Add vad with non-streaming ASR examples for Dart API (#1180)

@@ -4,6 +4,33 @@ set -ex
 
 
 cd dart-api-examples
 cd dart-api-examples
 
 
+pushd vad-with-non-streaming-asr
+echo '----------TeleSpeech CTC----------'
+./run-telespeech-ctc.sh
+rm -rf sherpa-onnx-*
+
+echo "----zipformer transducer----"
+./run-zipformer-transducer.sh
+rm -rf sherpa-onnx-*
+
+echo "----whisper----"
+./run-whisper.sh
+rm -rf sherpa-onnx-*
+
+echo "----paraformer----"
+./run-paraformer.sh
+rm -rf sherpa-onnx-*
+
+echo "----SenseVoice zh----"
+./run-sense-voice-zh.sh
+rm -rf sherpa-onnx-*
+
+echo "----SenseVoice en----"
+./run-sense-voice-en.sh
+rm -rf sherpa-onnx-*
+
+popd
+
 pushd keyword-spotter
 pushd keyword-spotter
 ./run-zh.sh
 ./run-zh.sh
 popd
 popd

@@ -109,6 +109,8 @@ jobs:
           cp scripts/dart/streaming-asr-pubspec.yaml dart-api-examples/streaming-asr/pubspec.yaml
           cp scripts/dart/streaming-asr-pubspec.yaml dart-api-examples/streaming-asr/pubspec.yaml
           cp scripts/dart/tts-pubspec.yaml dart-api-examples/tts/pubspec.yaml
           cp scripts/dart/tts-pubspec.yaml dart-api-examples/tts/pubspec.yaml
           cp scripts/dart/kws-pubspec.yaml dart-api-examples/keyword-spotter/pubspec.yaml
           cp scripts/dart/kws-pubspec.yaml dart-api-examples/keyword-spotter/pubspec.yaml
+          cp scripts/dart/vad-non-streaming-asr-pubspec.yaml dart-api-examples/vad-with-non-streaming-asr/pubspec.yaml
+
           cp scripts/dart/sherpa-onnx-pubspec.yaml flutter/sherpa_onnx/pubspec.yaml
           cp scripts/dart/sherpa-onnx-pubspec.yaml flutter/sherpa_onnx/pubspec.yaml
 
 
           .github/scripts/test-dart.sh
           .github/scripts/test-dart.sh

@@ -5,6 +5,17 @@ This directory contains examples for Dart API.
 You can find the package at
 You can find the package at
 https://pub.dev/packages/sherpa_onnx
 https://pub.dev/packages/sherpa_onnx
 
 
+## Descirption
+
+| Directory | Description |
+|-----------|-------------|
+| [./keyword-spotter](./keyword-spotter)| Example for keyword spotting|
+| [./non-streaming-asr](./non-streaming-asr)| Example for non-streaming speech recognition|
+| [./streaming-asr](./streaming-asr)| Example for streaming speech recognition|
+| [./tts](./tts)| Example for text to speech|
+| [./vad](./vad)| Example for voice activity detection|
+| [./vad-with-non-streaming-asr](./vad-with-non-streaming-asr)| Example for voice activity detection with non-streaming speech recognition. You can use it to generate subtitles.|
+
 ## How to create an example in this folder
 ## How to create an example in this folder
 
 
 ```bash
 ```bash

@@ -11,7 +11,7 @@ void main(List<String> arguments) async {
   await initSherpaOnnx();
   await initSherpaOnnx();
 
 
   final parser = ArgParser()
   final parser = ArgParser()
-    ..addOption('model', help: 'Path to the paraformer model')
+    ..addOption('model', help: 'Path to the SenseVoice model')
     ..addOption('tokens', help: 'Path to tokens.txt')
     ..addOption('tokens', help: 'Path to tokens.txt')
     ..addOption('language',
     ..addOption('language',
         help: 'auto, zh, en, ja, ko, yue, or leave it empty to use auto',
         help: 'auto, zh, en, ja, ko, yue, or leave it empty to use auto',

