实时语音转文字备忘录系统设计与实现 原创

实时语音转文字备忘录系统设计与实现

一、项目概述

基于鸿蒙分布式技术的实时语音转文字备忘录系统，能够将用户的语音输入实时转换为文字并同步到多个设备（手机、平板、智慧屏等）。系统利用鸿蒙的分布式能力实现多设备协同录音、语音识别和内容同步，为用户提供无缝的跨设备语音备忘录体验。

二、核心技术点
分布式语音采集与处理

// 分布式语音采集管理器
public class DistributedVoiceRecorder {
private static final String VOICE_DATA_KEY = “voice_data”;
private DistributedDataManager dataManager;
private List<AudioCapturer> capturers = new ArrayList<>();

public DistributedVoiceRecorder(Context context) {
    dataManager = DistributedDataManagerFactory.getInstance()
        .createDistributedDataManager(new ManagerConfig(context));

// 初始化多设备录音网络

public void initRecordingNetwork(List<DeviceInfo> devices) {
    for (DeviceInfo device : devices) {
        AudioCapturer capturer = createCapturerForDevice(device);
        capturers.add(capturer);
        
        capturer.setDataCallback(audioData -> {
            processAudioData(audioData, device);
        });

}

// 处理音频数据
private void processAudioData(byte[] audioData, DeviceInfo sourceDevice) {
    // 1. 音频预处理（降噪、增益等）
    byte[] processed = AudioProcessor.process(audioData);
    
    // 2. 分布式数据聚合
    VoiceDataPacket packet = new VoiceDataPacket(
        sourceDevice.getDeviceId(),
        System.currentTimeMillis(),
        processed
    );
    
    String json = new Gson().toJson(packet);
    dataManager.putString(VOICE_DATA_KEY + "_" + sourceDevice.getDeviceId(), json);
    
    // 3. 触发语音识别
    if (shouldTriggerRecognition()) {
        triggerDistributedRecognition();

}

// 触发分布式语音识别
private void triggerDistributedRecognition() {
    Map<String, VoiceDataPacket> allPackets = new HashMap<>();
    List<String> keys = dataManager.getKeysWithPrefix(VOICE_DATA_KEY);
    
    for (String key : keys) {
        String json = dataManager.getString(key);
        VoiceDataPacket packet = new Gson().fromJson(json, VoiceDataPacket.class);
        allPackets.put(packet.deviceId, packet);

// 执行多设备语音融合识别

    String recognizedText = VoiceRecognizer.recognize(allPackets.values());
    distributeRecognitionResult(recognizedText);

}

实时语音识别引擎

// 实时语音识别服务
public class RealTimeVoiceRecognizer {
private static final int SAMPLE_RATE = 16000;
private static final int BUFFER_SIZE_MS = 100; // 100ms缓冲区
private SpeechRecognizer recognizer;
private CircularAudioBuffer buffer;

public RealTimeVoiceRecognizer(Context context) {
    // 初始化语音识别器
    recognizer = SpeechRecognizer.createSpeechRecognizer(context);
    
    // 设置识别监听器
    recognizer.setRecognitionListener(new RecognitionListener() {
        @Override
        public void onResults(Bundle results) {
            List<String> matches = results.getStringArrayList(
                SpeechRecognizer.RESULTS_RECOGNITION);
            if (matches != null && !matches.isEmpty()) {
                String bestMatch = matches.get(0);
                onTextRecognized(bestMatch);

}

        // 其他回调方法...
    });
    
    // 初始化环形缓冲区
    buffer = new CircularAudioBuffer(SAMPLE_RATE * BUFFER_SIZE_MS / 1000);

// 处理音频数据流

public void processAudioStream(byte[] audioData) {
    buffer.write(audioData);
    
    // 当缓冲区足够时触发识别
    if (buffer.available() >= BUFFER_SIZE_MS * SAMPLE_RATE / 1000) {
        byte[] chunk = buffer.read();
        recognizeAudioChunk(chunk);

