Files
sherpa-onnx/java-api-examples/StreamingDecodeFileToneCtc.java
Fangjun Kuang 686b909e2f Add various language bindings for streaming T-one Russian ASR models (#2576)
This PR adds support for streaming T-one Russian ASR models across various language bindings in the sherpa-onnx library. The changes enable T-one CTC (Connectionist Temporal Classification) model integration by adding new configuration structures and example implementations.

- Introduces OnlineToneCtcModelConfig structures across all language bindings (C, C++, Swift, Java, Kotlin, Go, etc.)
- Adds T-one CTC model support to WASM implementations for both ASR and keyword spotting
- Provides comprehensive example implementations demonstrating T-one model usage in multiple programming languages
2025-09-09 16:51:18 +08:00

54 lines
1.8 KiB
Java

// Copyright 2024 Xiaomi Corporation
// This file shows how to use an online T-one CTC model, i.e.,
// streaming T-one CTC model, to decode files.
import com.k2fsa.sherpa.onnx.*;
public class StreamingDecodeFileToneCtc {
public static void main(String[] args) {
String model = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/model.onnx";
String tokens = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/tokens.txt";
String waveFilename = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/0.wav";
WaveReader reader = new WaveReader(waveFilename);
OnlineToneCtcModelConfig ctc = OnlineToneCtcModelConfig.builder().setModel(model).build();
OnlineModelConfig modelConfig =
OnlineModelConfig.builder()
.setToneCtc(ctc)
.setTokens(tokens)
.setNumThreads(1)
.setDebug(true)
.build();
OnlineRecognizerConfig config =
OnlineRecognizerConfig.builder()
.setOnlineModelConfig(modelConfig)
.setDecodingMethod("greedy_search")
.build();
OnlineRecognizer recognizer = new OnlineRecognizer(config);
OnlineStream stream = recognizer.createStream();
float[] leftPaddings = new float[(int) (0.3 * reader.getSampleRate())];
stream.acceptWaveform(leftPaddings, reader.getSampleRate());
stream.acceptWaveform(reader.getSamples(), reader.getSampleRate());
float[] tailPaddings = new float[(int) (0.6 * reader.getSampleRate())];
stream.acceptWaveform(tailPaddings, reader.getSampleRate());
while (recognizer.isReady(stream)) {
recognizer.decode(stream);
}
String text = recognizer.getResult(stream).getText();
System.out.printf("filename:%s\nresult:%s\n", waveFilename, text);
stream.release();
recognizer.release();
}
}