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

+# Introduction
+
+This folder contains examples for non-streaming ASR + voice activity detection
+with Dart API.
+
+| File | Description|
+|------|------------|
+|[./bin/paraformer.dart](./bin/paraformer.dart)| Use a Paraformer model for speech recognition. See [./run-paraformer.sh](./run-paraformer.sh)|
+|[./bin/sense-voice.dart](./bin/sense-voice.dart)| Use a SenseVoice Ctc model for speech recognition. See [./run-sense-voice-zh.sh](./run-sense-voice-zh.sh) and [./run-sense-voice-en.sh](./run-sense-voice-en.sh)|
+|[./bin/telespeech-ctc.dart](./bin/telespeech-ctc.dart)| Use a TeleSpeech CTC model for speech recognition. See [./run-telespeech-ctc.sh](./run-telespeech-ctc.sh)|
+|[./bin/whisper.dart](./bin/whisper.dart)| Use a Whisper model for speech recognition. See [./run-whisper.sh](./run-whisper.sh)|
+|[./bin/zipformer-transducer.dart](./bin/zipformer-transducer.dart)| Use a Zipformer transducer model for speech recognition. See [./run-zipformer-transducer.sh](./run-zipformer-transducer.sh)|
+

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

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

+// Copyright (c)  2024  Xiaomi Corporation
+import 'dart:io';
+import 'dart:typed_data';
+
+import 'package:args/args.dart';
+import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;
+
+import './init.dart';
+
+void main(List<String> arguments) async {
+  await initSherpaOnnx();
+
+  final parser = ArgParser()
+    ..addOption('silero-vad', help: 'Path to silero_vad.onnx')
+    ..addOption('model', help: 'Path to the paraformer model')
+    ..addOption('tokens', help: 'Path to tokens.txt')
+    ..addOption('input-wav', help: 'Path to input.wav to transcribe');
+
+  final res = parser.parse(arguments);
+  if (res['silero-vad'] == null ||
+      res['model'] == null ||
+      res['tokens'] == null ||
+      res['input-wav'] == null) {
+    print(parser.usage);
+    exit(1);
+  }
+
+  // create VAD
+  final sileroVad = res['silero-vad'] as String;
+
+  final sileroVadConfig = sherpa_onnx.SileroVadModelConfig(
+    model: sileroVad,
+    minSilenceDuration: 0.25,
+    minSpeechDuration: 0.5,
+  );
+
+  final vadConfig = sherpa_onnx.VadModelConfig(
+    sileroVad: sileroVadConfig,
+    numThreads: 1,
+    debug: true,
+  );
+
+  final vad = sherpa_onnx.VoiceActivityDetector(
+      config: vadConfig, bufferSizeInSeconds: 10);
+
+  // create paraformer recognizer
+  final model = res['model'] as String;
+  final tokens = res['tokens'] as String;
+  final inputWav = res['input-wav'] as String;
+
+  final paraformer = sherpa_onnx.OfflineParaformerModelConfig(
+    model: model,
+  );
+
+  final modelConfig = sherpa_onnx.OfflineModelConfig(
+    paraformer: paraformer,
+    tokens: tokens,
+    debug: true,
+    numThreads: 1,
+    modelType: 'paraformer',
+  );
+  final config = sherpa_onnx.OfflineRecognizerConfig(model: modelConfig);
+  final recognizer = sherpa_onnx.OfflineRecognizer(config);
+
+  final waveData = sherpa_onnx.readWave(inputWav);
+  if (waveData.sampleRate != 16000) {
+    print('Only 16000 Hz is supported. Given: ${waveData.sampleRate}');
+    exit(1);
+  }
+
+  int numSamples = waveData.samples.length;
+  int numIter = numSamples ~/ vadConfig.sileroVad.windowSize;
+
+  for (int i = 0; i != numIter; ++i) {
+    int start = i * vadConfig.sileroVad.windowSize;
+    vad.acceptWaveform(Float32List.sublistView(
+        waveData.samples, start, start + vadConfig.sileroVad.windowSize));
+
+    if (vad.isDetected()) {
+      while (!vad.isEmpty()) {
+        final samples = vad.front().samples;
+        final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+        final endTime =
+            startTime + samples.length.toDouble() / waveData.sampleRate;
+
+        final stream = recognizer.createStream();
+        stream.acceptWaveform(
+            samples: samples, sampleRate: waveData.sampleRate);
+        recognizer.decode(stream);
+
+        final result = recognizer.getResult(stream);
+        stream.free();
+        print(
+            '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+        vad.pop();
+      }
+    }
+  }
+
+  vad.flush();
+
+  while (!vad.isEmpty()) {
+    final samples = vad.front().samples;
+    final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+    final endTime = startTime + samples.length.toDouble() / waveData.sampleRate;
+
+    final stream = recognizer.createStream();
+    stream.acceptWaveform(samples: samples, sampleRate: waveData.sampleRate);
+    recognizer.decode(stream);
+
+    final result = recognizer.getResult(stream);
+    stream.free();
+    print(
+        '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+    vad.pop();
+  }
+
+  vad.free();
+
+  recognizer.free();
+}

