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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch.nn as nn
from modules.general.utils import Conv1d, zero_module
from .residual_block import ResidualBlock
class BiDilConv(nn.Module):
r"""Dilated CNN architecture with residual connections, default diffusion decoder.
Args:
input_channel: The number of input channels.
base_channel: The number of base channels.
n_res_block: The number of residual blocks.
conv_kernel_size: The kernel size of convolutional layers.
dilation_cycle_length: The cycle length of dilation.
conditioner_size: The size of conditioner.
"""
def __init__(
self,
input_channel,
base_channel,
n_res_block,
conv_kernel_size,
dilation_cycle_length,
conditioner_size,
output_channel: int = -1,
):
super().__init__()
self.input_channel = input_channel
self.base_channel = base_channel
self.n_res_block = n_res_block
self.conv_kernel_size = conv_kernel_size
self.dilation_cycle_length = dilation_cycle_length
self.conditioner_size = conditioner_size
self.output_channel = output_channel if output_channel > 0 else input_channel
self.input = nn.Sequential(
Conv1d(
input_channel,
base_channel,
1,
),
nn.ReLU(),
)
self.residual_blocks = nn.ModuleList(
[
ResidualBlock(
channels=base_channel,
kernel_size=conv_kernel_size,
dilation=2 ** (i % dilation_cycle_length),
d_context=conditioner_size,
)
for i in range(n_res_block)
]
)
self.out_proj = nn.Sequential(
Conv1d(
base_channel,
base_channel,
1,
),
nn.ReLU(),
zero_module(
Conv1d(
base_channel,
self.output_channel,
1,
),
),
)
def forward(self, x, y, context=None):
"""
Args:
x: Noisy mel-spectrogram [B x ``n_mel`` x L]
y: FILM embeddings with the shape of (B, ``base_channel``)
context: Context with the shape of [B x ``d_context`` x L], default to None.
"""
h = self.input(x)
skip = None
for i in range(self.n_res_block):
h, skip_connection = self.residual_blocks[i](h, y, context)
skip = skip_connection if skip is None else skip_connection + skip
out = skip / math.sqrt(self.n_res_block)
out = self.out_proj(out)
return out
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