Dart API for speaker diarization (#1418)

dart-api-examples/speaker-diarization/bin/init.dart

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+../../vad/bin/init.dart

dart-api-examples/speaker-diarization/bin/speaker-diarization.dart

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+// Copyright (c)  2024  Xiaomi Corporation
+import 'dart:io';
+import 'dart:typed_data';
+import 'dart:ffi';
+
+import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;
+import './init.dart';
+
+void main(List<String> arguments) async {
+  await initSherpaOnnx();
+
+  /* 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
+        */
+
+  final segmentationModel =
+      "./sherpa-onnx-pyannote-segmentation-3-0/model.onnx";
+
+  final embeddingModel =
+      "./3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx";
+
+  final waveFilename = "./0-four-speakers-zh.wav";
+
+  final segmentationConfig = sherpa_onnx.OfflineSpeakerSegmentationModelConfig(
+    pyannote: sherpa_onnx.OfflineSpeakerSegmentationPyannoteModelConfig(
+        model: segmentationModel),
+  );
+
+  final embeddingConfig =
+      sherpa_onnx.SpeakerEmbeddingExtractorConfig(model: embeddingModel);
+
+  // since we know there are 4 speakers in ./0-four-speakers-zh.wav, we set
+  // numClusters to 4. If you don't know the exact number, please set it to -1.
+  // in that case, you have to set threshold. A larger threshold leads to
+  // fewer clusters, i.e., fewer speakers.
+  final clusteringConfig =
+      sherpa_onnx.FastClusteringConfig(numClusters: 4, threshold: 0.5);
+
+  var config = sherpa_onnx.OfflineSpeakerDiarizationConfig(
+      segmentation: segmentationConfig,
+      embedding: embeddingConfig,
+      clustering: clusteringConfig,
+      minDurationOn: 0.2,
+      minDurationOff: 0.5);
+
+  final sd = sherpa_onnx.OfflineSpeakerDiarization(config);
+  if (sd.ptr == nullptr) {
+    return;
+  }
+
+  final waveData = sherpa_onnx.readWave(waveFilename);
+  if (sd.sampleRate != waveData.sampleRate) {
+    print(
+        'Expected sample rate: ${sd.sampleRate}, given: ${waveData.sampleRate}');
+    return;
+  }
+
+  print('started');
+
+  // Use the following statement if you don't want to use a callback
+  // final segments = sd.process(samples: waveData.samples);
+
+  final segments = sd.processWithCallback(
+      samples: waveData.samples,
+      callback: (int numProcessedChunk, int numTotalChunks) {
+        final progress = 100.0 * numProcessedChunk / numTotalChunks;
+
+        print('Progress ${progress.toStringAsFixed(2)}%');
+
+        return 0;
+      });
+
+  for (int i = 0; i < segments.length; ++i) {
+    print(
+        '${segments[i].start.toStringAsFixed(3)} -- ${segments[i].end.toStringAsFixed(3)}  speaker_${segments[i].speaker}');
+  }
+}