Add Java and Kotlin API for sense voice (#1164)

@@ -114,6 +114,16 @@ jobs:
           ./run-kws-from-file.sh
           rm -rf sherpa-onnx-*
 
+      - name:  Run java test (VAD + Non-streaming SenseVoice)
+        shell: bash
+        run: |
+          cd ./java-api-examples
+          ./run-vad-non-streaming-sense-voice.sh
+          rm *.onnx
+          ls -lh *.wav
+          rm *.wav
+          rm -rf sherpa-onnx-*
+
       - name:  Run java test (VAD + Non-streaming Paraformer)
         shell: bash
         run: |
@@ -193,6 +203,10 @@ jobs:
         shell: bash
         run: |
           cd ./java-api-examples
+
+          ./run-non-streaming-decode-file-sense-voice.sh
+          rm -rf sherpa-onnx-sense-voice-*
+
           ./run-inverse-text-normalization-paraformer.sh
 
           ./run-non-streaming-decode-file-paraformer.sh

+// Copyright 2024 Xiaomi Corporation
+
+// This file shows how to use an offline SenseVoice model,
+// i.e., non-streaming SenseVoice model,
+// to decode files.
+import com.k2fsa.sherpa.onnx.*;
+
+public class NonStreamingDecodeFileSenseVoice {
+  public static void main(String[] args) {
+    // please refer to
+    // https://k2-fsa.github.io/sherpa/onnx/sense-voice/index.html
+    // to download model files
+    String model = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/model.int8.onnx";
+    String tokens = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt";
+
+    String waveFilename = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/test_wavs/zh.wav";
+
+    WaveReader reader = new WaveReader(waveFilename);
+
+    OfflineSenseVoiceModelConfig senseVoice =
+        OfflineSenseVoiceModelConfig.builder().setModel(model).build();
+
+    OfflineModelConfig modelConfig =
+        OfflineModelConfig.builder()
+            .setSenseVoice(senseVoice)
+            .setTokens(tokens)
+            .setNumThreads(1)
+            .setDebug(true)
+            .build();
+
+    OfflineRecognizerConfig config =
+        OfflineRecognizerConfig.builder()
+            .setOfflineModelConfig(modelConfig)
+            .setDecodingMethod("greedy_search")
+            .build();
+
+    OfflineRecognizer recognizer = new OfflineRecognizer(config);
+    OfflineStream stream = recognizer.createStream();
+    stream.acceptWaveform(reader.getSamples(), reader.getSampleRate());
+
+    recognizer.decode(stream);
+
+    String text = recognizer.getResult(stream).getText();
+
+    System.out.printf("filename:%s\nresult:%s\n", waveFilename, text);
+
+    stream.release();
+    recognizer.release();
+  }
+}

@@ -18,6 +18,7 @@ This directory contains examples for the JAVA API of sherpa-onnx.
 
 ```bash
 ./run-non-streaming-decode-file-paraformer.sh
+./run-non-streaming-decode-file-sense-voice.sh
 ./run-non-streaming-decode-file-transducer.sh
 ./run-non-streaming-decode-file-whisper.sh
 ./run-non-streaming-decode-file-nemo.sh
@@ -64,6 +65,12 @@ The punctuation model supports both English and Chinese.
 ./run-vad-from-mic.sh
 ```
 
+## VAD with a microphone + Non-streaming SenseVoice for speech recognition
+
+```bash
+./run-vad-from-mic-non-streaming-sense-voice.sh
+```
+
 ## VAD with a microphone + Non-streaming Paraformer for speech recognition
 
 ```bash
@@ -82,6 +89,12 @@ The punctuation model supports both English and Chinese.
 ./run-vad-remove-slience.sh
 ```
 
+## VAD + Non-streaming SenseVoice for speech recognition
+
+```bash
+./run-vad-non-streaming-sense-voice.sh
+```
+
 ## VAD + Non-streaming Paraformer for speech recognition
 
 ```bash

+// Copyright 2024 Xiaomi Corporation
+
+// This file shows how to use a silero_vad model with a non-streaming
+// SenseVoice model for speech recognition.