+// Copyright (c)  2024  Xiaomi Corporation
+import 'dart:io';
+import 'dart:typed_data';
+
+import 'package:args/args.dart';
+import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;
+
+import './init.dart';
+
+void main(List<String> arguments) async {
+  await initSherpaOnnx();
+
+  final parser = ArgParser()
+    ..addOption('silero-vad', help: 'Path to silero_vad.onnx')
+    ..addOption('model', help: 'Path to the SenseVoice model')
+    ..addOption('tokens', help: 'Path to tokens.txt')
+    ..addOption('language',
+        help: 'auto, zh, en, ja, ko, yue, or leave it empty to use auto',
+        defaultsTo: '')
+    ..addOption('use-itn',
+        help: 'true to use inverse text normalization', defaultsTo: 'false')
+    ..addOption('input-wav', help: 'Path to input.wav to transcribe');
+
+  final res = parser.parse(arguments);
+  if (res['silero-vad'] == null ||
+      res['model'] == null ||
+      res['tokens'] == null ||
+      res['input-wav'] == null) {
+    print(parser.usage);
+    exit(1);
+  }
+
+  // create VAD
+  final sileroVad = res['silero-vad'] as String;
+
+  final sileroVadConfig = sherpa_onnx.SileroVadModelConfig(
+    model: sileroVad,
+    minSilenceDuration: 0.25,
+    minSpeechDuration: 0.5,
+  );
+
+  final vadConfig = sherpa_onnx.VadModelConfig(
+    sileroVad: sileroVadConfig,
+    numThreads: 1,
+    debug: true,
+  );
+
+  final vad = sherpa_onnx.VoiceActivityDetector(
+      config: vadConfig, bufferSizeInSeconds: 10);
+
+  // create SenseVoice
+  final model = res['model'] as String;
+  final tokens = res['tokens'] as String;
+  final inputWav = res['input-wav'] as String;
+  final language = res['language'] as String;
+  final useItn = (res['use-itn'] as String).toLowerCase() == 'true';
+
+  final senseVoice = sherpa_onnx.OfflineSenseVoiceModelConfig(
+      model: model, language: language, useInverseTextNormalization: useItn);
+
+  final modelConfig = sherpa_onnx.OfflineModelConfig(
+    senseVoice: senseVoice,
+    tokens: tokens,
+    debug: true,
+    numThreads: 1,
+  );
+  final config = sherpa_onnx.OfflineRecognizerConfig(model: modelConfig);
+  final recognizer = sherpa_onnx.OfflineRecognizer(config);
+
+  final waveData = sherpa_onnx.readWave(inputWav);
+  if (waveData.sampleRate != 16000) {
+    print('Only 16000 Hz is supported. Given: ${waveData.sampleRate}');
+    exit(1);
+  }
+
+  int numSamples = waveData.samples.length;
+  int numIter = numSamples ~/ vadConfig.sileroVad.windowSize;
+
+  for (int i = 0; i != numIter; ++i) {
+    int start = i * vadConfig.sileroVad.windowSize;
+    vad.acceptWaveform(Float32List.sublistView(
+        waveData.samples, start, start + vadConfig.sileroVad.windowSize));
+
+    if (vad.isDetected()) {
+      while (!vad.isEmpty()) {
+        final samples = vad.front().samples;
+        final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+        final endTime =
+            startTime + samples.length.toDouble() / waveData.sampleRate;
+
+        final stream = recognizer.createStream();
+        stream.acceptWaveform(
+            samples: samples, sampleRate: waveData.sampleRate);
+        recognizer.decode(stream);
+
+        final result = recognizer.getResult(stream);
+        stream.free();
+        print(
+            '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+        vad.pop();
+      }
+    }
+  }
+
+  vad.flush();
+
+  while (!vad.isEmpty()) {
+    final samples = vad.front().samples;
+    final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+    final endTime = startTime + samples.length.toDouble() / waveData.sampleRate;
+
+    final stream = recognizer.createStream();
+    stream.acceptWaveform(samples: samples, sampleRate: waveData.sampleRate);
+    recognizer.decode(stream);
+
+    final result = recognizer.getResult(stream);
+    stream.free();
+    print(
+        '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+    vad.pop();
+  }
+
+  vad.free();
+
+  recognizer.free();
+}

