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diff --git a/tensorflow/examples/speech_commands/label_wav_dir.py b/tensorflow/examples/speech_commands/label_wav_dir.py
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+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Runs a trained audio graph against WAVE files and reports the results.
+
+The model, labels and .wav files specified in the arguments will be loaded, and
+then the predictions from running the model against the audio data will be
+printed to the console. This is a useful script for sanity checking trained
+models, and as an example of how to use an audio model from Python.
+
+Here's an example of running it:
+
+python tensorflow/examples/speech_commands/label_wav_dir.py \
+--graph=/tmp/my_frozen_graph.pb \
+--labels=/tmp/speech_commands_train/conv_labels.txt \
+--wav_dir=/tmp/speech_dataset/left
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import glob
+import sys
+
+import tensorflow as tf
+
+# pylint: disable=unused-import
+from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
+# pylint: enable=unused-import
+
+FLAGS = None
+
+
+def load_graph(filename):
+  """Unpersists graph from file as default graph."""
+  with tf.gfile.FastGFile(filename, 'rb') as f:
+    graph_def = tf.GraphDef()
+    graph_def.ParseFromString(f.read())
+    tf.import_graph_def(graph_def, name='')
+
+
+def load_labels(filename):
+  """Read in labels, one label per line."""
+  return [line.rstrip() for line in tf.gfile.GFile(filename)]
+
+
+def run_graph(wav_dir, labels, input_layer_name, output_layer_name,
+              num_top_predictions):
+  """Runs the audio data through the graph and prints predictions."""
+  with tf.Session() as sess:
+    # Feed the audio data as input to the graph.
+    #   predictions  will contain a two-dimensional array, where one
+    #   dimension represents the input image count, and the other has
+    #   predictions per class
+    for wav_path in glob.glob(wav_dir + '/*.wav'):
+      if not wav_path or not tf.gfile.Exists(wav_path):
+        tf.logging.fatal('Audio file does not exist %s', wav_path)
+
+      with open(wav_path, 'rb') as wav_file:
+        wav_data = wav_file.read()
+
+      softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
+      predictions, = sess.run(softmax_tensor, {input_layer_name: wav_data})
+
+      # Sort to show labels in order of confidence
+      print('\n%s' % (wav_path.split('/')[-1]))
+      top_k = predictions.argsort()[-num_top_predictions:][::-1]
+      for node_id in top_k:
+        human_string = labels[node_id]
+        score = predictions[node_id]
+        print('%s (score = %.5f)' % (human_string, score))
+
+    return 0
+
+
+def label_wav(wav_dir, labels, graph, input_name, output_name, how_many_labels):
+  """Loads the model and labels, and runs the inference to print predictions."""
+  if not labels or not tf.gfile.Exists(labels):
+    tf.logging.fatal('Labels file does not exist %s', labels)
+
+  if not graph or not tf.gfile.Exists(graph):
+    tf.logging.fatal('Graph file does not exist %s', graph)
+
+  labels_list = load_labels(labels)
+
+  # load graph, which is stored in the default session
+  load_graph(graph)
+
+  run_graph(wav_dir, labels_list, input_name, output_name, how_many_labels)
+
+
+def main(_):
+  """Entry point for script, converts flags to arguments."""
+  label_wav(FLAGS.wav_dir, FLAGS.labels, FLAGS.graph, FLAGS.input_name,
+            FLAGS.output_name, FLAGS.how_many_labels)
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument(
+      '--wav_dir', type=str, default='', help='Audio file to be identified.')
+  parser.add_argument(
+      '--graph', type=str, default='', help='Model to use for identification.')
+  parser.add_argument(
+      '--labels', type=str, default='', help='Path to file containing labels.')
+  parser.add_argument(
+      '--input_name',
+      type=str,
+      default='wav_data:0',
+      help='Name of WAVE data input node in model.')
+  parser.add_argument(
+      '--output_name',
+      type=str,
+      default='labels_softmax:0',
+      help='Name of node outputting a prediction in the model.')
+  parser.add_argument(
+      '--how_many_labels',
+      type=int,
+      default=3,
+      help='Number of results to show.')
+
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)