File size: 5,496 Bytes
8c70653 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
from typing import List
import numpy as np
import torch
import torch.nn.functional as F
from TTS.vocoder.layers.lvc_block import LVCBlock
LRELU_SLOPE = 0.1
class UnivnetGenerator(torch.nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
hidden_channels: int,
cond_channels: int,
upsample_factors: List[int],
lvc_layers_each_block: int,
lvc_kernel_size: int,
kpnet_hidden_channels: int,
kpnet_conv_size: int,
dropout: float,
use_weight_norm=True,
):
"""Univnet Generator network.
Paper: https://arxiv.org/pdf/2106.07889.pdf
Args:
in_channels (int): Number of input tensor channels.
out_channels (int): Number of channels of the output tensor.
hidden_channels (int): Number of hidden network channels.
cond_channels (int): Number of channels of the conditioning tensors.
upsample_factors (List[int]): List of uplsample factors for the upsampling layers.
lvc_layers_each_block (int): Number of LVC layers in each block.
lvc_kernel_size (int): Kernel size of the LVC layers.
kpnet_hidden_channels (int): Number of hidden channels in the key-point network.
kpnet_conv_size (int): Number of convolution channels in the key-point network.
dropout (float): Dropout rate.
use_weight_norm (bool, optional): Enable/disable weight norm. Defaults to True.
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.cond_channels = cond_channels
self.upsample_scale = np.prod(upsample_factors)
self.lvc_block_nums = len(upsample_factors)
# define first convolution
self.first_conv = torch.nn.Conv1d(
in_channels, hidden_channels, kernel_size=7, padding=(7 - 1) // 2, dilation=1, bias=True
)
# define residual blocks
self.lvc_blocks = torch.nn.ModuleList()
cond_hop_length = 1
for n in range(self.lvc_block_nums):
cond_hop_length = cond_hop_length * upsample_factors[n]
lvcb = LVCBlock(
in_channels=hidden_channels,
cond_channels=cond_channels,
upsample_ratio=upsample_factors[n],
conv_layers=lvc_layers_each_block,
conv_kernel_size=lvc_kernel_size,
cond_hop_length=cond_hop_length,
kpnet_hidden_channels=kpnet_hidden_channels,
kpnet_conv_size=kpnet_conv_size,
kpnet_dropout=dropout,
)
self.lvc_blocks += [lvcb]
# define output layers
self.last_conv_layers = torch.nn.ModuleList(
[
torch.nn.Conv1d(
hidden_channels, out_channels, kernel_size=7, padding=(7 - 1) // 2, dilation=1, bias=True
),
]
)
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
def forward(self, c):
"""Calculate forward propagation.
Args:
c (Tensor): Local conditioning auxiliary features (B, C ,T').
Returns:
Tensor: Output tensor (B, out_channels, T)
"""
# random noise
x = torch.randn([c.shape[0], self.in_channels, c.shape[2]])
x = x.to(self.first_conv.bias.device)
x = self.first_conv(x)
for n in range(self.lvc_block_nums):
x = self.lvc_blocks[n](x, c)
# apply final layers
for f in self.last_conv_layers:
x = F.leaky_relu(x, LRELU_SLOPE)
x = f(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
"""Remove weight normalization module from all of the layers."""
def _remove_weight_norm(m):
try:
# print(f"Weight norm is removed from {m}.")
torch.nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(_remove_weight_norm)
def apply_weight_norm(self):
"""Apply weight normalization module from all of the layers."""
def _apply_weight_norm(m):
if isinstance(m, (torch.nn.Conv1d, torch.nn.Conv2d)):
torch.nn.utils.weight_norm(m)
# print(f"Weight norm is applied to {m}.")
self.apply(_apply_weight_norm)
@staticmethod
def _get_receptive_field_size(layers, stacks, kernel_size, dilation=lambda x: 2**x):
assert layers % stacks == 0
layers_per_cycle = layers // stacks
dilations = [dilation(i % layers_per_cycle) for i in range(layers)]
return (kernel_size - 1) * sum(dilations) + 1
@property
def receptive_field_size(self):
"""Return receptive field size."""
return self._get_receptive_field_size(self.layers, self.stacks, self.kernel_size)
@torch.no_grad()
def inference(self, c):
"""Perform inference.
Args:
c (Tensor): Local conditioning auxiliary features :math:`(B, C, T)`.
Returns:
Tensor: Output tensor (T, out_channels)
"""
x = torch.randn([c.shape[0], self.in_channels, c.shape[2]])
x = x.to(self.first_conv.bias.device)
c = c.to(next(self.parameters()))
return self.forward(c)
|