+// Copyright (c)  2024  Xiaomi Corporation
+import 'dart:io';
+import 'dart:typed_data';
+
+import 'package:args/args.dart';
+import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;
+
+import './init.dart';
+
+void main(List<String> arguments) async {
+  await initSherpaOnnx();
+
+  final parser = ArgParser()
+    ..addOption('silero-vad', help: 'Path to silero_vad.onnx')
+    ..addOption('model', help: 'Path to the telespeech CTC model')
+    ..addOption('tokens', help: 'Path to tokens.txt')
+    ..addOption('input-wav', help: 'Path to input.wav to transcribe');
+
+  final res = parser.parse(arguments);
+
+  if (res['silero-vad'] == null ||
+      res['model'] == null ||
+      res['tokens'] == null ||
+      res['input-wav'] == null) {
+    print(parser.usage);
+    exit(1);
+  }
+
+  // create VAD
+  final sileroVad = res['silero-vad'] as String;
+
+  final sileroVadConfig = sherpa_onnx.SileroVadModelConfig(
+    model: sileroVad,
+    minSilenceDuration: 0.25,
+    minSpeechDuration: 0.5,
+  );
+
+  final vadConfig = sherpa_onnx.VadModelConfig(
+    sileroVad: sileroVadConfig,
+    numThreads: 1,
+    debug: true,
+  );
+
+  final vad = sherpa_onnx.VoiceActivityDetector(
+      config: vadConfig, bufferSizeInSeconds: 10);
+
+  // create telespeech CTC recognizer
+  final model = res['model'] as String;
+  final tokens = res['tokens'] as String;
+  final inputWav = res['input-wav'] as String;
+
+  final modelConfig = sherpa_onnx.OfflineModelConfig(
+    telespeechCtc: model,
+    tokens: tokens,
+    debug: true,
+    numThreads: 1,
+    modelType: 'telespeech_ctc',
+  );
+  final config = sherpa_onnx.OfflineRecognizerConfig(model: modelConfig);
+  final recognizer = sherpa_onnx.OfflineRecognizer(config);
+
+  final waveData = sherpa_onnx.readWave(inputWav);
+  if (waveData.sampleRate != 16000) {
+    print('Only 16000 Hz is supported. Given: ${waveData.sampleRate}');
+    exit(1);
+  }
+
+  int numSamples = waveData.samples.length;
+  int numIter = numSamples ~/ vadConfig.sileroVad.windowSize;
+
+  for (int i = 0; i != numIter; ++i) {
+    int start = i * vadConfig.sileroVad.windowSize;
+    vad.acceptWaveform(Float32List.sublistView(
+        waveData.samples, start, start + vadConfig.sileroVad.windowSize));
+
+    if (vad.isDetected()) {
+      while (!vad.isEmpty()) {
+        final samples = vad.front().samples;
+        final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+        final endTime =
+            startTime + samples.length.toDouble() / waveData.sampleRate;
+
+        final stream = recognizer.createStream();
+        stream.acceptWaveform(
+            samples: samples, sampleRate: waveData.sampleRate);
+        recognizer.decode(stream);
+
+        final result = recognizer.getResult(stream);
+        stream.free();
+        print(
+            '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+        vad.pop();
+      }
+    }
+  }
+
+  vad.flush();
+
+  while (!vad.isEmpty()) {
+    final samples = vad.front().samples;
+    final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+    final endTime = startTime + samples.length.toDouble() / waveData.sampleRate;
+
+    final stream = recognizer.createStream();
+    stream.acceptWaveform(samples: samples, sampleRate: waveData.sampleRate);
+    recognizer.decode(stream);
+
+    final result = recognizer.getResult(stream);
+    stream.free();
+    print(
+        '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+    vad.pop();
+  }
+
+  vad.free();
+
+  recognizer.free();
+}

