mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
1277 lines
44 KiB
Python
1277 lines
44 KiB
Python
import warnings
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warnings.filterwarnings("ignore")
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import copy
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import math
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import os
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import pdb
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import torch
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from torch import nn
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from torch.nn import functional as F
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from module import commons
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from module import modules
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from module import attentions
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from f5_tts.model import DiT
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from module.commons import init_weights, get_padding
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from module.mrte_model import MRTE
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from module.quantize import ResidualVectorQuantizer
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# from text import symbols
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from text import symbols as symbols_v1
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from text import symbols2 as symbols_v2
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from torch.cuda.amp import autocast
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import contextlib,random
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class StochasticDurationPredictor(nn.Module):
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def __init__(
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self,
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in_channels,
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filter_channels,
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kernel_size,
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p_dropout,
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n_flows=4,
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gin_channels=0,
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):
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super().__init__()
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filter_channels = in_channels # it needs to be removed from future version.
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self.in_channels = in_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.log_flow = modules.Log()
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self.flows = nn.ModuleList()
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self.flows.append(modules.ElementwiseAffine(2))
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for i in range(n_flows):
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self.flows.append(
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modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
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)
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self.flows.append(modules.Flip())
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self.post_pre = nn.Conv1d(1, filter_channels, 1)
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self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
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self.post_convs = modules.DDSConv(
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filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
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)
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self.post_flows = nn.ModuleList()
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self.post_flows.append(modules.ElementwiseAffine(2))
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for i in range(4):
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self.post_flows.append(
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modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
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)
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self.post_flows.append(modules.Flip())
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self.pre = nn.Conv1d(in_channels, filter_channels, 1)
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self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
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self.convs = modules.DDSConv(
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filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
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)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
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def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
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x = torch.detach(x)
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x = self.pre(x)
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if g is not None:
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g = torch.detach(g)
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x = x + self.cond(g)
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x = self.convs(x, x_mask)
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x = self.proj(x) * x_mask
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if not reverse:
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flows = self.flows
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assert w is not None
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logdet_tot_q = 0
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h_w = self.post_pre(w)
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h_w = self.post_convs(h_w, x_mask)
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h_w = self.post_proj(h_w) * x_mask
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e_q = (
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torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
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* x_mask
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)
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z_q = e_q
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for flow in self.post_flows:
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z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
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logdet_tot_q += logdet_q
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z_u, z1 = torch.split(z_q, [1, 1], 1)
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u = torch.sigmoid(z_u) * x_mask
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z0 = (w - u) * x_mask
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logdet_tot_q += torch.sum(
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(F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
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)
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logq = (
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torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
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- logdet_tot_q
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)
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logdet_tot = 0
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z0, logdet = self.log_flow(z0, x_mask)
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logdet_tot += logdet
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z = torch.cat([z0, z1], 1)
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for flow in flows:
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z, logdet = flow(z, x_mask, g=x, reverse=reverse)
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logdet_tot = logdet_tot + logdet
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nll = (
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torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
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- logdet_tot
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)
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return nll + logq # [b]
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else:
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flows = list(reversed(self.flows))
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flows = flows[:-2] + [flows[-1]] # remove a useless vflow
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z = (
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torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
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* noise_scale
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)
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for flow in flows:
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z = flow(z, x_mask, g=x, reverse=reverse)
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z0, z1 = torch.split(z, [1, 1], 1)
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logw = z0
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return logw
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class DurationPredictor(nn.Module):
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def __init__(
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self, in_channels, filter_channels, kernel_size, p_dropout, 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.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.gin_channels = gin_channels
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self.drop = nn.Dropout(p_dropout)
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self.conv_1 = nn.Conv1d(
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in_channels, filter_channels, kernel_size, padding=kernel_size // 2
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)
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self.norm_1 = modules.LayerNorm(filter_channels)
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self.conv_2 = nn.Conv1d(
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filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
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)
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self.norm_2 = modules.LayerNorm(filter_channels)
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self.proj = nn.Conv1d(filter_channels, 1, 1)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, in_channels, 1)
