diff --git a/enhancer/cli/train.py b/enhancer/cli/train.py
index 6d5f182..131db4f 100644
--- a/enhancer/cli/train.py
+++ b/enhancer/cli/train.py
@@ -4,10 +4,14 @@ from types import MethodType
 import hydra
 from hydra.utils import instantiate
 from omegaconf import DictConfig, OmegaConf
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning.callbacks import (
+    EarlyStopping,
+    LearningRateMonitor,
+    ModelCheckpoint,
+)
 from pytorch_lightning.loggers import MLFlowLogger
 from torch.optim.lr_scheduler import ReduceLROnPlateau
-from torch_audiomentations import BandPassFilter, Compose, Shift
+from torch_audiomentations import Compose, Shift
 
 os.environ["HYDRA_FULL_ERROR"] = "1"
 JOB_ID = os.environ.get("SLURM_JOBID", "0")
@@ -29,7 +33,6 @@ def main(config: DictConfig):
     apply_augmentations = Compose(
         [
             Shift(min_shift=0.0, max_shift=1.0, shift_unit="seconds", p=0.5),
-            BandPassFilter(p=0.5),
         ]
     )
 
@@ -52,6 +55,8 @@ def main(config: DictConfig):
         every_n_epochs=1,
     )
     callbacks.append(checkpoint)
+    callbacks.append(LearningRateMonitor(logging_interval="epoch"))
+
     if parameters.get("Early_stop", False):
         early_stopping = EarlyStopping(
             monitor="val_loss",
@@ -63,11 +68,11 @@ def main(config: DictConfig):
         )
         callbacks.append(early_stopping)
 
-    def configure_optimizer(self):
+    def configure_optimizers(self):
         optimizer = instantiate(
             config.optimizer,
             lr=parameters.get("lr"),
-            parameters=self.parameters(),
+            params=self.parameters(),
         )
         scheduler = ReduceLROnPlateau(
             optimizer=optimizer,
@@ -77,9 +82,13 @@ def main(config: DictConfig):
             min_lr=parameters.get("min_lr", 1e-6),
             patience=parameters.get("ReduceLr_patience", 3),
         )
-        return {"optimizer": optimizer, "lr_scheduler": scheduler}
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": scheduler,
+            "monitor": f'valid_{parameters.get("ReduceLr_monitor", "loss")}',
+        }
 
-    model.configure_parameters = MethodType(configure_optimizer, model)
+    model.configure_optimizers = MethodType(configure_optimizers, model)
 
     trainer = instantiate(config.trainer, logger=logger, callbacks=callbacks)
     trainer.fit(model)