diff --git a/tts/phonetics/__init__.py b/tts/phonetics/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tts/phonetics/base.py b/tts/phonetics/base.py
deleted file mode 100644
index 7e35d8e..0000000
--- a/tts/phonetics/base.py
+++ /dev/null
@@ -1,15 +0,0 @@
-"""
-Base class for Arpabet Sequence Transformers.
-"""
-
-class ArpabetTransformer:
-
-    def transform_text(self, text):
-        """
-        Input: String text sequence
-        Output: 2-dimensional tensor sequence
-        """
-        pass
-
-
-
diff --git a/tts/phonetics/english_arpabet_v1.py b/tts/phonetics/english_arpabet_v1.py
deleted file mode 100644
index e0e1884..0000000
--- a/tts/phonetics/english_arpabet_v1.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import torch
-import numpy as np
-
-from base import ArpabetTransformer
-
-class EnglishMixedArpabetTransformerV1(ArpabetTransformer):
-
-    def transform_text(self, text):
-        pre_sequence = self._handle_multi_lines(text)
-        return self._convert_to_sequence(pre_sequence)
-
-    def _handle_multi_lines(self, text):
-        # NB: This code is adapted from vanilla Tacotron. It's not very graceful.
-        pre_sequence = ''
-        for line in text.split("\n"):
-            if len(line) < 1:
-                continue
-            if line[-1] != ";":
-                line = line + ";"
-            pre_sequence += line
-
-        return pre_sequence
-
-    def _convert_to_sequence(self, pre_sequence_text):
-        with torch.no_grad():
-            # save VRAM by not including gradients
-            sequence = np.array(text_to_sequence(pre_sequence_text, ['english_cleaners']))[None, :]
-            sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
-        return sequence
diff --git a/tts/phonetics/spanish_arpabet_v1.py b/tts/phonetics/spanish_arpabet_v1.py
deleted file mode 100644
index e69de29..0000000