Java语音识别案例

wen java案例 2026-07-30 173

本文目录导读：

Java语音识别案例

方案一：使用Google Cloud Speech-to-Text API
方案二：使用Java Speech API (JSAPI)
方案三：录音并保存为WAV文件
使用说明
运行配置

我来为您提供一个完整的Java语音识别案例，使用Google的Speech-to-Text API和Java Speech API。

使用Google Cloud Speech-to-Text API

Maven依赖配置

<dependencies>
    <!-- Google Cloud Speech -->
    <dependency>
        <groupId>com.google.cloud</groupId>
        <artifactId>google-cloud-speech</artifactId>
        <version>4.3.0</version>
    </dependency>
    <!-- 录音库 -->
    <dependency>
        <groupId>com.google.guava</groupId>
        <artifactId>guava</artifactId>
        <version>31.1-jre</version>
    </dependency>
</dependencies>

语音识别主类

package com.example.speech;
import com.google.cloud.speech.v1.*;
import com.google.protobuf.ByteString;
import javax.sound.sampled.*;
import java.io.*;
import java.util.List;
public class SpeechRecognitionExample {
    private static final String GOOGLE_CREDENTIALS_PATH = "path/to/your/service-account-key.json";
    public static void main(String[] args) {
        try {
            // 设置Google Cloud认证
            System.setProperty("GOOGLE_APPLICATION_CREDENTIALS", GOOGLE_CREDENTIALS_PATH);
            // 1. 录音
            System.out.println("开始录音，请说话...");
            byte[] audioData = recordAudio(5); // 录音5秒
            // 2. 语音识别
            String recognizedText = recognizeSpeech(audioData);
            System.out.println("识别结果: " + recognizedText);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
    /**
     * 录音方法
     */
    public static byte[] recordAudio(int durationSeconds) throws LineUnavailableException {
        // 音频格式设置
        AudioFormat format = new AudioFormat(
            AudioFormat.Encoding.PCM_SIGNED,
            16000, // 采样率
            16,    // 位深度
            1,     // 单声道
            2,     // 帧大小
            16000, // 帧率
            false  // big-endian
        );
        DataLine.Info info = new DataLine.Info(TargetDataLine.class, format);
        TargetDataLine line = (TargetDataLine) AudioSystem.getLine(info);
        line.open(format);
        line.start();
        System.out.println("录音中...");
        ByteArrayOutputStream out = new ByteArrayOutputStream();
        byte[] buffer = new byte[4096];
        int numBytesRead;
        long startTime = System.currentTimeMillis();
        while (System.currentTimeMillis() - startTime < durationSeconds * 1000) {
            numBytesRead = line.read(buffer, 0, buffer.length);
            out.write(buffer, 0, numBytesRead);
        }
        line.stop();
        line.close();
        System.out.println("录音结束");
        return out.toByteArray();
    }
    /**
     * 语音识别方法
     */
    public static String recognizeSpeech(byte[] audioData) throws Exception {
        // 创建Speech客户端
        try (SpeechClient speechClient = SpeechClient.create()) {
            // 构建识别配置
            RecognitionConfig config = RecognitionConfig.newBuilder()
                .setEncoding(RecognitionConfig.AudioEncoding.LINEAR16)
                .setSampleRateHertz(16000)
                .setLanguageCode("zh-CN") // 中文普通话
                .setModel("default")
                .build();
            // 构建音频数据
            RecognitionAudio audio = RecognitionAudio.newBuilder()
                .setContent(ByteString.copyFrom(audioData))
                .build();
            // 执行语音识别
            RecognizeResponse response = speechClient.recognize(config, audio);
            // 解析结果
            List<SpeechRecognitionResult> results = response.getResultsList();
            StringBuilder resultText = new StringBuilder();
            for (SpeechRecognitionResult result : results) {
                SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);
                resultText.append(alternative.getTranscript());
            }
            return resultText.toString();
        }
    }
}

使用Java Speech API (JSAPI)

Maven依赖

<dependencies>
    <!-- JSAPI 2.0 -->
    <dependency>
        <groupId>javax.speech</groupId>
        <artifactId>jsapi</artifactId>
        <version>1.0</version>
    </dependency>
    <!-- Sphinx-4 离线语音识别 -->
    <dependency>
        <groupId>edu.cmu.sphinx</groupId>
        <artifactId>sphinx4-core</artifactId>
        <version>5prealpha-SNAPSHOT</version>
    </dependency>
    <!-- 中文语音模型 -->
    <dependency>
        <groupId>edu.cmu.sphinx</groupId>
        <artifactId>sphinx4-data</artifactId>
        <version>5prealpha-SNAPSHOT</version>
    </dependency>
</dependencies>