+// Copyright (c)  2024  Xiaomi Corporation
+import 'dart:io';
+import 'dart:typed_data';
+
+import 'package:args/args.dart';
+import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;
+
+import './init.dart';
+
+void main(List<String> arguments) async {
+  await initSherpaOnnx();
+
+  final parser = ArgParser()
+    ..addOption('silero-vad', help: 'Path to silero_vad.onnx')
+    ..addOption('encoder', help: 'Path to the whisper encoder model')
+    ..addOption('decoder', help: 'Path to whisper decoder model')
+    ..addOption('tokens', help: 'Path to tokens.txt')
+    ..addOption('input-wav', help: 'Path to input.wav to transcribe');
+
+  final res = parser.parse(arguments);
+  if (res['silero-vad'] == null ||
+      res['encoder'] == null ||
+      res['decoder'] == null ||
+      res['tokens'] == null ||
+      res['input-wav'] == null) {
+    print(parser.usage);
+    exit(1);
+  }
+
+  // create VAD
+  final sileroVad = res['silero-vad'] as String;
+
+  final sileroVadConfig = sherpa_onnx.SileroVadModelConfig(
+    model: sileroVad,
+    minSilenceDuration: 0.25,
+    minSpeechDuration: 0.5,
+  );
+
+  final vadConfig = sherpa_onnx.VadModelConfig(
+    sileroVad: sileroVadConfig,
+    numThreads: 1,
+    debug: true,
+  );
+
+  final vad = sherpa_onnx.VoiceActivityDetector(
+      config: vadConfig, bufferSizeInSeconds: 10);
+
+  // create whisper recognizer
+  final encoder = res['encoder'] as String;
+  final decoder = res['decoder'] as String;
+  final tokens = res['tokens'] as String;
+  final inputWav = res['input-wav'] as String;
+
+  final whisper = sherpa_onnx.OfflineWhisperModelConfig(
+    encoder: encoder,
+    decoder: decoder,
+  );
+
+  final modelConfig = sherpa_onnx.OfflineModelConfig(
+    whisper: whisper,
+    tokens: tokens,
+    modelType: 'whisper',
+    debug: false,
+    numThreads: 1,
+  );
+  final config = sherpa_onnx.OfflineRecognizerConfig(model: modelConfig);
+  final recognizer = sherpa_onnx.OfflineRecognizer(config);
+
+  final waveData = sherpa_onnx.readWave(inputWav);
+  if (waveData.sampleRate != 16000) {
+    print('Only 16000 Hz is supported. Given: ${waveData.sampleRate}');
+    exit(1);
+  }
+
+  int numSamples = waveData.samples.length;
+  int numIter = numSamples ~/ vadConfig.sileroVad.windowSize;
+
+  for (int i = 0; i != numIter; ++i) {
+    int start = i * vadConfig.sileroVad.windowSize;
+    vad.acceptWaveform(Float32List.sublistView(
+        waveData.samples, start, start + vadConfig.sileroVad.windowSize));
+
+    if (vad.isDetected()) {
+      while (!vad.isEmpty()) {
+        final samples = vad.front().samples;
+        final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+        final endTime =
+            startTime + samples.length.toDouble() / waveData.sampleRate;
+
+        final stream = recognizer.createStream();
+        stream.acceptWaveform(
+            samples: samples, sampleRate: waveData.sampleRate);
+        recognizer.decode(stream);
+
+        final result = recognizer.getResult(stream);
+        stream.free();
+        print(
+            '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+        vad.pop();
+      }
+    }
+  }
+
+  vad.flush();
+
+  while (!vad.isEmpty()) {
+    final samples = vad.front().samples;
+    final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+    final endTime = startTime + samples.length.toDouble() / waveData.sampleRate;
+
+    final stream = recognizer.createStream();
+    stream.acceptWaveform(samples: samples, sampleRate: waveData.sampleRate);
+    recognizer.decode(stream);
+
+    final result = recognizer.getResult(stream);
+    stream.free();
+    print(
+        '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+    vad.pop();
+  }
+
+  vad.free();
+
+  recognizer.free();
+}

