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enginex-mr_series-sherpa-onnx/nodejs-addon-examples/test_asr_non_streaming_zipformer_ctc.js
Fangjun Kuang 3bf986d08d Support non-streaming zipformer CTC ASR models (#2340)
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
2025-07-04 15:57:07 +08:00

47 lines
1.4 KiB
JavaScript

// Copyright (c) 2025 Xiaomi Corporation
const sherpa_onnx = require('sherpa-onnx-node');
// Please download test files from
// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
const config = {
'featConfig': {
'sampleRate': 16000,
'featureDim': 80,
},
'modelConfig': {
'zipformerCtc': {
'model': './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx',
},
'tokens': './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt',
'numThreads': 2,
'provider': 'cpu',
'debug': 1,
}
};
const waveFilename =
'./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav';
const recognizer = new sherpa_onnx.OfflineRecognizer(config);
console.log('Started')
let start = Date.now();
const stream = recognizer.createStream();
const wave = sherpa_onnx.readWave(waveFilename);
stream.acceptWaveform({sampleRate: wave.sampleRate, samples: wave.samples});
recognizer.decode(stream);
result = recognizer.getResult(stream)
let stop = Date.now();
console.log('Done')
const elapsed_seconds = (stop - start) / 1000;
const duration = wave.samples.length / wave.sampleRate;
const real_time_factor = elapsed_seconds / duration;
console.log('Wave duration', duration.toFixed(3), 'seconds')
console.log('Elapsed', elapsed_seconds.toFixed(3), 'seconds')
console.log(
`RTF = ${elapsed_seconds.toFixed(3)}/${duration.toFixed(3)} =`,
real_time_factor.toFixed(3))
console.log(waveFilename)
console.log('result\n', result)