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def forward(self, x, x_mask, g=None):
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x = torch.detach(x)
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if g is not None:
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g = torch.detach(g)
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x = x + self.cond(g)
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x = self.conv_1(x * x_mask)
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x = torch.relu(x)
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x = self.norm_1(x)
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x = self.drop(x)
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x = self.conv_2(x * x_mask)
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x = torch.relu(x)
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x = self.norm_2(x)
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x = self.drop(x)
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x = self.proj(x * x_mask)
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return x * x_mask
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class TextEncoder(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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latent_channels=192,
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version = "v2",
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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.latent_channels = latent_channels
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self.version = version
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self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
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self.encoder_ssl = attentions.Encoder(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers // 2,
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kernel_size,
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p_dropout,
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)
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self.encoder_text = 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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if self.version == "v1":
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symbols = symbols_v1.symbols
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else:
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symbols = symbols_v2.symbols
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self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
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self.mrte = MRTE()
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self.encoder2 = attentions.Encoder(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers // 2,
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kernel_size,
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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, y, y_lengths, text, text_lengths, ge, speed=1,test=None):
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
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y.dtype
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)
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y = self.ssl_proj(y * y_mask) * y_mask
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y = self.encoder_ssl(y * y_mask, y_mask)
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text_mask = torch.unsqueeze(
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commons.sequence_mask(text_lengths, text.size(1)), 1
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).to(y.dtype)
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if test == 1:
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text[:, :] = 0
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text = self.text_embedding(text).transpose(1, 2)
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text = self.encoder_text(text * text_mask, text_mask)
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y = self.mrte(y, y_mask, text, text_mask, ge)
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y = self.encoder2(y * y_mask, y_mask)
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if(speed!=1):
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y = F.interpolate(y, size=int(y.shape[-1] / speed)+1, mode="linear")
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y_mask = F.interpolate(y_mask, size=y.shape[-1], mode="nearest")
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stats = self.proj(y) * y_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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return y, m, logs, y_mask
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def extract_latent(self, x):
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x = self.ssl_proj(x)
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quantized, codes, commit_loss, quantized_list = self.quantizer(x)
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return codes.transpose(0, 1)
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def decode_latent(self, codes, y_mask, refer, refer_mask, ge):
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quantized = self.quantizer.decode(codes)
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y = self.vq_proj(quantized) * y_mask
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y = self.encoder_ssl(y * y_mask, y_mask)
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y = self.mrte(y, y_mask, refer, refer_mask, ge)
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y = self.encoder2(y * y_mask, y_mask)
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stats = self.proj(y) * y_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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return y, m, logs, y_mask, quantized
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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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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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if g != None:
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g = g.detach()
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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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class Encoder(nn.Module):
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def __init__(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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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(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
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self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
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def forward(self, x, x_lengths, g=None):
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if(g!=None):
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g = g.detach()
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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return stats, x_mask
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class WNEncoder(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,
|
||
n_layers,
|
||
gin_channels=0,
|
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):
|
||
super().__init__()
|
||
self.in_channels = in_channels
|
||
self.out_channels = out_channels
|
||
self.hidden_channels = hidden_channels
|
||
self.kernel_size = kernel_size
|
||
self.dilation_rate = dilation_rate
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||
self.n_layers = n_layers
|
||
self.gin_channels = gin_channels
|
||
|
||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||
self.enc = modules.WN(
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||
hidden_channels,
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||
kernel_size,
|
||
dilation_rate,
|
||
n_layers,
|
||
gin_channels=gin_channels,
|
||
)
|
||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||
self.norm = modules.LayerNorm(out_channels)
|
||
|
||
def forward(self, x, x_lengths, g=None):
|
||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
|
||
x.dtype
|
||
)
|
||
x = self.pre(x) * x_mask
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||
x = self.enc(x, x_mask, g=g)
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out = self.proj(x) * x_mask
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out = self.norm(out)
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||
return out
|
||
|
||
|
||
class Generator(torch.nn.Module):
|
||
def __init__(
|
||
self,
|
||
initial_channel,
|
||
resblock,
|
||
resblock_kernel_sizes,
|
||
resblock_dilation_sizes,
|
||
upsample_rates,
|
||
upsample_initial_channel,
|
||
upsample_kernel_sizes,
|
||
gin_channels=0,
|
||
):
|
||
super(Generator, self).__init__()
|
||
self.num_kernels = len(resblock_kernel_sizes)
|
||
self.num_upsamples = len(upsample_rates)
|
||
self.conv_pre = Conv1d(
|
||
initial_channel, upsample_initial_channel, 7, 1, padding=3
|
||
)
|
||
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
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||
|
||
self.ups = nn.ModuleList()
|
||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||
self.ups.append(
|
||
weight_norm(
|
||
ConvTranspose1d(
|
||
upsample_initial_channel // (2**i),
|
||
upsample_initial_channel // (2 ** (i + 1)),
|
||
k,
|
||
u,
|
||
padding=(k - u) // 2,
|
||
)
|
||
)
|
||
)
|
||
|
||
self.resblocks = nn.ModuleList()
|
||
for i in range(len(self.ups)):
|
||
ch = upsample_initial_channel // (2 ** (i + 1))
|
||
for j, (k, d) in enumerate(
|
||
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
||
):
|
||
self.resblocks.append(resblock(ch, k, d))
|
||
|
||
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
||
self.ups.apply(init_weights)
|
||
|
||
if gin_channels != 0:
|
||
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
||
|
||
def forward(self, x, g=None):
|
||
x = self.conv_pre(x)
|
||
if g is not None:
|
||
x = x + self.cond(g)
|
||
|
||
for i in range(self.num_upsamples):
|
||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||
x = self.ups[i](x)
|
||
xs = None
|
||
for j in range(self.num_kernels):
|
||
if xs is None:
|
||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||
else:
|
||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||
x = xs / self.num_kernels
|
||
x = F.leaky_relu(x)
|
||
x = self.conv_post(x)
|
||
x = torch.tanh(x)
|
||
|
||
return x
|
||
|
||
def remove_weight_norm(self):
|
||
print("Removing weight norm...")
