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diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java
deleted file mode 100644
index eb4dc69d63..0000000000
--- a/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java
+++ /dev/null
@@ -1,353 +0,0 @@
-/*
- * 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.
- */
-
-/* Demonstrates how to run an audio recognition model in Android.
-
-This example loads a simple speech recognition model trained by the tutorial at
-https://www.tensorflow.org/tutorials/audio_training
-
-The model files should be downloaded automatically from the TensorFlow website,
-but if you have a custom model you can update the LABEL_FILENAME and
-MODEL_FILENAME constants to point to your own files.
-
-The example application displays a list view with all of the known audio labels,
-and highlights each one when it thinks it has detected one through the
-microphone. The averaging of results to give a more reliable signal happens in
-the RecognizeCommands helper class.
-*/
-
-package org.tensorflow.demo;
-
-import android.animation.ValueAnimator;
-import android.app.Activity;
-import android.content.pm.PackageManager;
-import android.media.AudioFormat;
-import android.media.AudioRecord;
-import android.media.MediaRecorder;
-import android.os.Bundle;
-import android.util.Log;
-import android.view.View;
-import android.widget.ArrayAdapter;
-import android.widget.Button;
-import android.widget.ListView;
-import java.io.BufferedReader;
-import java.io.IOException;
-import java.io.InputStreamReader;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.concurrent.locks.ReentrantLock;
-import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
-import org.tensorflow.demo.R;
-
-/**
- * An activity that listens for audio and then uses a TensorFlow model to detect particular classes,
- * by default a small set of action words.
- */
-public class SpeechActivity extends Activity {
-
-  // Constants that control the behavior of the recognition code and model
-  // settings. See the audio recognition tutorial for a detailed explanation of
-  // all these, but you should customize them to match your training settings if
-  // you are running your own model.
-  private static final int SAMPLE_RATE = 16000;
-  private static final int SAMPLE_DURATION_MS = 1000;
-  private static final int RECORDING_LENGTH = (int) (SAMPLE_RATE * SAMPLE_DURATION_MS / 1000);
-  private static final long AVERAGE_WINDOW_DURATION_MS = 500;
-  private static final float DETECTION_THRESHOLD = 0.70f;
-  private static final int SUPPRESSION_MS = 1500;
-  private static final int MINIMUM_COUNT = 3;
-  private static final long MINIMUM_TIME_BETWEEN_SAMPLES_MS = 30;
-  private static final String LABEL_FILENAME = "file:///android_asset/conv_actions_labels.txt";
-  private static final String MODEL_FILENAME = "file:///android_asset/conv_actions_frozen.pb";
-  private static final String INPUT_DATA_NAME = "decoded_sample_data:0";
-  private static final String SAMPLE_RATE_NAME = "decoded_sample_data:1";
-  private static final String OUTPUT_SCORES_NAME = "labels_softmax";
-
-  // UI elements.
-  private static final int REQUEST_RECORD_AUDIO = 13;
-  private Button quitButton;
-  private ListView labelsListView;
-  private static final String LOG_TAG = SpeechActivity.class.getSimpleName();
-
-  // Working variables.
-  short[] recordingBuffer = new short[RECORDING_LENGTH];
-  int recordingOffset = 0;
-  boolean shouldContinue = true;
-  private Thread recordingThread;
-  boolean shouldContinueRecognition = true;
-  private Thread recognitionThread;
-  private final ReentrantLock recordingBufferLock = new ReentrantLock();
-  private TensorFlowInferenceInterface inferenceInterface;
-  private List<String> labels = new ArrayList<String>();
-  private List<String> displayedLabels = new ArrayList<>();
-  private RecognizeCommands recognizeCommands = null;
-
-  @Override
-  protected void onCreate(Bundle savedInstanceState) {
-    // Set up the UI.
-    super.onCreate(savedInstanceState);
-    setContentView(R.layout.activity_speech);
-    quitButton = (Button) findViewById(R.id.quit);
-    quitButton.setOnClickListener(
-        new View.OnClickListener() {
-          @Override
-          public void onClick(View view) {
-            moveTaskToBack(true);
-            android.os.Process.killProcess(android.os.Process.myPid());
-            System.exit(1);
-          }
-        });
-    labelsListView = (ListView) findViewById(R.id.list_view);
-
-    // Load the labels for the model, but only display those that don't start
-    // with an underscore.