+
+import com.k2fsa.sherpa.onnx.*;
+import javax.sound.sampled.*;
+
+public class VadFromMicWithNonStreamingSenseVoice {
+  private static final int sampleRate = 16000;
+  private static final int windowSize = 512;
+
+  public static Vad createVad() {
+    // please download ./silero_vad.onnx from
+    // https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
+    String model = "./silero_vad.onnx";
+    SileroVadModelConfig sileroVad =
+        SileroVadModelConfig.builder()
+            .setModel(model)
+            .setThreshold(0.5f)
+            .setMinSilenceDuration(0.25f)
+            .setMinSpeechDuration(0.5f)
+            .setWindowSize(windowSize)
+            .build();
+
+    VadModelConfig config =
+        VadModelConfig.builder()
+            .setSileroVadModelConfig(sileroVad)
+            .setSampleRate(sampleRate)
+            .setNumThreads(1)
+            .setDebug(true)
+            .setProvider("cpu")
+            .build();
+
+    return new Vad(config);
+  }
+
+  public static OfflineRecognizer createOfflineRecognizer() {
+    // please refer to
+    // https://k2-fsa.github.io/sherpa/onnx/sense-voice/index.html
+    // to download model files
+    String model = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/model.int8.onnx";
+    String tokens = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt";
+
+    OfflineSenseVoiceModelConfig senseVoice =
+        OfflineSenseVoiceModelConfig.builder().setModel(model).build();
+
+    OfflineModelConfig modelConfig =
+        OfflineModelConfig.builder()
+            .setSenseVoice(senseVoice)
+            .setTokens(tokens)
+            .setNumThreads(1)
+            .setDebug(true)
+            .build();
+
+    OfflineRecognizerConfig config =
+        OfflineRecognizerConfig.builder()
+            .setOfflineModelConfig(modelConfig)
+            .setDecodingMethod("greedy_search")
+            .build();
+
+    return new OfflineRecognizer(config);
+  }
+
+  public static void main(String[] args) {
+    Vad vad = createVad();
+    OfflineRecognizer recognizer = createOfflineRecognizer();
+
+    // https://docs.oracle.com/javase/8/docs/api/javax/sound/sampled/AudioFormat.html
+    // Linear PCM, 16000Hz, 16-bit, 1 channel, signed, little endian
+    AudioFormat format = new AudioFormat(sampleRate, 16, 1, true, false);
+
+    // https://docs.oracle.com/javase/8/docs/api/javax/sound/sampled/DataLine.Info.html#Info-java.lang.Class-javax.sound.sampled.AudioFormat-int-
+    DataLine.Info info = new DataLine.Info(TargetDataLine.class, format);
+    TargetDataLine targetDataLine;
+    try {
+      targetDataLine = (TargetDataLine) AudioSystem.getLine(info);
+      targetDataLine.open(format);
+      targetDataLine.start();
+    } catch (LineUnavailableException e) {
+      System.out.println("Failed to open target data line: " + e.getMessage());
+      vad.release();
+      recognizer.release();
+      return;
+    }
+
+    boolean printed = false;
+    byte[] buffer = new byte[windowSize * 2];
+    float[] samples = new float[windowSize];
+
+    System.out.println("Started. Please speak");
+    boolean running = true;
+    while (targetDataLine.isOpen() && running) {
+      int n = targetDataLine.read(buffer, 0, buffer.length);
+      if (n <= 0) {
+        System.out.printf("Got %d bytes. Expected %d bytes.\n", n, buffer.length);
+        continue;
+      }
+      for (int i = 0; i != windowSize; ++i) {
+        short low = buffer[2 * i];
+        short high = buffer[2 * i + 1];
+        int s = (high << 8) + low;
+        samples[i] = (float) s / 32768;
+      }
+
+      vad.acceptWaveform(samples);
+      if (vad.isSpeechDetected() && !printed) {
+        System.out.println("Detected speech");
+        printed = true;
+      }
+
+      if (!vad.isSpeechDetected()) {
+        printed = false;
+      }
+
+      while (!vad.empty()) {
+        SpeechSegment segment = vad.front();
+        float startTime = segment.getStart() / (float) sampleRate;
+        float duration = segment.getSamples().length / (float) sampleRate;
+
+        OfflineStream stream = recognizer.createStream();
+        stream.acceptWaveform(segment.getSamples(), sampleRate);
+        recognizer.decode(stream);
+        String text = recognizer.getResult(stream).getText();
+        stream.release();
+
+        if (!text.isEmpty()) {
+          System.out.printf("%.3f--%.3f: %s\n", startTime, startTime + duration, text);
+        }
+
+        if (text.contains("退出程序")) {
+          running = false;
+        }
+
+        vad.pop();
+      }
+    }
+
+    vad.release();
+    recognizer.release();
+  }
+}

+// Copyright 2024 Xiaomi Corporation
+
+// This file shows how to use a silero_vad model with a non-streaming SenseVoiceModel
+// for speech recognition.
