Update ONNXVITS_infer.py

ONNXVITS_infer.py

@@ -91,3 +91,64 @@ class SynthesizerTrn(models.SynthesizerTrn):
     o = torch.from_numpy(o[0])
 
     return o, attn, y_mask, (z, z_p, m_p, logs_p)
+
+  def predict_duration(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
+              emotion_embedding=None):
+    from ONNXVITS_utils import runonnx
+
+    #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    x, m_p, logs_p, x_mask = runonnx("ONNX_net/enc_p.onnx", x=x.numpy(), x_lengths=x_lengths.numpy())
+    x = torch.from_numpy(x)
+    m_p = torch.from_numpy(m_p)
+    logs_p = torch.from_numpy(logs_p)
+    x_mask = torch.from_numpy(x_mask)
+
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    #logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
+    logw = runonnx("ONNX_net/dp.onnx", x=x.numpy(), x_mask=x_mask.numpy(), g=g.numpy())
+    logw = torch.from_numpy(logw[0])
+
+    w = torch.exp(logw) * x_mask * length_scale
+    w_ceil = torch.ceil(w)
+    return list(w_ceil.squeeze())
+
+  def infer_with_duration(self, x, x_lengths, w_ceil, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
+              emotion_embedding=None):
+    from ONNXVITS_utils import runonnx
+
+    #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    x, m_p, logs_p, x_mask = runonnx("ONNX_net/enc_p.onnx", x=x.numpy(), x_lengths=x_lengths.numpy())
+    x = torch.from_numpy(x)
+    m_p = torch.from_numpy(m_p)
+    logs_p = torch.from_numpy(logs_p)
+    x_mask = torch.from_numpy(x_mask)
+
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+    assert len(w_ceil) == x.shape[2]
+    w_ceil = torch.FloatTensor(w_ceil).reshape(1, 1, -1)
+    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
+    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+    attn = commons.generate_path(w_ceil, attn_mask)
+
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+
+    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+    
+    #z = self.flow(z_p, y_mask, g=g, reverse=True)
+    z = runonnx("ONNX_net/flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g.numpy())
+    z = torch.from_numpy(z[0])
+
+    #o = self.dec((z * y_mask)[:,:,:max_len], g=g)
+    o = runonnx("ONNX_net/dec.onnx", z_in=(z * y_mask)[:,:,:max_len].numpy(), g=g.numpy())
+    o = torch.from_numpy(o[0])
+
+    return o, attn, y_mask, (z, z_p, m_p, logs_p)