Diarization

final result = await RunAnywhere.diarization.diarize(AudioInput.pcm16(pcm));

print('${result.speakerCount} speakers');
for (final segment in result.segments) {
  print('${segment.speakerId}: ${segment.startMs}-${segment.endMs}ms');
}

Future<DiarizationResult> diarize(
  AudioInput audio, {
  DiarizationOptions? options,
});

final result = await RunAnywhere.diarization.diarize(
  audio,
  options: DiarizationOptions(
    threshold: 0.6,
    minimumDurationMs: 250,
    mergeGapMs: 500,
  ),
);

final turns = await RunAnywhere.diarization.diarize(AudioInput.pcm16(pcm));

for (final turn in turns.segments) {
  final slice = sliceMs(pcm, turn.startMs, turn.endMs);
  final text = await RunAnywhere.stt.transcribe(AudioInput.pcm16(slice));
  print('${turn.speakerId}: ${text.text}');
}

await RunAnywhere.models.register(
  ModelRegistration.archive(
    id: 'sherpa-onnx-speaker-diarization',
    name: 'Speaker Diarization',
    url: 'https://example.com/speaker-diarization.tar.bz2',
    archiveType: ArchiveType.ARCHIVE_TYPE_TAR_BZ2,
    structure: ArchiveStructure.ARCHIVE_STRUCTURE_NESTED_DIRECTORY,
    framework: InferenceFramework.INFERENCE_FRAMEWORK_SHERPA,
    category: ModelCategory.MODEL_CATEGORY_SPEAKER_DIARIZATION,
    memoryRequirementBytes: 90000000,
  ),
);

await RunAnywhere.models.load('sherpa-onnx-speaker-diarization');