-    String actualFilename = LABEL_FILENAME.split("file:///android_asset/")[1];
-    Log.i(LOG_TAG, "Reading labels from: " + actualFilename);
-    BufferedReader br = null;
-    try {
-      br = new BufferedReader(new InputStreamReader(getAssets().open(actualFilename)));
-      String line;
-      while ((line = br.readLine()) != null) {
-        labels.add(line);
-        if (line.charAt(0) != '_') {
-          displayedLabels.add(line.substring(0, 1).toUpperCase() + line.substring(1));
-        }
-      }
-      br.close();
-    } catch (IOException e) {
-      throw new RuntimeException("Problem reading label file!", e);
-    }
-
-    // Build a list view based on these labels.
-    ArrayAdapter<String> arrayAdapter =
-        new ArrayAdapter<String>(this, R.layout.list_text_item, displayedLabels);
-    labelsListView.setAdapter(arrayAdapter);
-
-    // Set up an object to smooth recognition results to increase accuracy.
-    recognizeCommands =
-        new RecognizeCommands(
-            labels,
-            AVERAGE_WINDOW_DURATION_MS,
-            DETECTION_THRESHOLD,
-            SUPPRESSION_MS,
-            MINIMUM_COUNT,
-            MINIMUM_TIME_BETWEEN_SAMPLES_MS);
-
-    // Load the TensorFlow model.
-    inferenceInterface = new TensorFlowInferenceInterface(getAssets(), MODEL_FILENAME);
-
-    // Start the recording and recognition threads.
-    requestMicrophonePermission();
-    startRecognition();
-  }
-
-  private void requestMicrophonePermission() {
-    requestPermissions(
-        new String[] {android.Manifest.permission.RECORD_AUDIO}, REQUEST_RECORD_AUDIO);
-  }
-
-  @Override
-  public void onRequestPermissionsResult(
-      int requestCode, String[] permissions, int[] grantResults) {
-    if (requestCode == REQUEST_RECORD_AUDIO
-        && grantResults.length > 0
-        && grantResults[0] == PackageManager.PERMISSION_GRANTED) {
-      startRecording();
-      startRecognition();
-    }
-  }
-
-  public synchronized void startRecording() {
-    if (recordingThread != null) {
-      return;
-    }
-    shouldContinue = true;
-    recordingThread =
-        new Thread(
-            new Runnable() {
-              @Override
-              public void run() {
-                record();
-              }
-            });
-    recordingThread.start();
-  }
-
-  public synchronized void stopRecording() {
-    if (recordingThread == null) {
-      return;
-    }
-    shouldContinue = false;
-    recordingThread = null;
-  }
-
-  private void record() {
-    android.os.Process.setThreadPriority(android.os.Process.THREAD_PRIORITY_AUDIO);
-
-    // Estimate the buffer size we'll need for this device.
-    int bufferSize =
-        AudioRecord.getMinBufferSize(
-            SAMPLE_RATE, AudioFormat.CHANNEL_IN_MONO, AudioFormat.ENCODING_PCM_16BIT);
-    if (bufferSize == AudioRecord.ERROR || bufferSize == AudioRecord.ERROR_BAD_VALUE) {
-      bufferSize = SAMPLE_RATE * 2;
-    }
-    short[] audioBuffer = new short[bufferSize / 2];
-
-    AudioRecord record =
-        new AudioRecord(
-            MediaRecorder.AudioSource.DEFAULT,
-            SAMPLE_RATE,
-            AudioFormat.CHANNEL_IN_MONO,
-            AudioFormat.ENCODING_PCM_16BIT,
-            bufferSize);
-
-    if (record.getState() != AudioRecord.STATE_INITIALIZED) {
-      Log.e(LOG_TAG, "Audio Record can't initialize!");
-      return;
-    }
-
-    record.startRecording();
-
-    Log.v(LOG_TAG, "Start recording");
-
-    // Loop, gathering audio data and copying it to a round-robin buffer.
-    while (shouldContinue) {
-      int numberRead = record.read(audioBuffer, 0, audioBuffer.length);
-      int maxLength = recordingBuffer.length;
-      int newRecordingOffset = recordingOffset + numberRead;
-      int secondCopyLength = Math.max(0, newRecordingOffset - maxLength);
-      int firstCopyLength = numberRead - secondCopyLength;
-      // We store off all the data for the recognition thread to access. The ML
-      // thread will copy out of this buffer into its own, while holding the
-      // lock, so this should be thread safe.