+// Copyright (c)  2024  Xiaomi Corporation
+import 'dart:io';
+import 'dart:typed_data';
+
+import 'package:args/args.dart';
+import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;
+
+import './init.dart';
+
+void main(List<String> arguments) async {
+  await initSherpaOnnx();
+
+  final parser = ArgParser()
+    ..addOption('silero-vad', help: 'Path to silero_vad.onnx')
+    ..addOption('encoder', help: 'Path to the encoder model')
+    ..addOption('decoder', help: 'Path to decoder model')
+    ..addOption('joiner', help: 'Path to joiner model')
+    ..addOption('tokens', help: 'Path to tokens.txt')
+    ..addOption('input-wav', help: 'Path to input.wav to transcribe');
+
+  final res = parser.parse(arguments);
+
+  if (res['silero-vad'] == null ||
+      res['encoder'] == null ||
+      res['decoder'] == null ||
+      res['joiner'] == null ||
+      res['tokens'] == null ||
+      res['input-wav'] == null) {
+    print(parser.usage);
+    exit(1);
+  }
+
+  // create VAD
+  final sileroVad = res['silero-vad'] as String;
+
+  final sileroVadConfig = sherpa_onnx.SileroVadModelConfig(
+    model: sileroVad,
+    minSilenceDuration: 0.25,
+    minSpeechDuration: 0.5,
+  );
+
+  final vadConfig = sherpa_onnx.VadModelConfig(
+    sileroVad: sileroVadConfig,
+    numThreads: 1,
+    debug: true,
+  );
+
+  final vad = sherpa_onnx.VoiceActivityDetector(
+      config: vadConfig, bufferSizeInSeconds: 10);
+
+  // create zipformer transducer recognizer
+  final encoder = res['encoder'] as String;
+  final decoder = res['decoder'] as String;
+  final joiner = res['joiner'] as String;
+  final tokens = res['tokens'] as String;
+  final inputWav = res['input-wav'] as String;
+
+  final transducer = sherpa_onnx.OfflineTransducerModelConfig(
+    encoder: encoder,
+    decoder: decoder,
+    joiner: joiner,
+  );
+
+  final modelConfig = sherpa_onnx.OfflineModelConfig(
+    transducer: transducer,
+    tokens: tokens,
+    debug: true,
+    numThreads: 1,
+  );
+  final config = sherpa_onnx.OfflineRecognizerConfig(model: modelConfig);
+  final recognizer = sherpa_onnx.OfflineRecognizer(config);
+
+  final waveData = sherpa_onnx.readWave(inputWav);
+  if (waveData.sampleRate != 16000) {
+    print('Only 16000 Hz is supported. Given: ${waveData.sampleRate}');
+    exit(1);
+  }
+
+  int numSamples = waveData.samples.length;
+  int numIter = numSamples ~/ vadConfig.sileroVad.windowSize;
+
+  for (int i = 0; i != numIter; ++i) {
+    int start = i * vadConfig.sileroVad.windowSize;
+    vad.acceptWaveform(Float32List.sublistView(
+        waveData.samples, start, start + vadConfig.sileroVad.windowSize));
+
+    if (vad.isDetected()) {
+      while (!vad.isEmpty()) {
+        final samples = vad.front().samples;
+        final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+        final endTime =
+            startTime + samples.length.toDouble() / waveData.sampleRate;
+
+        final stream = recognizer.createStream();
+        stream.acceptWaveform(
+            samples: samples, sampleRate: waveData.sampleRate);
+        recognizer.decode(stream);
+
+        final result = recognizer.getResult(stream);
+        stream.free();
+        print(
+            '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+        vad.pop();
+      }
+    }
+  }
+
+  vad.flush();
+
+  while (!vad.isEmpty()) {
+    final samples = vad.front().samples;
+    final startTime = vad.front().start.toDouble() / waveData.sampleRate;
+    final endTime = startTime + samples.length.toDouble() / waveData.sampleRate;
+
+    final stream = recognizer.createStream();
+    stream.acceptWaveform(samples: samples, sampleRate: waveData.sampleRate);
+    recognizer.decode(stream);
+
+    final result = recognizer.getResult(stream);
+    stream.free();
+    print(
+        '${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
+
+    vad.pop();
+  }
+
+  vad.free();
+
+  recognizer.free();
+}