|
||
for l in self.ups:
|
||
remove_weight_norm(l)
|
||
for l in self.resblocks:
|
||
l.remove_weight_norm()
|
||
|
||
|
||
class DiscriminatorP(torch.nn.Module):
|
||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||
super(DiscriminatorP, self).__init__()
|
||
self.period = period
|
||
self.use_spectral_norm = use_spectral_norm
|
||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||
self.convs = nn.ModuleList(
|
||
[
|
||
norm_f(
|
||
Conv2d(
|
||
1,
|
||
32,
|
||
(kernel_size, 1),
|
||
(stride, 1),
|
||
padding=(get_padding(kernel_size, 1), 0),
|
||
)
|
||
),
|
||
norm_f(
|
||
Conv2d(
|
||
32,
|
||
128,
|
||
(kernel_size, 1),
|
||
(stride, 1),
|
||
padding=(get_padding(kernel_size, 1), 0),
|
||
)
|
||
),
|
||
norm_f(
|
||
Conv2d(
|
||
128,
|
||
512,
|
||
(kernel_size, 1),
|
||
(stride, 1),
|
||
padding=(get_padding(kernel_size, 1), 0),
|
||
)
|
||
),
|
||
norm_f(
|
||
Conv2d(
|
||
512,
|
||
1024,
|
||
(kernel_size, 1),
|
||
(stride, 1),
|
||
padding=(get_padding(kernel_size, 1), 0),
|
||
)
|
||
),
|
||
norm_f(
|
||
Conv2d(
|
||
1024,
|
||
1024,
|
||
(kernel_size, 1),
|
||
1,
|
||
padding=(get_padding(kernel_size, 1), 0),
|
||
)
|
||
),
|
||
]
|
||
)
|
||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||
|
||
def forward(self, x):
|
||
fmap = []
|
||
|
||
# 1d to 2d
|
||
b, c, t = x.shape
|
||
if t % self.period != 0: # pad first
|
||
n_pad = self.period - (t % self.period)
|
||
x = F.pad(x, (0, n_pad), "reflect")
|
||
t = t + n_pad
|
||
x = x.view(b, c, t // self.period, self.period)
|
||
|
||
for l in self.convs:
|
||
x = l(x)
|
||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||
fmap.append(x)
|
||
x = self.conv_post(x)
|
||
fmap.append(x)
|
||
x = torch.flatten(x, 1, -1)
|
||
|
||
return x, fmap
|
||
|
||
|
||
class DiscriminatorS(torch.nn.Module):
|
||
def __init__(self, use_spectral_norm=False):
|
||
super(DiscriminatorS, self).__init__()
|
||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||
self.convs = nn.ModuleList(
|
||
[
|
||
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
||
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
||
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
||
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
||
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||
]
|
||
)
|
||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||
|
||
def forward(self, x):
|
||
fmap = []
|
||
|
||
for l in self.convs:
|
||
x = l(x)
|
||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||
fmap.append(x)
|
||
x = self.conv_post(x)
|
||
fmap.append(x)
|
||
x = torch.flatten(x, 1, -1)
|
||
|
||
return x, fmap
|
||
|
||
|
||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||
def __init__(self, use_spectral_norm=False):
|
||
super(MultiPeriodDiscriminator, self).__init__()
|
||
periods = [2, 3, 5, 7, 11]
|
||
|
||
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
||
discs = discs + [
|
||
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
|
||
]
|
||
self.discriminators = nn.ModuleList(discs)
|
||
|
||
def forward(self, y, y_hat):
|
||
y_d_rs = []
|
||
y_d_gs = []
|
||
fmap_rs = []
|
||
fmap_gs = []
|
||
for i, d in enumerate(self.discriminators):
|
||
y_d_r, fmap_r = d(y)
|
||
y_d_g, fmap_g = d(y_hat)
|
||
y_d_rs.append(y_d_r)
|
||
y_d_gs.append(y_d_g)
|
||
fmap_rs.append(fmap_r)
|
||
fmap_gs.append(fmap_g)
|
||
|
||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||
|
||
|
||
class ReferenceEncoder(nn.Module):
|
||
"""
|
||
inputs --- [N, Ty/r, n_mels*r] mels
|
||
outputs --- [N, ref_enc_gru_size]
|
||
"""
|
||
|
||
def __init__(self, spec_channels, gin_channels=0):
|
||
super().__init__()
|
||
self.spec_channels = spec_channels
|
||
ref_enc_filters = [32, 32, 64, 64, 128, 128]
|
||
K = len(ref_enc_filters)
|
||
filters = [1] + ref_enc_filters
|
||
convs = [
|
||
weight_norm(
|
||
nn.Conv2d(
|
||
in_channels=filters[i],
|
||
out_channels=filters[i + 1],
|
||
kernel_size=(3, 3),
|
||
stride=(2, 2),
|
||
padding=(1, 1),
|
||
)
|
||
