From 58784ad09c05a5d07d3b86cd53b89e34972e34bc Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 19 Jun 2020 12:25:03 +0200
Subject: [PATCH] renaming for melgan generator

---
 vocoder/layers/melgan.py           |  2 +-
 vocoder/layers/pqmf.py             |  2 --
 vocoder/models/melgan_generator.py | 14 +++++++-------
 3 files changed, 8 insertions(+), 10 deletions(-)

diff --git a/vocoder/layers/melgan.py b/vocoder/layers/melgan.py
index 61f7a96b..58c12a2e 100644
--- a/vocoder/layers/melgan.py
+++ b/vocoder/layers/melgan.py
@@ -21,7 +21,7 @@ class ResidualStack(nn.Module):
                     nn.Conv1d(channels,
                               channels,
                               kernel_size=kernel_size,
-                              dilation=layer_padding,
+                              dilation=layer_dilation,
                               bias=True)),
                 nn.LeakyReLU(0.2),
                 weight_norm(
diff --git a/vocoder/layers/pqmf.py b/vocoder/layers/pqmf.py
index 3985aad4..ef5a3507 100644
--- a/vocoder/layers/pqmf.py
+++ b/vocoder/layers/pqmf.py
@@ -1,5 +1,3 @@
-"""Pseudo QMF modules."""
-
 import numpy as np
 import torch
 import torch.nn.functional as F
diff --git a/vocoder/models/melgan_generator.py b/vocoder/models/melgan_generator.py
index e69e6ef3..01b52ea8 100644
--- a/vocoder/models/melgan_generator.py
+++ b/vocoder/models/melgan_generator.py
@@ -77,16 +77,16 @@ class MelganGenerator(nn.Module):
         ]
         self.layers = nn.Sequential(*layers)
 
-    def forward(self, cond_features):
-        return self.layers(cond_features)
+    def forward(self, c):
+        return self.layers(c)
 
-    def inference(self, cond_features):
-        cond_features = cond_features.to(self.layers[1].weight.device)
-        cond_features = torch.nn.functional.pad(
-            cond_features,
+    def inference(self, c):
+        c = c.to(self.layers[1].weight.device)
+        c = torch.nn.functional.pad(
+            c,
             (self.inference_padding, self.inference_padding),
             'replicate')
-        return self.layers(cond_features)
+        return self.layers(c)
 
     def remove_weight_norm(self):
         for _, layer in enumerate(self.layers):