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# refer to：
# https://github.com/CNChTu/Diffusion-SVC/blob/v2.0_dev/diffusion/naive_v2/model_conformer_naive.py
# https://github.com/CNChTu/Diffusion-SVC/blob/v2.0_dev/diffusion/naive_v2/naive_v2_diff.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules.commons.common_layers import SinusoidalPosEmb, SwiGLU
from utils.hparams import hparams
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
nn.init.kaiming_normal_(self.weight)
class Transpose(nn.Module):
def __init__(self, dims):
super().__init__()
assert len(dims) == 2, 'dims must be a tuple of two dimensions'
self.dims = dims
def forward(self, x):
return x.transpose(*self.dims)
class LYNXConvModule(nn.Module):
@staticmethod
def calc_same_padding(kernel_size):
pad = kernel_size // 2
return pad, pad - (kernel_size + 1) % 2
def __init__(self, dim, expansion_factor, kernel_size=31, activation='PReLU', dropout=0.0):
super().__init__()
inner_dim = dim * expansion_factor
activation_classes = {
'SiLU': nn.SiLU,
'ReLU': nn.ReLU,
'PReLU': lambda: nn.PReLU(inner_dim)
}
activation = activation if activation is not None else 'PReLU'
if activation not in activation_classes:
raise ValueError(f'{activation} is not a valid activation')
_activation = activation_classes[activation]()
padding = self.calc_same_padding(kernel_size)
if float(dropout) > 0.:
_dropout = nn.Dropout(dropout)
else:
_dropout = nn.Identity()
self.net = nn.Sequential(
nn.LayerNorm(dim),
Transpose((1, 2)),
nn.Conv1d(dim, inner_dim * 2, 1),
SwiGLU(dim=1),
nn.Conv1d(inner_dim, inner_dim, kernel_size=kernel_size, padding=padding[0], groups=inner_dim),
_activation,
nn.Conv1d(inner_dim, dim, 1),
Transpose((1, 2)),
_dropout
)
def forward(self, x):
return self.net(x)
class LYNXNetResidualLayer(nn.Module):
def __init__(self, dim_cond, dim, expansion_factor, kernel_size=31, activation='PReLU', dropout=0.0):
super().__init__()
self.diffusion_projection = nn.Conv1d(dim, dim, 1)
self.conditioner_projection = nn.Conv1d(dim_cond, dim, 1)
self.convmodule = LYNXConvModule(dim=dim, expansion_factor=expansion_factor, kernel_size=kernel_size,
activation=activation, dropout=dropout)
def forward(self, x, conditioner, diffusion_step, front_cond_inject=False):
if front_cond_inject:
x = x + self.conditioner_projection(conditioner)
res_x = x
else:
res_x = x
x = x + self.conditioner_projection(conditioner)
x = x + self.diffusion_projection(diffusion_step)
x = x.transpose(1, 2)
x = self.convmodule(x) # (#batch, dim, length)
x = x.transpose(1, 2) + res_x
return x # (#batch, length, dim)
class LYNXNet(nn.Module):
def __init__(self, in_dims, n_feats, *, num_layers=6, num_channels=512, expansion_factor=2, kernel_size=31,
activation='PReLU', dropout=0.0, strong_cond=False):
"""
LYNXNet(Linear Gated Depthwise Separable Convolution Network)
TIPS:You can control the style of the generated results by modifying the 'activation',
- 'PReLU'(default) : Similar to WaveNet
- 'SiLU' : Voice will be more pronounced, not recommended for use under DDPM
- 'ReLU' : Contrary to 'SiLU', Voice will be weakened
"""
super().__init__()
self.in_dims = in_dims
self.n_feats = n_feats
self.input_projection = Conv1d(in_dims * n_feats, num_channels, 1)
self.diffusion_embedding = nn.Sequential(
SinusoidalPosEmb(num_channels),
nn.Linear(num_channels, num_channels * 4),
nn.GELU(),
nn.Linear(num_channels * 4, num_channels),
)
self.residual_layers = nn.ModuleList(
[
LYNXNetResidualLayer(
dim_cond=hparams['hidden_size'],
dim=num_channels,
expansion_factor=expansion_factor,
kernel_size=kernel_size,
activation=activation,
dropout=dropout
)
for i in range(num_layers)
]
)
self.norm = nn.LayerNorm(num_channels)
self.output_projection = Conv1d(num_channels, in_dims * n_feats, kernel_size=1)
self.strong_cond = strong_cond
nn.init.zeros_(self.output_projection.weight)
def forward(self, spec, diffusion_step, cond):
"""
:param spec: [B, F, M, T]
:param diffusion_step: [B, 1]
:param cond: [B, H, T]
:return:
"""
if self.n_feats == 1:
x = spec[:, 0] # [B, M, T]
else:
x = spec.flatten(start_dim=1, end_dim=2) # [B, F x M, T]
x = self.input_projection(x) # x [B, residual_channel, T]
if not self.strong_cond:
x = F.gelu(x)
diffusion_step = self.diffusion_embedding(diffusion_step).unsqueeze(-1)
for layer in self.residual_layers:
x = layer(x, cond, diffusion_step, front_cond_inject=self.strong_cond)
# post-norm
x = self.norm(x.transpose(1, 2)).transpose(1, 2)
# MLP and GLU
x = self.output_projection(x) # [B, 128, T]
if self.n_feats == 1:
x = x[:, None, :, :]
else:
# This is the temporary solution since PyTorch 1.13
# does not support exporting aten::unflatten to ONNX
# x = x.unflatten(dim=1, sizes=(self.n_feats, self.in_dims))
x = x.reshape(-1, self.n_feats, self.in_dims, x.shape[2])
return x
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