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Add Kotlin and Java API for Dolphin CTC models (#2086)
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-18
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‎.github/workflows/apk-asr-2pass.yaml‎

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fail-fast: false
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matrix:
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os: [ubuntu-latest]
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total: ["4"]
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index: ["0", "1", "2", "3"]
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total: ["16"]
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index: ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15"]
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steps:
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- uses: actions/checkout@v4

‎.github/workflows/apk-vad-asr.yaml‎

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fail-fast: false
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matrix:
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os: [ubuntu-latest]
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total: ["10"]
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index: ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
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total: ["18"]
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index: ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17"]
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steps:
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- uses: actions/checkout@v4

‎.github/workflows/run-java-test.yaml‎

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make -j4
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ls -lh lib
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- name: Run java test (VAD + Non-streaming Dolphin CTC)
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shell: bash
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run: |
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cd ./java-api-examples
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./run-vad-non-streaming-dolphin-ctc.sh
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rm *.onnx
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ls -lh *.wav
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rm *.wav
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rm -rf sherpa-onnx-dolphin-*
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- name: Run speech enhancement (GTCRN)
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shell: bash
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run: |
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run: |
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cd ./java-api-examples
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./run-non-streaming-decode-file-dolphin-ctc.sh
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rm -rf sherpa-onnx-dolphin-*
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./run-non-streaming-decode-file-moonshine.sh
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rm -rf sherpa-onnx-moonshine-*
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‎.gitignore‎

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*.jit
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##clion
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.idea
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sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02

‎java-api-examples/NonStreamingDecodeFileDolphinCtc.java‎

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// Copyright 2025 Xiaomi Corporation
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// This file shows how to use an offline Dolphin CTC model, i.e.,
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// non-streaming Dolphin CTC model, to decode files.
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import com.k2fsa.sherpa.onnx.*;
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public class NonStreamingDecodeFileDolphinCtc {
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public static void main(String[] args) {
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// please refer to
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// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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// to download model files
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String model = "./sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02/model.int8.onnx";
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String tokens = "./sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02/tokens.txt";
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String waveFilename =
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"./sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02/test_wavs/0.wav";
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WaveReader reader = new WaveReader(waveFilename);
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OfflineDolphinModelConfig dolphin = OfflineDolphinModelConfig.builder().setModel(model).build();
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OfflineModelConfig modelConfig =
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OfflineModelConfig.builder()
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.setDolphin(dolphin)
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.setTokens(tokens)
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.setNumThreads(1)
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.setDebug(true)
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.build();
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OfflineRecognizerConfig config =
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OfflineRecognizerConfig.builder()
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.setOfflineModelConfig(modelConfig)
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.setDecodingMethod("greedy_search")
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.build();
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OfflineRecognizer recognizer = new OfflineRecognizer(config);
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OfflineStream stream = recognizer.createStream();
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stream.acceptWaveform(reader.getSamples(), reader.getSampleRate());
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recognizer.decode(stream);
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String text = recognizer.getResult(stream).getText();
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System.out.printf("filename:%s\nresult:%s\n", waveFilename, text);
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stream.release();
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recognizer.release();
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}
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}

‎java-api-examples/README.md‎

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## Non-Streaming Speech recognition
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```bash
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./run-non-streaming-decode-file-dolphin-ctc.sh
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./run-non-streaming-decode-file-paraformer.sh
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./run-non-streaming-decode-file-sense-voice.sh
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./run-non-streaming-decode-file-transducer.sh
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./run-vad-remove-slience.sh
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```
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## VAD + Non-streaming Dolphin CTC for speech recognition
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```bash
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./run-vad-non-streaming-dolphin-ctc.sh
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```
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## VAD + Non-streaming SenseVoice for speech recognition
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```bash