)
|
||
for i in range(K)
|
||
]
|
||
self.convs = nn.ModuleList(convs)
|
||
# self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
|
||
|
||
out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
|
||
self.gru = nn.GRU(
|
||
input_size=ref_enc_filters[-1] * out_channels,
|
||
hidden_size=256 // 2,
|
||
batch_first=True,
|
||
)
|
||
self.proj = nn.Linear(128, gin_channels)
|
||
|
||
def forward(self, inputs):
|
||
N = inputs.size(0)
|
||
out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
|
||
for conv in self.convs:
|
||
out = conv(out)
|
||
# out = wn(out)
|
||
out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
|
||
|
||
out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
|
||
T = out.size(1)
|
||
N = out.size(0)
|
||
out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
|
||
|
||
self.gru.flatten_parameters()
|
||
memory, out = self.gru(out) # out --- [1, N, 128]
|
||
|
||
return self.proj(out.squeeze(0)).unsqueeze(-1)
|
||
|
||
def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
|
||
for i in range(n_convs):
|
||
L = (L - kernel_size + 2 * pad) // stride + 1
|
||
return L
|
||
|
||
|
||
class Quantizer_module(torch.nn.Module):
|
||
def __init__(self, n_e, e_dim):
|
||
super(Quantizer_module, self).__init__()
|
||
self.embedding = nn.Embedding(n_e, e_dim)
|
||
self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e)
|
||
|
||
def forward(self, x):
|
||
d = (
|
||
torch.sum(x**2, 1, keepdim=True)
|
||
+ torch.sum(self.embedding.weight**2, 1)
|
||
- 2 * torch.matmul(x, self.embedding.weight.T)
|
||
)
|
||
min_indicies = torch.argmin(d, 1)
|
||
z_q = self.embedding(min_indicies)
|
||
return z_q, min_indicies
|
||
|
||
|
||
class Quantizer(torch.nn.Module):
|
||
def __init__(self, embed_dim=512, n_code_groups=4, n_codes=160):
|
||
super(Quantizer, self).__init__()
|
||
assert embed_dim % n_code_groups == 0
|
||
self.quantizer_modules = nn.ModuleList(
|
||
[
|
||
Quantizer_module(n_codes, embed_dim // n_code_groups)
|
||
for _ in range(n_code_groups)
|
||
]
|
||
)
|
||
self.n_code_groups = n_code_groups
|
||
self.embed_dim = embed_dim
|
||
|
||
def forward(self, xin):
|
||
# B, C, T
|
||
B, C, T = xin.shape
|
||
xin = xin.transpose(1, 2)
|
||
x = xin.reshape(-1, self.embed_dim)
|
||
x = torch.split(x, self.embed_dim // self.n_code_groups, dim=-1)
|
||
min_indicies = []
|
||
z_q = []
|
||
for _x, m in zip(x, self.quantizer_modules):
|
||
_z_q, _min_indicies = m(_x)
|
||
z_q.append(_z_q)
|
||
min_indicies.append(_min_indicies) # B * T,
|
||
z_q = torch.cat(z_q, -1).reshape(xin.shape)
|
||
loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean(
|
||
(z_q - xin.detach()) ** 2
|
||
)
|
||
z_q = xin + (z_q - xin).detach()
|
||
z_q = z_q.transpose(1, 2)
|
||
codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups)
|
||
return z_q, loss, codes.transpose(1, 2)
|
||
|
||
def embed(self, x):
|
||
# idx: N, 4, T
|
||
x = x.transpose(1, 2)
|
||
x = torch.split(x, 1, 2)
|
||
ret = []
|
||
for q, embed in zip(x, self.quantizer_modules):
|
||
q = embed.embedding(q.squeeze(-1))
|
||
ret.append(q)
|
||
ret = torch.cat(ret, -1)
|
||
return ret.transpose(1, 2) # N, C, T
|
||
|
||
|
||
class CodePredictor(nn.Module):
|
||
def __init__(
|
||
self,
|
||
hidden_channels,
|
||
filter_channels,
|
||
n_heads,
|
||
n_layers,
|
||
kernel_size,
|
||
p_dropout,
|
||
n_q=8,
|
||
dims=1024,
|
||
ssl_dim=768,
|
||
):
|
||
super().__init__()
|
||
self.hidden_channels = hidden_channels
|
||
self.filter_channels = filter_channels
|
||
self.n_heads = n_heads
|
||
self.n_layers = n_layers
|
||
self.kernel_size = kernel_size
|
||
self.p_dropout = p_dropout
|
||
|
||
self.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1)
|
||
self.ref_enc = modules.MelStyleEncoder(
|
||
ssl_dim, style_vector_dim=hidden_channels
|
||
)
|
||
|
||
self.encoder = attentions.Encoder(
|
||
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
|
||
)
|
||
|
||
self.out_proj = nn.Conv1d(hidden_channels, (n_q - 1) * dims, 1)
|
