src/models/SoulX-LiveAct/kokoro/modules.py
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# https://github.com/yl4579/StyleTTS2/blob/main/models.py
from .istftnet import AdainResBlk1d
from torch.nn.utils import weight_norm
from transformers import AlbertModel
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearNorm(nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__()
self.linear_layer = nn.Linear(in_dim, out_dim, bias=bias)
nn.init.xavier_uniform_(self.linear_layer.weight, gain=nn.init.calculate_gain(w_init_gain))
def forward(self, x):
return self.linear_layer(x)
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
class TextEncoder(nn.Module):
def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)):
super().__init__()
self.embedding = nn.Embedding(n_symbols, channels)
padding = (kernel_size - 1) // 2
self.cnn = nn.ModuleList()
for _ in range(depth):
self.cnn.append(nn.Sequential(
weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)),
LayerNorm(channels),
actv,
nn.Dropout(0.2),
))
self.lstm = nn.LSTM(channels, channels//2, 1, batch_first=True, bidirectional=True)
def forward(self, x, input_lengths, m):
x = self.embedding(x) # [B, T, emb]
x = x.transpose(1, 2) # [B, emb, T]
m = m.unsqueeze(1)
x.masked_fill_(m, 0.0)
for c in self.cnn:
x = c(x)
x.masked_fill_(m, 0.0)
x = x.transpose(1, 2) # [B, T, chn]
lengths = input_lengths if input_lengths.device == torch.device('cpu') else input_lengths.to('cpu')
x = nn.utils.rnn.pack_padded_sequence(x, lengths, batch_first=True, enforce_sorted=False)
self.lstm.flatten_parameters()
x, _ = self.lstm(x)
x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
x = x.transpose(-1, -2)
x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]], device=x.device)
x_pad[:, :, :x.shape[-1]] = x
x = x_pad
x.masked_fill_(m, 0.0)
return x
class AdaLayerNorm(nn.Module):
def __init__(self, style_dim, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.fc = nn.Linear(style_dim, channels*2)
def forward(self, x, s):
x = x.transpose(-1, -2)
x = x.transpose(1, -1)
h = self.fc(s)
h = h.view(h.size(0), h.size(1), 1)
gamma, beta = torch.chunk(h, chunks=2, dim=1)
gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), eps=self.eps)
x = (1 + gamma) * x + beta
return x.transpose(1, -1).transpose(-1, -2)
class ProsodyPredictor(nn.Module):
def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1):
super().__init__()
self.text_encoder = DurationEncoder(sty_dim=style_dim, d_model=d_hid,nlayers=nlayers, dropout=dropout)
self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
self.duration_proj = LinearNorm(d_hid, max_dur)
self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
self.F0 = nn.ModuleList()
self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
self.N = nn.ModuleList()
self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
def forward(self, texts, style, text_lengths, alignment, m):
d = self.text_encoder(texts, style, text_lengths, m)
m = m.unsqueeze(1)
lengths = text_lengths if text_lengths.device == torch.device('cpu') else text_lengths.to('cpu')
x = nn.utils.rnn.pack_padded_sequence(d, lengths, batch_first=True, enforce_sorted=False)
self.lstm.flatten_parameters()
x, _ = self.lstm(x)
x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]], device=x.device)
x_pad[:, :x.shape[1], :] = x
x = x_pad
duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=False))
en = (d.transpose(-1, -2) @ alignment)
return duration.squeeze(-1), en
def F0Ntrain(self, x, s):
x, _ = self.shared(x.transpose(-1, -2))
F0 = x.transpose(-1, -2)
for block in self.F0:
F0 = block(F0, s)
F0 = self.F0_proj(F0)
N = x.transpose(-1, -2)
for block in self.N:
N = block(N, s)
N = self.N_proj(N)
return F0.squeeze(1), N.squeeze(1)
class DurationEncoder(nn.Module):
def __init__(self, sty_dim, d_model, nlayers, dropout=0.1):
super().__init__()
self.lstms = nn.ModuleList()
for _ in range(nlayers):
self.lstms.append(nn.LSTM(d_model + sty_dim, d_model // 2, num_layers=1, batch_first=True, bidirectional=True, dropout=dropout))
self.lstms.append(AdaLayerNorm(sty_dim, d_model))
self.dropout = dropout
self.d_model = d_model
self.sty_dim = sty_dim
def forward(self, x, style, text_lengths, m):
masks = m
x = x.permute(2, 0, 1)
s = style.expand(x.shape[0], x.shape[1], -1)
x = torch.cat([x, s], axis=-1)
x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0)
x = x.transpose(0, 1)
x = x.transpose(-1, -2)
for block in self.lstms:
if isinstance(block, AdaLayerNorm):
x = block(x.transpose(-1, -2), style).transpose(-1, -2)
x = torch.cat([x, s.permute(1, 2, 0)], axis=1)
x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0)
else:
lengths = text_lengths if text_lengths.device == torch.device('cpu') else text_lengths.to('cpu')
x = x.transpose(-1, -2)
x = nn.utils.rnn.pack_padded_sequence(
x, lengths, batch_first=True, enforce_sorted=False)
block.flatten_parameters()
x, _ = block(x)
x, _ = nn.utils.rnn.pad_packed_sequence(
x, batch_first=True)
x = F.dropout(x, p=self.dropout, training=False)
x = x.transpose(-1, -2)
x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]], device=x.device)
x_pad[:, :, :x.shape[-1]] = x
x = x_pad
return x.transpose(-1, -2)
# https://github.com/yl4579/StyleTTS2/blob/main/Utils/PLBERT/util.py
class CustomAlbert(AlbertModel):
def forward(self, *args, **kwargs):
outputs = super().forward(*args, **kwargs)
return outputs.last_hidden_state