+name: vad_with_non_streaming_asr
+
+description: >
+  This example demonstrates how to use the Dart API for VAD (voice activity detection)
+  with non-streaming speech recognition.
+
+version: 1.0.0
+
+environment:
+  sdk: ^3.4.0
+
+dependencies:
+  sherpa_onnx: ^1.10.19
+  path: ^1.9.0
+  args: ^2.5.0
+
+dev_dependencies:
+  lints: ^3.0.0

+#!/usr/bin/env bash
+
+set -ex
+
+dart pub get
+
+if [ ! -f ./sherpa-onnx-paraformer-zh-2023-09-14/tokens.txt ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-paraformer-zh-2023-09-14.tar.bz2
+
+  tar xvf sherpa-onnx-paraformer-zh-2023-09-14.tar.bz2
+  rm sherpa-onnx-paraformer-zh-2023-09-14.tar.bz2
+fi
+
+if [ ! -f ./lei-jun-test.wav ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/lei-jun-test.wav
+fi
+
+if [[ ! -f ./silero_vad.onnx ]]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
+fi
+
+dart run \
+  ./bin/paraformer.dart \
+  --silero-vad ./silero_vad.onnx \
+  --model ./sherpa-onnx-paraformer-zh-2023-09-14/model.int8.onnx \
+  --tokens ./sherpa-onnx-paraformer-zh-2023-09-14/tokens.txt \
+  --input-wav ./lei-jun-test.wav

+#!/usr/bin/env bash
+
+set -ex
+
+dart pub get
+
+if [ ! -f ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  tar xvf sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  rm sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+fi
+
+if [ ! -f ./Obama.wav ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/Obama.wav
+fi
+
+if [[ ! -f ./silero_vad.onnx ]]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
+fi
+
+dart run \
+  ./bin/sense-voice.dart \
+  --silero-vad ./silero_vad.onnx \
+  --model ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/model.onnx \
+  --tokens ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt \
+  --use-itn true \
+  --input-wav ./Obama.wav
+

