diff --git a/config.json b/config.json
index 3505e114..d9ffc5c8 100644
--- a/config.json
+++ b/config.json
@@ -12,7 +12,7 @@
   "text_cleaner": "english_cleaners",
 
   "epochs": 2000,
-  "lr": 0.005,
+  "lr": 0.0006,
   "warmup_steps": 4000,
   "batch_size": 180,
   "r": 5,
diff --git a/datasets/LJSpeech.py b/datasets/LJSpeech.py
index 3fe07078..81c2c9e9 100644
--- a/datasets/LJSpeech.py
+++ b/datasets/LJSpeech.py
@@ -1,4 +1,3 @@
-import pandas as pd
 import os
 import numpy as np
 import collections
@@ -16,7 +15,10 @@ class LJSpeechDataset(Dataset):
     def __init__(self, csv_file, root_dir, outputs_per_step, sample_rate,
                 text_cleaner, num_mels, min_level_db, frame_shift_ms,
                 frame_length_ms, preemphasis, ref_level_db, num_freq, power):
-        self.frames = pd.read_csv(csv_file, sep='|', header=None)
+        
+        f = open(csv_file, "r")
+        self.frames = [line.split('|') for line in f]
+        f.close()
         self.root_dir = root_dir
         self.outputs_per_step = outputs_per_step
         self.sample_rate = sample_rate
@@ -40,7 +42,7 @@ class LJSpeechDataset(Dataset):
     def __getitem__(self, idx):
         wav_name = os.path.join(self.root_dir,
                                 self.frames.ix[idx, 0]) + '.wav'
-        text = self.frames.ix[idx, 1]
+        text = self.frames[idx][1]
         text = np.asarray(text_to_sequence(text, [self.cleaners]), dtype=np.int32)
         wav = np.asarray(self.load_wav(wav_name)[0], dtype=np.float32)
         sample = {'text': text, 'wav': wav, 'item_idx': self.frames.ix[idx, 0]}
diff --git a/train.py b/train.py
index 9b8e8c9f..09c0e402 100644
--- a/train.py
+++ b/train.py
@@ -73,7 +73,8 @@ def main(args):
 
     dataloader = DataLoader(dataset, batch_size=c.batch_size,
                             shuffle=True, collate_fn=dataset.collate_fn,
-                            drop_last=True, num_workers=c.num_loader_workers)
+                            drop_last=True, num_workers=c.num_loader_workers,
+                            pin_memory=True)
 
     # setup the model
     model = Tacotron(c.embedding_size,
@@ -108,6 +109,7 @@ def main(args):
         start_epoch = checkpoint['step'] // len(dataloader)
         best_loss = checkpoint['linear_loss']
         start_epoch = 0
+        args.restore_step = checkpoint['step']
     else:
         print("\n > Starting a new training")
 
diff --git a/utils/visual.py b/utils/visual.py
index 935ed369..b0143fc9 100644
--- a/utils/visual.py
+++ b/utils/visual.py
@@ -5,7 +5,7 @@ import matplotlib.pyplot as plt
 
 
 def plot_alignment(alignment, info=None):
-    fig, ax = plt.subplots()
+    fig, ax = plt.subplots(figsize=(16,10))
     im = ax.imshow(alignment.T, aspect='auto', origin='lower',
                    interpolation='none')
     fig.colorbar(im, ax=ax)