mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-04-05 04:08:58 +08:00
116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
# Copyright (c) 2025 SparkAudio
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# 2025 Xinsheng Wang (w.xinshawn@gmail.com)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class SamplingBlock(nn.Module):
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"""Sampling block for upsampling or downsampling"""
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def __init__(
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self,
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dim: int,
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groups: int = 1,
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upsample_scale: int = 1,
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downsample_scale: int = 1,
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) -> None:
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"""
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Args:
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dim: input dimension
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groups: number of groups
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upsample_scale: upsampling scale
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downsample_scale: downsampling scale
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"""
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super(SamplingBlock, self).__init__()
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self.upsample_scale = upsample_scale
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self.downsample_scale = downsample_scale
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if self.upsample_scale > 1:
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self.de_conv_upsampler = nn.Sequential(
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nn.LeakyReLU(0.2),
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nn.ConvTranspose1d(
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dim,
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dim,
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kernel_size=upsample_scale * 2,
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stride=upsample_scale,
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padding=upsample_scale // 2 + upsample_scale % 2,
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output_padding=upsample_scale % 2,
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groups=groups,
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),
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)
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if self.downsample_scale > 1:
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self.conv_downsampler = nn.Sequential(
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nn.LeakyReLU(0.2),
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nn.Conv1d(
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dim,
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dim,
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kernel_size=2 * downsample_scale,
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stride=downsample_scale,
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padding=downsample_scale // 2 + downsample_scale % 2,
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groups=groups,
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),
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)
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@staticmethod
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def repeat_upsampler(x, upsample_scale):
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return x.repeat_interleave(upsample_scale, dim=2)
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@staticmethod
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def skip_downsampler(x, downsample_scale):
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return F.avg_pool1d(x, kernel_size=downsample_scale, stride=downsample_scale)
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def forward(self, x):
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x = x.transpose(1, 2)
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if self.upsample_scale > 1:
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repeat_res = self.repeat_upsampler(x, self.upsample_scale)
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deconv_res = self.de_conv_upsampler(x)
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upmerge_res = repeat_res + deconv_res
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else:
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upmerge_res = x
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repeat_res = x
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if self.downsample_scale > 1:
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conv_res = self.conv_downsampler(upmerge_res)
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skip2_res = self.skip_downsampler(upmerge_res, self.downsample_scale)
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skip1_res = self.skip_downsampler(repeat_res, self.downsample_scale)
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else:
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conv_res = upmerge_res
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skip2_res = upmerge_res
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skip1_res = repeat_res
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final_res = conv_res + skip1_res + skip2_res
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return final_res
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# test
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if __name__ == "__main__":
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test_input = torch.randn(8, 1024, 50) # Batch size = 8, 1024 channels, length = 50
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model = SamplingBlock(1024, 1024, upsample_scale=2)
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model_down = SamplingBlock(1024, 1024, downsample_scale=2)
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output = model(test_input)
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output_down = model_down(test_input)
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print("shape after upsample * 2", output.shape) # torch.Size([8, 1024, 100])
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print("shape after downsample * 2", output_down.shape) # torch.Size([8, 1024, 25])
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if output.shape == torch.Size([8, 1024, 100]) and output_down.shape == torch.Size(
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[8, 1024, 25]
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):
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print("test successful")
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