, (z, z_p, m_p, logs_p) |
|
|
|
|
|
class SynthesizerTrnMs256NSFsid_nono(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=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.gin_channels = gin_channels |
|
|
|
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, |
|
f0=False, |
|
) |
|
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, 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, y, y_lengths, ds): |
|
g = self.emb_g(ds).unsqueeze(-1) |
|
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) |
|
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) |
|
z_p = self.flow(z, y_mask, g=g) |
|
z_slice, ids_slice = commons.rand_slice_segments( |
|
z, y_lengths, self.segment_size |
|
) |
|
o = self.dec(z_slice, g=g) |
|
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
|
|
|
def infer(self, phone, phone_lengths, sid, rate=None): |
|
g = self.emb_g(sid).unsqueeze(-1) |
|
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) |
|
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask |
|
if rate: |
|
head = int(z_p.shape[2] * rate) |
|
z_p = z_p[:, :, -head:] |
|
x_mask = x_mask[:, :, -head:] |
|
z = self.flow(z_p, x_mask, g=g, reverse=True) |
|
o = self.dec(z * x_mask, g=g) |
|
return o, x_mask, (z, z_p, m_p, logs_p) |
|
|
|
|
|
class SynthesizerTrnMs768NSFsid_nono(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=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.gin_channels = gin_channels |
|
|
|
self.spk_embed_dim = spk_embed_dim |
|
self.enc_p = TextEncoder768( |
|
inter_channels, |
|
hidden_channels, |
|
filter_channels, |
|
n_heads, |
|
n_layers, |
|
kernel_size, |
|
p_dropout, |
|
f0=False, |
|
) |
|
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, 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, y, y_lengths, ds): |
|
g = self.emb_g(ds).unsqueeze(-1) |
|
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) |
|
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) |
|
z_p = self.flow(z, y_mask, g=g) |
|
z_slice, ids_slice = commons.rand_slice_segments( |
|
z, y_lengths, self.segment_size |
|
) |
|
o = self.dec(z_slice, g=g) |
|
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
|
|
|
def infer(self, phone, phone_lengths, sid, rate=None): |
|
g = self.emb_g(sid).unsqueeze(-1) |
|
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) |
|
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask |
|
if rate: |
|
head = int(z_p.shape[2] * rate) |
|
z_p = z_p[:, :, -head:] |
|
x_mask = x_mask[:, :, -head:] |
|
z = self.flow(z_p, x_mask, g=g, reverse=True) |
|
o = self.dec(z * x_mask, g=g) |
|
return o, x_mask, (z, z_p, m_p, logs_p) |
|
|
|
|
|
class MultiPeriodDiscriminator(torch.nn.Module): |
|
def __init__(self, use_spectral_norm=False): |
|
super(MultiPeriodDiscriminator, self).__init__() |
|
periods = [2, 3, 5, 7, 11, 17] |
|
|
|
|
|
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 MultiPeriodDiscriminatorV2(torch.nn.Module): |
|
def __init__(self, use_spectral_norm=False): |
|
super(MultiPeriodDiscriminatorV2, self).__init__() |
|
|
|
periods = [2, 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) |
|
|
|
|
|
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 = [] |
|
|
|
|
|
b, c, t = x.shape |
|
if t % self.period != 0: |
|
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 |
|
|
