Java API for speaker diarization (#1416)

@@ -107,6 +107,13 @@ jobs:
             make -j4
             ls -lh lib
 
+      - name:  Run java test (speaker diarization)
+        shell: bash
+        run: |
+          cd ./java-api-examples
+          ./run-offline-speaker-diarization.sh
+          rm -rfv *.onnx *.wav sherpa-onnx-pyannote-*
+
       - name:  Run java test (kws)
         shell: bash
         run: |

+// Copyright 2024 Xiaomi Corporation
+
+// This file shows how to use sherpa-onnx Java API for speaker diarization,
+import com.k2fsa.sherpa.onnx.*;
+
+public class OfflineSpeakerDiarizationDemo {
+  public static void main(String[] args) {
+    /* Please use the following commands to download files used in this file
+    Step 1: Download a speaker segmentation model
+
+    Please visit https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-segmentation-models
+    for a list of available models. The following is an example
+
+      wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+      tar xvf sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+      rm sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+
+    Step 2: Download a speaker embedding extractor model
+
+    Please visit https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-recongition-models
+    for a list of available models. The following is an example
+
+      wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-recongition-models/3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx
+
+    Step 3. Download test wave files
+
+    Please visit https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-segmentation-models
+    for a list of available test wave files. The following is an example
+
+      wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/0-four-speakers-zh.wav
+
+    Step 4. Run it
+        */
+
+    String segmentationModel = "./sherpa-onnx-pyannote-segmentation-3-0/model.onnx";
+    String embeddingModel = "./3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx";
+    String waveFilename = "./0-four-speakers-zh.wav";
+
+    WaveReader reader = new WaveReader(waveFilename);
+
+    OfflineSpeakerSegmentationPyannoteModelConfig pyannote =
+        OfflineSpeakerSegmentationPyannoteModelConfig.builder().setModel(segmentationModel).build();
+
+    OfflineSpeakerSegmentationModelConfig segmentation =
+        OfflineSpeakerSegmentationModelConfig.builder()
+            .setPyannote(pyannote)
+            .setDebug(true)
+            .build();
+
+    SpeakerEmbeddingExtractorConfig embedding =
+        SpeakerEmbeddingExtractorConfig.builder().setModel(embeddingModel).setDebug(true).build();
+
+    // The test wave file ./0-four-speakers-zh.wav contains four speakers, so
+    // we use numClusters=4 here. If you don't know the number of speakers
+    // in the test wave file, please set the numClusters to -1 and provide
+    // threshold for clustering
+    FastClusteringConfig clustering =
+        FastClusteringConfig.builder()
+            .setNumClusters(4) // set it to -1 if you don't know the actual number
+            .setThreshold(0.5f)
+            .build();
+
+    OfflineSpeakerDiarizationConfig config =
+        OfflineSpeakerDiarizationConfig.builder()
+            .setSegmentation(segmentation)
+            .setEmbedding(embedding)
+            .setClustering(clustering)
+            .setMinDurationOn(0.2f)
+            .setMinDurationOff(0.5f)
+            .build();
+
+    OfflineSpeakerDiarization sd = new OfflineSpeakerDiarization(config);
+    if (sd.getSampleRate() != reader.getSampleRate()) {
+      System.out.printf(
+          "Expected sample rate: %d, given: %d\n", sd.getSampleRate(), reader.getSampleRate());
+      return;
+    }
+
+    // OfflineSpeakerDiarizationSegment[] segments = sd.process(reader.getSamples());
+    // without callback is also ok
+
+    // or you can use a callback to show the progress
+    OfflineSpeakerDiarizationSegment[] segments =
+        sd.processWithCallback(
+            reader.getSamples(),
+            (int numProcessedChunks, int numTotalChunks, long arg) -> {
+              float progress = 100.0f * numProcessedChunks / numTotalChunks;
+              System.out.printf("Progress: %.2f%%\n", progress);
+
+              return 0;
+            });
+
+    for (OfflineSpeakerDiarizationSegment s : segments) {
+      System.out.printf("%.3f -- %.3f speaker_%02d\n", s.getStart(), s.getEnd(), s.getSpeaker());
+    }
+
+    sd.release();
+  }
+}

@@ -4,6 +4,12 @@ This directory contains examples for the JAVA API of sherpa-onnx.
 
