373 lines
14 KiB
Python
373 lines
14 KiB
Python
# https://github.com/yl4579/StyleTTS2/blob/main/models.py
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from istftnet import AdaIN1d, Decoder
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from munch import Munch
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from pathlib import Path
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from plbert import load_plbert
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from torch.nn.utils import weight_norm, spectral_norm
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import json
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import numpy as np
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import os
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import os.path as osp
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class LinearNorm(torch.nn.Module):
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def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
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super(LinearNorm, self).__init__()
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self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
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torch.nn.init.xavier_uniform_(
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self.linear_layer.weight,
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gain=torch.nn.init.calculate_gain(w_init_gain))
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def forward(self, x):
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return self.linear_layer(x)
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class LayerNorm(nn.Module):
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def __init__(self, channels, eps=1e-5):
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super().__init__()
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self.channels = channels
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self.eps = eps
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self.gamma = nn.Parameter(torch.ones(channels))
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self.beta = nn.Parameter(torch.zeros(channels))
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def forward(self, x):
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x = x.transpose(1, -1)
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x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
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return x.transpose(1, -1)
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class TextEncoder(nn.Module):
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def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)):
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super().__init__()
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self.embedding = nn.Embedding(n_symbols, channels)
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padding = (kernel_size - 1) // 2
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self.cnn = nn.ModuleList()
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for _ in range(depth):
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self.cnn.append(nn.Sequential(
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weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)),
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LayerNorm(channels),
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actv,
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nn.Dropout(0.2),
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))
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# self.cnn = nn.Sequential(*self.cnn)
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self.lstm = nn.LSTM(channels, channels//2, 1, batch_first=True, bidirectional=True)
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def forward(self, x, input_lengths, m):
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x = self.embedding(x) # [B, T, emb]
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x = x.transpose(1, 2) # [B, emb, T]
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m = m.to(input_lengths.device).unsqueeze(1)
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x.masked_fill_(m, 0.0)
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for c in self.cnn:
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x = c(x)
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x.masked_fill_(m, 0.0)
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x = x.transpose(1, 2) # [B, T, chn]
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input_lengths = input_lengths.cpu().numpy()
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x = nn.utils.rnn.pack_padded_sequence(
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x, input_lengths, batch_first=True, enforce_sorted=False)
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self.lstm.flatten_parameters()
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x, _ = self.lstm(x)
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x, _ = nn.utils.rnn.pad_packed_sequence(
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x, batch_first=True)
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x = x.transpose(-1, -2)
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x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
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x_pad[:, :, :x.shape[-1]] = x
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x = x_pad.to(x.device)
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x.masked_fill_(m, 0.0)
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return x
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def inference(self, x):
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x = self.embedding(x)
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x = x.transpose(1, 2)
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x = self.cnn(x)
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x = x.transpose(1, 2)
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self.lstm.flatten_parameters()
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x, _ = self.lstm(x)
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return x
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def length_to_mask(self, lengths):
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mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
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mask = torch.gt(mask+1, lengths.unsqueeze(1))
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return mask
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class UpSample1d(nn.Module):
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def __init__(self, layer_type):
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super().__init__()
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self.layer_type = layer_type
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def forward(self, x):
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if self.layer_type == 'none':
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return x
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else:
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return F.interpolate(x, scale_factor=2, mode='nearest')
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class AdainResBlk1d(nn.Module):
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def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2),
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upsample='none', dropout_p=0.0):
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super().__init__()
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self.actv = actv
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self.upsample_type = upsample
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self.upsample = UpSample1d(upsample)
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self.learned_sc = dim_in != dim_out
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self._build_weights(dim_in, dim_out, style_dim)
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self.dropout = nn.Dropout(dropout_p)
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if upsample == 'none':
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self.pool = nn.Identity()
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else:
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self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1))
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def _build_weights(self, dim_in, dim_out, style_dim):
