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import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn.utils.parametrizations import weight_norm
from ..hparams import HParams
from .lvcnet import LVCBlock
from .mrstft import MRSTFTLoss
class UnivNet(nn.Module):
@property
def d_noise(self):
return 128
@property
def strides(self):
return [7, 5, 4, 3]
@property
def dilations(self):
return [1, 3, 9, 27]
@property
def nc(self):
return self.hp.univnet_nc
@property
def scale_factor(self) -> int:
return self.hp.hop_size
def __init__(self, hp: HParams, d_input):
super().__init__()
self.d_input = d_input
self.hp = hp
self.blocks = nn.ModuleList(
[
LVCBlock(
self.nc,
d_input,
stride=stride,
dilations=self.dilations,
cond_hop_length=hop_length,
kpnet_conv_size=3,
)
for stride, hop_length in zip(self.strides, np.cumprod(self.strides))
]
)
self.conv_pre = weight_norm(nn.Conv1d(self.d_noise, self.nc, 7, padding=3, padding_mode="reflect"))
self.conv_post = nn.Sequential(
nn.LeakyReLU(0.2),
weight_norm(nn.Conv1d(self.nc, 1, 7, padding=3, padding_mode="reflect")),
nn.Tanh(),
)
self.mrstft = MRSTFTLoss(hp)
@property
def eps(self):
return 1e-5
def forward(self, x: Tensor, y: Tensor | None = None, npad=10):
"""
Args:
x: (b c t), acoustic features
y: (b t), waveform
Returns:
z: (b t), waveform
"""
assert x.ndim == 3, "x must be 3D tensor"
assert y is None or y.ndim == 2, "y must be 2D tensor"
assert x.shape[1] == self.d_input, f"x.shape[1] must be {self.d_input}, but got {x.shape}"
assert npad >= 0, "npad must be positive or zero"
x = F.pad(x, (0, npad), "constant", 0)
z = torch.randn(x.shape[0], self.d_noise, x.shape[2]).to(x)
z = self.conv_pre(z) # (b c t)
for block in self.blocks:
z = block(z, x) # (b c t)
z = self.conv_post(z) # (b 1 t)
z = z[..., : -self.scale_factor * npad]
z = z.squeeze(1) # (b t)
if y is not None:
self.losses = self.mrstft(z, y)
return z
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