‎java-api-examples/VadNonStreamingDolphinCtc.java‎

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// Copyright 2025 Xiaomi Corporation
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// This file shows how to use a silero_vad model with a non-streaming Dolphin
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// CTC model for speech recognition.
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import com.k2fsa.sherpa.onnx.*;
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import java.util.Arrays;
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public class VadNonStreamingSenseVoice {
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public static Vad createVad() {
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// please download ./silero_vad.onnx from
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// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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String model = "./silero_vad.onnx";
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SileroVadModelConfig sileroVad =
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SileroVadModelConfig.builder()
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.setModel(model)
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.setThreshold(0.5f)
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.setMinSilenceDuration(0.25f)
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.setMinSpeechDuration(0.5f)
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.setWindowSize(512)
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.setMaxSpeechDuration(5.0f)
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.build();
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VadModelConfig config =
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VadModelConfig.builder()
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.setSileroVadModelConfig(sileroVad)
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.setSampleRate(16000)
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.setNumThreads(1)
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.setDebug(true)
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.setProvider("cpu")
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.build();
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return new Vad(config);
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}
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public static OfflineRecognizer createOfflineRecognizer() {
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// please refer to
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// https://k2-fsa.github.io/sherpa/onnx/dolphin/index.html
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// to download model files
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String model = "./sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02/model.int8.onnx";
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String tokens = "./sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02/tokens.txt";
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OfflineDolphinModelConfig dolphin = OfflineDolphinModelConfig.builder().setModel(model).build();
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OfflineModelConfig modelConfig =
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OfflineModelConfig.builder()
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.setDolphin(dolphin)
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.setTokens(tokens)
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.setNumThreads(1)
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.setDebug(true)
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.build();
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OfflineRecognizerConfig config =
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OfflineRecognizerConfig.builder()
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.setOfflineModelConfig(modelConfig)
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.setDecodingMethod("greedy_search")
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.build();
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return new OfflineRecognizer(config);
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}
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public static void main(String[] args) {
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Vad vad = createVad();
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OfflineRecognizer recognizer = createOfflineRecognizer();
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// You can download the test file from
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// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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String testWaveFilename = "./lei-jun-test.wav";
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WaveReader reader = new WaveReader(testWaveFilename);
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int numSamples = reader.getSamples().length;
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int numIter = numSamples / 512;
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for (int i = 0; i != numIter; ++i) {
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int start = i * 512;
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int end = start + 512;
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float[] samples = Arrays.copyOfRange(reader.getSamples(), start, end);
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vad.acceptWaveform(samples);
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if (vad.isSpeechDetected()) {
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while (!vad.empty()) {
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SpeechSegment segment = vad.front();
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float startTime = segment.getStart() / 16000.0f;
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float duration = segment.getSamples().length / 16000.0f;
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OfflineStream stream = recognizer.createStream();
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stream.acceptWaveform(segment.getSamples(), 16000);
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recognizer.decode(stream);
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String text = recognizer.getResult(stream).getText();
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stream.release();
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if (!text.isEmpty()) {
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System.out.printf("%.3f--%.3f: %s\n", startTime, startTime + duration, text);
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}
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vad.pop();
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}
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}
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}
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vad.flush();
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while (!vad.empty()) {
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SpeechSegment segment = vad.front();
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float startTime = segment.getStart() / 16000.0f;
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float duration = segment.getSamples().length / 16000.0f;
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OfflineStream stream = recognizer.createStream();
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stream.acceptWaveform(segment.getSamples(), 16000);
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recognizer.decode(stream);
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String text = recognizer.getResult(stream).getText();
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stream.release();
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if (!text.isEmpty()) {
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System.out.printf("%.3f--%.3f: %s\n", startTime, startTime + duration, text);
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}
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vad.pop();
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}
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vad.release();
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recognizer.release();
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}
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}

‎java-api-examples/run-non-streaming-decode-file-dolphin-ctc.sh‎

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#!/usr/bin/env bash
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set -ex
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if [[ ! -f ../build/lib/libsherpa-onnx-jni.dylib && ! -f ../build/lib/libsherpa-onnx-jni.so ]]; then
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mkdir -p ../build
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pushd ../build
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cmake \
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-DSHERPA_ONNX_ENABLE_PYTHON=OFF \
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-DSHERPA_ONNX_ENABLE_TESTS=OFF \
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-DSHERPA_ONNX_ENABLE_CHECK=OFF \
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-DBUILD_SHARED_LIBS=ON \
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-DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
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-DSHERPA_ONNX_ENABLE_JNI=ON \
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..
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make -j4
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ls -lh lib
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popd
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fi
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if [ ! -f ../sherpa-onnx/java-api/build/sherpa-onnx.jar ]; then
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pushd ../sherpa-onnx/java-api
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make
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popd
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fi
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if [ ! -f ./sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02/model.int8.onnx ]; then
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curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
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tar xvf sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
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rm sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
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ls -lh sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02
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fi
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java \
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-Djava.library.path=$PWD/../build/lib \
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-cp ../sherpa-onnx/java-api/build/sherpa-onnx.jar \
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NonStreamingDecodeFileDolphinCtc.java

‎java-api-examples/run-vad-non-streaming-dolphin-ctc.sh‎

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#!/usr/bin/env bash
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set -ex
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if [[ ! -f ../build/lib/libsherpa-onnx-jni.dylib && ! -f ../build/lib/libsherpa-onnx-jni.so ]]; then
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mkdir -p ../build
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pushd ../build
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cmake \
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-DSHERPA_ONNX_ENABLE_PYTHON=OFF \
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-DSHERPA_ONNX_ENABLE_TESTS=OFF \
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-DSHERPA_ONNX_ENABLE_CHECK=OFF \
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-DBUILD_SHARED_LIBS=ON \
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-DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
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-DSHERPA_ONNX_ENABLE_JNI=ON \
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..
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make -j4
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ls -lh lib
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popd
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fi
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if [ ! -f ../sherpa-onnx/java-api/build/sherpa-onnx.jar ]; then
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pushd ../sherpa-onnx/java-api
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make
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popd
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fi
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if [ ! -f ./silero_vad.onnx ]; then
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curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
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fi
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if [ ! -f ./lei-jun-test.wav ]; then
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curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/lei-jun-test.wav
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fi
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if [ ! -f ./sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02/model.int8.onnx ]; then
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curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
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tar xvf sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
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rm sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
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ls -lh sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02
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fi
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java \
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-Djava.library.path=$PWD/../build/lib \
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-cp ../sherpa-onnx/java-api/build/sherpa-onnx.jar \
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./VadNonStreamingDolphinCtc.java

‎kotlin-api-examples/run.sh‎

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}
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function testOfflineAsr() {
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if [ ! -f ./sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02/model.int8.onnx ]; then
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curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
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tar xvf sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
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rm sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
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ls -lh sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02
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fi
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if [ ! -f ./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/encoder.int8.onnx ]; then
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curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16.tar.bz2
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tar xvf sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16.tar.bz2

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