 # Usage
 
+## Non-streaming speaker diarization
+
+```bash
+./run-offline-speaker-diarization.sh
+```
+
 ## Streaming Speech recognition
 
 ```

+#!/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-pyannote-segmentation-3-0/model.onnx ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+  tar xvf sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+  rm sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+fi
+
+if [ ! -f ./3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-recongition-models/3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx
+fi
+
+if [ ! -f ./0-four-speakers-zh.wav ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/0-four-speakers-zh.wav
+fi
+
+java \
+  -Djava.library.path=$PWD/../build/lib \
+  -cp ../sherpa-onnx/java-api/build/sherpa-onnx.jar \
+  ./OfflineSpeakerDiarizationDemo.java

@@ -68,6 +68,15 @@ java_files += KeywordSpotterConfig.java
 java_files += KeywordSpotterResult.java
 java_files += KeywordSpotter.java
 
+java_files += OfflineSpeakerSegmentationPyannoteModelConfig.java
+java_files += OfflineSpeakerSegmentationModelConfig.java
+java_files += FastClusteringConfig.java
+java_files += OfflineSpeakerDiarizationConfig.java
+java_files += OfflineSpeakerDiarizationSegment.java
+java_files += OfflineSpeakerDiarizationCallback.java
+java_files += OfflineSpeakerDiarization.java
+
+
 class_files := $(java_files:%.java=%.class)
 
 java_files := $(addprefix src/$(package_dir)/,$(java_files))

+// Copyright 2024 Xiaomi Corporation
+
+package com.k2fsa.sherpa.onnx;
+
+public class FastClusteringConfig {
+    private final int numClusters;
+    private final float threshold;
+
+    private FastClusteringConfig(Builder builder) {
+        this.numClusters = builder.numClusters;
+        this.threshold = builder.threshold;
+    }
+
+    public static Builder builder() {
+        return new Builder();
+    }
+
+    public int getNumClusters() {
+        return numClusters;
+    }
+
+    public float getThreshold() {
+        return threshold;
+    }
+
+    public static class Builder {
+        private int numClusters = -1;
+        private float threshold = 0.5f;
+
+        public FastClusteringConfig build() {
+            return new FastClusteringConfig(this);
+        }
+
+        public Builder setNumClusters(int numClusters) {
+            this.numClusters = numClusters;
+            return this;
+        }
+
+        public Builder setThreshold(float threshold) {
+            this.threshold = threshold;
+            return this;
+        }
+    }
+}

+// Copyright 2024 Xiaomi Corporation
+
+package com.k2fsa.sherpa.onnx;
+
+public class OfflineSpeakerDiarization {
+    static {
+        System.loadLibrary("sherpa-onnx-jni");
+    }
+
+    private long ptr = 0;
+
+    public OfflineSpeakerDiarization(OfflineSpeakerDiarizationConfig config) {
+        ptr = newFromFile(config);
+    }
+
+    public int getSampleRate() {
+        return getSampleRate(ptr);
+    }
+
+    // Only config.clustering is used. All other fields are ignored
+    public void setConfig(OfflineSpeakerDiarizationConfig config) {
+        setConfig(ptr, config);
+    }
+
+    public OfflineSpeakerDiarizationSegment[] process(float[] samples) {
+        return process(ptr, samples);
+    }
+
+    public OfflineSpeakerDiarizationSegment[] processWithCallback(float[] samples, OfflineSpeakerDiarizationCallback callback) {
+        return processWithCallback(ptr, samples, callback, 0);
+    }
+
+    public OfflineSpeakerDiarizationSegment[] processWithCallback(float[] samples, OfflineSpeakerDiarizationCallback callback, long arg) {
+        return processWithCallback(ptr, samples, callback, arg);
+    }
+
+    protected void finalize() throws Throwable {
+        release();
+    }
+
+    // You'd better call it manually if it is not used anymore
+    public void release() {
+        if (this.ptr == 0) {
+            return;
+        }
+        delete(this.ptr);
+        this.ptr = 0;
+    }
+
+    private native int getSampleRate(long ptr);
+
+    private native void delete(long ptr);
+
+    private native long newFromFile(OfflineSpeakerDiarizationConfig config);
+
+    private native void setConfig(long ptr, OfflineSpeakerDiarizationConfig config);
+
+    private native OfflineSpeakerDiarizationSegment[] process(long ptr, float[] samples);
+
+    private native OfflineSpeakerDiarizationSegment[] processWithCallback(long ptr, float[] samples, OfflineSpeakerDiarizationCallback callback, long arg);
+}
\ No newline at end of file

+// Copyright 2024 Xiaomi Corporation
+
+package com.k2fsa.sherpa.onnx;
+
+@FunctionalInterface
+public interface OfflineSpeakerDiarizationCallback {
+    Integer invoke(int numProcessedChunks, int numTotalCunks, long arg);
+}

+package com.k2fsa.sherpa.onnx;
+
+public class OfflineSpeakerDiarizationConfig {
+    private final OfflineSpeakerSegmentationModelConfig segmentation;
+    private final SpeakerEmbeddingExtractorConfig embedding;
+    private final FastClusteringConfig clustering;
+    private final float minDurationOn;
+    private final float minDurationOff;
+
+    private OfflineSpeakerDiarizationConfig(Builder builder) {
+        this.segmentation = builder.segmentation;
+        this.embedding = builder.embedding;
+        this.clustering = builder.clustering;
