diff --git a/vocoder/utils/io.py b/vocoder/utils/io.py
index b6ea7484..9d350238 100644
--- a/vocoder/utils/io.py
+++ b/vocoder/utils/io.py
@@ -2,18 +2,27 @@ import os
 import torch
 import datetime
 
-def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
-               epoch, output_path, **kwargs):
+
+def save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
+               scheduler_disc, current_step, epoch, output_path, **kwargs):
     model_state = model.state_dict()
-    model_disc_state = model_disc.state_dict()
-    optimizer_state = optimizer.state_dict() if optimizer is not None else None
-    optimizer_disc_state = optimizer_disc.state_dict(
-    ) if optimizer_disc is not None else None
+    model_disc_state = model_disc.state_dict()\
+         if model_disc is not None else None
+    optimizer_state = optimizer.state_dict()\
+         if optimizer is not None else None
+    optimizer_disc_state = optimizer_disc.state_dict()\
+        if optimizer_disc is not None else None
+    scheduler_state = scheduler.state_dict()\
+        if scheduler is not None else None
+    scheduler_disc_state = scheduler_disc.state_dict()\
+        if scheduler_disc is not None else None
     state = {
         'model': model_state,
         'optimizer': optimizer_state,
+        'scheduler': scheduler_state,
         'model_disc': model_disc_state,
         'optimizer_disc': optimizer_disc_state,
+        'scheduler_disc': scheduler_disc_state,
         'step': current_step,
         'epoch': epoch,
         'date': datetime.date.today().strftime("%B %d, %Y"),
@@ -22,26 +31,29 @@ def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
     torch.save(state, output_path)
 
 
-def save_checkpoint(model, optimizer, model_disc, optimizer_disc, current_step,
-                    epoch, output_folder, **kwargs):
+def save_checkpoint(model, optimizer, scheduler, model_disc, optimizer_disc,
+                    scheduler_disc, current_step, epoch, output_folder,
+                    **kwargs):
     file_name = 'checkpoint_{}.pth.tar'.format(current_step)
     checkpoint_path = os.path.join(output_folder, file_name)
     print(" > CHECKPOINT : {}".format(checkpoint_path))
-    save_model(model, optimizer, model_disc, optimizer_disc, current_step,
-               epoch, checkpoint_path, **kwargs)
+    save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
+               scheduler_disc, current_step, epoch, checkpoint_path, **kwargs)
 
 
-def save_best_model(target_loss, best_loss, model, optimizer, model_disc,
-                    optimizer_disc, current_step, epoch, output_folder,
-                    **kwargs):
+def save_best_model(target_loss, best_loss, model, optimizer, scheduler,
+                    model_disc, optimizer_disc, scheduler_disc, current_step,
+                    epoch, output_folder, **kwargs):
     if target_loss < best_loss:
         file_name = 'best_model.pth.tar'
         checkpoint_path = os.path.join(output_folder, file_name)
         print(" > BEST MODEL : {}".format(checkpoint_path))
         save_model(model,
                    optimizer,
+                   scheduler,
                    model_disc,
                    optimizer_disc,
+                   scheduler_disc,
                    current_step,
                    epoch,
                    checkpoint_path,