3bf986d08d
This PR adds support for non-streaming Zipformer CTC ASR models across multiple language bindings, WebAssembly, examples, and CI workflows. - Introduces a new OfflineZipformerCtcModelConfig in C/C++, Python, Swift, Java, Kotlin, Go, Dart, Pascal, and C# APIs - Updates initialization, freeing, and recognition logic to include Zipformer CTC in WASM and Node.js - Adds example scripts and CI steps for downloading, building, and running Zipformer CTC models Model doc is available at https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html
53 lines
1.4 KiB
Dart
53 lines
1.4 KiB
Dart
// Copyright (c) 2025 Xiaomi Corporation
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import 'dart:io';
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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('model', help: 'Path to the Zipformer CTC model')
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..addOption('tokens', help: 'Path to tokens.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['model'] == null ||
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res['tokens'] == 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 model = res['model'] as String;
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final tokens = res['tokens'] as String;
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final inputWav = res['input-wav'] as String;
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final zipformerCtc = sherpa_onnx.OfflineZipformerCtcModelConfig(model: model);
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final modelConfig = sherpa_onnx.OfflineModelConfig(
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zipformerCtc: zipformerCtc,
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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.OfflineRecognizerConfig(model: modelConfig);
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final recognizer = sherpa_onnx.OfflineRecognizer(config);
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final waveData = sherpa_onnx.readWave(inputWav);
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final stream = recognizer.createStream();
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stream.acceptWaveform(
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samples: waveData.samples, sampleRate: waveData.sampleRate);
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recognizer.decode(stream);
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final result = recognizer.getResult(stream);
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print(result.text);
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stream.free();
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recognizer.free();
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}
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