Update App.java

examples/java-example/src/main/java/org/example/App.java

@@ -2,68 +2,263 @@ package org.example;
 
 import ai.onnxruntime.OrtException;
 import javax.sound.sampled.*;
+import java.io.File;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
 import java.util.Map;
 
+/**
+ * Silero VAD Java Example
+ * Voice Activity Detection using ONNX model
+ * 
+ * @author VvvvvGH
+ */
 public class App {
 
-    private static final String MODEL_PATH = "src/main/resources/silero_vad.onnx";
+    // ONNX model path - using the model file from the project
+    private static final String MODEL_PATH = "../../src/silero_vad/data/silero_vad.onnx";
+    // Test audio file path
+    private static final String AUDIO_FILE_PATH = "../../en_example.wav";
+    // Sampling rate
     private static final int SAMPLE_RATE = 16000;
-    private static final float START_THRESHOLD = 0.6f;
+    // Speech threshold (consistent with Python default)
-    private static final float END_THRESHOLD = 0.45f;
+    private static final float THRESHOLD = 0.5f;
-    private static final int MIN_SILENCE_DURATION_MS = 600;
+    // Negative threshold (used to determine speech end)
-    private static final int SPEECH_PAD_MS = 500;
+    private static final float NEG_THRESHOLD = 0.35f; // threshold - 0.15
-    private static final int WINDOW_SIZE_SAMPLES = 2048;
+    // Minimum speech duration (milliseconds)
+    private static final int MIN_SPEECH_DURATION_MS = 250;
+    // Minimum silence duration (milliseconds)
+    private static final int MIN_SILENCE_DURATION_MS = 100;
+    // Speech padding (milliseconds)
+    private static final int SPEECH_PAD_MS = 30;
+    // Window size (samples) - 512 samples for 16kHz
+    private static final int WINDOW_SIZE_SAMPLES = 512;
 
     public static void main(String[] args) {
-        // Initialize the Voice Activity Detector
+        System.out.println("=".repeat(60));
-        SlieroVadDetector vadDetector;
+        System.out.println("Silero VAD Java ONNX Example");
+        System.out.println("=".repeat(60));
+        
+        // Load ONNX model
+        SlieroVadOnnxModel model;
         try {
-            vadDetector = new SlieroVadDetector(MODEL_PATH, START_THRESHOLD, END_THRESHOLD, SAMPLE_RATE, MIN_SILENCE_DURATION_MS, SPEECH_PAD_MS);
+            System.out.println("Loading ONNX model: " + MODEL_PATH);
+            model = new SlieroVadOnnxModel(MODEL_PATH);
+            System.out.println("Model loaded successfully!");
         } catch (OrtException e) {
-            System.err.println("Error initializing the VAD detector: " + e.getMessage());
+            System.err.println("Failed to load model: " + e.getMessage());
+            e.printStackTrace();
             return;
         }
 
-        // Set audio format
+        // Read WAV file
-        AudioFormat format = new AudioFormat(SAMPLE_RATE, 16, 1, true, false);
+        float[] audioData;
-        DataLine.Info info = new DataLine.Info(TargetDataLine.class, format);
-
-        // Get the target data line and open it with the specified format
-        TargetDataLine targetDataLine;
         try {
-            targetDataLine = (TargetDataLine) AudioSystem.getLine(info);
+            System.out.println("\nReading audio file: " + AUDIO_FILE_PATH);
-            targetDataLine.open(format);
+            audioData = readWavFileAsFloatArray(AUDIO_FILE_PATH);
-            targetDataLine.start();
+            System.out.println("Audio file read successfully, samples: " + audioData.length);
-        } catch (LineUnavailableException e) {
+            System.out.println("Audio duration: " + String.format("%.2f", (audioData.length / (float) SAMPLE_RATE)) + " seconds");
-            System.err.println("Error opening target data line: " + e.getMessage());
+        } catch (Exception e) {
+            System.err.println("Failed to read audio file: " + e.getMessage());
+            e.printStackTrace();
             return;
         }
 
