我正在尝试在使用tensorflow的移动应用程序上运行语音识别模型。我面临以下问题： 当我在应用程序中讲话时，它会提取并打印MFCC功能，但随后会崩溃并给出以下错误：
04-03 12:56:16.754 24654-24815/org.tensorflow.demo E/TensorFlowInferenceInterface: Failed to run TensorFlow inference with inputs:[SeqLen], outputs:[SparseToDense]
--------- beginning of crash
04-03 12:56:16.755 24654-24815/org.tensorflow.demo E/AndroidRuntime: FATAL EXCEPTION: Thread-7556
Process: org.tensorflow.demo, PID: 24654
java.lang.IllegalArgumentException: Expects arg[0] to be int32 but float is provided
at org.tensorflow.Session.run(Native Method)
at org.tensorflow.Session.access$100(Session.java:48)
at org.tensorflow.Session$Runner.runHelper(Session.java:314)
at org.tensorflow.Session$Runner.run(Session.java:264)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.run(TensorFlowInferenceInterface.java:228)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.run(TensorFlowInferenceInterface.java:197)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.run(TensorFlowInferenceInterface.java:187)
at org.tensorflow.demo.SpeechActivity.recognize(SpeechActivity.java:229)
at org.tensorflow.demo.SpeechActivity.access$100(SpeechActivity.java:48)
at org.tensorflow.demo.SpeechActivity$3.run(SpeechActivity.java:193)
at java.lang.Thread.run(Thread.java:818)
这是我的代码：
/**
* An activity that listens for audio and then uses a Pretrained model to detect speech content,
* and turn it into text.
*/
public class SpeechActivity extends Activity {
// Constants that control the behavior of the recognition code and model
// settings.
private static final int SAMPLE_RATE = 16000;
private static final int SAMPLE_DURATION_MS = 5000;
private static final int RECORDING_LENGTH = (int) (SAMPLE_RATE * SAMPLE_DURATION_MS / 1000);
private static final String MODEL_FILENAME = "file:///android_asset/april_model.pb"; // my model
private static final String INPUT_DATA_NAME = "SeqLen";
private static final String OUTPUT_SCORES_NAME = "SparseToDense";
private static final char[] map = new char[]{'0', ' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g',
'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q',
'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'};
// UI elements.
private static final int REQUEST_RECORD_AUDIO = 13;
private Button startButton;
private TextView outputText;
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;
@Override
protected void onCreate(Bundle savedInstanceState) {
// Set up the UI.
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_speech);
startButton = (Button) findViewById(R.id.start);
startButton.setOnClickListener(
new View.OnClickListener() {
@Override
public void onClick(View view) {
startRecording();
}
});
outputText = (TextView) findViewById(R.id.output_text);
// Load the Pretrained WaveNet model.
inferenceInterface = new TensorFlowInferenceInterface(getAssets(), MODEL_FILENAME);
requestMicrophonePermission();
}
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) {
}
}
public synchronized void startRecording() {
if (recordingThread != null) {
return;
}
shouldContinue = true;
recordingThread =
new Thread(
new Runnable() {
@Override
public void run() {
record();
}
});
recordingThread.start();
}
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");
while (shouldContinue) {
int numberRead = record.read(audioBuffer, 0, audioBuffer.length);
Log.v(LOG_TAG, "read: " + numberRead);
int maxLength = recordingBuffer.length;
recordingBufferLock.lock();
try {
if (recordingOffset + numberRead < maxLength) {
System.arraycopy(audioBuffer, 0, recordingBuffer, recordingOffset, numberRead);
} else {
shouldContinue = false;
}
recordingOffset += numberRead;
} finally {
recordingBufferLock.unlock();
}
}
record.stop();
record.release();
startRecognition();
}
public synchronized void startRecognition() {
if (recognitionThread != null) {
return;
}
shouldContinueRecognition = true;
recognitionThread =
new Thread(
new Runnable() {
@Override
public void run() {
recognize();
}
});
recognitionThread.start();
}
private void recognize() {
Log.v(LOG_TAG, "Start recognition");
short[] inputBuffer = new short[RECORDING_LENGTH];
double[] doubleInputBuffer = new double[RECORDING_LENGTH];
long[] outputScores = new long[157];
String[] outputScoresNames = {OUTPUT_SCORES_NAME};
recordingBufferLock.lock();
try {
int maxLength = recordingBuffer.length;
System.arraycopy(recordingBuffer, 0, inputBuffer, 0, maxLength);
} finally {
recordingBufferLock.unlock();
}
// We need to feed in float values between -1.0 and 1.0, so divide the
// signed 16-bit inputs.
for (int i = 0; i < RECORDING_LENGTH; ++i) {
doubleInputBuffer[i] = inputBuffer[i] / 32767.0;
}
//MFCC java library.
MFCC mfccConvert = new MFCC();
float[] mfccInput = mfccConvert.process(doubleInputBuffer);
Log.v(LOG_TAG, "MFCC Input======> " + Arrays.toString(mfccInput));
// Run the model.
inferenceInterface.feed(INPUT_DATA_NAME, mfccInput, 1, 157, 20);
inferenceInterface.run(outputScoresNames); //This is where the error message points
inferenceInterface.fetch(OUTPUT_SCORES_NAME, outputScores);
Log.v(LOG_TAG, "OUTPUT======> " + Arrays.toString(outputScores));
//Output the result.
String result = "";
for (int i = 0;i<outputScores.length;i++) {
if (outputScores[i] == 0)
break;
result += map[(int) outputScores[i]];
}
final String r = result;
this.runOnUiThread(new Runnable() {
@Override
public void run() {
outputText.setText(r);
}
});
Log.v(LOG_TAG, "End recognition: " +result);
}
}
有人可以帮我解决这个问题吗？
这是我为Android代码所引用的链接： https://github.com/chiachunfu/speech 我只是用我的模型代替了他们使用的股票模型。