||
self.n_q = n_q
|
||
self.dims = dims
|
||
|
||
def forward(self, x, x_mask, refer, codes, infer=False):
|
||
x = x.detach()
|
||
x = self.vq_proj(x * x_mask) * x_mask
|
||
g = self.ref_enc(refer, x_mask)
|
||
x = x + g
|
||
x = self.encoder(x * x_mask, x_mask)
|
||
x = self.out_proj(x * x_mask) * x_mask
|
||
logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose(
|
||
2, 3
|
||
)
|
||
target = codes[1:].transpose(0, 1)
|
||
if not infer:
|
||
logits = logits.reshape(-1, self.dims)
|
||
target = target.reshape(-1)
|
||
loss = torch.nn.functional.cross_entropy(logits, target)
|
||
return loss
|
||
else:
|
||
_, top10_preds = torch.topk(logits, 10, dim=-1)
|
||
correct_top10 = torch.any(top10_preds == target.unsqueeze(-1), dim=-1)
|
||
top3_acc = 100 * torch.mean(correct_top10.float()).detach().cpu().item()
|
||
|
||
print("Top-10 Accuracy:", top3_acc, "%")
|
||
|
||
pred_codes = torch.argmax(logits, dim=-1)
|
||
acc = 100 * torch.mean((pred_codes == target).float()).detach().cpu().item()
|
||
print("Top-1 Accuracy:", acc, "%")
|
||
|
||
return pred_codes.transpose(0, 1)
|
||
|
||
|
||
class SynthesizerTrn(nn.Module):
|
||
"""
|
||
Synthesizer for Training
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
spec_channels,
|
||
segment_size,
|
||
inter_channels,
|
||
hidden_channels,
|
||
filter_channels,
|
||
n_heads,
|
||
n_layers,
|
||
kernel_size,
|
||
p_dropout,
|
||
resblock,
|
||
resblock_kernel_sizes,
|
||
resblock_dilation_sizes,
|
||
upsample_rates,
|
||
upsample_initial_channel,
|
||
upsample_kernel_sizes,
|
||
n_speakers=0,
|
||
gin_channels=0,
|
||
use_sdp=True,
|
||
semantic_frame_rate=None,
|
||
freeze_quantizer=None,
|
||
version = "v2",
|
||
**kwargs
|
||
):
|
||
super().__init__()
|
||
self.spec_channels = spec_channels
|
||
self.inter_channels = inter_channels
|
||
self.hidden_channels = hidden_channels
|
||
self.filter_channels = filter_channels
|
||
self.n_heads = n_heads
|
||
self.n_layers = n_layers
|
||
self.kernel_size = kernel_size
|
||
self.p_dropout = p_dropout
|
||
self.resblock = resblock
|
||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||
self.upsample_rates = upsample_rates
|
||
self.upsample_initial_channel = upsample_initial_channel
|
||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||
self.segment_size = segment_size
|
||
self.n_speakers = n_speakers
|
||
self.gin_channels = gin_channels
|
||
self.version = version
|
||
|
||
self.use_sdp = use_sdp
|
||
self.enc_p = TextEncoder(
|
||
inter_channels,
|
||
hidden_channels,
|
||
filter_channels,
|
||
n_heads,
|
||
n_layers,
|
||
kernel_size,
|
||
p_dropout,
|
||
version = version,
|
||
)
|
||
self.dec = Generator(
|
||
inter_channels,
|
||
resblock,
|
||
resblock_kernel_sizes,
|
||
resblock_dilation_sizes,
|
||
upsample_rates,
|
||
upsample_initial_channel,
|
||
upsample_kernel_sizes,
|
||
gin_channels=gin_channels,
|
||
)
|
||
self.enc_q = PosteriorEncoder(
|
||
spec_channels,
|
||
inter_channels,
|
||
hidden_channels,
|
||
5,
|
||
1,
|
||
16,
|
||
gin_channels=gin_channels,
|
||
)
|
||
self.flow = ResidualCouplingBlock(
|
||
inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
|
||
)
|
||
|
||
# self.version=os.environ.get("version","v1")
|
||
if(self.version=="v1"):
|
||
self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
|
||
else:
|
||
self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)
|
||
|
||
ssl_dim = 768
|
||
assert semantic_frame_rate in ["25hz", "50hz"]
|
||
self.semantic_frame_rate = semantic_frame_rate
|
||
if semantic_frame_rate == "25hz":
|
||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
|
||
else:
|
||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
|
||
|
||
self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024)
|
||
self.freeze_quantizer = freeze_quantizer
|
||
|
||
def forward(self, ssl, y, y_lengths, text, text_lengths):
|
||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
|
||
y.dtype
|
||
)
|
||
if(self.version=="v1"):
|
||
ge = self.ref_enc(y * y_mask, y_mask)