-        // Main loop to continuously read data and apply Voice Activity Detection
+        // Get speech timestamps (batch mode, consistent with Python's get_speech_timestamps)
-        while (targetDataLine.isOpen()) {
+        System.out.println("\nDetecting speech segments...");
-            byte[] data = new byte[WINDOW_SIZE_SAMPLES];
+        List<Map<String, Integer>> speechTimestamps;
-
+        try {
-            int numBytesRead = targetDataLine.read(data, 0, data.length);
+            speechTimestamps = getSpeechTimestamps(
-            if (numBytesRead <= 0) {
+                audioData,
-                System.err.println("Error reading data from target data line.");
+                model,
-                continue;
+                THRESHOLD,
-            }
+                SAMPLE_RATE,
-
+                MIN_SPEECH_DURATION_MS,
-            // Apply the Voice Activity Detector to the data and get the result
+                MIN_SILENCE_DURATION_MS,
-            Map<String, Double> detectResult;
+                SPEECH_PAD_MS,
-            try {
+                NEG_THRESHOLD
-                detectResult = vadDetector.apply(data, true);
+            );
-            } catch (Exception e) {
+        } catch (OrtException e) {
-                System.err.println("Error applying VAD detector: " + e.getMessage());
+            System.err.println("Failed to detect speech timestamps: " + e.getMessage());
-                continue;
+            e.printStackTrace();
-            }
+            return;
-
-            if (!detectResult.isEmpty()) {
-                System.out.println(detectResult);
-            }
         }
 
