diff --git a/chatterbot/trainers.py b/chatterbot/trainers.py
index 18cdf5ff5..48d2ab7ce 100644
--- a/chatterbot/trainers.py
+++ b/chatterbot/trainers.py
@@ -2,7 +2,6 @@
 import sys
 import csv
 import time
-from multiprocessing import Pool, Manager
 from dateutil import parser as date_parser
 from chatterbot.conversation import Statement
 from chatterbot.tagging import PosLemmaTagger
@@ -174,41 +173,6 @@ def train(self, *corpus_paths):
             self.chatbot.storage.create_many(statements_to_create)
 
 
-def read_file(files, queue, preprocessors, tagger):
-
-    statements_from_file = []
-
-    for tsv_file in files:
-        with open(tsv_file, 'r', encoding='utf-8') as tsv:
-            reader = csv.reader(tsv, delimiter='\t')
-
-            previous_statement_text = None
-            previous_statement_search_text = ''
-
-            for row in reader:
-                if len(row) > 0:
-                    statement = Statement(
-                        text=row[3],
-                        in_response_to=previous_statement_text,
-                        conversation='training',
-                        created_at=date_parser.parse(row[0]),
-                        persona=row[1]
-                    )
-
-                    for preprocessor in preprocessors:
-                        statement = preprocessor(statement)
-
-                    statement.search_text = tagger.get_bigram_pair_string(statement.text)
-                    statement.search_in_response_to = previous_statement_search_text
-
-                    previous_statement_text = statement.text
-                    previous_statement_search_text = statement.search_text
-
-                    statements_from_file.append(statement)
-
-    queue.put(tuple(statements_from_file))
-
-
 class UbuntuCorpusTrainer(Trainer):
     """
     Allow chatbots to be trained with the data from the Ubuntu Dialog Corpus.
@@ -337,9 +301,6 @@ def train(self):
             '**', '**', '*.tsv'
         )
 
-        manager = Manager()
-        queue = manager.Queue()
-
         def chunks(items, items_per_chunk):
             for start_index in range(0, len(items), items_per_chunk):
                 end_index = start_index + items_per_chunk
@@ -349,55 +310,40 @@ def chunks(items, items_per_chunk):
 
         file_groups = tuple(chunks(file_list, 10000))
 
-        argument_groups = tuple(
-            (
-                file_names,
-                queue,
-                self.chatbot.preprocessors,
-                tagger,
-            ) for file_names in file_groups
-        )
-
-        pool_batches = chunks(argument_groups, 9)
-
-        total_batches = len(file_groups)
-        batch_number = 0
-
         start_time = time.time()
 
-        with Pool() as pool:
-            for pool_batch in pool_batches:
-                pool.starmap(read_file, pool_batch)
+        for tsv_files in file_groups:
+
+            statements_from_file = []
 
-                while True:
+            for tsv_file in tsv_files:
+                with open(tsv_file, 'r', encoding='utf-8') as tsv:
+                    reader = csv.reader(tsv, delimiter='\t')
 
-                    if queue.empty():
-                        break
+                    previous_statement_text = None
+                    previous_statement_search_text = ''
 
-                    batch_number += 1
+                    for row in reader:
+                        if len(row) > 0:
+                            statement = Statement(
+                                text=row[3],
+                                in_response_to=previous_statement_text,
+                                conversation='training',
+                                created_at=date_parser.parse(row[0]),
+                                persona=row[1]
+                            )
 
-                    print('Training with batch {} with {} batches remaining...'.format(
-                        batch_number,
-                        total_batches - batch_number
-                    ))
+                            for preprocessor in self.chatbot.preprocessors:
+                                statement = preprocessor(statement)
 
-                    self.chatbot.storage.create_many(queue.get())
+                            statement.search_text = tagger.get_bigram_pair_string(statement.text)
+                            statement.search_in_response_to = previous_statement_search_text
 
-                elapsed_time = time.time() - start_time
-                time_per_batch = elapsed_time / batch_number
-                remaining_time = time_per_batch * (total_batches - batch_number)
+                            previous_statement_text = statement.text
+                            previous_statement_search_text = statement.search_text
 
-                print('{:.0f} hours {:.0f} minutes {:.0f} seconds elapsed.'.format(
-                    elapsed_time // 3600 % 24,
-                    elapsed_time // 60 % 60,
-                    elapsed_time % 60
-                ))
+                            statements_from_file.append(statement)
 
-                print('{:.0f} hours {:.0f} minutes {:.0f} seconds remaining.'.format(
-                    remaining_time // 3600 % 24,
-                    remaining_time // 60 % 60,
-                    remaining_time % 60
-                ))
-                print('---')
+            self.chatbot.storage.create_many(statements_from_file)
 
         print('Training took', time.time() - start_time, 'seconds.')