diff --git a/train_ms.py b/train_ms.py
index 297fea6..0da546d 100644
--- a/train_ms.py
+++ b/train_ms.py
@@ -157,12 +157,20 @@ def run(rank, n_gpus, hps):
         net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
     try:
         if net_dur_disc is not None:
-            _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=True)
-        _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
-                                                   optim_g, skip_optimizer=True)
-        _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
-                                                   optim_d, skip_optimizer=True)
-            
+            _, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
+                utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc,
+                skip_optimizer=True)
+            _, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
+                utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
+                optim_g, skip_optimizer=True)
+            _, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
+                utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
+                optim_d, skip_optimizer=True)
+            if not optim_g.param_groups[0].get("initial_lr"):
+                optim_g.param_groups[0]["initial_lr"] = g_resume_lr
+            if not optim_d.param_groups[0].get("initial_lr"):
+                optim_d.param_groups[0]["initial_lr"] = d_resume_lr
+
         epoch_str = max(epoch_str, 1)
         global_step = (epoch_str - 1) * len(train_loader)
     except Exception as e:
@@ -174,6 +182,8 @@ def run(rank, n_gpus, hps):
     scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
     scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
     if net_dur_disc is not None:
+        if not optim_dur_disc.param_groups[0].get("initial_lr"):
+            optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
         scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
     else:
         scheduler_dur_disc = None