-        // Close the target data line to release audio resources
+        // Output detection results
-        targetDataLine.close();
+        System.out.println("\nDetected speech timestamps (in samples):");
+        for (Map<String, Integer> timestamp : speechTimestamps) {
+            System.out.println(timestamp);
+        }
+
+        // Output summary
+        System.out.println("\n" + "=".repeat(60));
+        System.out.println("Detection completed!");
+        System.out.println("Total detected " + speechTimestamps.size() + " speech segments");
+        System.out.println("=".repeat(60));
+        
+        // Close model
+        try {
+            model.close();
+        } catch (OrtException e) {
+            System.err.println("Error closing model: " + e.getMessage());
+        }
     }
+
+    /**
+     * Get speech timestamps
+     * Implements the same logic as Python's get_speech_timestamps
+     * 
+     * @param audio Audio data (float array)
+     * @param model ONNX model
+     * @param threshold Speech threshold
+     * @param samplingRate Sampling rate
+     * @param minSpeechDurationMs Minimum speech duration (milliseconds)
+     * @param minSilenceDurationMs Minimum silence duration (milliseconds)
+     * @param speechPadMs Speech padding (milliseconds)
+     * @param negThreshold Negative threshold (used to determine speech end)
+     * @return List of speech timestamps
+     */
+    private static List<Map<String, Integer>> getSpeechTimestamps(
+            float[] audio,
+            SlieroVadOnnxModel model,
+            float threshold,
+            int samplingRate,
+            int minSpeechDurationMs,
+            int minSilenceDurationMs,
+            int speechPadMs,
+            float negThreshold) throws OrtException {
+        
+        // Reset model states
+        model.resetStates();
+        
+        // Calculate parameters
+        int minSpeechSamples = samplingRate * minSpeechDurationMs / 1000;
+        int speechPadSamples = samplingRate * speechPadMs / 1000;
+        int minSilenceSamples = samplingRate * minSilenceDurationMs / 1000;
+        int windowSizeSamples = samplingRate == 16000 ? 512 : 256;
+        int audioLengthSamples = audio.length;
+        
+        // Calculate speech probabilities for all audio chunks
+        List<Float> speechProbs = new ArrayList<>();
+        for (int currentStart = 0; currentStart < audioLengthSamples; currentStart += windowSizeSamples) {
+            float[] chunk = new float[windowSizeSamples];
+            int chunkLength = Math.min(windowSizeSamples, audioLengthSamples - currentStart);
+            System.arraycopy(audio, currentStart, chunk, 0, chunkLength);
+            
+            // Pad with zeros if chunk is shorter than window size
+            if (chunkLength < windowSizeSamples) {
+                for (int i = chunkLength; i < windowSizeSamples; i++) {
+                    chunk[i] = 0.0f;
+                }
+            }
+            
+            float speechProb = model.call(new float[][]{chunk}, samplingRate)[0];
+            speechProbs.add(speechProb);
+        }
+        
+        // Detect speech segments using the same algorithm as Python
+        boolean triggered = false;
+        List<Map<String, Integer>> speeches = new ArrayList<>();
+        Map<String, Integer> currentSpeech = null;
+        int tempEnd = 0;
+        
+        for (int i = 0; i < speechProbs.size(); i++) {
+            float speechProb = speechProbs.get(i);
+            
+            // Reset temporary end if speech probability exceeds threshold
+            if (speechProb >= threshold && tempEnd != 0) {
+                tempEnd = 0;
+            }
+            
+            // Detect speech start
+            if (speechProb >= threshold && !triggered) {
+                triggered = true;
+                currentSpeech = new HashMap<>();
+                currentSpeech.put("start", windowSizeSamples * i);
+                continue;
+            }
+            
+            // Detect speech end
+            if (speechProb < negThreshold && triggered) {
+                if (tempEnd == 0) {
+                    tempEnd = windowSizeSamples * i;
+                }
+                if (windowSizeSamples * i - tempEnd < minSilenceSamples) {
+                    continue;
+                } else {
+                    currentSpeech.put("end", tempEnd);
+                    if (currentSpeech.get("end") - currentSpeech.get("start") > minSpeechSamples) {
+                        speeches.add(currentSpeech);
+                    }
+                    currentSpeech = null;
+                    tempEnd = 0;
+                    triggered = false;
+                }
+            }
+        }
+        
+        // Handle the last speech segment
+        if (currentSpeech != null && 
+            (audioLengthSamples - currentSpeech.get("start")) > minSpeechSamples) {
+            currentSpeech.put("end", audioLengthSamples);
+            speeches.add(currentSpeech);
+        }
+        
+        // Add speech padding - same logic as Python
+        for (int i = 0; i < speeches.size(); i++) {
+            Map<String, Integer> speech = speeches.get(i);
+            if (i == 0) {
+                speech.put("start", Math.max(0, speech.get("start") - speechPadSamples));
+            }
+            if (i != speeches.size() - 1) {
+                int silenceDuration = speeches.get(i + 1).get("start") - speech.get("end");
+                if (silenceDuration < 2 * speechPadSamples) {
+                    speech.put("end", speech.get("end") + silenceDuration / 2);
+                    speeches.get(i + 1).put("start", 
+                        Math.max(0, speeches.get(i + 1).get("start") - silenceDuration / 2));
+                } else {
+                    speech.put("end", Math.min(audioLengthSamples, speech.get("end") + speechPadSamples));
+                    speeches.get(i + 1).put("start", 
+                        Math.max(0, speeches.get(i + 1).get("start") - speechPadSamples));
+                }
+            } else {
+                speech.put("end", Math.min(audioLengthSamples, speech.get("end") + speechPadSamples));
+            }
+        }
+        
+        return speeches;
+    }
+
+    /**
+     * Read WAV file and return as float array
+     * 
+     * @param filePath WAV file path
+     * @return Audio data as float array (normalized to -1.0 to 1.0)
+     */
+    private static float[] readWavFileAsFloatArray(String filePath) 
+            throws UnsupportedAudioFileException, IOException {
+        File audioFile = new File(filePath);
+        AudioInputStream audioStream = AudioSystem.getAudioInputStream(audioFile);
+        
+        // Get audio format information
+        AudioFormat format = audioStream.getFormat();
+        System.out.println("Audio format: " + format);
+        
+        // Read all audio data
+        byte[] audioBytes = audioStream.readAllBytes();
+        audioStream.close();
+        
+        // Convert to float array
+        float[] audioData = new float[audioBytes.length / 2];
+        for (int i = 0; i < audioData.length; i++) {
+            // 16-bit PCM: two bytes per sample (little-endian)
+            short sample = (short) ((audioBytes[i * 2] & 0xff) | (audioBytes[i * 2 + 1] << 8));
+            audioData[i] = sample / 32768.0f; // Normalize to -1.0 to 1.0
+        }
+        
+        return audioData;
+    }
+
 }