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self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
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self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
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self.norm1 = AdaIN1d(style_dim, dim_in)
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self.norm2 = AdaIN1d(style_dim, dim_out)
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if self.learned_sc:
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self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
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def _shortcut(self, x):
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x = self.upsample(x)
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if self.learned_sc:
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x = self.conv1x1(x)
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return x
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def _residual(self, x, s):
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x = self.norm1(x, s)
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x = self.actv(x)
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x = self.pool(x)
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x = self.conv1(self.dropout(x))
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x = self.norm2(x, s)
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x = self.actv(x)
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x = self.conv2(self.dropout(x))
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return x
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def forward(self, x, s):
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out = self._residual(x, s)
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out = (out + self._shortcut(x)) / np.sqrt(2)
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return out
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class AdaLayerNorm(nn.Module):
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def __init__(self, style_dim, channels, eps=1e-5):
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super().__init__()
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self.channels = channels
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self.eps = eps
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self.fc = nn.Linear(style_dim, channels*2)
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def forward(self, x, s):
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x = x.transpose(-1, -2)
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x = x.transpose(1, -1)
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h = self.fc(s)
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h = h.view(h.size(0), h.size(1), 1)
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gamma, beta = torch.chunk(h, chunks=2, dim=1)
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gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1)
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x = F.layer_norm(x, (self.channels,), eps=self.eps)
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x = (1 + gamma) * x + beta
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return x.transpose(1, -1).transpose(-1, -2)
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class ProsodyPredictor(nn.Module):
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def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1):
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super().__init__()
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self.text_encoder = DurationEncoder(sty_dim=style_dim,
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d_model=d_hid,
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nlayers=nlayers,
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dropout=dropout)
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self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
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self.duration_proj = LinearNorm(d_hid, max_dur)
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self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
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self.F0 = nn.ModuleList()
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self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
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self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
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self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
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self.N = nn.ModuleList()
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self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
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self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
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self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
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self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
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self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
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def forward(self, texts, style, text_lengths, alignment, m):
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d = self.text_encoder(texts, style, text_lengths, m)
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batch_size = d.shape[0]
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text_size = d.shape[1]
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# predict duration
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input_lengths = text_lengths.cpu().numpy()
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x = nn.utils.rnn.pack_padded_sequence(
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d, input_lengths, batch_first=True, enforce_sorted=False)
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m = m.to(text_lengths.device).unsqueeze(1)
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self.lstm.flatten_parameters()
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x, _ = self.lstm(x)
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x, _ = nn.utils.rnn.pad_packed_sequence(
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x, batch_first=True)
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x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]])
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x_pad[:, :x.shape[1], :] = x
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x = x_pad.to(x.device)
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duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=self.training))
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en = (d.transpose(-1, -2) @ alignment)
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return duration.squeeze(-1), en
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def F0Ntrain(self, x, s):
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x, _ = self.shared(x.transpose(-1, -2))
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F0 = x.transpose(-1, -2)
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for block in self.F0:
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F0 = block(F0, s)
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F0 = self.F0_proj(F0)
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N = x.transpose(-1, -2)
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for block in self.N:
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N = block(N, s)
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N = self.N_proj(N)
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return F0.squeeze(1), N.squeeze(1)
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def length_to_mask(self, lengths):
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mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
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mask = torch.gt(mask+1, lengths.unsqueeze(1))
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return mask
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class DurationEncoder(nn.Module):
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def __init__(self, sty_dim, d_model, nlayers, dropout=0.1):
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super().__init__()
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self.lstms = nn.ModuleList()