}

private void recognizeAudioChunk(byte[] audioChunk) {
    // 将音频数据转换为识别器需要的格式
    ByteArrayInputStream stream = new ByteArrayInputStream(audioChunk);
    AudioFormat format = new AudioFormat.Builder()
        .setEncoding(AudioFormat.ENCODING_PCM_16BIT)
        .setSampleRate(SAMPLE_RATE)
        .setChannelMask(AudioFormat.CHANNEL_IN_MONO)
        .build();
    
    AudioRecord audioRecord = new AudioRecord.Builder()
        .setAudioFormat(format)
        .build();
    
    // 开始识别
    recognizer.startListening(new Intent(RecognizerIntent.ACTION_RECOGNIZE_SPEECH)
        .putExtra(RecognizerIntent.EXTRA_LANGUAGE_MODEL, 
                 RecognizerIntent.LANGUAGE_MODEL_FREE_FORM)
        .putExtra(RecognizerIntent.EXTRA_PARTIAL_RESULTS, true));

// 分布式识别结果处理

private void onTextRecognized(String text) {
    // 发送到所有设备
    MemoSyncService.syncTextUpdate(text);

}

三、鸿蒙跨端同步实现
备忘录同步服务

// 备忘录数据同步服务
public class MemoSyncService extends Ability {
private static final String MEMO_DATA_KEY = “memo_data”;
private DistributedDataManager dataManager;

@Override
public void onStart(Intent intent) {
    super.onStart(intent);
    initSyncService();

private void initSyncService() {

    dataManager = DistributedDataManagerFactory.getInstance()
        .createDistributedDataManager(new ManagerConfig(this));
        
    // 注册数据变更监听
    dataManager.registerDataChangeListener(new DataChangeListener() {
        @Override
        public void onDataChanged(String deviceId, String key, String value) {
            if (MEMO_DATA_KEY.equals(key)) {
                processMemoUpdate(value);

}

    });

// 同步文本更新

public static void syncTextUpdate(String text) {
    MemoUpdate update = new MemoUpdate(
        DeviceManager.getLocalDeviceId(),
        System.currentTimeMillis(),
        text
    );
    
    String json = new Gson().toJson(update);
    DistributedDataManager.getInstance().putString(MEMO_DATA_KEY, json);

// 处理备忘录更新

private void processMemoUpdate(String jsonData) {
    MemoUpdate update = new Gson().fromJson(jsonData, MemoUpdate.class);
    
    // 忽略本设备发出的更新
    if (!update.deviceId.equals(DeviceManager.getLocalDeviceId())) {
        EventBus.getDefault().post(new MemoUpdateEvent(update));

}

// 获取完整备忘录内容
public String getCombinedMemo() {
    List<MemoUpdate> allUpdates = getAllUpdates();
    Collections.sort(allUpdates, (u1, u2) -> Long.compare(u1.timestamp, u2.timestamp));
    
    StringBuilder combined = new StringBuilder();
    for (MemoUpdate update : allUpdates) {
        combined.append(update.text).append(" ");

return combined.toString();

private List<MemoUpdate> getAllUpdates() {

    List<String> allData = dataManager.getAllValues(MEMO_DATA_KEY);
    List<MemoUpdate> updates = new ArrayList<>();
    
    for (String json : allData) {
        updates.add(new Gson().fromJson(json, MemoUpdate.class));

return updates;

}

多设备备忘录UI

// 分布式备忘录UI组件
public class VoiceMemoComponent extends ComponentContainer {
private Text memoDisplay;
private Button recordButton;
private RealTimeVoiceRecognizer recognizer;
private DistributedVoiceRecorder recorder;

public VoiceMemoComponent(Context context) {
    super(context);
    initUI();
    initSpeechComponents();
    registerEventHandlers();

private void initUI() {

    // 初始化UI组件
    memoDisplay = new Text(getContext());
    memoDisplay.setTextSize(16);
    
    recordButton = new Button(getContext());
    recordButton.setText("按住说话");
    
