需要源码和配置文件请点赞关注收藏后评论区留言~~~

一、基于机器学习的语音推断

Tensorflow基于分层和模块化的设计思想，整个框架以C语言的编程接口为界，分为前端和后端两大部分 Tensorflow框架结构如下图

二、Tensorflow Lite简介

虽然Tensorflow是一款十分优秀的机器学习框架，但是它层次众多，不适合在单个设备上独立运行，为此Google推出了Tensorflow Lite，也就是Tensorflow的精简版，它可以在移动设备，嵌入式设备和物联网设备上运行Tensorflow模型

Tensorflow Lite包括下列两个主要组件

Tensorflow Lite解释器 允许在设备端的不同硬件上运行优化过的模型

Tensorflow Lite转换器 将Tensorflow模型转换为解释器使用的格式 同时通过优化提高应用性能

Tensorflow Lite允许在网络边缘的设备上执行机器学习任务，无须在设备与服务器之间来回发送数据 对开发者来说 在设备端执行机器学习任务有以下好处

缩短延迟 数组无须往返服务器

保护隐私 任何数据都不会离开设备

减少连接 不需要互联网连接

降低功耗 网络连接非常耗电

三、从语音中识别指令实战

首先给App工程手工添加Tensorflow Lite支持

implementation 'org.tensorflow:tensorflow-lite:2.5.0'

