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@ -631,12 +631,17 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
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o = self.dec(z_slice, pitchf, g=g)
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return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
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def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
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def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
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g = self.emb_g(sid).unsqueeze(-1)
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m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
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z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
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if(rate):
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head=int(z_p.shape[2]*rate)
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z_p=z_p[:,:,-head:]
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x_mask=x_mask[:,:,-head:]
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nsff0=nsff0[:,-head:]
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
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o = self.dec(z * x_mask, nsff0, g=g)
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return o, x_mask, (z, z_p, m_p, logs_p)
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@ -742,12 +747,17 @@ class SynthesizerTrnMs768NSFsid(nn.Module):
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o = self.dec(z_slice, pitchf, g=g)
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return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
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def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
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def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
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g = self.emb_g(sid).unsqueeze(-1)
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m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
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z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
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if(rate):
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head=int(z_p.shape[2]*rate)
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z_p=z_p[:,:,-head:]
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x_mask=x_mask[:,:,-head:]
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nsff0=nsff0[:,-head:]
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
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o = self.dec(z * x_mask, nsff0, g=g)
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return o, x_mask, (z, z_p, m_p, logs_p)
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@ -844,12 +854,16 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
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o = self.dec(z_slice, g=g)
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return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
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def infer(self, phone, phone_lengths, sid, max_len=None):
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def infer(self, phone, phone_lengths, sid, rate=None):
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g = self.emb_g(sid).unsqueeze(-1)
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m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
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z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
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if(rate):
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head=int(z_p.shape[2]*rate)
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z_p=z_p[:,:,-head:]
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x_mask=x_mask[:,:,-head:]
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec((z * x_mask)[:, :, :max_len], g=g)
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o = self.dec(z * x_mask, g=g)
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return o, x_mask, (z, z_p, m_p, logs_p)
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@ -946,12 +960,16 @@ class SynthesizerTrnMs768NSFsid_nono(nn.Module):
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o = self.dec(z_slice, g=g)
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return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
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def infer(self, phone, phone_lengths, sid, max_len=None):
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def infer(self, phone, phone_lengths, sid, rate=None):
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g = self.emb_g(sid).unsqueeze(-1)
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m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
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z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
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if(rate):
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head=int(z_p.shape[2]*rate)
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z_p=z_p[:,:,-head:]
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x_mask=x_mask[:,:,-head:]
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec((z * x_mask)[:, :, :max_len], g=g)
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o = self.dec(z * x_mask, g=g)
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return o, x_mask, (z, z_p, m_p, logs_p)
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