diff --git a/DeepSpeech.py b/DeepSpeech.py
index 9f9b5ce1..47809d54 100755
--- a/DeepSpeech.py
+++ b/DeepSpeech.py
@@ -12,7 +12,6 @@ import evaluate
 import numpy as np
 import progressbar
 import shutil
-import tempfile
 import tensorflow as tf
 import traceback
 
@@ -30,9 +29,9 @@ from util.text import Alphabet
 
 #TODO: remove once fully switched to 1.13
 try:
-    from tensorflow.contrib.lite.python import tflite_convert # 1.12
+    import tensorflow.lite as lite # 1.13
 except ImportError:
-    from tensorflow.lite.python import tflite_convert # 1.13
+    import tensorflow.contrib.lite as lite # 1.12
 
 
 # Graph Creation
@@ -804,7 +803,7 @@ def export():
                 os.makedirs(FLAGS.export_dir)
 
             def do_graph_freeze(output_file=None, output_node_names=None, variables_blacklist=None):
-                freeze_graph.freeze_graph_with_def_protos(
+                return freeze_graph.freeze_graph_with_def_protos(
                     input_graph_def=session.graph_def,
                     input_saver_def=saver.as_saver_def(),
                     input_checkpoint=checkpoint_path,
@@ -819,39 +818,16 @@ def export():
             if not FLAGS.export_tflite:
                 do_graph_freeze(output_file=output_graph_path, output_node_names=output_names, variables_blacklist='previous_state_c,previous_state_h')
             else:
-                temp_fd, temp_freeze = tempfile.mkstemp(dir=FLAGS.export_dir)
-                os.close(temp_fd)
-                do_graph_freeze(output_file=temp_freeze, output_node_names=output_names, variables_blacklist='')
+                frozen_graph = do_graph_freeze(output_node_names=output_names, variables_blacklist='')
                 output_tflite_path = os.path.join(FLAGS.export_dir, output_filename.replace('.pb', '.tflite'))
-                class TFLiteFlags():
-                    def __init__(self):
-                        self.graph_def_file = temp_freeze
-                        self.inference_type = 'FLOAT'
-                        self.input_arrays   = input_names
-                        self.input_shapes   = input_shapes
-                        self.output_arrays  = output_names
-                        self.output_file    = output_tflite_path
-                        self.output_format  = 'TFLITE'
-                        self.post_training_quantize = True
 
-                        default_empty = [
-                            'inference_input_type',
-                            'mean_values',
-                            'default_ranges_min', 'default_ranges_max',
-                            'drop_control_dependency',
-                            'reorder_across_fake_quant',
-                            'change_concat_input_ranges',
-                            'allow_custom_ops',
-                            'converter_mode',
-                            'dump_graphviz_dir',
-                            'dump_graphviz_video'
-                        ]
-                        for e in default_empty:
-                            self.__dict__[e] = None
+                converter = lite.TFLiteConverter(frozen_graph, input_tensors=inputs.values(), output_tensors=outputs.values())
+                converter.post_training_quantize = True
+                tflite_model = converter.convert()
+
+                with open(output_tflite_path, 'wb') as fout:
+                    fout.write(tflite_model)
 
-                flags = TFLiteFlags()
-                tflite_convert._convert_model(flags)
-                os.unlink(temp_freeze)
                 log_info('Exported model for TF Lite engine as {}'.format(os.path.basename(output_tflite_path)))
 
             log_info('Models exported at %s' % (FLAGS.export_dir))