diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
index de7e439d..133346f6 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -37,8 +37,8 @@ In the worse case provide steps to reproduce the behaviour.
 You can either run `TTS/bin/collect_env_info.py`
 
 ```bash
-wget https://raw.githubusercontent.com/coqui-ai/TTS/main/TTS/bin/collect_env_details.py
-python collect_env_details.py
+wget https://raw.githubusercontent.com/coqui-ai/TTS/main/TTS/bin/collect_env_info.py
+python collect_env_info.py
 ```
 
 or fill in the fields below manually.
diff --git a/TTS/vocoder/configs/shared_configs.py b/TTS/vocoder/configs/shared_configs.py
index c5d6a8b4..9ff6f790 100644
--- a/TTS/vocoder/configs/shared_configs.py
+++ b/TTS/vocoder/configs/shared_configs.py
@@ -113,8 +113,10 @@ class BaseGANVocoderConfig(BaseVocoderConfig):
             Parameters for the generator learning rate scheduler. Defaults to `{"gamma": 0.999, "last_epoch": -1}`.
         lr_scheduler_disc (torch.optim.Scheduler):
             Learning rate scheduler for the discriminator. Defaults to `ExponentialLR`.
-        lr_scheduler_dict_params (dict):
+        lr_scheduler_disc_params (dict):
             Parameters for the discriminator learning rate scheduler. Defaults to `{"gamma": 0.999, "last_epoch": -1}`.
+        scheduler_after_epoch (bool):
+            Whether to update the learning rate schedulers after each epoch. Defaults to True.
         use_pqmf (bool):
             enable / disable PQMF for subband approximation at training. Defaults to False.
         steps_to_start_discriminator (int):
@@ -173,6 +175,7 @@ class BaseGANVocoderConfig(BaseVocoderConfig):
     lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999, "last_epoch": -1})
     lr_scheduler_disc: str = "ExponentialLR"  # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
     lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999, "last_epoch": -1})
+    scheduler_after_epoch: bool = True
 
     use_pqmf: bool = False  # enable/disable using pqmf for multi-band training. (Multi-band MelGAN)
     steps_to_start_discriminator = 0  # start training the discriminator after this number of steps.
diff --git a/TTS/vocoder/models/gan.py b/TTS/vocoder/models/gan.py
index e36c2cd1..76fee505 100644
--- a/TTS/vocoder/models/gan.py
+++ b/TTS/vocoder/models/gan.py
@@ -202,7 +202,9 @@ class GAN(BaseVocoder):
     ) -> Tuple[Dict, np.ndarray]:
         """Call `_log()` for training."""
         ap = assets["audio_processor"]
-        self._log("train", ap, batch, outputs)
+        figures, audios = self._log("eval", ap, batch, outputs)
+        logger.eval_figures(steps, figures)
+        logger.eval_audios(steps, audios, ap.sample_rate)
 
     @torch.no_grad()
     def eval_step(self, batch: Dict, criterion: nn.Module, optimizer_idx: int) -> Tuple[Dict, Dict]:
@@ -214,7 +216,9 @@ class GAN(BaseVocoder):
     ) -> Tuple[Dict, np.ndarray]:
         """Call `_log()` for evaluation."""
         ap = assets["audio_processor"]
-        self._log("eval", ap, batch, outputs)
+        figures, audios = self._log("eval", ap, batch, outputs)
+        logger.eval_figures(steps, figures)
+        logger.eval_audios(steps, audios, ap.sample_rate)
 
     def load_checkpoint(
         self,