55 lines
2.0 KiB
Java
55 lines
2.0 KiB
Java
// Copyright 2024 Xiaomi Corporation
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// This file shows how to use an offline paraformer, i.e., non-streaming paraformer,
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// to decode files with inverse text normalization.
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import com.k2fsa.sherpa.onnx.*;
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public class InverseTextNormalizationNonStreamingParaformer {
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public static void main(String[] args) {
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// please refer to
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// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/paraformer-models.html#csukuangfj-sherpa-onnx-paraformer-zh-2023-09-14-chinese-english
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// to download model files
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String model = "./sherpa-onnx-paraformer-zh-2023-09-14/model.int8.onnx";
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String tokens = "./sherpa-onnx-paraformer-zh-2023-09-14/tokens.txt";
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// https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/itn-zh-number.wav
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String waveFilename = "./itn-zh-number.wav";
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// https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/itn_zh_number.fst
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String ruleFsts = "./itn_zh_number.fst";
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WaveReader reader = new WaveReader(waveFilename);
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OfflineParaformerModelConfig paraformer =
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OfflineParaformerModelConfig.builder().setModel(model).build();
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OfflineModelConfig modelConfig =
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OfflineModelConfig.builder()
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.setParaformer(paraformer)
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.setTokens(tokens)
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.setNumThreads(1)
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.setDebug(true)
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.build();
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OfflineRecognizerConfig config =
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OfflineRecognizerConfig.builder()
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.setOfflineModelConfig(modelConfig)
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.setDecodingMethod("greedy_search")
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.setRuleFsts(ruleFsts)
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.build();
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OfflineRecognizer recognizer = new OfflineRecognizer(config);
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OfflineStream stream = recognizer.createStream();
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stream.acceptWaveform(reader.getSamples(), reader.getSampleRate());
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recognizer.decode(stream);
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String text = recognizer.getResult(stream).getText();
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System.out.printf("filename:%s\nresult:%s\n", waveFilename, text);
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stream.release();
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recognizer.release();
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}
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}
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