+
+import com.k2fsa.sherpa.onnx.*;
+import java.util.Arrays;
+
+public class VadNonStreamingSenseVoice {
+  public static Vad createVad() {
+    // please download ./silero_vad.onnx from
+    // https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
+    String model = "./silero_vad.onnx";
+    SileroVadModelConfig sileroVad =
+        SileroVadModelConfig.builder()
+            .setModel(model)
+            .setThreshold(0.5f)
+            .setMinSilenceDuration(0.25f)
+            .setMinSpeechDuration(0.5f)
+            .setWindowSize(512)
+            .build();
+
+    VadModelConfig config =
+        VadModelConfig.builder()
+            .setSileroVadModelConfig(sileroVad)
+            .setSampleRate(16000)
+            .setNumThreads(1)
+            .setDebug(true)
+            .setProvider("cpu")
+            .build();
+
+    return new Vad(config);
+  }
+
+  public static OfflineRecognizer createOfflineRecognizer() {
+    // please refer to
+    // https://k2-fsa.github.io/sherpa/onnx/sense-voice/index.html
+    // to download model files
+    String model = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/model.int8.onnx";
+    String tokens = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt";
+
+    OfflineSenseVoiceModelConfig senseVoice =
+        OfflineSenseVoiceModelConfig.builder().setModel(model).build();
+
+    OfflineModelConfig modelConfig =
+        OfflineModelConfig.builder()
+            .setSenseVoice(senseVoice)
+            .setTokens(tokens)
+            .setNumThreads(1)
+            .setDebug(true)
+            .build();
+
+    OfflineRecognizerConfig config =
+        OfflineRecognizerConfig.builder()
+            .setOfflineModelConfig(modelConfig)
+            .setDecodingMethod("greedy_search")
+            .build();
+
+    return new OfflineRecognizer(config);
+  }
+
+  public static void main(String[] args) {
+
+    Vad vad = createVad();
+    OfflineRecognizer recognizer = createOfflineRecognizer();
+
+    // You can download the test file from
+    // https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
+    String testWaveFilename = "./lei-jun-test.wav";
+    WaveReader reader = new WaveReader(testWaveFilename);
+
+    int numSamples = reader.getSamples().length;
+    int numIter = numSamples / 512;
+
+    for (int i = 0; i != numIter; ++i) {
+      int start = i * 512;
+      int end = start + 512;
+      float[] samples = Arrays.copyOfRange(reader.getSamples(), start, end);
+      vad.acceptWaveform(samples);
+      if (vad.isSpeechDetected()) {
+        while (!vad.empty()) {
+          SpeechSegment segment = vad.front();
+          float startTime = segment.getStart() / 16000.0f;
+          float duration = segment.getSamples().length / 16000.0f;
+
+          OfflineStream stream = recognizer.createStream();
+          stream.acceptWaveform(segment.getSamples(), 16000);
+          recognizer.decode(stream);
+          String text = recognizer.getResult(stream).getText();
+          stream.release();
+
+          if (!text.isEmpty()) {
+            System.out.printf("%.3f--%.3f: %s\n", startTime, startTime + duration, text);
+          }
+
+          vad.pop();
+        }
+      }
+    }
+
+    vad.flush();
+    while (!vad.empty()) {
+      SpeechSegment segment = vad.front();
+      float startTime = segment.getStart() / 16000.0f;
+      float duration = segment.getSamples().length / 16000.0f;
+
+      OfflineStream stream = recognizer.createStream();
+      stream.acceptWaveform(segment.getSamples(), 16000);
+      recognizer.decode(stream);
+      String text = recognizer.getResult(stream).getText();
+      stream.release();
+
+      if (!text.isEmpty()) {
+        System.out.printf("%.3f--%.3f: %s\n", startTime, startTime + duration, text);
+      }
+
+      vad.pop();
+    }
+
+    vad.release();
+    recognizer.release();
+  }
+}

+#!/usr/bin/env bash
+
+set -ex
+
+if [[ ! -f ../build/lib/libsherpa-onnx-jni.dylib  && ! -f ../build/lib/libsherpa-onnx-jni.so ]]; then
+  mkdir -p ../build
+  pushd ../build
+  cmake \
+    -DSHERPA_ONNX_ENABLE_PYTHON=OFF \
+    -DSHERPA_ONNX_ENABLE_TESTS=OFF \
+    -DSHERPA_ONNX_ENABLE_CHECK=OFF \
+    -DBUILD_SHARED_LIBS=ON \
+    -DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
+    -DSHERPA_ONNX_ENABLE_JNI=ON \
+    ..
