How to add multiple models to the app and use it at once so that it improves accuracy and helpful for wide range of users

import 'dart:async';
import 'dart:convert';
import 'dart:developer';
import 'dart:io';
import 'dart:typed_data';
import 'package:edolms/modules/screens/student/ai/conversation/chat/functions/model/vosk_result_model.dart';
import 'package:flutter/services.dart';
import 'package:vosk_flutter/vosk_flutter.dart';

class VoskTranscriber {
late VoskFlutterPlugin _vosk;
Recognizer? _recognizer;
SpeechService? _speechService;
Model? _model;
String? _error;
late StreamSubscription? _resultSubscription;

VoskTranscriber() {
initializeVosk();
}

Future initializeVosk() async {
const _sampleRate = 16000;

print("_initializeVosk called");
_vosk = VoskFlutterPlugin.instance();
final modelPath = await ModelLoader()
.loadFromAssets('assets/models/vosk-model-small-en-in-0.4.zip')
.then(
(modelPath) => _vosk.createModel(modelPath)) // create model object
.then((model) => _model = model)
.then((_) => _vosk.createRecognizer(
model: _model!, sampleRate: _sampleRate)) // create recognizer
.then((value) => _recognizer = value)
.then((recognizer) {
if (Platform.isAndroid) {
_vosk
.initSpeechService(_recognizer!) // init speech service
.then((speechService) => _speechService = speechService)
.catchError((e) => _error = e.toString());
print("sampleRate : ${_sampleRate}");
}
}).catchError((e) async {
_error = e.toString();
print("catchError : $e");
return null;
});

// print("modelPath :$modelPath");
// _recognizer = await _vosk.createRecognizer(
// model: Model(modelPath, const MethodChannel("")), sampleRate: 16000);
}

Future transcribeAudio(Uint8List audioBytes) async {
print("transcribeAudio function called");
print("audioBytes length : ${audioBytes.length}");
List results = [];
int chunkSize = 348135;
int pos = 0;

while (pos + chunkSize < audioBytes.length) {
final resultReady = await _recognizer!.acceptWaveformBytes(
Uint8List.fromList(audioBytes.sublist(pos, pos + chunkSize)),
);
// print("resultReady : ${resultReady}");
pos += chunkSize;

if (resultReady) {
String result = await _recognizer!.getResult();
dynamic decodedData = jsonDecode(result);

if (decodedData["text"].isEmpty ||
decodedData["text"] == "" ||
decodedData["text"] == null) {
print(" transcribed text empty ");
} else {
log("complete result in vosk : ${result}");
results.add(result);
}
} else {
String result = await _recognizer!.getPartialResult();
dynamic decodedData = jsonDecode(result);
if (decodedData["partial"].isEmpty ||
decodedData["partial"] == "" ||
decodedData["partial"] == null) {
print("empty partial result");
} else {
log("partial result in vosk : ${result}");
results.add(result);
}
}
}

// await _recognizer!
// .acceptWaveformBytes(Uint8List.fromList(audioBytes.sublist(pos)));
results.add(await _recognizer!.getFinalResult());

return results.join(' ');
}

startSpeechToText(Function(String) onResult) async {
String result = "";

// final speechService = await _vosk.initSpeechService(_recognizer!);
_speechService?.onPartial().forEach((partial) => print(partial));
_speechService?.onResult().forEach((result) => print(result));
await _speechService?.start();

_resultSubscription = _speechService?.onResult().listen((event) {
result = _onSpeechResult(event);
onResult(result);
});
}

String _onSpeechResult(dynamic result) {
String _sttAllWords = "";
Map decoded = json.decode(result);
VoskResultModel voskResult = VoskResultModel.fromJson(decoded);
if (voskResult.text != null && voskResult.text != "") {
_sttAllWords += voskResult.text?.trim() ?? "";
print("final text: ${_sttAllWords.trim()}");
}

return _sttAllWords;
}

Future stopSpeechToText() async {
await _resultSubscription?.cancel();
await _speechService?.stop();

print("stopped speechToText wosk");
}
}