diff --git a/requirements.txt b/requirements.txt
index 9cb570ec..e0ba7699 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,4 +3,3 @@ numpy==1.10.4
 argparse==1.2.1
 h5py==2.5.0
 six==1.10.0
-wsgiref==0.1.2
diff --git a/scripts/novel_substrings.py b/scripts/novel_substrings.py
index 9333e339..695cfc3d 100644
--- a/scripts/novel_substrings.py
+++ b/scripts/novel_substrings.py
@@ -1,4 +1,7 @@
+from __future__ import print_function
+
 import argparse
+import six
 
 """
 Check how many substrings in sampled text are novel, not appearing in training
@@ -18,14 +21,14 @@
 with open(args.training_text, 'r') as f:
   s2 = f.read()
 
-for L in xrange(1, 50):
+for L in six.moves.range(1, 50):
   num_searched = 0
   num_found = 0
-  for i in xrange(len(s1) - L + 1):
+  for i in six.moves.range(len(s1) - L + 1):
     num_searched += 1
     sub = s1[i:(i+L)]
     assert len(sub) == L
     if sub in s2:
       num_found += 1
   novel_frac = (num_searched - num_found) / float(num_searched)
-  print L, novel_frac
+  print(L, novel_frac)
diff --git a/scripts/preprocess.py b/scripts/preprocess.py
index 90b834b6..fecd56e5 100644
--- a/scripts/preprocess.py
+++ b/scripts/preprocess.py
@@ -1,6 +1,10 @@
 # -*- coding: utf-8 -*-
+from __future__ import print_function
 
-import argparse, json, os
+import argparse
+import json
+import os
+import six
 import numpy as np
 import h5py
 import codecs
@@ -34,20 +38,20 @@
   val_size = int(args.val_frac * total_size)
   test_size = int(args.test_frac * total_size)
   train_size = total_size - val_size - test_size
- 
+
   if not args.quiet:
-    print 'Total vocabulary size: %d' % len(token_to_idx)
-    print 'Total tokens in file: %d' % total_size
-    print '  Training size: %d' % train_size
-    print '  Val size: %d' % val_size
-    print '  Test size: %d' % test_size
+    print('Total vocabulary size: %d' % len(token_to_idx))
+    print('Total tokens in file: %d' % total_size)
+    print('  Training size: %d' % train_size)
+    print('  Val size: %d' % val_size)
+    print('  Test size: %d' % test_size)
 
   # Choose the datatype based on the vocabulary size
   dtype = np.uint8
   if len(token_to_idx) > 255:
     dtype = np.uint32
   if not args.quiet:
-    print 'Using dtype ', dtype
+    print('Using dtype ', dtype)
 
   # Just load data into memory ... we'll have to do something more clever
   # for huge datasets but this should be fine for now
@@ -77,7 +81,7 @@
   # doesn't crash
   if args.encoding is None:
     new_token_to_idx = {}
-    for token, idx in token_to_idx.iteritems():
+    for token, idx in six.iteritems(token_to_idx):
       if ord(token) > 127:
         new_token_to_idx['[%d]' % ord(token)] = idx
       else:
@@ -87,7 +91,7 @@
   # Dump a JSON file for the vocab
   json_data = {
     'token_to_idx': token_to_idx,
-    'idx_to_token': {v: k for k, v in token_to_idx.iteritems()},
+    'idx_to_token': {v: k for k, v in six.iteritems(token_to_idx)},
   }
   with open(args.output_json, 'w') as f:
     json.dump(json_data, f)