+
+  make -j4
+  ls -lh lib
+  popd
+fi
+
+if [ ! -f ../sherpa-onnx/java-api/build/sherpa-onnx.jar ]; then
+  pushd ../sherpa-onnx/java-api
+  make
+  popd
+fi
+
+if [ ! -f ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  tar xvf sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  rm sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+fi
+
+java \
+  -Djava.library.path=$PWD/../build/lib \
+  -cp ../sherpa-onnx/java-api/build/sherpa-onnx.jar \
+  NonStreamingDecodeFileSenseVoice.java

+#!/usr/bin/env bash
+
+set -ex
+
+if [[ ! -f ../build/lib/libsherpa-onnx-jni.dylib  && ! -f ../build/lib/libsherpa-onnx-jni.so ]]; then
+  mkdir -p ../build
+  pushd ../build
+  cmake \
+    -DSHERPA_ONNX_ENABLE_PYTHON=OFF \
+    -DSHERPA_ONNX_ENABLE_TESTS=OFF \
+    -DSHERPA_ONNX_ENABLE_CHECK=OFF \
+    -DBUILD_SHARED_LIBS=ON \
+    -DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
+    -DSHERPA_ONNX_ENABLE_JNI=ON \
+    ..
+
+  make -j4
+  ls -lh lib
+  popd
+fi
+
+if [ ! -f ../sherpa-onnx/java-api/build/sherpa-onnx.jar ]; then
+  pushd ../sherpa-onnx/java-api
+  make
+  popd
+fi
+
+if [ ! -f ./silero_vad.onnx ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
+fi
+
+if [ ! -f ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  tar xvf sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  rm sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+fi
+
+java \
+  -Djava.library.path=$PWD/../build/lib \
+  -cp ../sherpa-onnx/java-api/build/sherpa-onnx.jar \
+  ./VadFromMicWithNonStreamingSenseVoice.java

+#!/usr/bin/env bash
+
+set -ex
+
+if [[ ! -f ../build/lib/libsherpa-onnx-jni.dylib  && ! -f ../build/lib/libsherpa-onnx-jni.so ]]; then
+  mkdir -p ../build
+  pushd ../build
+  cmake \
+    -DSHERPA_ONNX_ENABLE_PYTHON=OFF \
+    -DSHERPA_ONNX_ENABLE_TESTS=OFF \
+    -DSHERPA_ONNX_ENABLE_CHECK=OFF \
+    -DBUILD_SHARED_LIBS=ON \
+    -DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
+    -DSHERPA_ONNX_ENABLE_JNI=ON \
+    ..
