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Add Dart API for keyword spotter (k2-fsa#1162)
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‎.github/scripts/test-dart.sh‎

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cd dart-api-examples
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pushd keyword-spotter
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./run-zh.sh
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popd
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pushd non-streaming-asr
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echo '----------SenseVoice----------'

‎.github/workflows/test-dart.yaml‎

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cp scripts/dart/non-streaming-asr-pubspec.yaml dart-api-examples/non-streaming-asr/pubspec.yaml
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cp scripts/dart/streaming-asr-pubspec.yaml dart-api-examples/streaming-asr/pubspec.yaml
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cp scripts/dart/tts-pubspec.yaml dart-api-examples/tts/pubspec.yaml
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cp scripts/dart/kws-pubspec.yaml dart-api-examples/keyword-spotter/pubspec.yaml
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cp scripts/dart/sherpa-onnx-pubspec.yaml flutter/sherpa_onnx/pubspec.yaml
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.github/scripts/test-dart.sh

‎CHANGELOG.md‎

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## 1.10.17
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* Support SenseVoice CTC models.
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* Add Dart API for keyword spotter.
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## 1.10.16
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‎dart-api-examples/.gitignore‎

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!run*.sh
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# See https://www.dartlang.org/guides/libraries/private-files
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# Files and directories created by pub
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.dart_tool/
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.packages
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build/
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# If you're building an application, you may want to check-in your pubspec.lock
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pubspec.lock
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# Directory created by dartdoc
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# If you don't generate documentation locally you can remove this line.
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doc/api/
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# dotenv environment variables file
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.env*
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# Avoid committing generated Javascript files:
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*.dart.js
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*.info.json # Produced by the --dump-info flag.
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*.js # When generated by dart2js. Don't specify *.js if your
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# project includes source files written in JavaScript.
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*.js_
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*.js.deps
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*.js.map
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.flutter-plugins
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.flutter-plugins-dependencies

‎dart-api-examples/keyword-spotter/.gitignore‎

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# https://dart.dev/guides/libraries/private-files
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# Created by `dart pub`
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.dart_tool/

‎dart-api-examples/keyword-spotter/CHANGELOG.md‎

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## 1.0.0
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- Initial version.

‎dart-api-examples/keyword-spotter/README.md‎

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# Introduction
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This directory contains keyword spotting examples using
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Dart API from [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx)

‎dart-api-examples/keyword-spotter/analysis_options.yaml‎

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# This file configures the static analysis results for your project (errors,
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# warnings, and lints).
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#
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# This enables the 'recommended' set of lints from `package:lints`.
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# This set helps identify many issues that may lead to problems when running
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# or consuming Dart code, and enforces writing Dart using a single, idiomatic
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# style and format.
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#
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# If you want a smaller set of lints you can change this to specify
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# 'package:lints/core.yaml'. These are just the most critical lints
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# (the recommended set includes the core lints).
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# The core lints are also what is used by pub.dev for scoring packages.
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include: package:lints/recommended.yaml
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# Uncomment the following section to specify additional rules.
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# linter:
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# rules:
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# - camel_case_types
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# analyzer:
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# exclude:
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# - path/to/excluded/files/**
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# For more information about the core and recommended set of lints, see
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# https://dart.dev/go/core-lints
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# For additional information about configuring this file, see
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# https://dart.dev/guides/language/analysis-options

‎dart-api-examples/keyword-spotter/bin/init.dart‎

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

‎dart-api-examples/keyword-spotter/bin/zipformer-transducer.dart‎

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// 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 'package:args/args.dart';
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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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final parser = ArgParser()
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..addOption('encoder', help: 'Path to the encoder model')
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..addOption('decoder', help: 'Path to decoder model')
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..addOption('joiner', help: 'Path to joiner model')
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..addOption('tokens', help: 'Path to tokens.txt')
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..addOption('keywords-file', help: 'Path to keywords.txt')
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..addOption('input-wav', help: 'Path to input.wav to transcribe');
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final res = parser.parse(arguments);
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if (res['encoder'] == null ||
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res['decoder'] == null ||
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res['joiner'] == null ||
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res['tokens'] == null ||
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res['keywords-file'] == null ||
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res['input-wav'] == null) {
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print(parser.usage);
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exit(1);
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}
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final encoder = res['encoder'] as String;
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final decoder = res['decoder'] as String;
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final joiner = res['joiner'] as String;
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final tokens = res['tokens'] as String;
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final keywordsFile = res['keywords-file'] as String;
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final inputWav = res['input-wav'] as String;
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final transducer = sherpa_onnx.OnlineTransducerModelConfig(
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encoder: encoder,
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decoder: decoder,
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joiner: joiner,
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);
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final modelConfig = sherpa_onnx.OnlineModelConfig(
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transducer: transducer,
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tokens: tokens,
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debug: true,
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numThreads: 1,
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);
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final config = sherpa_onnx.KeywordSpotterConfig(
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model: modelConfig,
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keywordsFile: keywordsFile,
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);
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final spotter = sherpa_onnx.KeywordSpotter(config);
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final waveData = sherpa_onnx.readWave(inputWav);
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var stream = spotter.createStream();
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// simulate streaming. You can choose an arbitrary chunk size.
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// chunkSize of a single sample is also ok, i.e, chunkSize = 1
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final chunkSize = 1600; // 0.1 second for 16kHz
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final numChunks = waveData.samples.length ~/ chunkSize;
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for (int i = 0; i != numChunks; ++i) {
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int start = i * chunkSize;
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stream.acceptWaveform(
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samples:
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Float32List.sublistView(waveData.samples, start, start + chunkSize),
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sampleRate: waveData.sampleRate,
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);
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while (spotter.isReady(stream)) {
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spotter.decode(stream);
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final result = spotter.getResult(stream);
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if (result.keyword != '') {
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print('Detected: ${result.keyword}');
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}
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}
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}
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// 0.5 seconds, assume sampleRate is 16kHz
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final tailPaddings = Float32List(8000);
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stream.acceptWaveform(
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samples: tailPaddings,
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sampleRate: waveData.sampleRate,
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);
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while (spotter.isReady(stream)) {
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spotter.decode(stream);
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final result = spotter.getResult(stream);
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if (result.keyword != '') {
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print('Detected: ${result.keyword}');
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}
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}
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stream.free();
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spotter.free();
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}

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