-      recordingBufferLock.lock();
-      try {
-        System.arraycopy(audioBuffer, 0, recordingBuffer, recordingOffset, firstCopyLength);
-        System.arraycopy(audioBuffer, firstCopyLength, recordingBuffer, 0, secondCopyLength);
-        recordingOffset = newRecordingOffset % maxLength;
-      } finally {
-        recordingBufferLock.unlock();
-      }
-    }
-
-    record.stop();
-    record.release();
-  }
-
-  public synchronized void startRecognition() {
-    if (recognitionThread != null) {
-      return;
-    }
-    shouldContinueRecognition = true;
-    recognitionThread =
-        new Thread(
-            new Runnable() {
-              @Override
-              public void run() {
-                recognize();
-              }
-            });
-    recognitionThread.start();
-  }
-
-  public synchronized void stopRecognition() {
-    if (recognitionThread == null) {
-      return;
-    }
-    shouldContinueRecognition = false;
-    recognitionThread = null;
-  }
-
-  private void recognize() {
-    Log.v(LOG_TAG, "Start recognition");
-
-    short[] inputBuffer = new short[RECORDING_LENGTH];
-    float[] floatInputBuffer = new float[RECORDING_LENGTH];
-    float[] outputScores = new float[labels.size()];
-    String[] outputScoresNames = new String[] {OUTPUT_SCORES_NAME};
-    int[] sampleRateList = new int[] {SAMPLE_RATE};
-
-    // Loop, grabbing recorded data and running the recognition model on it.
-    while (shouldContinueRecognition) {
-      // The recording thread places data in this round-robin buffer, so lock to
-      // make sure there's no writing happening and then copy it to our own
-      // local version.
-      recordingBufferLock.lock();
-      try {
-        int maxLength = recordingBuffer.length;
-        int firstCopyLength = maxLength - recordingOffset;
-        int secondCopyLength = recordingOffset;
-        System.arraycopy(recordingBuffer, recordingOffset, inputBuffer, 0, firstCopyLength);
-        System.arraycopy(recordingBuffer, 0, inputBuffer, firstCopyLength, secondCopyLength);
-      } finally {
-        recordingBufferLock.unlock();
-      }
-
-      // We need to feed in float values between -1.0f and 1.0f, so divide the
-      // signed 16-bit inputs.
-      for (int i = 0; i < RECORDING_LENGTH; ++i) {
-        floatInputBuffer[i] = inputBuffer[i] / 32767.0f;
-      }
-
-      // Run the model.
-      inferenceInterface.feed(SAMPLE_RATE_NAME, sampleRateList);
-      inferenceInterface.feed(INPUT_DATA_NAME, floatInputBuffer, RECORDING_LENGTH, 1);
-      inferenceInterface.run(outputScoresNames);
-      inferenceInterface.fetch(OUTPUT_SCORES_NAME, outputScores);
-
-      // Use the smoother to figure out if we've had a real recognition event.
-      long currentTime = System.currentTimeMillis();
-      final RecognizeCommands.RecognitionResult result =
-          recognizeCommands.processLatestResults(outputScores, currentTime);
-
-      runOnUiThread(
-          new Runnable() {
-            @Override
-            public void run() {
-              // If we do have a new command, highlight the right list entry.
-              if (!result.foundCommand.startsWith("_") && result.isNewCommand) {
-                int labelIndex = -1;
-                for (int i = 0; i < labels.size(); ++i) {
-                  if (labels.get(i).equals(result.foundCommand)) {
-                    labelIndex = i;
-                  }
-                }
-                final View labelView = (View) labelsListView.getChildAt(labelIndex - 2);
-                ValueAnimator colorAnimation =
-                     ValueAnimator.ofArgb(0x00b3ccff, 0xffb3ccff, 0x00b3ccff);
-                colorAnimation.setDuration(750);
-                colorAnimation.addUpdateListener(
-                    new ValueAnimator.AnimatorUpdateListener() {
-                      @Override
-                      public void onAnimationUpdate(ValueAnimator animator) {
-                        labelView.setBackgroundColor((int) animator.getAnimatedValue());
-                      }
-                    });
-                colorAnimation.start();
-              }
-            }
-          });
-      try {
-        // We don't need to run too frequently, so snooze for a bit.
-        Thread.sleep(MINIMUM_TIME_BETWEEN_SAMPLES_MS);
-      } catch (InterruptedException e) {
-        // Ignore
-      }
-    }
-
-    Log.v(LOG_TAG, "End recognition");
-  }
-}