+
+  make -j4
+  ls -lh lib
+  popd
+fi
+
+if [ ! -f ../sherpa-onnx/java-api/build/sherpa-onnx.jar ]; then
+  pushd ../sherpa-onnx/java-api
+  make
+  popd
+fi
+
+if [ ! -f ./silero_vad.onnx ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
+fi
+
+if [ ! -f ./lei-jun-test.wav ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/lei-jun-test.wav
+fi
+
+if [ ! -f ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  tar xvf sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  rm sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+fi
+
+java \
+  -Djava.library.path=$PWD/../build/lib \
+  -cp ../sherpa-onnx/java-api/build/sherpa-onnx.jar \
+  ./VadNonStreamingSenseVoice.java

@@ -167,6 +167,12 @@ function testSpokenLanguageIdentification() {
 }
 
 function testOfflineAsr() {
+  if [ ! -f ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt ]; then
+    curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+    tar xvf sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+    rm sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  fi
+
   if [ ! -f ./sherpa-onnx-whisper-tiny.en/tiny.en-encoder.int8.onnx ]; then
     curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.en.tar.bz2
     tar xvf sherpa-onnx-whisper-tiny.en.tar.bz2

 package com.k2fsa.sherpa.onnx
 
 fun main() {
-  val types = arrayOf(0, 2, 5, 6)
+  val types = arrayOf(0, 2, 5, 6, 15)
   for (type in types) {
     test(type)
   }
@@ -15,6 +15,7 @@ fun test(type: Int) {
     2 -> "./sherpa-onnx-whisper-tiny.en/test_wavs/0.wav"
     5 -> "./sherpa-onnx-zipformer-multi-zh-hans-2023-9-2/test_wavs/1.wav"
     6 -> "./sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/8k.wav"
+    15 -> "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/test_wavs/zh.wav"
     else -> null
   }
 

@@ -90,6 +90,23 @@ def get_models():
             """,
         ),
         Model(
+            model_name="sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17",
+            idx=15,
+            lang="zh_en_ko_ja_yue",
+            short_name="sense_voice",
+            cmd="""
+            pushd $model_name
+
+            rm -rfv test_wavs
+            rm -fv model.onnx
+            rm -fv *.py
+
+            ls -lh
+
+            popd
+            """,
+        ),
+        Model(
             model_name="sherpa-onnx-paraformer-zh-small-2024-03-09",
             idx=14,
             lang="zh",

@@ -27,6 +27,7 @@ java_files += OfflineTransducerModelConfig.java
 java_files += OfflineParaformerModelConfig.java
 java_files += OfflineWhisperModelConfig.java
 java_files += OfflineNemoEncDecCtcModelConfig.java
+java_files += OfflineSenseVoiceModelConfig.java
 java_files += OfflineModelConfig.java
 java_files += OfflineRecognizerConfig.java
 java_files += OfflineRecognizerResult.java

@@ -7,6 +7,7 @@ public class OfflineModelConfig {
     private final OfflineParaformerModelConfig paraformer;
     private final OfflineWhisperModelConfig whisper;
     private final OfflineNemoEncDecCtcModelConfig nemo;
+    private final OfflineSenseVoiceModelConfig senseVoice;
     private final String teleSpeech;
     private final String tokens;
     private final int numThreads;
@@ -22,6 +23,7 @@ public class OfflineModelConfig {
         this.paraformer = builder.paraformer;
         this.whisper = builder.whisper;
         this.nemo = builder.nemo;
+        this.senseVoice = builder.senseVoice;
         this.teleSpeech = builder.teleSpeech;
         this.tokens = builder.tokens;
         this.numThreads = builder.numThreads;
@@ -48,6 +50,10 @@ public class OfflineModelConfig {
         return whisper;
     }
 
+    public OfflineSenseVoiceModelConfig getSenseVoice() {
+        return senseVoice;
+    }
+
     public String getTokens() {
         return tokens;
     }
@@ -85,6 +91,7 @@ public class OfflineModelConfig {
         private OfflineTransducerModelConfig transducer = OfflineTransducerModelConfig.builder().build();
         private OfflineWhisperModelConfig whisper = OfflineWhisperModelConfig.builder().build();
         private OfflineNemoEncDecCtcModelConfig nemo = OfflineNemoEncDecCtcModelConfig.builder().build();
+        private OfflineSenseVoiceModelConfig senseVoice = OfflineSenseVoiceModelConfig.builder().build();
         private String teleSpeech = "";
         private String tokens = "";
         private int numThreads = 1;
@@ -113,7 +120,6 @@ public class OfflineModelConfig {
             return this;
         }
 
-
         public Builder setTeleSpeech(String teleSpeech) {
             this.teleSpeech = teleSpeech;
             return this;
@@ -124,6 +130,11 @@ public class OfflineModelConfig {