|
||
else:
|
||
ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
|
||
with autocast(enabled=False):
|
||
maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext()
|
||
with maybe_no_grad:
|
||
if self.freeze_quantizer:
|
||
self.ssl_proj.eval()
|
||
self.quantizer.eval()
|
||
ssl = self.ssl_proj(ssl)
|
||
quantized, codes, commit_loss, quantized_list = self.quantizer(
|
||
ssl, layers=[0]
|
||
)
|
||
|
||
if self.semantic_frame_rate == "25hz":
|
||
quantized = F.interpolate(
|
||
quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
|
||
)
|
||
|
||
x, m_p, logs_p, y_mask = self.enc_p(
|
||
quantized, y_lengths, text, text_lengths, ge
|
||
)
|
||
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge)
|
||
z_p = self.flow(z, y_mask, g=ge)
|
||
|
||
z_slice, ids_slice = commons.rand_slice_segments(
|
||
z, y_lengths, self.segment_size
|
||
)
|
||
o = self.dec(z_slice, g=ge)
|
||
return (
|
||
o,
|
||
commit_loss,
|
||
ids_slice,
|
||
y_mask,
|
||
y_mask,
|
||
(z, z_p, m_p, logs_p, m_q, logs_q),
|
||
quantized,
|
||
)
|
||
|
||
def infer(self, ssl, y, y_lengths, text, text_lengths, test=None, noise_scale=0.5):
|
||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
|
||
y.dtype
|
||
)
|
||
if(self.version=="v1"):
|
||
ge = self.ref_enc(y * y_mask, y_mask)
|
||
else:
|
||
ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
|
||
|
||
ssl = self.ssl_proj(ssl)
|
||
quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0])
|
||
if self.semantic_frame_rate == "25hz":
|
||
quantized = F.interpolate(
|
||
quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
|
||
)
|
||
|
||
x, m_p, logs_p, y_mask = self.enc_p(
|
||
quantized, y_lengths, text, text_lengths, ge, test=test
|
||
)
|
||
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
||
|
||
z = self.flow(z_p, y_mask, g=ge, reverse=True)
|
||
|
||
o = self.dec((z * y_mask)[:, :, :], g=ge)
|
||
return o, y_mask, (z, z_p, m_p, logs_p)
|
||
|
||
@torch.no_grad()
|
||
def decode(self, codes, text, refer, noise_scale=0.5,speed=1):
|
||
def get_ge(refer):
|
||
ge = None
|
||
if refer is not None:
|
||
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
|
||
refer_mask = torch.unsqueeze(
|
||
commons.sequence_mask(refer_lengths, refer.size(2)), 1
|
||
).to(refer.dtype)
|
||
if (self.version == "v1"):
|
||
ge = self.ref_enc(refer * refer_mask, refer_mask)
|
||
else:
|
||
ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
|
||
return ge
|
||
if(type(refer)==list):
|
||
ges=[]
|
||
for _refer in refer:
|
||
ge=get_ge(_refer)
|
||
ges.append(ge)
|
||
ge=torch.stack(ges,0).mean(0)
|
||
else:
|
||
ge=get_ge(refer)
|
||
|
||
y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
|
||
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
|
||
|
||
quantized = self.quantizer.decode(codes)
|
||
if self.semantic_frame_rate == "25hz":
|
||
quantized = F.interpolate(
|
||
quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
|
||
)
|
||
x, m_p, logs_p, y_mask = self.enc_p(
|
||
quantized, y_lengths, text, text_lengths, ge,speed
|
||
)
|
||
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
||
|
||
z = self.flow(z_p, y_mask, g=ge, reverse=True)
|
||
|
||
o = self.dec((z * y_mask)[:, :, :], g=ge)
|
||
return o
|
||
|
||
def extract_latent(self, x):
|
||
ssl = self.ssl_proj(x)
|
||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
|
||
return codes.transpose(0, 1)
|
||
class CFM(torch.nn.Module):
|
||
def __init__(
|
||
self,
|
||
in_channels,dit
|
||
):
|
||
super().__init__()
|
||
self.sigma_min = 1e-6
|
||
|
||
self.estimator = dit
|
||
|
||
self.in_channels = in_channels
|
||
|
||
self.criterion = torch.nn.MSELoss()
|
||
|
||
@torch.inference_mode()
|
||
def inference(self, mu, x_lens, prompt, n_timesteps, temperature=1.0, inference_cfg_rate=0):
|
||
"""Forward diffusion"""
|
||
B, T = mu.size(0), mu.size(1)
|
||
x = torch.randn([B, self.in_channels, T], device=mu.device,dtype=mu.dtype) * temperature
|
||
prompt_len = prompt.size(-1)
|
