101 lines
3.5 KiB
Dart
101 lines
3.5 KiB
Dart
// Copyright (c) 2024 Xiaomi Corporation
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import 'dart:io';
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import 'dart:typed_data';
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import 'dart:ffi';
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import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;
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import './init.dart';
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void main(List<String> arguments) async {
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await initSherpaOnnx();
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/* Please use the following commands to download files used in this file
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Step 1: Download a speaker segmentation model
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Please visit https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-segmentation-models
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for a list of available models. The following is an example
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
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tar xvf sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
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rm sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
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Step 2: Download a speaker embedding extractor model
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Please visit https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-recongition-models
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for a list of available models. The following is an example
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-recongition-models/3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx
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Step 3. Download test wave files
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Please visit https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-segmentation-models
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for a list of available test wave files. The following is an example
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/0-four-speakers-zh.wav
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Step 4. Run it
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*/
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final segmentationModel =
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"./sherpa-onnx-pyannote-segmentation-3-0/model.onnx";
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final embeddingModel =
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"./3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx";
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final waveFilename = "./0-four-speakers-zh.wav";
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final segmentationConfig = sherpa_onnx.OfflineSpeakerSegmentationModelConfig(
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pyannote: sherpa_onnx.OfflineSpeakerSegmentationPyannoteModelConfig(
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model: segmentationModel),
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);
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final embeddingConfig =
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sherpa_onnx.SpeakerEmbeddingExtractorConfig(model: embeddingModel);
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// since we know there are 4 speakers in ./0-four-speakers-zh.wav, we set
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// numClusters to 4. If you don't know the exact number, please set it to -1.
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// in that case, you have to set threshold. A larger threshold leads to
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// fewer clusters, i.e., fewer speakers.
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final clusteringConfig =
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sherpa_onnx.FastClusteringConfig(numClusters: 4, threshold: 0.5);
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var config = sherpa_onnx.OfflineSpeakerDiarizationConfig(
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segmentation: segmentationConfig,
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embedding: embeddingConfig,
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clustering: clusteringConfig,
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minDurationOn: 0.2,
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minDurationOff: 0.5);
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final sd = sherpa_onnx.OfflineSpeakerDiarization(config);
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if (sd.ptr == nullptr) {
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return;
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}
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final waveData = sherpa_onnx.readWave(waveFilename);
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if (sd.sampleRate != waveData.sampleRate) {
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print(
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'Expected sample rate: ${sd.sampleRate}, given: ${waveData.sampleRate}');
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return;
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}
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print('started');
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// Use the following statement if you don't want to use a callback
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// final segments = sd.process(samples: waveData.samples);
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final segments = sd.processWithCallback(
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samples: waveData.samples,
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callback: (int numProcessedChunk, int numTotalChunks) {
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final progress = 100.0 * numProcessedChunk / numTotalChunks;
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print('Progress ${progress.toStringAsFixed(2)}%');
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return 0;
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});
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for (int i = 0; i < segments.length; ++i) {
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print(
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'${segments[i].start.toStringAsFixed(3)} -- ${segments[i].end.toStringAsFixed(3)} speaker_${segments[i].speaker}');
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}
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}
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