             return this;
         }
 
+        public Builder setSenseVoice(OfflineSenseVoiceModelConfig senseVoice) {
+            this.senseVoice = senseVoice;
+            return this;
+        }
+
         public Builder setTokens(String tokens) {
             this.tokens = tokens;
             return this;

+// Copyright 2024 Xiaomi Corporation
+
+package com.k2fsa.sherpa.onnx;
+
+public class OfflineSenseVoiceModelConfig {
+    private final String model;
+    private final String language;
+    private final boolean useInverseTextNormalization;
+
+    private OfflineSenseVoiceModelConfig(Builder builder) {
+        this.model = builder.model;
+        this.language = builder.language;
+        this.useInverseTextNormalization = builder.useInverseTextNormalization;
+    }
+
+    public static Builder builder() {
+        return new Builder();
+    }
+
+    public String getModel() {
+        return model;
+    }
+
+    public String getLanguage() {
+        return language;
+    }
+
+    public boolean getUseInverseTextNormalization() {
+        return useInverseTextNormalization;
+    }
+
+    public static class Builder {
+        private String model = "";
+        private String language = "";
+        private boolean useInverseTextNormalization = true;
+
+        public OfflineSenseVoiceModelConfig build() {
+            return new OfflineSenseVoiceModelConfig(this);
+        }
+
+        public Builder setModel(String model) {
+            this.model = model;
+            return this;
+        }
+
+        public Builder setLanguage(String language) {
+            this.language = language;
+            return this;
+        }
+
+        public Builder setInverseTextNormalization(boolean useInverseTextNormalization) {
+            this.useInverseTextNormalization = useInverseTextNormalization;
+            return this;
+        }
+    }
+}

@@ -171,6 +171,31 @@ static OfflineRecognizerConfig GetOfflineConfig(JNIEnv *env, jobject config) {
   ans.model_config.whisper.tail_paddings =
       env->GetIntField(whisper_config, fid);
 
+  // sense voice
+  fid = env->GetFieldID(model_config_cls, "senseVoice",
+                        "Lcom/k2fsa/sherpa/onnx/OfflineSenseVoiceModelConfig;");
+  jobject sense_voice_config = env->GetObjectField(model_config, fid);
+  jclass sense_voice_config_cls = env->GetObjectClass(sense_voice_config);
+
+  fid = env->GetFieldID(sense_voice_config_cls, "model", "Ljava/lang/String;");
+  s = (jstring)env->GetObjectField(sense_voice_config, fid);
+  p = env->GetStringUTFChars(s, nullptr);
+  ans.model_config.sense_voice.model = p;
+  env->ReleaseStringUTFChars(s, p);
+
+  fid =
+      env->GetFieldID(sense_voice_config_cls, "language", "Ljava/lang/String;");
+  s = (jstring)env->GetObjectField(sense_voice_config, fid);
+  p = env->GetStringUTFChars(s, nullptr);
+  ans.model_config.sense_voice.language = p;
+  env->ReleaseStringUTFChars(s, p);
+
+  fid = env->GetFieldID(sense_voice_config_cls, "useInverseTextNormalization",
+                        "Z");
+  ans.model_config.sense_voice.use_itn =
+      env->GetBooleanField(sense_voice_config, fid);
+
+  // nemo
   fid = env->GetFieldID(
       model_config_cls, "nemo",
       "Lcom/k2fsa/sherpa/onnx/OfflineNemoEncDecCtcModelConfig;");

@@ -30,11 +30,18 @@ data class OfflineWhisperModelConfig(
     var tailPaddings: Int = 1000, // Padding added at the end of the samples
 )
 
+data class OfflineSenseVoiceModelConfig(
+    var model: String = "",
+    var language: String = "",
+    var useInverseTextNormalization: Boolean = true,
+)
+
 data class OfflineModelConfig(
     var transducer: OfflineTransducerModelConfig = OfflineTransducerModelConfig(),
     var paraformer: OfflineParaformerModelConfig = OfflineParaformerModelConfig(),
     var whisper: OfflineWhisperModelConfig = OfflineWhisperModelConfig(),
     var nemo: OfflineNemoEncDecCtcModelConfig = OfflineNemoEncDecCtcModelConfig(),
+    var senseVoice: OfflineSenseVoiceModelConfig = OfflineSenseVoiceModelConfig(),
     var teleSpeech: String = "",
     var numThreads: Int = 1,
     var debug: Boolean = false,
@@ -321,6 +328,16 @@ fun getOfflineModelConfig(type: Int): OfflineModelConfig? {
                 modelType = "paraformer",
             )
         }
+
+        15 -> {
+            val modelDir = "sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17"
+            return OfflineModelConfig(
+                senseVoice = OfflineSenseVoiceModelConfig(
+                    model = "$modelDir/model.int8.onnx",
+                ),
+                tokens = "$modelDir/tokens.txt",
+            )
+        }
     }
     return null
 }