Fix UnivNet inference code

TTS/vocoder/models/hifigan_generator.py

@@ -15,7 +15,7 @@ def get_padding(k, d):
 class ResBlock1(torch.nn.Module):
     """Residual Block Type 1. It has 3 convolutional layers in each convolutiona block.
 
-    Network:
+    Network::
 
         x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
         |--------------------------------------------------------------------------------------------------|
@@ -105,7 +105,7 @@ class ResBlock1(torch.nn.Module):
 class ResBlock2(torch.nn.Module):
     """Residual Block Type 1. It has 3 convolutional layers in each convolutiona block.
 
-    Network:
+    Network::
 
         x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
         |---------------------------------------------------|

TTS/vocoder/models/univnet_generator.py

@@ -122,24 +122,16 @@ class UnivnetGenerator(torch.nn.Module):
         """Return receptive field size."""
         return self._get_receptive_field_size(self.layers, self.stacks, self.kernel_size)
 
-    def inference(self, c=None, x=None):
+    @torch.no_grad()
+    def inference(self, c):
         """Perform inference.
         Args:
-            c (Union[Tensor, ndarray]): Local conditioning auxiliary features (T' ,C).
-            x (Union[Tensor, ndarray]): Input noise signal (T, 1).
+            c (Tensor): Local conditioning auxiliary features :math:`(B, C, T)`.
         Returns:
             Tensor: Output tensor (T, out_channels)
         """
-        if x is not None:
-            if not isinstance(x, torch.Tensor):
-                x = torch.tensor(x, dtype=torch.float).to(next(self.parameters()).device)
-            x = x.transpose(1, 0).unsqueeze(0)
-        else:
-            assert c is not None
-            x = torch.randn(1, 1, len(c) * self.upsample_factor).to(next(self.parameters()).device)
-        if c is not None:
-            if not isinstance(c, torch.Tensor):
-                c = torch.tensor(c, dtype=torch.float).to(next(self.parameters()).device)
-            c = c.transpose(1, 0).unsqueeze(0)
-            c = torch.nn.ReplicationPad1d(self.aux_context_window)(c)
-        return self.forward(c).squeeze(0).transpose(1, 0)
+        x = torch.randn([c.shape[0], self.in_channels, c.shape[2]])
+        x = x.to(self.first_conv.bias.device)
+
+        c = c.to(next(self.parameters()))
+        return self.forward(c)

recipes/ljspeech/univnet/train.py

@@ -1,5 +1,6 @@
 import os
 
+from TTS.config.shared_configs import BaseAudioConfig
 from TTS.trainer import Trainer, TrainingArgs, init_training
 from TTS.vocoder.configs import UnivnetConfig