fix the issue when using load_pretrained_model

espnet2/gan_tts/hifigan/hifigan.py

@@ -620,43 +620,60 @@ def _load_state_dict_pre_hook(
 
         See also:
             - https://github.com/espnet/espnet/pull/5240
+            - https://github.com/espnet/espnet/pull/5249
             - https://github.com/kan-bayashi/ParallelWaveGAN/pull/409
 
         """
         current_module_keys = [x for x in state_dict.keys() if x.startswith(prefix)]
-        if self.use_weight_norm and not any(
-            ["weight_g" in k for k in current_module_keys]
+        if self.use_weight_norm and any(
+            [k.endswith("weight") for k in current_module_keys]
         ):
             logging.warning(
                 "It seems weight norm is not applied in the pretrained model but the"
                 " current model uses it. To keep the compatibility, we remove the norm"
-                " from the current model. This may causes training error due to the"
-                " parameter mismatch when finetuning. To avoid this issue, please"
-                " change the following parameters in config to false:\n"
+                " from the current model. This may cause unexpected behavior due to the"
+                " parameter mismatch in finetuning. To avoid this issue, please change"
+                " the following parameters in config to false:\n"
                 " - discriminator_params.follow_official_norm\n"
                 " - discriminator_params.scale_discriminator_params.use_weight_norm\n"
                 " - discriminator_params.scale_discriminator_params.use_spectral_norm\n"
-                " See also: https://github.com/espnet/espnet/pull/5240"
+                "\n"
+                "See also:\n"
+                " - https://github.com/espnet/espnet/pull/5240\n"
+                " - https://github.com/espnet/espnet/pull/5249"
             )
             self.remove_weight_norm()
             self.use_weight_norm = False
+            for k in current_module_keys:
+                if k.endswith("weight_g") or k.endswith("weight_v"):
+                    del state_dict[k]
 
-        if self.use_spectral_norm and not any(
-            ["weight_u" in k for k in current_module_keys]
+        if self.use_spectral_norm and any(
+            [k.endswith("weight") for k in current_module_keys]
         ):
             logging.warning(
                 "It seems spectral norm is not applied in the pretrained model but the"
                 " current model uses it. To keep the compatibility, we remove the norm"
-                " from the current model. This may causes training error due to the"
-                " parameter mismatch when finetuning. To avoid this issue, please"
-                " change the following parameters in config to false:\n"
+                " from the current model. This may cause unexpected behavior due to the"
+                " parameter mismatch in finetuning. To avoid this issue, please change"
+                " the following parameters in config to false:\n"
                 " - discriminator_params.follow_official_norm\n"
                 " - discriminator_params.scale_discriminator_params.use_weight_norm\n"
                 " - discriminator_params.scale_discriminator_params.use_spectral_norm\n"
-                " See also: https://github.com/espnet/espnet/pull/5240"
+                "\n"
+                "See also:\n"
+                " - https://github.com/espnet/espnet/pull/5240\n"
+                " - https://github.com/espnet/espnet/pull/5249"
             )
             self.remove_spectral_norm()
             self.use_spectral_norm = False
+            for k in current_module_keys:
+                if (
+                    k.endswith("weight_u")
+                    or k.endswith("weight_v")
+                    or k.endswith("weight_orig")
+                ):
+                    del state_dict[k]
 
 
 class HiFiGANMultiScaleDiscriminator(torch.nn.Module):