diff --git a/config.json b/config.json
index 1b873b98..89a30a6b 100644
--- a/config.json
+++ b/config.json
@@ -25,5 +25,5 @@
   "text_cleaner": "english_cleaners",
 
   "data_path": "/data/shared/KeithIto/LJSpeech-1.0",
-  "output_path": "./result"
+  "output_path": "result"
 }
diff --git a/datasets/.LJSpeech.py.swp b/datasets/.LJSpeech.py.swp
index b80a29a9..c909a602 100644
Binary files a/datasets/.LJSpeech.py.swp and b/datasets/.LJSpeech.py.swp differ
diff --git a/datasets/LJSpeech.py b/datasets/LJSpeech.py
index 7f9aca36..6202c570 100644
--- a/datasets/LJSpeech.py
+++ b/datasets/LJSpeech.py
@@ -4,7 +4,6 @@ import numpy as np
 import collections
 from torch.utils.data import Dataset
 
-import train_config as c
 from Tacotron.utils.text import text_to_sequence
 from Tacotron.utils.audio import *
 from Tacotron.utils.data import prepare_data, pad_data, pad_per_step
@@ -12,16 +11,19 @@ from Tacotron.utils.data import prepare_data, pad_data, pad_per_step
 
 class LJSpeechDataset(Dataset):
 
-    def __init__(self, csv_file, root_dir, outputs_per_step):
+    def __init__(self, csv_file, root_dir, outputs_per_step, sample_rate,
+                 cleaners):
         self.frames = pd.read_csv(csv_file, sep='|', header=None)
         self.root_dir = root_dir
         self.outputs_per_step = outputs_per_step
+        self.sample_rate = sample_rate
+        self.cleaners = cleaners
         print(" > Reading LJSpeech from - {}".format(root_dir))
         print(" | > Number of instances : {}".format(len(self.frames)))
 
     def load_wav(self, filename):
         try:
-            audio = librosa.load(filename, sr=c.sample_rate)
+            audio = librosa.load(filename, sr=self.sample_rate)
             return audio
         except RuntimeError as e:
             print(" !! Cannot read file : {}".format(filename))
@@ -33,7 +35,7 @@ class LJSpeechDataset(Dataset):
         wav_name = os.path.join(self.root_dir,
                                 self.frames.ix[idx, 0]) + '.wav'
         text = self.frames.ix[idx, 1]
-        text = np.asarray(text_to_sequence(text, [c.cleaners]), dtype=np.int32)
+        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}
         return sample
diff --git a/train.py b/train.py
index 6ca1f1fe..69545300 100644
--- a/train.py
+++ b/train.py
@@ -42,7 +42,9 @@ def main(args):
 
     dataset = LJSpeechDataset(os.path.join(c.data_path, 'metadata.csv'),
                               os.path.join(c.data_path, 'wavs'),
-                              c.r
+                              c.r,
+                              c.sample_rate,
+                              c.text_cleaner
                              )
 
     model = Tacotron(c.embedding_size,
diff --git a/utils/.generic_utils.py.swp b/utils/.generic_utils.py.swp
index 4b46ae8a..a1e05635 100644
Binary files a/utils/.generic_utils.py.swp and b/utils/.generic_utils.py.swp differ
diff --git a/utils/audio.py b/utils/audio.py
index 1d6b24f7..49a5bc34 100644
--- a/utils/audio.py
+++ b/utils/audio.py
@@ -1,8 +1,6 @@
 import librosa
 import numpy as np
 from scipy import signal
-import Tacotron.train_config as c
-
 
 _mel_basis = None
 
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index e5fc0cb4..4a10b9a2 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -33,7 +33,7 @@ def remove_experiment_folder(experiment_path):
     """Check folder if there is a checkpoint, otherwise remove the folder"""
 
     checkpoint_files = glob.glob(experiment_path+"/*.pth.tar")
-    if len(checkpoint_files) < 2:
+    if len(checkpoint_files) < 1:
         shutil.rmtree(experiment_path)
         print(" ! Run is removed from {}".format(experiment_path))
     else: