RVC/Retrieval-based-Voice-Conversion-WebUI
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Reformat and rewrite _get_name_params (#57)

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* Reformat

* rewrite _get_name_params

* Add workflow for automatic formatting

* Revert "Add workflow for automatic formatting"

This reverts commit 9111c5dbc1.

* revert Retrieval_based_Voice_Conversion_WebUI.ipynb

---------

Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
This commit is contained in:
Ftps
2023-04-15 20:44:24 +09:00
committed by GitHub
parent aaa893c4b1
commit c8261b2ccc
45 changed files with 4878 additions and 2456 deletions
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40
uvr5_pack/lib_v5/nets.py
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@@ -7,7 +7,6 @@ from uvr5_pack.lib_v5 import spec_utils
class BaseASPPNet(nn.Module):
def __init__(self, nin, ch, dilations=(4, 8, 16)):
super(BaseASPPNet, self).__init__()
self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
@@ -39,7 +38,6 @@ class BaseASPPNet(nn.Module):
class CascadedASPPNet(nn.Module):
def __init__(self, n_fft):
super(CascadedASPPNet, self).__init__()
self.stg1_low_band_net = BaseASPPNet(2, 16)
@@ -64,13 +62,16 @@ class CascadedASPPNet(nn.Module):
mix = x.detach()
x = x.clone()
x = x[:, :, :self.max_bin]
x = x[:, :, : self.max_bin]
bandw = x.size()[2] // 2
aux1 = torch.cat([
self.stg1_low_band_net(x[:, :, :bandw]),
self.stg1_high_band_net(x[:, :, bandw:])
], dim=2)
aux1 = torch.cat(
[
self.stg1_low_band_net(x[:, :, :bandw]),
self.stg1_high_band_net(x[:, :, bandw:]),
],
dim=2,
)
h = torch.cat([x, aux1], dim=1)
aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
@@ -82,24 +83,33 @@ class CascadedASPPNet(nn.Module):
mask = F.pad(
input=mask,
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
mode='replicate')
mode="replicate",
)
if self.training:
aux1 = torch.sigmoid(self.aux1_out(aux1))
aux1 = F.pad(
input=aux1,
pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
mode='replicate')
mode="replicate",
)
aux2 = torch.sigmoid(self.aux2_out(aux2))
aux2 = F.pad(
input=aux2,
pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
mode='replicate')
mode="replicate",
)
return mask * mix, aux1 * mix, aux2 * mix
else:
else:
if aggressiveness:
mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3)
mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value'])
mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
mask[:, :, : aggressiveness["split_bin"]],
1 + aggressiveness["value"] / 3,
)
mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
mask[:, :, aggressiveness["split_bin"] :],
1 + aggressiveness["value"],
)
return mask * mix
@@ -107,7 +117,7 @@ class CascadedASPPNet(nn.Module):
h = self.forward(x_mag, aggressiveness)
if self.offset > 0:
h = h[:, :, :, self.offset:-self.offset]
h = h[:, :, :, self.offset : -self.offset]
assert h.size()[3] > 0
return h