fumiama/Retrieval-based-Voice-Conversion-WebUI
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fix(dml): train extract_f0_print error

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ModuleNotFoundError: No module named 'torch.privateuseone' due to new prosess
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源文雨
2025-11-21 16:52:17 +08:00
parent 7fa122045f
commit 43d19eb00e
13 changed files with 50 additions and 39 deletions
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32
rvc/layers/synthesizers.py
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@@ -34,7 +34,7 @@ class SynthesizerTrnMsNSFsid(nn.Module):
upsample_kernel_sizes: List[int],
spk_embed_dim: int,
gin_channels: int,
sr: Optional[Union[str, int]],
sr: Union[str, int],
encoder_dim: int,
use_f0: bool,
):
@@ -143,7 +143,7 @@ class SynthesizerTrnMsNSFsid(nn.Module):
torch.nn.utils.remove_weight_norm(self.enc_q)
return self
@torch.jit.ignore
@torch.jit.ignore()
def forward(
self,
phone: torch.Tensor,
@@ -155,18 +155,20 @@ class SynthesizerTrnMsNSFsid(nn.Module):
pitchf: Optional[torch.Tensor] = None,
): # 这里ds是id，[bs,1]
# print(1,pitch.shape)#[bs,t]
g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
embg = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
z_p = self.flow(z, y_mask, g=g)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=embg)
z_p = self.flow(z, y_mask, g=embg)
z_slice, ids_slice = rand_slice_segments_on_last_dim(
z, y_lengths, self.segment_size
)
if pitchf is not None:
if pitchf is not None and isinstance(self.dec, NSFGenerator):
pitchf = slice_on_last_dim(pitchf, ids_slice, self.segment_size)
o = self.dec(z_slice, pitchf, g=g)
o = self.dec(z_slice, pitchf, g=embg) # type: ignore
elif isinstance(self.dec, Generator):
o = self.dec(z_slice, g=embg)
else:
o = self.dec(z_slice, g=g)
raise KeyError(f"unknown dec type: {type(self.dec).__name__}")
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
@torch.jit.export
@@ -201,15 +203,17 @@ class SynthesizerTrnMsNSFsid(nn.Module):
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
z = self.flow(z_p, x_mask, g=g, reverse=True)
del z_p, m_p, logs_p
if pitchf is not None:
o = self.dec(
if pitchf is not None and isinstance(self.dec, NSFGenerator):
o = self.dec(
z * x_mask,
pitchf,
g=g,
n_res=return_length2,
)
else:
elif isinstance(self.dec, Generator):
o = self.dec(z * x_mask, g=g, n_res=return_length2)
else:
raise KeyError(f"unknown dec type: {type(self.dec).__name__}")
del x_mask, z
return o # , x_mask, (z, z_p, m_p, logs_p)
@@ -326,7 +330,7 @@ class SynthesizerTrnMs256NSFsid_nono(SynthesizerTrnMsNSFsid):
upsample_kernel_sizes: List[int],
spk_embed_dim: int,
gin_channels: int,
sr=None,
sr: Union[str, int],
):
super().__init__(
spec_channels,
@@ -346,6 +350,7 @@ class SynthesizerTrnMs256NSFsid_nono(SynthesizerTrnMsNSFsid):
upsample_kernel_sizes,
spk_embed_dim,
gin_channels,
sr,
256,
False,
)
@@ -371,7 +376,7 @@ class SynthesizerTrnMs768NSFsid_nono(SynthesizerTrnMsNSFsid):
upsample_kernel_sizes: List[int],
spk_embed_dim: int,
gin_channels: int,
sr=None,
sr: Union[str, int],
):
super().__init__(
spec_channels,
@@ -391,6 +396,7 @@ class SynthesizerTrnMs768NSFsid_nono(SynthesizerTrnMsNSFsid):
upsample_kernel_sizes,
spk_embed_dim,
gin_channels,
sr,
768,
False,
)