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for _ in range(nlayers):
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self.lstms.append(nn.LSTM(d_model + sty_dim,
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d_model // 2,
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num_layers=1,
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batch_first=True,
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bidirectional=True,
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dropout=dropout))
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self.lstms.append(AdaLayerNorm(sty_dim, d_model))
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self.dropout = dropout
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self.d_model = d_model
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self.sty_dim = sty_dim
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def forward(self, x, style, text_lengths, m):
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masks = m.to(text_lengths.device)
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x = x.permute(2, 0, 1)
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s = style.expand(x.shape[0], x.shape[1], -1)
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x = torch.cat([x, s], axis=-1)
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x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0)
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x = x.transpose(0, 1)
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input_lengths = text_lengths.cpu().numpy()
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x = x.transpose(-1, -2)
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for block in self.lstms:
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if isinstance(block, AdaLayerNorm):
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x = block(x.transpose(-1, -2), style).transpose(-1, -2)
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x = torch.cat([x, s.permute(1, -1, 0)], axis=1)
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x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0)
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else:
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x = x.transpose(-1, -2)
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x = nn.utils.rnn.pack_padded_sequence(
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x, input_lengths, batch_first=True, enforce_sorted=False)
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block.flatten_parameters()
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x, _ = block(x)
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x, _ = nn.utils.rnn.pad_packed_sequence(
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x, batch_first=True)
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = x.transpose(-1, -2)
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x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
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x_pad[:, :, :x.shape[-1]] = x
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x = x_pad.to(x.device)
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return x.transpose(-1, -2)
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def inference(self, x, style):
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x = self.embedding(x.transpose(-1, -2)) * np.sqrt(self.d_model)
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style = style.expand(x.shape[0], x.shape[1], -1)
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x = torch.cat([x, style], axis=-1)
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src = self.pos_encoder(x)
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output = self.transformer_encoder(src).transpose(0, 1)
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return output
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def length_to_mask(self, lengths):
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mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
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mask = torch.gt(mask+1, lengths.unsqueeze(1))
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return mask
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# https://github.com/yl4579/StyleTTS2/blob/main/utils.py
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def recursive_munch(d):
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if isinstance(d, dict):
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return Munch((k, recursive_munch(v)) for k, v in d.items())
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elif isinstance(d, list):
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return [recursive_munch(v) for v in d]
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else:
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return d
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def build_model(path, device):
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config = Path(__file__).parent / 'config.json'
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assert config.exists(), f'Config path incorrect: config.json not found at {config}'
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with open(config, 'r') as r:
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args = recursive_munch(json.load(r))
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assert args.decoder.type == 'istftnet', f'Unknown decoder type: {args.decoder.type}'
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decoder = Decoder(dim_in=args.hidden_dim, style_dim=args.style_dim, dim_out=args.n_mels,
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resblock_kernel_sizes = args.decoder.resblock_kernel_sizes,
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upsample_rates = args.decoder.upsample_rates,
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upsample_initial_channel=args.decoder.upsample_initial_channel,
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resblock_dilation_sizes=args.decoder.resblock_dilation_sizes,
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upsample_kernel_sizes=args.decoder.upsample_kernel_sizes,
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gen_istft_n_fft=args.decoder.gen_istft_n_fft, gen_istft_hop_size=args.decoder.gen_istft_hop_size)
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text_encoder = TextEncoder(channels=args.hidden_dim, kernel_size=5, depth=args.n_layer, n_symbols=args.n_token)
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predictor = ProsodyPredictor(style_dim=args.style_dim, d_hid=args.hidden_dim, nlayers=args.n_layer, max_dur=args.max_dur, dropout=args.dropout)
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bert = load_plbert()
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bert_encoder = nn.Linear(bert.config.hidden_size, args.hidden_dim)
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for parent in [bert, bert_encoder, predictor, decoder, text_encoder]:
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for child in parent.children():
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if isinstance(child, nn.RNNBase):
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child.flatten_parameters()
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model = Munch(
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bert=bert.to(device).eval(),
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bert_encoder=bert_encoder.to(device).eval(),
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predictor=predictor.to(device).eval(),
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decoder=decoder.to(device).eval(),
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text_encoder=text_encoder.to(device).eval(),
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)
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for key, state_dict in torch.load(path, map_location='cpu', weights_only=True)['net'].items():
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assert key in model, key
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try:
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model[key].load_state_dict(state_dict)
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except:
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state_dict = {k[7:]: v for k, v in state_dict.items()}
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model[key].load_state_dict(state_dict, strict=False)
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return model
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