refactor(vocoder): remove duplicate function

TTS/vocoder/models/parallel_wavegan_generator.py

@@ -12,6 +12,13 @@ from TTS.vocoder.layers.upsample import ConvUpsample
 logger = logging.getLogger(__name__)
 
 
+def _get_receptive_field_size(layers, stacks, kernel_size, dilation=lambda x: 2**x):
+    assert layers % stacks == 0
+    layers_per_cycle = layers // stacks
+    dilations = [dilation(i % layers_per_cycle) for i in range(layers)]
+    return (kernel_size - 1) * sum(dilations) + 1
+
+
 class ParallelWaveganGenerator(torch.nn.Module):
     """PWGAN generator as in https://arxiv.org/pdf/1910.11480.pdf.
     It is similar to WaveNet with no causal convolution.
@@ -144,16 +151,9 @@ class ParallelWaveganGenerator(torch.nn.Module):
 
         self.apply(_apply_weight_norm)
 
-    @staticmethod
-    def _get_receptive_field_size(layers, stacks, kernel_size, dilation=lambda x: 2**x):
-        assert layers % stacks == 0
-        layers_per_cycle = layers // stacks
-        dilations = [dilation(i % layers_per_cycle) for i in range(layers)]
-        return (kernel_size - 1) * sum(dilations) + 1
-
     @property
     def receptive_field_size(self):
-        return self._get_receptive_field_size(self.layers, self.stacks, self.kernel_size)
+        return _get_receptive_field_size(self.layers, self.stacks, self.kernel_size)
 
     def load_checkpoint(
         self, config, checkpoint_path, eval=False, cache=False

TTS/vocoder/models/univnet_generator.py

@@ -7,6 +7,7 @@ import torch.nn.functional as F
 from torch.nn.utils import parametrize
 
 from TTS.vocoder.layers.lvc_block import LVCBlock
+from TTS.vocoder.models.parallel_wavegan_generator import _get_receptive_field_size
 
 logger = logging.getLogger(__name__)
 
@@ -133,17 +134,10 @@ class UnivnetGenerator(torch.nn.Module):
 
         self.apply(_apply_weight_norm)
 
-    @staticmethod
-    def _get_receptive_field_size(layers, stacks, kernel_size, dilation=lambda x: 2**x):
-        assert layers % stacks == 0
-        layers_per_cycle = layers // stacks
-        dilations = [dilation(i % layers_per_cycle) for i in range(layers)]
-        return (kernel_size - 1) * sum(dilations) + 1
-
     @property
     def receptive_field_size(self):
         """Return receptive field size."""
-        return self._get_receptive_field_size(self.layers, self.stacks, self.kernel_size)
+        return _get_receptive_field_size(self.layers, self.stacks, self.kernel_size)
 
     @torch.no_grad()
     def inference(self, c):