// Copyright (c) 2025 Xiaomi Corporation import 'dart:io'; import 'package:args/args.dart'; import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx; import './init.dart'; void main(List arguments) async { await initSherpaOnnx(); print('sherpa-onnx version: ${sherpa_onnx.getVersion()}'); print('sherpa-onnx gitSha1: ${sherpa_onnx.getGitSha1()}'); print('sherpa-onnx gitDate: ${sherpa_onnx.getGitDate()}'); final parser = ArgParser() ..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') ..addOption('hr-dict-dir', help: 'Path to jieba dict for homophone replacer') ..addOption('hr-lexicon', help: 'Path to lexicon.txt for homophone replacer') ..addOption('hr-rule-fsts', help: 'Path to replace.fst for homophone replacer'); final res = parser.parse(arguments); if (res['model'] == null || res['tokens'] == null || res['hr-dict-dir'] == null || res['hr-lexicon'] == null || res['hr-rule-fsts'] == null || res['input-wav'] == null) { print(parser.usage); exit(1); } 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 hrDictDir = res['hr-dict-dir'] as String; final hrLexicon = res['hr-lexicon'] as String; final hrRuleFsts = res['hr-rule-fsts'] as String; 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 hr = sherpa_onnx.HomophoneReplacerConfig( dictDir: hrDictDir, lexicon: hrLexicon, ruleFsts: hrRuleFsts); final config = sherpa_onnx.OfflineRecognizerConfig(model: modelConfig, hr: hr); final recognizer = sherpa_onnx.OfflineRecognizer(config); final waveData = sherpa_onnx.readWave(inputWav); final stream = recognizer.createStream(); stream.acceptWaveform( samples: waveData.samples, sampleRate: waveData.sampleRate); recognizer.decode(stream); final result = recognizer.getResult(stream); print(result.text); stream.free(); recognizer.free(); }