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~~~

 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 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 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 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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