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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Mish(nn.Module):
def forward(self, x):
return x * torch.tanh(F.softplus(x))
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super(SinusoidalPosEmb, self).__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim-1)
emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
emb = x.unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class ResidualBlock(nn.Module):
def __init__(self, encoder_hidden, residual_channels, dilation):
super().__init__()
self.dilated_conv = nn.Conv1d(
residual_channels,
2 * residual_channels,
3,
padding=dilation,
dilation=dilation
)
self.diffusion_projection = nn.Linear(
residual_channels, residual_channels
)
self.conditioner_projection = nn.Conv1d(
encoder_hidden, 2 * residual_channels, 1
)
self.output_projection = nn.Conv1d(
residual_channels, 2 * residual_channels, 1
)
def forward(self, x, conditioner, diffusion_step):
diffusion_step = self.diffusion_projection(diffusion_step
).unsqueeze(-1)
conditioner = self.conditioner_projection(conditioner)
y = x + diffusion_step
y = self.dilated_conv(y) + conditioner
gate, filter = torch.chunk(y, 2, dim=1)
y = torch.sigmoid(gate) * torch.tanh(filter)
y = self.output_projection(y)
residual, skip = torch.chunk(y, 2, dim=1)
return (x+residual) / math.sqrt(2.0), skip
class DiffSingerNet(nn.Module):
def __init__(
self,
in_dims=128,
residual_channels=256,
encoder_hidden=256,
dilation_cycle_length=4,
residual_layers=20,
):
super().__init__()
# self.pe_scale = pe_scale
self.input_projection = nn.Conv1d(in_dims, residual_channels, 1)
self.time_pos_emb = SinusoidalPosEmb(residual_channels)
dim = residual_channels
self.mlp = nn.Sequential(
nn.Linear(dim, dim * 4), Mish(), nn.Linear(dim * 4, dim)
)
self.residual_layers = nn.ModuleList([
ResidualBlock(
encoder_hidden, residual_channels,
2**(i % dilation_cycle_length)
) for i in range(residual_layers)
])
self.skip_projection = nn.Conv1d(
residual_channels, residual_channels, 1
)
self.output_projection = nn.Conv1d(residual_channels, in_dims, 1)
nn.init.zeros_(self.output_projection.weight)
def forward(self, x, timesteps, context, x_mask=None, context_mask=None):
# make it compatible with int time step during inference
if timesteps.dim() == 0:
timesteps = timesteps.expand(x.shape[0]
).to(x.device, dtype=torch.long)
x = self.input_projection(x) # x [B, residual_channel, T]
x = F.relu(x)
t = self.time_pos_emb(timesteps)
t = self.mlp(t)
cond = context
skip = []
for layer_id, layer in enumerate(self.residual_layers):
x, skip_connection = layer(x, cond, t)
skip.append(skip_connection)
x = torch.sum(torch.stack(skip),
dim=0) / math.sqrt(len(self.residual_layers))
x = self.skip_projection(x)
x = F.relu(x)
x = self.output_projection(x) # [B, M, T]
return x * x_mask.unsqueeze(1)
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