    // 设置布局
    setOrientation(Component.VERTICAL);
    addComponent(memoDisplay);
    addComponent(recordButton);

private void initSpeechComponents() {

    recognizer = new RealTimeVoiceRecognizer(getContext());
    recorder = new DistributedVoiceRecorder(getContext());

private void registerEventHandlers() {

    // 录音按钮事件
    recordButton.setTouchEventListener(new Component.TouchEventListener() {
        @Override
        public void onTouchEvent(Component component, TouchEvent event) {
            if (event.getAction() == TouchEvent.PRESS) {
                startRecording();

else if (event.getAction() == TouchEvent.RELEASE) {

                stopRecording();

return true;

});

    // 备忘录更新事件
    EventBus.getDefault().addSubscriber(this, MemoUpdateEvent.class, event -> {
        updateMemoDisplay(event.getUpdate());
    });

private void startRecording() {

    // 1. 初始化录音设备网络
    List<DeviceInfo> devices = DeviceManager.getPairedDevices();
    recorder.initRecordingNetwork(devices);
    
    // 2. 开始录音
    recorder.startRecording();
    
    // 3. 更新UI状态
    recordButton.setText("录音中...");

private void stopRecording() {

    // 1. 停止录音
    recorder.stopRecording();
    
    // 2. 更新UI状态
    recordButton.setText("按住说话");
    
    // 3. 显示完整备忘录
    String fullMemo = MemoSyncService.getCombinedMemo();
    memoDisplay.setText(fullMemo);

private void updateMemoDisplay(MemoUpdate update) {

    // 在UI线程更新显示
    getContext().getUITaskDispatcher().asyncDispatch(() -> {
        String currentText = memoDisplay.getText();
        memoDisplay.setText(currentText + " " + update.text);
    });

// 开始分布式语音备忘录会话

public void startDistributedSession(List<DeviceInfo> devices) {
    // 1. 初始化分布式组件
    recorder.initRecordingNetwork(devices);
    
    // 2. 设置音频处理回调
    recorder.setProcessedAudioCallback(audioData -> {
        recognizer.processAudioStream(audioData);
    });

}

四、系统架构设计

±------------------+ ±------------------+ ±------------------+
手机: 主录音设备 <—> 平板: 实时显示 <—> 智慧屏: 大屏展示
±------------------+ ±------------------+ ±------------------+

v v

±--------------------------------------------------------------+
鸿蒙分布式语音处理中间层
±--------------------------------------------------------------+

v v

±------------------+ ±------------------+ ±------------------+
语音采集处理 实时语音识别 文本同步存储

±------------------+ ±------------------+ ±------------------+

五、关键技术创新点
多设备协同降噪：利用多麦克风阵列提升语音质量

实时流式识别：低延迟语音转文字技术

智能断句合并：自然语言处理优化备忘录可读性

分布式文本同步：多设备间实时内容同步

六、应用场景
会议记录：多人会议实时转录为文字纪要

课堂笔记：讲课内容自动转换为文字笔记

灵感捕捉：随时语音记录创意想法

无障碍辅助：为听障人士提供语音转文字服务

七、性能优化方案

// 自适应语音处理策略
public class AdaptiveVoiceProcessor {
private static final int MODE_HIGH_QUALITY = 0;
private static final int MODE_BALANCED = 1;
private static final int MODE_LOW_LATENCY = 2;

private int currentMode = MODE_BALANCED;
private NetworkQualityMonitor networkMonitor;
private DevicePerformanceMonitor perfMonitor;

public AdaptiveVoiceProcessor(Context context) {
    networkMonitor = new NetworkQualityMonitor(context);
    perfMonitor = new DevicePerformanceMonitor(context);

