Update ONNXVITS_infer.py

ONNXVITS_infer.py

@@ -13,17 +13,18 @@ from torch.nn import Conv1d, ConvTranspose1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from commons import init_weights, get_padding
 
+
 class TextEncoder(nn.Module):
   def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout,
-      emotion_embedding):
+               n_vocab,
+               out_channels,
+               hidden_channels,
+               filter_channels,
+               n_heads,
+               n_layers,
+               kernel_size,
+               p_dropout,
+               emotion_embedding):
     super().__init__()
     self.n_vocab = n_vocab
     self.out_channels = out_channels
@@ -34,12 +35,12 @@ class TextEncoder(nn.Module):
     self.kernel_size = kernel_size
     self.p_dropout = p_dropout
     self.emotion_embedding = emotion_embedding
-    
-    if self.n_vocab!=0:
+
+    if self.n_vocab != 0:
       self.emb = nn.Embedding(n_vocab, hidden_channels)
       if emotion_embedding:
         self.emo_proj = nn.Linear(1024, hidden_channels)
-      nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+      nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
 
     self.encoder = attentions.Encoder(
       hidden_channels,
@@ -48,15 +49,15 @@ class TextEncoder(nn.Module):
       n_layers,
       kernel_size,
       p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
 
   def forward(self, x, x_lengths, emotion_embedding=None):
-    if self.n_vocab!=0:
-      x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
+    if self.n_vocab != 0:
+      x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
     if emotion_embedding is not None:
       print("emotion added")
       x = x + self.emo_proj(emotion_embedding.unsqueeze(1))
-    x = torch.transpose(x, 1, -1) # [b, h, t]
+    x = torch.transpose(x, 1, -1)  # [b, h, t]
     x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
 
     x = self.encoder(x * x_mask, x_mask)
@@ -65,15 +66,16 @@ class TextEncoder(nn.Module):
     m, logs = torch.split(stats, self.out_channels, dim=1)
     return x, m, logs, x_mask
 
+
 class PosteriorEncoder(nn.Module):
   def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
+               in_channels,
+               out_channels,
+               hidden_channels,
+               kernel_size,
+               dilation_rate,
+               n_layers,
+               gin_channels=0):
     super().__init__()
     self.in_channels = in_channels
     self.out_channels = out_channels
@@ -96,35 +98,36 @@ class PosteriorEncoder(nn.Module):
     z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
     return z, m, logs, x_mask
 
+
 class SynthesizerTrn(models.SynthesizerTrn):
   """
   Synthesizer for Training
   """
 
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    emotion_embedding=False,
-    **kwargs):
-
-    super().__init__(    
+  def __init__(self,
+               n_vocab,
+               spec_channels,
+               segment_size,
+               inter_channels,
+               hidden_channels,
+               filter_channels,
+               n_heads,
+               n_layers,
+               kernel_size,
+               p_dropout,
+               resblock,
+               resblock_kernel_sizes,
+               resblock_dilation_sizes,
+               upsample_rates,
+               upsample_initial_channel,
+               upsample_kernel_sizes,
+               n_speakers=0,
+               gin_channels=0,
+               use_sdp=True,
+               emotion_embedding=False,
+               **kwargs):
+
+    super().__init__(
       n_vocab,
       spec_channels,
       segment_size,
@@ -135,11 +138,11 @@ class SynthesizerTrn(models.SynthesizerTrn):
       n_layers,
       kernel_size,
       p_dropout,
-      resblock, 
-      resblock_kernel_sizes, 
-      resblock_dilation_sizes, 
-      upsample_rates, 
-      upsample_initial_channel, 
+      resblock,
+      resblock_kernel_sizes,
+      resblock_dilation_sizes,
+      upsample_rates,
+      upsample_initial_channel,
       upsample_kernel_sizes,
       n_speakers=n_speakers,
       gin_channels=gin_channels,
@@ -147,27 +150,28 @@ class SynthesizerTrn(models.SynthesizerTrn):
       **kwargs
     )
     self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        emotion_embedding)
+                             inter_channels,
+                             hidden_channels,
+                             filter_channels,
+                             n_heads,
+                             n_layers,
+                             kernel_size,
+                             p_dropout,
+                             emotion_embedding)
     self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
 
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None, emotion_embedding=None):
+  def infer(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, emotion_embedding)
+    with torch.no_grad():
+      x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
 
     if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+      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 = 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])
 
@@ -178,26 +182,27 @@ class SynthesizerTrn(models.SynthesizerTrn):
     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']
+    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 = 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 = 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)
 
   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):
+                       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 = 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)
@@ -205,68 +210,14 @@ class SynthesizerTrn(models.SynthesizerTrn):
     x_mask = torch.from_numpy(x_mask)
 
     if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+      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 = 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)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    from ONNXVITS_utils import runonnx
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    # z_p = self.flow(z, y_mask, g=g_src)
-    z_p = runonnx("ONNX_net/flow.onnx", z_p=z.numpy(), y_mask=y_mask.numpy(), g=g_src.numpy())
-    z_p = torch.from_numpy(z_p[0])
-    # z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    z_hat = runonnx("ONNX_net/flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g_tgt.numpy())
-    z_hat = torch.from_numpy(z_hat[0])
-    # o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    o_hat = runonnx("ONNX_net/dec.onnx", z_in=(z_hat * y_mask).numpy(), g=g_tgt.numpy())
-    o_hat = torch.from_numpy(o_hat[0])
-    return o_hat, y_mask, (z, z_p, z_hat)
+    return list(w_ceil.squeeze())