diff --git a/config.json b/config.json
index a8ee4f96..dde96b94 100644
--- a/config.json
+++ b/config.json
@@ -25,10 +25,10 @@
         "do_trim_silence": true  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
     },
 
-    "embedding_size": 256,    
+    "embedding_size": 256,
     "text_cleaner": "english_cleaners",
     "epochs": 1000,
-    
+
     "lr": 0.001,
     "lr_decay": false,
     "warmup_steps": 4000,
@@ -52,4 +52,4 @@
     "output_path": "../keep/",      // output path for all training outputs.
     "num_loader_workers": 8,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
     "num_val_loader_workers": 4     // number of evaluation data loader processes.
-}
\ No newline at end of file
+}
diff --git a/datasets/TTSDatasetCached.py b/datasets/TTSDatasetCached.py
deleted file mode 100644
index 28033a80..00000000
--- a/datasets/TTSDatasetCached.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import random
-import numpy as np
-import collections
-import librosa
-import torch
-from torch.utils.data import Dataset
-
-from utils.text import text_to_sequence
-from datasets.preprocess import tts_cache
-from utils.data import (prepare_data, pad_per_step, prepare_tensor,
-                        prepare_stop_target)
-
-
-class MyDataset(Dataset):
-    # TODO: Not finished yet.
-    def __init__(self,
-                 root_path,
-                 meta_file,
-                 outputs_per_step,
-                 text_cleaner,
-                 ap,
-                 batch_group_size=0,                 
-                 min_seq_len=0,
-                 **kwargs
-                 ):
-        self.ap = ap
-        self.root_path = root_path
-        self.batch_group_size = batch_group_size
-        self.feat_dir = os.path.join(root_path, 'loader_data')
-        self.items = tts_cache(root_path, meta_file)
-        self.outputs_per_step = outputs_per_step
-        self.sample_rate = ap.sample_rate
-        self.cleaners = text_cleaner
-        self.min_seq_len = min_seq_len
-        print(" > Reading LJSpeech from - {}".format(root_path))
-        print(" | > Number of instances : {}".format(len(self.items)))
-        self.sort_items()
-
-    def load_wav(self, filename):
-        try:
-            audio = self.ap.load_wav(filename)
-            return audio
-        except RuntimeError as e:
-            print(" !! Cannot read file : {}".format(filename))
-
-    def load_np(self, filename):
-        data = np.load(filename).astype('float32')
-        return data
-
-    def sort_items(self):
-        r"""Sort text sequences in ascending order"""
-        lengths = np.array([len(ins[-1]) for ins in self.items])
-
-        print(" | > Max length sequence {}".format(np.max(lengths)))
-        print(" | > Min length sequence {}".format(np.min(lengths)))
-        print(" | > Avg length sequence {}".format(np.mean(lengths)))
-
-        idxs = np.argsort(lengths)
-        new_frames = []
-        ignored = []
-        for i, idx in enumerate(idxs):
-            length = lengths[idx]
-            if length < self.min_seq_len:
-                ignored.append(idx)
-            else:
-                new_frames.append(self.items[idx])
-        print(" | > {} instances are ignored by min_seq_len ({})".format(
-            len(ignored), self.min_seq_len))
-        # shuffle batch groups
-        if self.batch_group_size > 0:
-            print(" | > Batch group shuffling is active.")
-            for i in range(len(new_frames) // self.batch_group_size):
-                offset = i * self.batch_group_size
-                end_offset = offset + self.batch_group_size
-                temp_frames = new_frames[offset : end_offset]
-                random.shuffle(temp_frames)
-                new_frames[offset : end_offset] = temp_frames
-        self.items = new_frames
-
-    def __len__(self):
-        return len(self.items)
-
-    def __getitem__(self, idx):
-        wav_name = self.items[idx][0]
-        mel_name = self.items[idx][1]
-        linear_name = self.items[idx][2]
-        text = self.items[idx][-1]
-        text = np.asarray(
-            text_to_sequence(text, [self.cleaners]), dtype=np.int32)
-        if wav_name.split('.')[-1] == 'npy':
-            wav = self.load_np(wav_name)
-        else:
-            wav = np.asarray(self.load_wav(wav_name), dtype=np.float32)
-        mel = self.load_np(mel_name)
-        linear = self.load_np(linear_name)
-        sample = {
-            'text': text,
-            'wav': wav,
-            'item_idx': self.items[idx][0],
-            'mel': mel,
-            'linear': linear
-        }
-        return sample
-
-    def collate_fn(self, batch):
-        r"""
-            Perform preprocessing and create a final data batch:
-            1. PAD sequences with the longest sequence in the batch
-            2. Convert Audio signal to Spectrograms.
-            3. PAD sequences that can be divided by r.
-            4. Convert Numpy to Torch tensors.
-        """
-
-        # Puts each data field into a tensor with outer dimension batch size
-        if isinstance(batch[0], collections.Mapping):
-            keys = list()
-
-            wav = [d['wav'] for d in batch]
-            item_idxs = [d['item_idx'] for d in batch]
-            text = [d['text'] for d in batch]
-            mel = [d['mel'] for d in batch]
-            linear = [d['linear'] for d in batch]
-
-            text_lenghts = np.array([len(x) for x in text])
-            max_text_len = np.max(text_lenghts)
-            mel_lengths = [m.shape[1] + 1 for m in mel]  # +1 for zero-frame
-
-            # compute 'stop token' targets
-            stop_targets = [
-                np.array([0.] * (mel_len - 1)) for mel_len in mel_lengths
-            ]
-
-            # PAD stop targets
-            stop_targets = prepare_stop_target(stop_targets,
-                                               self.outputs_per_step)
-
-            # PAD sequences with largest length of the batch
-            text = prepare_data(text).astype(np.int32)
-            wav = prepare_data(wav)
-
-            # PAD features with largest length + a zero frame
-            linear = prepare_tensor(linear, self.outputs_per_step)
-            mel = prepare_tensor(mel, self.outputs_per_step)
-            timesteps = mel.shape[2]
-
-            # B x T x D
-            linear = linear.transpose(0, 2, 1)
-            mel = mel.transpose(0, 2, 1)
-
-            # convert things to pytorch
-            text_lenghts = torch.LongTensor(text_lenghts)
-            text = torch.LongTensor(text)
-            linear = torch.FloatTensor(linear).contiguous()
-            mel = torch.FloatTensor(mel).contiguous()
-            mel_lengths = torch.LongTensor(mel_lengths)
-            stop_targets = torch.FloatTensor(stop_targets)
-
-            return text, text_lenghts, linear, mel, mel_lengths, stop_targets, item_idxs
-
-        raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
-                         found {}".format(type(batch[0]))))
diff --git a/datasets/TTSDatasetMemory.py b/datasets/TTSDatasetMemory.py
index 1d770dfd..0c799f46 100644
--- a/datasets/TTSDatasetMemory.py
+++ b/datasets/TTSDatasetMemory.py
@@ -14,7 +14,7 @@ from utils.data import (prepare_data, pad_per_step, prepare_tensor,
 
 
 class MyDataset(Dataset):
-    # TODO: Not finished yet.
+    # TODO: Merge to TTSDataset.py, but it is not fast as it is supposed to be
     def __init__(self,
                  root_path,
                  meta_file,