+        this.minDurationOff = builder.minDurationOff;
+        this.minDurationOn = builder.minDurationOn;
+    }
+
+    public static Builder builder() {
+        return new Builder();
+    }
+
+    public OfflineSpeakerSegmentationModelConfig getSegmentation() {
+        return segmentation;
+    }
+
+    public SpeakerEmbeddingExtractorConfig getEmbedding() {
+        return embedding;
+    }
+
+    public FastClusteringConfig getClustering() {
+        return clustering;
+    }
+
+    public float getMinDurationOff() {
+        return minDurationOff;
+    }
+
+    public float getMinDurationOn() {
+        return minDurationOn;
+    }
+
+    public static class Builder {
+        private OfflineSpeakerSegmentationModelConfig segmentation = OfflineSpeakerSegmentationModelConfig.builder().build();
+        private SpeakerEmbeddingExtractorConfig embedding = SpeakerEmbeddingExtractorConfig.builder().build();
+        private FastClusteringConfig clustering = FastClusteringConfig.builder().build();
+        private float minDurationOn = 0.2f;
+        private float minDurationOff = 0.5f;
+
+        public OfflineSpeakerDiarizationConfig build() {
+            return new OfflineSpeakerDiarizationConfig(this);
+        }
+
+        public Builder setSegmentation(OfflineSpeakerSegmentationModelConfig segmentation) {
+            this.segmentation = segmentation;
+            return this;
+        }
+
+        public Builder setEmbedding(SpeakerEmbeddingExtractorConfig embedding) {
+            this.embedding = embedding;
+            return this;
+        }
+
+        public Builder setClustering(FastClusteringConfig clustering) {
+            this.clustering = clustering;
+            return this;
+        }
+
+        public Builder setMinDurationOff(float minDurationOff) {
+            this.minDurationOff = minDurationOff;
+            return this;
+        }
+
+        public Builder setMinDurationOn(float minDurationOn) {
+            this.minDurationOn = minDurationOn;
+            return this;
+        }
+    }
+
+}

+// Copyright 2024 Xiaomi Corporation
+
+package com.k2fsa.sherpa.onnx;
+
+public class OfflineSpeakerDiarizationSegment {
+    private final float start;
+    private final float end;
+    private final int speaker;
+
+    public OfflineSpeakerDiarizationSegment(float start, float end, int speaker) {
+        this.start = start;
+        this.end = end;
+        this.speaker = speaker;
+    }
+
+    public float getStart() {
+        return start;
+    }
+
+    public float getEnd() {
+        return end;
+    }
+
+    public int getSpeaker() {
+        return speaker;
+    }
+}

+// Copyright 2024 Xiaomi Corporation
+
+package com.k2fsa.sherpa.onnx;
+
+public class OfflineSpeakerSegmentationModelConfig {
+    private final OfflineSpeakerSegmentationPyannoteModelConfig pyannote;
+    private final int numThreads;
+    private final boolean debug;
+    private final String provider;
+
+    private OfflineSpeakerSegmentationModelConfig(Builder builder) {
+        this.pyannote = builder.pyannote;
+        this.numThreads = builder.numThreads;
+        this.debug = builder.debug;
+        this.provider = builder.provider;
+    }
+
+    public static Builder builder() {
+        return new Builder();
+    }
+
+    public static class Builder {
+        private OfflineSpeakerSegmentationPyannoteModelConfig pyannote = OfflineSpeakerSegmentationPyannoteModelConfig.builder().build();
+        private int numThreads = 1;
+        private boolean debug = true;
+        private String provider = "cpu";
+
+        public OfflineSpeakerSegmentationModelConfig build() {
+            return new OfflineSpeakerSegmentationModelConfig(this);
+        }
+
+        public Builder setPyannote(OfflineSpeakerSegmentationPyannoteModelConfig pyannote) {
+            this.pyannote = pyannote;
+            return this;
+        }
+
+        public Builder setNumThreads(int numThreads) {
+            this.numThreads = numThreads;
+            return this;
+        }
+
+        public Builder setDebug(boolean debug) {
+            this.debug = debug;
+            return this;
+        }
+
+        public Builder setProvider(String provider) {
+            this.provider = provider;
+            return this;
+        }
+    }
+}
\ No newline at end of file

+// Copyright 2024 Xiaomi Corporation
+
+package com.k2fsa.sherpa.onnx;
+
+public class OfflineSpeakerSegmentationPyannoteModelConfig {
+    private final String model;
+
+    private OfflineSpeakerSegmentationPyannoteModelConfig(Builder builder) {
+        this.model = builder.model;
+    }
+
+    public static Builder builder() {
+        return new Builder();
+    }
+
+    public String getModel() {
+        return model;
+    }
+
+    public static class Builder {
+        private String model = "";
+
+        public OfflineSpeakerSegmentationPyannoteModelConfig build() {
+            return new OfflineSpeakerSegmentationPyannoteModelConfig(this);
+        }
+
+        public Builder setModel(String model) {
+            this.model = model;
+            return this;
+        }
+    }
+}

+// Copyright 2024 Xiaomi Corporation
+
 package com.k2fsa.sherpa.onnx;
 
 @FunctionalInterface

@@ -50,5 +50,4 @@ public class SpeakerEmbeddingExtractorConfig {
             return this;
         }
     }
-
 }