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support gpt-sovits v4

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support gpt-sovits v4
This commit is contained in:
RVC-Boss
2025-04-20 14:53:42 +08:00
committed by GitHub
parent c6cb6b45f3
commit 50e9ba0218
3 changed files with 255 additions and 62 deletions
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12
GPT_SoVITS/module/models.py
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@@ -414,7 +414,7 @@ class Generator(torch.nn.Module):
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=0,
gin_channels=0,is_bias=False,
):
super(Generator, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
@@ -442,7 +442,7 @@ class Generator(torch.nn.Module):
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
self.resblocks.append(resblock(ch, k, d))
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=is_bias)
self.ups.apply(init_weights)
if gin_channels != 0:
@@ -1173,7 +1173,7 @@ class SynthesizerTrnV3(nn.Module):
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest") ##BCT
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
fea = self.bridge(x)
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest") ##BCT
fea = F.interpolate(fea, scale_factor=(1.875 if self.version=="v3"else 2), mode="nearest") ##BCT
fea, y_mask_ = self.wns1(
fea, mel_lengths, ge
) ##If the 1-minute fine-tuning works fine, no need to manually adjust the learning rate.
@@ -1196,9 +1196,9 @@ class SynthesizerTrnV3(nn.Module):
ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
y_lengths = torch.LongTensor([int(codes.size(2) * 2)]).to(codes.device)
if speed == 1:
sizee = int(codes.size(2) * 2.5 * 1.5)
sizee = int(codes.size(2) * (3.875 if self.version=="v3"else 4))
else:
sizee = int(codes.size(2) * 2.5 * 1.5 / speed) + 1
sizee = int(codes.size(2) * (3.875 if self.version=="v3"else 4) / speed) + 1
y_lengths1 = torch.LongTensor([sizee]).to(codes.device)
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
@@ -1207,7 +1207,7 @@ class SynthesizerTrnV3(nn.Module):
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest") ##BCT
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge, speed)
fea = self.bridge(x)
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest") ##BCT
fea = F.interpolate(fea, scale_factor=(1.875 if self.version=="v3"else 2), mode="nearest") ##BCT
####more wn paramter to learn mel
fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
return fea, ge