diff --git a/config/default.yaml b/config/default.yaml
index 4152ec4..5c3771d 100644
--- a/config/default.yaml
+++ b/config/default.yaml
@@ -7,8 +7,10 @@ train:
   num_workers: 32
   batch_size: 16
   optimizer: 'adam'
+  epochs: 1500
   adam:
-    lr: 0.0001
+    init_lr: 0.0001
+    final_lr: 0.00001
     beta1: 0.5
     beta2: 0.9
 ---
diff --git a/utils/train.py b/utils/train.py
index 19bd3d5..5c1f5a3 100644
--- a/utils/train.py
+++ b/utils/train.py
@@ -4,7 +4,6 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import itertools
 import traceback
 
 from model.generator import Generator
@@ -13,18 +12,35 @@
 from .validation import validate
 
 
+def cosine_decay(init_val, final_val, step, decay_steps):
+    alpha = final_val / init_val
+    cosine_decay = 0.5 * (1 + math.cos(math.pi * step / decay_steps))
+    decayed = (1 - alpha) * cosine_decay + alpha
+    return init_val * decayed
+
+
+def adjust_learning_rate(optimizer, epoch, hp):
+    init_lr = hp.train.adam.init_lr
+    final_lr = hp.train.adam.final_lr
+    decay_steps = hp.train.epochs
+    lr = cosine_decay(init_lr, final_lr, epoch, decay_steps)
+
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+
 def train(args, pt_dir, chkpt_path, trainloader, valloader, writer, logger, hp, hp_str):
     model_g = Generator(hp.audio.n_mel_channels).cuda()
     model_d = MultiScaleDiscriminator().cuda()
 
     optim_g = torch.optim.Adam(model_g.parameters(),
-        lr=hp.train.adam.lr, betas=(hp.train.adam.beta1, hp.train.adam.beta2))
+        lr=hp.train.adam.init_lr, betas=(hp.train.adam.beta1, hp.train.adam.beta2))
     optim_d = torch.optim.Adam(model_d.parameters(),
-        lr=hp.train.adam.lr, betas=(hp.train.adam.beta1, hp.train.adam.beta2))
+        lr=hp.train.adam.init_lr, betas=(hp.train.adam.beta1, hp.train.adam.beta2))
 
     githash = get_commit_hash()
 
-    init_epoch = -1
+    elapsed_epochs = 0
     step = 0
 
     if chkpt_path is not None:
@@ -35,7 +51,7 @@ def train(args, pt_dir, chkpt_path, trainloader, valloader, writer, logger, hp,
         optim_g.load_state_dict(checkpoint['optim_g'])
         optim_d.load_state_dict(checkpoint['optim_d'])
         step = checkpoint['step']
-        init_epoch = checkpoint['epoch']
+        elapsed_epochs = checkpoint['epoch']
 
         if hp_str != checkpoint['hp_str']:
             logger.warning("New hparams is different from checkpoint. Will use new.")
@@ -54,11 +70,18 @@ def train(args, pt_dir, chkpt_path, trainloader, valloader, writer, logger, hp,
     try:
         model_g.train()
         model_d.train()
-        for epoch in itertools.count(init_epoch+1):
+
+        epochs = hp.train.epochs - elapsed_epochs
+        for epoch in range(epochs + 1):
             if epoch % hp.log.validation_interval == 0:
                 with torch.no_grad():
                     validate(hp, args, model_g, model_d, valloader, writer, step)
 
+            epoch += elapsed_epochs
+
+            adjust_learning_rate(optim_g, epoch, hp)
+            adjust_learning_rate(optim_d, epoch, hp)
+
             trainloader.dataset.shuffle_mapping()
             loader = tqdm.tqdm(trainloader, desc='Loading train data')
             for (melG, audioG), (melD, audioD) in loader: