mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-04-05 04:08:58 +08:00
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
# Copyright (c) 2024 Xinsheng Wang (w.xinshawn@gmail.com)
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
# Adapted from https://github.com/descriptinc/descript-audio-codec under the Apache License 2.0
|
|
|
|
|
|
import torch.nn as nn
|
|
|
|
from spark.sparktts.modules.blocks.layers import (
|
|
Snake1d,
|
|
WNConv1d,
|
|
ResidualUnit,
|
|
WNConvTranspose1d,
|
|
init_weights,
|
|
)
|
|
|
|
|
|
class DecoderBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
input_dim: int = 16,
|
|
output_dim: int = 8,
|
|
kernel_size: int = 2,
|
|
stride: int = 1,
|
|
):
|
|
super().__init__()
|
|
self.block = nn.Sequential(
|
|
Snake1d(input_dim),
|
|
WNConvTranspose1d(
|
|
input_dim,
|
|
output_dim,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=(kernel_size - stride) // 2,
|
|
),
|
|
ResidualUnit(output_dim, dilation=1),
|
|
ResidualUnit(output_dim, dilation=3),
|
|
ResidualUnit(output_dim, dilation=9),
|
|
)
|
|
|
|
def forward(self, x):
|
|
return self.block(x)
|
|
|
|
|
|
class WaveGenerator(nn.Module):
|
|
def __init__(
|
|
self,
|
|
input_channel,
|
|
channels,
|
|
rates,
|
|
kernel_sizes,
|
|
d_out: int = 1,
|
|
):
|
|
super().__init__()
|
|
|
|
# Add first conv layer
|
|
layers = [WNConv1d(input_channel, channels, kernel_size=7, padding=3)]
|
|
|
|
# Add upsampling + MRF blocks
|
|
for i, (kernel_size, stride) in enumerate(zip(kernel_sizes, rates)):
|
|
input_dim = channels // 2**i
|
|
output_dim = channels // 2 ** (i + 1)
|
|
layers += [DecoderBlock(input_dim, output_dim, kernel_size, stride)]
|
|
|
|
# Add final conv layer
|
|
layers += [
|
|
Snake1d(output_dim),
|
|
WNConv1d(output_dim, d_out, kernel_size=7, padding=3),
|
|
nn.Tanh(),
|
|
]
|
|
|
|
self.model = nn.Sequential(*layers)
|
|
|
|
self.apply(init_weights)
|
|
|
|
def forward(self, x):
|
|
return self.model(x)
|