+#!/usr/bin/env bash
+
+set -ex
+
+dart pub get
+
+if [ ! -f ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  tar xvf sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+  rm sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2
+fi
+
+if [ ! -f ./lei-jun-test.wav ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/lei-jun-test.wav
+fi
+
+if [[ ! -f ./silero_vad.onnx ]]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
+fi
+
+dart run \
+  ./bin/sense-voice.dart \
+  --silero-vad ./silero_vad.onnx \
+  --model ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/model.onnx \
+  --tokens ./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt \
+  --use-itn true \
+  --input-wav ./lei-jun-test.wav
+

+#!/usr/bin/env bash
+
+set -ex
+
+dart pub get
+
+if [ ! -f ./sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04/tokens.txt ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04.tar.bz2
+
+  tar xvf sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04.tar.bz2
+  rm sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04.tar.bz2
+fi
+
+if [ ! -f ./lei-jun-test.wav ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/lei-jun-test.wav
+fi
+
+if [[ ! -f ./silero_vad.onnx ]]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
+fi
+
+dart run \
+  ./bin/telespeech-ctc.dart \
+  --silero-vad ./silero_vad.onnx \
+  --model ./sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04/model.int8.onnx \
+  --tokens ./sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04/tokens.txt \
+  --input-wav ./lei-jun-test.wav

+#!/usr/bin/env bash
+
+set -ex
+
+dart pub get
+
+if [ ! -f ./sherpa-onnx-whisper-tiny.en/tiny.en-tokens.txt ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.en.tar.bz2
+
+  tar xvf sherpa-onnx-whisper-tiny.en.tar.bz2
+  rm sherpa-onnx-whisper-tiny.en.tar.bz2
+fi
+
+
+
+if [ ! -f ./Obama.wav ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/Obama.wav
+fi
+
+if [[ ! -f ./silero_vad.onnx ]]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
+fi
+
+dart run \
+  ./bin/whisper.dart \
+  --silero-vad ./silero_vad.onnx \
+  --encoder ./sherpa-onnx-whisper-tiny.en/tiny.en-encoder.int8.onnx \
+  --decoder ./sherpa-onnx-whisper-tiny.en/tiny.en-decoder.int8.onnx \
+  --tokens ./sherpa-onnx-whisper-tiny.en/tiny.en-tokens.txt \
+  --input-wav ./Obama.wav

+#!/usr/bin/env bash
+
+set -ex
+
+dart pub get
+
+if [ ! -f ./sherpa-onnx-zipformer-gigaspeech-2023-12-12/tokens.txt ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-gigaspeech-2023-12-12.tar.bz2
+
+  tar xvf sherpa-onnx-zipformer-gigaspeech-2023-12-12.tar.bz2
+  rm sherpa-onnx-zipformer-gigaspeech-2023-12-12.tar.bz2
+fi
+
+if [ ! -f ./Obama.wav ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/Obama.wav
+fi
+
+if [[ ! -f ./silero_vad.onnx ]]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
+fi
+
+dart run \
+  ./bin/zipformer-transducer.dart \
+  --silero-vad ./silero_vad.onnx \
+  --encoder ./sherpa-onnx-zipformer-gigaspeech-2023-12-12/encoder-epoch-30-avg-1.int8.onnx \
+  --decoder ./sherpa-onnx-zipformer-gigaspeech-2023-12-12/decoder-epoch-30-avg-1.onnx \
+  --joiner ./sherpa-onnx-zipformer-gigaspeech-2023-12-12/joiner-epoch-30-avg-1.int8.onnx \
+  --tokens ./sherpa-onnx-zipformer-gigaspeech-2023-12-12/tokens.txt \
+  --input-wav ./Obama.wav
+

+name: vad_with_non_streaming_asr
+
+description: >
+  This example demonstrates how to use the Dart API for VAD (voice activity detection)
+  with non-streaming speech recognition.
+
+version: 1.0.0
+
+environment:
+  sdk: ^3.4.0
+
+dependencies:
+  sherpa_onnx:
+    path: ../../flutter/sherpa_onnx
+  path: ^1.9.0
+  args: ^2.5.0
+
+dev_dependencies:
+  lints: ^3.0.0