离线语音识别实现

package com.example.speech;
import edu.cmu.sphinx.api.Configuration;
import edu.cmu.sphinx.api.LiveSpeechRecognizer;
import edu.cmu.sphinx.api.SpeechResult;
import edu.cmu.sphinx.api.StreamSpeechRecognizer;
import java.io.File;
import java.io.FileInputStream;
import java.io.InputStream;
public class OfflineSpeechRecognition {
    public static void main(String[] args) {
        try {
            // 实时语音识别
            liveSpeechRecognition();
            // 或从文件识别
            // fileSpeechRecognition("path/to/audio.wav");
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
    /**
     * 实时语音识别（麦克风输入）
     */
    public static void liveSpeechRecognition() throws Exception {
        // 配置语音识别器
        Configuration configuration = new Configuration();
        // 设置模型路径（需要下载中文模型）
        configuration.setAcousticModelPath("resource:/edu/cmu/sphinx/models/en-us/en-us");
        configuration.setDictionaryPath("resource:/edu/cmu/sphinx/models/en-us/cmudict-en-us.dict");
        configuration.setLanguageModelPath("resource:/edu/cmu/sphinx/models/en-us/en-us.lm.bin");
        // 对于中文，需要使用中文模型
        // configuration.setAcousticModelPath("resource:/models/zh-cn/mandarin");
        // configuration.setDictionaryPath("resource:/models/zh-cn/mandarin.dict");
        // configuration.setLanguageModelPath("resource:/models/zh-cn/mandarin.lm.bin");
        LiveSpeechRecognizer recognizer = new LiveSpeechRecognizer(configuration);
        System.out.println("语音识别已启动，请说话...");
        recognizer.startRecognition(true);
        SpeechResult result;
        while ((result = recognizer.getResult()) != null) {
            System.out.println("识别结果: " + result.getHypothesis());
        }
        recognizer.stopRecognition();
    }
    /**
     * 从音频文件识别
     */
    public static void fileSpeechRecognition(String audioFilePath) throws Exception {
        Configuration configuration = new Configuration();
        configuration.setAcousticModelPath("resource:/edu/cmu/sphinx/models/en-us/en-us");
        configuration.setDictionaryPath("resource:/edu/cmu/sphinx/models/en-us/cmudict-en-us.dict");
        configuration.setLanguageModelPath("resource:/edu/cmu/sphinx/models/en-us/en-us.lm.bin");
        StreamSpeechRecognizer recognizer = new StreamSpeechRecognizer(configuration);
        InputStream stream = new FileInputStream(new File(audioFilePath));
        recognizer.startRecognition(stream);
        SpeechResult result;
        while ((result = recognizer.getResult()) != null) {
            System.out.println("识别结果: " + result.getHypothesis());
        }
        recognizer.stopRecognition();
    }
}

录音并保存为WAV文件

package com.example.speech;
import javax.sound.sampled.*;
import java.io.*;
public class AudioRecorder {
    private static final int SAMPLE_RATE = 16000;
    private static final int BITS_PER_SAMPLE = 16;
    private static final int CHANNELS = 1;
    public static void main(String[] args) {
        try {
            // 录制音频并保存
            recordAndSave("recorded_audio.wav", 5);
            System.out.println("音频已保存到 recorded_audio.wav");
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
    /**
     * 录音并保存为WAV文件
     */
    public static void recordAndSave(String filePath, int durationSeconds) 
            throws LineUnavailableException, IOException {
        AudioFormat format = new AudioFormat(
            AudioFormat.Encoding.PCM_SIGNED,
            SAMPLE_RATE,
            BITS_PER_SAMPLE,
            CHANNELS,
            CHANNELS * BITS_PER_SAMPLE / 8,
            SAMPLE_RATE,
            false
        );
        DataLine.Info info = new DataLine.Info(TargetDataLine.class, format);
        TargetDataLine line = (TargetDataLine) AudioSystem.getLine(info);
        line.open(format);
        line.start();
        System.out.println("录音开始...");
        ByteArrayOutputStream out = new ByteArrayOutputStream();
        byte[] buffer = new byte[4096];
        int bytesRead;
        long startTime = System.currentTimeMillis();
        while (System.currentTimeMillis() - startTime < durationSeconds * 1000) {
            bytesRead = line.read(buffer, 0, buffer.length);
            out.write(buffer, 0, bytesRead);
        }
        line.stop();
        line.close();
        // 保存为WAV文件
        byte[] audioData = out.toByteArray();
        writeWavFile(audioData, filePath, format);
    }
    /**
     * 写入WAV文件
     */
    private static void writeWavFile(byte[] audioData, String filePath, AudioFormat format) 
            throws IOException {
        ByteArrayInputStream bais = new ByteArrayInputStream(audioData);
        AudioInputStream audioInputStream = new AudioInputStream(
            bais, format, audioData.length / format.getFrameSize()
        );
        AudioSystem.write(audioInputStream, AudioFileFormat.Type.WAVE, new File(filePath));
        audioInputStream.close();
    }
}

使用说明

方案一（Google API）：

在Google Cloud Console创建项目并启用Speech-to-Text API
下载服务账号密钥JSON文件
修改代码中的GOOGLE_CREDENTIALS_PATH路径

方案二（离线）：

需要下载中文语音模型文件
配置正确的模型路径
支持实时和文件两种模式

注意事项：

在线API需要网络连接和API密钥
离线识别准确率可能不如在线API
音频格式建议使用16kHz采样率、16位深度、单声道

运行配置

# 编译
javac -cp "lib/*" com/example/speech/*.java
# 运行
java -cp ".:lib/*" com.example.speech.SpeechRecognitionExample

这个案例提供了三种不同的语音识别实现方案,您可以根据实际需求选择适合的方案。

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