// 根据环境条件调整处理策略

public void adjustProcessingStrategy() {
    // 评估网络条件
    int networkScore = networkMonitor.getQualityScore();
    // 评估设备性能
    int perfScore = perfMonitor.getPerformanceScore();
    
    if (networkScore > 80 && perfScore > 80) {
        currentMode = MODE_HIGH_QUALITY;

else if (networkScore < 30 || perfScore < 30) {

        currentMode = MODE_LOW_LATENCY;

else {

        currentMode = MODE_BALANCED;

applyCurrentMode();

private void applyCurrentMode() {

    switch (currentMode) {
        case MODE_HIGH_QUALITY:
            // 启用高质量模式设置
            AudioProcessor.setSampleRate(44100);
            AudioProcessor.setNoiseReductionLevel(0.9f);
            VoiceRecognizer.setPrecisionMode(true);
            break;
        case MODE_BALANCED:
            // 启用平衡模式设置
            AudioProcessor.setSampleRate(16000);
            AudioProcessor.setNoiseReductionLevel(0.7f);
            VoiceRecognizer.setPrecisionMode(false);
            break;
        case MODE_LOW_LATENCY:
            // 启用低延迟模式设置
            AudioProcessor.setSampleRate(8000);
            AudioProcessor.setNoiseReductionLevel(0.5f);
            VoiceRecognizer.setLowLatencyMode(true);
            break;

}

// 分布式策略同步
public void syncStrategyAcrossDevices() {
    List<DeviceInfo> devices = DeviceManager.getPairedDevices();
    for (DeviceInfo device : devices) {
        if (!device.getDeviceId().equals(DeviceManager.getLocalDeviceId())) {
            sendStrategyToDevice(device, currentMode);

}

}

// 智能语音数据缓存
public class VoiceDataCache {
private static final long MAX_CACHE_SIZE = 50 1024 1024; // 50MB
private LruCache<String, CachedAudio> memoryCache;
private Map<String, Long> accessTimes = new HashMap<>();

public VoiceDataCache() {
    memoryCache = new LruCache<String, CachedAudio>((int) (MAX_CACHE_SIZE / 1024)) {
        @Override
        protected int sizeOf(String key, CachedAudio audio) {
            return audio.sizeInKB();

};

// 缓存音频数据

public void cacheAudio(String sessionId, byte[] audioData) {
    CachedAudio cached = new CachedAudio(audioData, System.currentTimeMillis());
    memoryCache.put(sessionId, cached);
    accessTimes.put(sessionId, System.currentTimeMillis());

// 获取缓存的音频

public byte[] getCachedAudio(String sessionId) {
    CachedAudio cached = memoryCache.get(sessionId);
    if (cached != null) {
        accessTimes.put(sessionId, System.currentTimeMillis());
        return cached.getData();

return null;

// 定期清理旧缓存

public void cleanUpOldCache() {
    long now = System.currentTimeMillis();
    long threshold = now - (24  60  60 * 1000); // 24小时前
    
    for (Map.Entry<String, Long> entry : accessTimes.entrySet()) {
        if (entry.getValue() < threshold) {
            memoryCache.remove(entry.getKey());
            accessTimes.remove(entry.getKey());

}

// 分布式缓存同步

public void syncCacheWithDevice(DeviceInfo device) {
    // 实现缓存同步逻辑

}

八、总结

本实时语音转文字备忘录系统基于鸿蒙分布式技术，实现了以下创新价值：
多设备协同：利用设备集群提升语音识别准确率

实时体验：语音到文字的极低延迟转换

无缝同步：跨设备内容实时保持一致

场景智能适配：根据使用环境自动优化处理策略

该系统充分展现了鸿蒙分布式能力在语音交互场景中的应用潜力，未来可结合更多AI技术实现说话人分离、语音指令识别、自动摘要等增强功能，并通过鸿蒙原子化服务实现更灵活的语音备忘录使用方式。