||
prompt_x = torch.zeros_like(x,dtype=mu.dtype)
|
||
prompt_x[..., :prompt_len] = prompt[..., :prompt_len]
|
||
x[..., :prompt_len] = 0
|
||
mu=mu.transpose(2,1)
|
||
t = 0
|
||
d = 1 / n_timesteps
|
||
for j in range(n_timesteps):
|
||
t_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * t
|
||
d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
|
||
# v_pred = model(x, t_tensor, d_tensor, **extra_args)
|
||
v_pred = self.estimator(x, prompt_x, x_lens, t_tensor,d_tensor, mu,drop_audio_cond=False,drop_text=False).transpose(2, 1)
|
||
if inference_cfg_rate>1e-5:
|
||
neg = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu, drop_audio_cond=True, drop_text=True).transpose(2, 1)
|
||
v_pred=v_pred+(v_pred-neg)*inference_cfg_rate
|
||
x = x + d * v_pred
|
||
t = t + d
|
||
x[:, :, :prompt_len] = 0
|
||
return x
|
||
def forward(self, x1, x_lens, prompt_lens, mu):
|
||
b, _, t = x1.shape
|
||
|
||
# random timestep
|
||
t = torch.rand([b], device=mu.device, dtype=x1.dtype)
|
||
x0 = torch.randn_like(x1,device=mu.device)
|
||
vt = x1 - x0
|
||
xt = x0 + t[:, None, None] * vt
|
||
dt = torch.zeros_like(t,device=mu.device)
|
||
prompt = torch.zeros_like(x1)
|
||
for bib in range(b):
|
||
prompt[bib, :, :prompt_lens[bib]] = x1[bib, :, :prompt_lens[bib]]
|
||
xt[bib, :, :prompt_lens[bib]] = 0
|
||
gailv=0.3# if ttime()>1736250488 else 0.1
|
||
if random.random() < gailv:
|
||
base = torch.randint(2, 8, (t.shape[0],), device=mu.device)
|
||
d = 1/torch.pow(2, base)
|
||
d_input = d.clone()
|
||
d_input[d_input < 1e-2] = 0
|
||
# with torch.no_grad():
|
||
v_pred_1 = self.estimator(xt, prompt, x_lens, t, d_input, mu).transpose(2, 1).detach()
|
||
# v_pred_1 = self.diffusion(xt, t, d_input, cond=conditioning).detach()
|
||
x_mid = xt + d[:, None, None] * v_pred_1
|
||
# v_pred_2 = self.diffusion(x_mid, t+d, d_input, cond=conditioning).detach()
|
||
v_pred_2 = self.estimator(x_mid, prompt, x_lens, t+d, d_input, mu).transpose(2, 1).detach()
|
||
vt = (v_pred_1 + v_pred_2) / 2
|
||
vt = vt.detach()
|
||
dt = 2*d
|
||
|
||
vt_pred = self.estimator(xt, prompt, x_lens, t,dt, mu).transpose(2,1)
|
||
loss = 0
|
||
|
||
# print(45555555,estimator_out.shape,u.shape,x_lens,prompt_lens)#45555555 torch.Size([7, 465, 100]) torch.Size([7, 100, 465]) tensor([461, 461, 451, 451, 442, 442, 442], device='cuda:0') tensor([ 96, 93, 185, 59, 244, 262, 294], device='cuda:0')
|
||
for bib in range(b):
|
||
loss += self.criterion(vt_pred[bib, :, prompt_lens[bib]:x_lens[bib]], vt[bib, :, prompt_lens[bib]:x_lens[bib]])
|
||
loss /= b
|
||
|
||
return loss#, estimator_out + (1 - self.sigma_min) * z
|
||
|
||
|
||
class SynthesizerTrnV3(nn.Module):
|
||
"""
|
||
Synthesizer for Training
|
||
"""
|
||
|
||
def __init__(self,
|
||
spec_channels,
|
||
segment_size,
|
||
inter_channels,
|
||
hidden_channels,
|
||
filter_channels,
|
||
n_heads,
|
||
n_layers,
|
||
kernel_size,
|
||
p_dropout,
|
||
resblock,
|
||
resblock_kernel_sizes,
|
||
resblock_dilation_sizes,
|
||
upsample_rates,
|
||
upsample_initial_channel,
|
||
upsample_kernel_sizes,
|
||
n_speakers=0,
|
||
gin_channels=0,
|
||
use_sdp=True,
|
||
semantic_frame_rate=None,
|
||
freeze_quantizer=None,
|
||
**kwargs):
|
||
|
||
super().__init__()
|
||
self.spec_channels = spec_channels
|
||
self.inter_channels = inter_channels
|
||
self.hidden_channels = hidden_channels
|
||
self.filter_channels = filter_channels
|
||
self.n_heads = n_heads
|
||
self.n_layers = n_layers
|
||
self.kernel_size = kernel_size
|
||
self.p_dropout = p_dropout
|
||
self.resblock = resblock
|
||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||
self.upsample_rates = upsample_rates
|
||
self.upsample_initial_channel = upsample_initial_channel
|
||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||
self.segment_size = segment_size
|
||
self.n_speakers = n_speakers
|
||
self.gin_channels = gin_channels
|
||
|
||
self.model_dim=512
|
||
self.use_sdp = use_sdp
|
||
