VadNonStreamingParaformer.java
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// Copyright 2024 Xiaomi Corporation
// This file shows how to use a silero_vad model with a non-streaming Paraformer
// for speech recognition.
import com.k2fsa.sherpa.onnx.*;
import java.util.Arrays;
public class VadNonStreamingParaformer {
public static Vad createVad() {
// please download ./silero_vad.onnx from
// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
String model = "./silero_vad.onnx";
SileroVadModelConfig sileroVad =
SileroVadModelConfig.builder()
.setModel(model)
.setThreshold(0.5f)
.setMinSilenceDuration(0.25f)
.setMinSpeechDuration(0.5f)
.setWindowSize(512)
.build();
VadModelConfig config =
VadModelConfig.builder()
.setSileroVadModelConfig(sileroVad)
.setSampleRate(16000)
.setNumThreads(1)
.setDebug(true)
.setProvider("cpu")
.build();
return new Vad(config);
}
public static OfflineRecognizer createOfflineRecognizer() {
// please refer to
// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/paraformer-models.html#csukuangfj-sherpa-onnx-paraformer-zh-2023-03-28-chinese-english
// to download model files
String model = "./sherpa-onnx-paraformer-zh-2023-03-28/model.int8.onnx";
String tokens = "./sherpa-onnx-paraformer-zh-2023-03-28/tokens.txt";
String waveFilename = "./sherpa-onnx-paraformer-zh-2023-03-28/test_wavs/3-sichuan.wav";
WaveReader reader = new WaveReader(waveFilename);
OfflineParaformerModelConfig paraformer =
OfflineParaformerModelConfig.builder().setModel(model).build();
OfflineModelConfig modelConfig =
OfflineModelConfig.builder()
.setParaformer(paraformer)
.setTokens(tokens)
.setNumThreads(1)
.setDebug(true)
.build();
OfflineRecognizerConfig config =
OfflineRecognizerConfig.builder()
.setOfflineModelConfig(modelConfig)
.setDecodingMethod("greedy_search")
.build();
return new OfflineRecognizer(config);
}
public static void main(String[] args) {
Vad vad = createVad();
OfflineRecognizer recognizer = createOfflineRecognizer();
// You can download the test file from
// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
String testWaveFilename = "./lei-jun-test.wav";
WaveReader reader = new WaveReader(testWaveFilename);
int numSamples = reader.getSamples().length;
int numIter = numSamples / 512;
for (int i = 0; i != numIter; ++i) {
int start = i * 512;
int end = start + 512;
float[] samples = Arrays.copyOfRange(reader.getSamples(), start, end);
vad.acceptWaveform(samples);
if (vad.isSpeechDetected()) {
while (!vad.empty()) {
SpeechSegment segment = vad.front();
float startTime = segment.getStart() / 16000.0f;
float duration = segment.getSamples().length / 16000.0f;
OfflineStream stream = recognizer.createStream();
stream.acceptWaveform(segment.getSamples(), 16000);
recognizer.decode(stream);
String text = recognizer.getResult(stream).getText();
stream.release();
if (!text.isEmpty()) {
System.out.printf("%.3f--%.3f: %s\n", startTime, startTime + duration, text);
}
vad.pop();
}
}
}
vad.release();
recognizer.release();
}
}