diff --git a/requirements.txt b/requirements.txt
index edd640b8..ec7a1092 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -15,6 +15,7 @@ tqdm
 inflect
 pysbd
 bokeh==1.4.0
+pysbd
 soundfile
 nose==1.3.7
 cardboardlint==1.3.0
diff --git a/server/synthesizer.py b/server/synthesizer.py
index 0c402609..0f743d87 100644
--- a/server/synthesizer.py
+++ b/server/synthesizer.py
@@ -1,5 +1,4 @@
 import io
-import re
 import sys
 import time
 
diff --git a/setup.py b/setup.py
index 11fef51c..3f02dd09 100644
--- a/setup.py
+++ b/setup.py
@@ -93,6 +93,7 @@ requirements = {
         "inflect",
         "pysbd",
         "bokeh==1.4.0",
+        "pysbd",
         "soundfile",
         "phonemizer>=2.2.0",
         "nose==1.3.7",
diff --git a/vocoder/tf/convert_melgan_tflite.py b/vocoder/tf/convert_melgan_tflite.py
index d84aae19..9a652b57 100644
--- a/vocoder/tf/convert_melgan_tflite.py
+++ b/vocoder/tf/convert_melgan_tflite.py
@@ -3,7 +3,6 @@
 import argparse
 
 from TTS.utils.io import load_config
-from TTS.utils.text.symbols import symbols, phonemes
 from TTS.vocoder.tf.utils.generic_utils import setup_generator
 from TTS.vocoder.tf.utils.io import load_checkpoint
 from TTS.vocoder.tf.utils.tflite import convert_melgan_to_tflite
diff --git a/vocoder/tf/utils/tflite.py b/vocoder/tf/utils/tflite.py
index 3669434e..d0637596 100644
--- a/vocoder/tf/utils/tflite.py
+++ b/vocoder/tf/utils/tflite.py
@@ -1,10 +1,9 @@
 import tensorflow as tf
 
 
-
 def convert_melgan_to_tflite(model,
-                                output_path=None,
-                                experimental_converter=True):
+                             output_path=None,
+                             experimental_converter=True):
     """Convert Tensorflow MelGAN model to TFLite. Save a binary file if output_path is
     provided, else return TFLite model."""
 
@@ -25,6 +24,7 @@ def convert_melgan_to_tflite(model,
         return None
     return tflite_model
 
+
 def load_tflite_model(tflite_path):
     tflite_model = tf.lite.Interpreter(model_path=tflite_path)
     tflite_model.allocate_tensors()