self.enc_p = TextEncoder(inter_channels,hidden_channels,filter_channels,n_heads,n_layers,kernel_size,p_dropout)
|
||
# self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)###<23>ع<EFBFBD><D8B9><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
|
||
self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)###<23>ع<EFBFBD><D8B9><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
|
||
# self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
|
||
# upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
||
# self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
|
||
# gin_channels=gin_channels)
|
||
# self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
||
|
||
|
||
ssl_dim = 768
|
||
assert semantic_frame_rate in ['25hz', "50hz"]
|
||
self.semantic_frame_rate = semantic_frame_rate
|
||
if semantic_frame_rate == '25hz':
|
||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
|
||
else:
|
||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
|
||
|
||
self.quantizer = ResidualVectorQuantizer(
|
||
dimension=ssl_dim,
|
||
n_q=1,
|
||
bins=1024
|
||
)
|
||
self.freeze_quantizer=freeze_quantizer
|
||
|
||
inter_channels2=512
|
||
self.bridge=nn.Sequential(
|
||
nn.Conv1d(inter_channels, inter_channels2, 1, stride=1),
|
||
nn.LeakyReLU()
|
||
)
|
||
self.wns1=Encoder(inter_channels2, inter_channels2, inter_channels2, 5, 1, 8,gin_channels=gin_channels)
|
||
self.linear_mel=nn.Conv1d(inter_channels2,100,1,stride=1)
|
||
self.cfm = CFM(100,DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),)#text_dim is condition feature dim
|
||
|
||
def forward(self, ssl, y, mel,ssl_lengths,y_lengths, text, text_lengths,mel_lengths):#ssl_lengths no need now
|
||
with autocast(enabled=False):
|
||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
|
||
ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
|
||
maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext()
|
||
with maybe_no_grad:
|
||
if self.freeze_quantizer:
|
||
self.ssl_proj.eval()#
|
||
self.quantizer.eval()
|
||
ssl = self.ssl_proj(ssl)
|
||
quantized, codes, commit_loss, quantized_list = self.quantizer(
|
||
ssl, layers=[0]
|
||
)
|
||
with maybe_no_grad:
|
||
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
|
||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
|
||
fea=self.bridge(x)
|
||
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
|
||
fea, y_mask_ = self.wns1(fea, mel_lengths, ge)###<23><><EFBFBD><EFBFBD>1min<6E><CEA2>û<EFBFBD><C3BB><EFBFBD><EFBFBD><EFBFBD>Ͳ<EFBFBD><CDB2><EFBFBD>Ҫ<D2AA><CEA2>ѧϰ<D1A7><CFB0><EFBFBD><EFBFBD>
|
||
B=ssl.shape[0]
|
||
prompt_len_max = mel_lengths*2/3
|
||
prompt_len = (torch.rand([B], device=fea.device) * prompt_len_max).floor().to(dtype=torch.long)
|
||
minn=min(mel.shape[-1],fea.shape[-1])
|
||
mel=mel[:,:,:minn]
|
||
fea=fea[:,:,:minn]
|
||
cfm_loss= self.cfm(mel, mel_lengths, prompt_len, fea)
|
||
return cfm_loss
|
||
|
||
@torch.no_grad()
|
||
def decode_encp(self, codes,text, refer,ge=None):
|
||
# print(2333333,refer.shape)
|
||
# ge=None
|
||
if(ge==None):
|
||
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
|
||
refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
|
||
ge = self.ref_enc(refer[:,:704] * refer_mask, refer_mask)
|
||
y_lengths = torch.LongTensor([int(codes.size(2)*2)]).to(codes.device)
|
||
y_lengths1 = torch.LongTensor([int(codes.size(2)*2.5*1.5)]).to(codes.device)
|
||
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
|
||
|
||
quantized = self.quantizer.decode(codes)
|
||
if self.semantic_frame_rate == '25hz':
|
||
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
|
||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
|
||
fea=self.bridge(x)
|
||
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
|
||
####more wn paramter to learn mel
|
||
fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
|
||
return fea,ge
|
||
|
||
def extract_latent(self, x):
|
||
ssl = self.ssl_proj(x)
|
||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
|
||
return codes.transpose(0,1)
|