Tensorflow Android Speech Recognition Sample 中的 Logging 和 deque 操作问题

Question

我正在研究 tensorflow 语音命令示例。 我使用的Android代码库与tensorflow GitHub android sample相同，主要集中在SpeechActivity.java和RecognizeCommands.java。除了记录消息，我没有更改任何内容。

据我所知，

(1) SpeechActivity.java 将模型引用结果 (outputScores) 和 currentTime 传递给 recognizeCommands.processLatestResults 以进行后平滑。

// 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);

(2) 在 ProcessLatestResults() 中，previousResults 用于存储在最近 500 毫秒内推断出的输出分数 (averageWindowDurationMs == 500)，averageScores 将是我们想要/使用的最终分数未来。

// Add the latest results to the head of the queue.
previousResults.addLast(new Pair<Long, float[]>(currentTimeMS, currentResults));

// Prune any earlier results that are too old for the averaging window.
final long timeLimit = currentTimeMS - averageWindowDurationMs;
while (previousResults.getFirst().first < timeLimit) {
  previousResults.removeFirst();
}

...

// Calculate the average score across all the results in the window.
float[] averageScores = new float[labelsCount];
for (Pair<Long, float[]> previousResult : previousResults) {
  final float[] scoresTensor = previousResult.second;
  int i = 0;
  while (i < scoresTensor.length) {
    averageScores[i] += scoresTensor[i] / howManyResults;
    ++i;
  }
}

我的问题/疑问是

(1) 当 for 循环计算平均值时，从每个项目读取的 previousResult.second 值是相同的。但是，这是不可能的。我的问题是我是否错过了记录信息中的某些内容，从而打印出错误的previousResult.second 值？或者那些分数数组真的是一样的？ 这是我的记录方式：

Log.d("tmp", "start average");
// Calculate the average score across all the results in the window.
float[] averageScores = new float[labelsCount];
for (Pair<Long, float[]> previousResult : previousResults) {
  final float[] scoresTensor = previousResult.second;
  Log.d("tmp", "previousResult("+previousResult.first+"): ["+Arrays.toString(previousResult.second)+"]" );
  int i = 0;
  while (i < scoresTensor.length) {
    averageScores[i] += scoresTensor[i] / howManyResults;
    ++i;
  }
}

这里是平均循环过程中的日志消息两次。第一次，previousResults中的scores数组和[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]一样，这是不可能的。

start avarage
previousResult(1520998400247): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
previousResult(1520998400301): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
previousResult(1520998400354): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
previousResult(1520998400408): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
previousResult(1520998400466): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
previousResult(1520998400520): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
previousResult(1520998400574): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
previousResult(1520998400629): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
previousResult(1520998400683): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
previousResult(1520998400737): [[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]]
....
....
start average
previousResult(1520998400301): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
previousResult(1520998400354): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
previousResult(1520998400408): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
previousResult(1520998400466): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
previousResult(1520998400520): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
previousResult(1520998400574): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
previousResult(1520998400629): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
previousResult(1520998400683): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
previousResult(1520998400737): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
previousResult(1520998400791): [[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]]
...
...

(2)根据消息日志可以看到，第一次1520998400301对应的scores数组是[0.16993265, 0.15456167, 0.027866788, 0.107177936, 0.12646474, 0.053816866, 0.082612425, 0.059116375, 0.038425073, 0.06992877, 0.033074524, 0.07702225]，但是下一次sscores数组变成了[0.14775836, 0.18298364, 0.026629224, 0.12195902, 0.111195154, 0.058891248, 0.07295453, 0.05453651, 0.04063993, 0.06559348, 0.032576166, 0.084282786]

我的第二个问题是我不知道这是怎么发生的。我的代码与RecognizeCommands.java 相同。任何线索或建议都会非常有帮助，谢谢。

Answer 1

尝试更改此代码：

  // Add the latest results to the head of the queue.
    previousResults.addLast(new Pair<Long, float[]>(currentTimeMS, currentResults));

如下：

  // Add the latest results to the head of the queue.
    previousResults.addLast(new Pair<Long, float[]>(currentTimeMS, 
                            Arrays.copyOf(currentResults, currentResults.length)));