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Format code (#727)

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github-actions[bot]
2023-07-13 14:35:24 +08:00
committed by GitHub
parent 6c13f1fe52
commit 9739f3085d
5 changed files with 418 additions and 184 deletions
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36
lib/infer_pack/models.py
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@@ -635,11 +635,11 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
g = self.emb_g(sid).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if(rate):
head=int(z_p.shape[2]*rate)
z_p=z_p[:,:,-head:]
x_mask=x_mask[:,:,-head:]
nsff0=nsff0[:,-head:]
if rate:
head = int(z_p.shape[2] * rate)
z_p = z_p[:, :, -head:]
x_mask = x_mask[:, :, -head:]
nsff0 = nsff0[:, -head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, nsff0, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
@@ -751,11 +751,11 @@ class SynthesizerTrnMs768NSFsid(nn.Module):
g = self.emb_g(sid).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if(rate):
head=int(z_p.shape[2]*rate)
z_p=z_p[:,:,-head:]
x_mask=x_mask[:,:,-head:]
nsff0=nsff0[:,-head:]
if rate:
head = int(z_p.shape[2] * rate)
z_p = z_p[:, :, -head:]
x_mask = x_mask[:, :, -head:]
nsff0 = nsff0[:, -head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, nsff0, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
@@ -858,10 +858,10 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
g = self.emb_g(sid).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if(rate):
head=int(z_p.shape[2]*rate)
z_p=z_p[:,:,-head:]
x_mask=x_mask[:,:,-head:]
if rate:
head = int(z_p.shape[2] * rate)
z_p = z_p[:, :, -head:]
x_mask = x_mask[:, :, -head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
@@ -964,10 +964,10 @@ class SynthesizerTrnMs768NSFsid_nono(nn.Module):
g = self.emb_g(sid).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if(rate):
head=int(z_p.shape[2]*rate)
z_p=z_p[:,:,-head:]
x_mask=x_mask[:,:,-head:]
if rate:
head = int(z_p.shape[2] * rate)
z_p = z_p[:, :, -head:]
x_mask = x_mask[:, :, -head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)