Add Java and Kotlin API for sense voice (#1164)

2024-07-22 14:08:40 +08:00
parent ac8223bd8a
commit dd300b1de5
16 changed files with 601 additions and 2 deletions
--- a/java-api-examples/VadNonStreamingSenseVoice.java
+++ b/java-api-examples/VadNonStreamingSenseVoice.java
@@ -0,0 +1,123 @@
+// Copyright 2024 Xiaomi Corporation
+
+// This file shows how to use a silero_vad model with a non-streaming SenseVoiceModel
+// for speech recognition.
+
+import com.k2fsa.sherpa.onnx.*;
+import java.util.Arrays;
+
+public class VadNonStreamingSenseVoice {
+  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/sense-voice/index.html
+    // to download model files
+    String model = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/model.int8.onnx";
+    String tokens = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt";
+
+    OfflineSenseVoiceModelConfig senseVoice =
+        OfflineSenseVoiceModelConfig.builder().setModel(model).build();
+
+    OfflineModelConfig modelConfig =
+        OfflineModelConfig.builder()
+            .setSenseVoice(senseVoice)
+            .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.flush();
+    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();
+  }
+}