同时还要引入语音识别的配置文件请点赞关注收藏后评论区留言私信博主

然后在活动代码中初始化Tensorflow Lite 分别读取标签配置 加载模型文件

运行效果如下

语音识别支持App支持识别英文单词指令 识别到的指令会高亮显示在App界面 并且标出吻合度

需要对着手机大声朗读上述英文单词 就可观察到语音推断结果

所以此处连接真机测试效果更好 模拟机不好录制语音~~~

演示视频如下

Android机器学习语音推断

四、代码

部分源码如下 需要全部代码请点赞关注收藏后评论区留言~~~

package com.example.voice;import android.content.res.AssetFileDescriptor;import android.content.res.AssetManager;import android.media.AudioFormat;import android.media.AudioRecord;import android.media.MediaRecorder;import android.os.Bundle;import android.os.Handler;import android.os.Looper;import android.util.Log;import android.widget.TextView;import androidx.appcompat.app.AppCompatActivity;import androidx.recyclerview.widget.GridLayoutManager;import androidx.recyclerview.widget.RecyclerView;import com.example.voice.adapter.WordRecyclerAdapter;import com.example.voice.bean.WordInfo;import com.example.voice.tensorflow.RecognizeCommands;import org.tensorflow.lite.Interpreter;import java.io.BufferedReader;import java.io.FileInputStream;import java.io.InputStreamReader;import java.nio.MappedByteBuffer;import java.nio.channels.FileChannel;import java.util.ArrayList;import java.util.HashMap;import java.util.List;import java.util.Map;import java.util.concurrent.locks.ReentrantLock;public class VoiceInferenceActivity extends AppCompatActivity {private final static String TAG = "VoiceInferenceActivity";private TextView tv_cost; // 声明一个文本视图对象private WordRecyclerAdapter mAdapter; // 英语单词的循环适配器private String[] mWordArray = new String[]{"Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go"};private List<WordInfo> mWordList = new ArrayList<>(); // 单词信息列表@Overrideprotected void onCreate(Bundle savedInstanceState) {super.onCreate(savedInstanceState);setContentView(R.layout.activity_voice_inference);initView(); // 初始化视图initTensorflow(); // 初始化Tensorflow}// 初始化视图private void initView() {TextView tv_rate = findViewById(R.id.tv_rate);tv_rate.setText(SAMPLE_RATE + " Hz");tv_cost = findViewById(R.id.tv_cost);for (String word : mWordArray) {mWordList.add(new WordInfo(word, null));}RecyclerView rv_word = findViewById(R.id.rv_word);GridLayoutManager manager = new GridLayoutManager(this, 2);rv_word.setLayoutManager(manager);mAdapter = new WordRecyclerAdapter(this, mWordList);rv_word.setAdapter(mAdapter);}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 = 1000;private static final float DETECTION_THRESHOLD = 0.50f;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 = "conv_actions_labels.txt";private static final String MODEL_FILENAME = "conv_actions_frozen.tflite";// Working variables.private short[] recordBuffer = new short[RECORDING_LENGTH];private int recordOffset = 0;private boolean continueRecord = true;private Thread recordThread;private boolean continueRecognize = true;private Thread recognizeThread;private final ReentrantLock recordBufferLock = new ReentrantLock();private List<String> labelList = new ArrayList<>(); // 指令标签列表private RecognizeCommands recognizeCommands = null; // 待识别的指令private Interpreter.Options tfLiteOptions = new Interpreter.Options(); // 解释器选项private Interpreter tfLite; // Tensorflow Lite的解释器private long costTime; // 每次语音识别的耗费时间// 初始化Tensorflowprivate void initTensorflow() {Log.d(TAG, "Reading labels from: " + LABEL_FILENAME);try (BufferedReader br = new BufferedReader(new InputStreamReader(getAssets().open(LABEL_FILENAME)))) {String line;while ((line = br.readLine()) != null) {labelList.add(line);}} catch (Exception e) {throw new RuntimeException("Problem reading label file!", e);}Log.d(TAG, "labelList.size()=" + labelList.size());// 设置一个对象来平滑识别结果，以提高准确率recognizeCommands = new RecognizeCommands(labelList,AVERAGE_WINDOW_DURATION_MS,DETECTION_THRESHOLD,SUPPRESSION_MS,MINIMUM_COUNT,MINIMUM_TIME_BETWEEN_SAMPLES_MS);try {MappedByteBuffer tfLiteModel = loadModelFile(getAssets(), MODEL_FILENAME);tfLite = new Interpreter(tfLiteModel, tfLiteOptions);} catch (Exception e) {throw new RuntimeException(e);}tfLite.resizeInput(0, new int[]{RECORDING_LENGTH, 1});tfLite.resizeInput(1, new int[]{1});startRecord(); // 开始录音startRecognize(); // 开始识别}private MappedByteBuffer loadModelFile(AssetManager assets, String modelFilename) throws Exception {Log.d(TAG, "modelFilename="+modelFilename);AssetFileDescriptor descriptor = assets.openFd(modelFilename);FileInputStream fis = new FileInputStream(descriptor.getFileDescriptor());FileChannel fileChannel = fis.getChannel();long startOffset = descriptor.getStartOffset();long declaredLength = descriptor.getDeclaredLength();return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);}// 开始录音public synchronized void startRecord() {if (recordThread != null) {return;}continueRecord = true;recordThread = new Thread(() -> record());recordThread.start();}// 停止录音public synchronized void stopRecord() {if (recordThread == null) {return;}continueRecord = false;recordThread = null;}// 录制音频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(TAG, "Audio Record can't initialize!");return;}record.startRecording();Log.d(TAG, "Start record");// Loop, gathering audio data and copying it to a round-robin buffer.while (continueRecord) {int numberRead = record.read(audioBuffer, 0, audioBuffer.length);int maxLength = recordBuffer.length;int newRecordOffset = recordOffset + numberRead;int secondCopyLength = Math.max(0, newRecordOffset - 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.recordBufferLock.lock();try {System.arraycopy(audioBuffer, 0, recordBuffer, recordOffset, firstCopyLength);System.arraycopy(audioBuffer, firstCopyLength, recordBuffer, 0, secondCopyLength);recordOffset = newRecordOffset % maxLength;} finally {recordBufferLock.unlock();}}record.stop();record.release();}// 开始识别public synchronized void startRecognize() {if (recognizeThread != null) {return;}continueRecognize = true;recognizeThread = new Thread(() -> recognize());recognizeThread.start();}// 停止识别public synchronized void stopRecognize() {if (recognizeThread == null) {return;}continueRecognize = false;recognizeThread = null;}// 识别语音private void recognize() {Log.d(TAG, "Start recognition");short[] inputBuffer = new short[RECORDING_LENGTH];float[][] floatInputBuffer = new float[RECORDING_LENGTH][1];float[][] outputScores = new float[1][labelList.size()];int[] sampleRateList = new int[]{SAMPLE_RATE};// Loop, grabbing recorded data and running the recognition model on it.while (continueRecognize) {long startTime = System.currentTimeMillis();// The record 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.recordBufferLock.lock();try {int maxLength = recordBuffer.length;int firstCopyLength = maxLength - recordOffset;int secondCopyLength = recordOffset;System.arraycopy(recordBuffer, recordOffset, inputBuffer, 0, firstCopyLength);System.arraycopy(recordBuffer, 0, inputBuffer, firstCopyLength, secondCopyLength);} finally {recordBufferLock.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][0] = inputBuffer[i] / 32767.0f;}Object[] inputArray = {floatInputBuffer, sampleRateList};Map<Integer, Object> outputMap = new HashMap<>();outputMap.put(0, outputScores);// Run the model.tfLite.runForMultipleInputsOutputs(inputArray, outputMap);// Use the smoother to figure out if we've had a real recognition event.final RecognizeCommands.RecognitionResult result =recognizeCommands.processLatestResults(outputScores[0], System.currentTimeMillis());costTime = System.currentTimeMillis() - startTime;runOnUiThread( () -> {tv_cost.setText(costTime + " ms");// If we do have a new command, highlight the right list entry.if (!result.foundCommand.startsWith("_") && result.isNewCommand) {int position = labelList.indexOf(result.foundCommand) - 2;WordInfo word = mWordList.get(position);word.percent = Math.round(result.score * 100) + "%";mWordList.set(position, word);mAdapter.notifyItemChanged(position);new Handler(Looper.myLooper()).postDelayed(() -> {word.percent = "";mWordList.set(position, word);mAdapter.notifyItemChanged(position);}, 1500);}});try {// We don't need to run too frequently, so snooze for a bit.Thread.sleep(MINIMUM_TIME_BETWEEN_SAMPLES_MS);} catch (InterruptedException e) {}}Log.d(TAG, "End recognition");}}

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【Android +Tensroflow Lite】实现从基于机器学习语音中识别指令讲解及实战（超详细 附源码和演示视频）