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https://github.com/snakers4/silero-vad.git
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Update App.java
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GH
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@@ -2,68 +2,263 @@ package org.example;
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import ai.onnxruntime.OrtException;
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import javax.sound.sampled.*;
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import java.io.File;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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/**
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* Silero VAD Java Example
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* Voice Activity Detection using ONNX model
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*
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* @author VvvvvGH
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*/
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public class App {
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private static final String MODEL_PATH = "src/main/resources/silero_vad.onnx";
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// ONNX model path - using the model file from the project
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private static final String MODEL_PATH = "../../src/silero_vad/data/silero_vad.onnx";
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// Test audio file path
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private static final String AUDIO_FILE_PATH = "../../en_example.wav";
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// Sampling rate
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private static final int SAMPLE_RATE = 16000;
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private static final float START_THRESHOLD = 0.6f;
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private static final float END_THRESHOLD = 0.45f;
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private static final int MIN_SILENCE_DURATION_MS = 600;
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private static final int SPEECH_PAD_MS = 500;
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private static final int WINDOW_SIZE_SAMPLES = 2048;
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// Speech threshold (consistent with Python default)
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private static final float THRESHOLD = 0.5f;
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// Negative threshold (used to determine speech end)
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private static final float NEG_THRESHOLD = 0.35f; // threshold - 0.15
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// Minimum speech duration (milliseconds)
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private static final int MIN_SPEECH_DURATION_MS = 250;
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// Minimum silence duration (milliseconds)
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private static final int MIN_SILENCE_DURATION_MS = 100;
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// Speech padding (milliseconds)
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private static final int SPEECH_PAD_MS = 30;
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// Window size (samples) - 512 samples for 16kHz
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private static final int WINDOW_SIZE_SAMPLES = 512;
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public static void main(String[] args) {
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// Initialize the Voice Activity Detector
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SlieroVadDetector vadDetector;
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System.out.println("=".repeat(60));
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System.out.println("Silero VAD Java ONNX Example");
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System.out.println("=".repeat(60));
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// Load ONNX model
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SlieroVadOnnxModel model;
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try {
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vadDetector = new SlieroVadDetector(MODEL_PATH, START_THRESHOLD, END_THRESHOLD, SAMPLE_RATE, MIN_SILENCE_DURATION_MS, SPEECH_PAD_MS);
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System.out.println("Loading ONNX model: " + MODEL_PATH);
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model = new SlieroVadOnnxModel(MODEL_PATH);
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System.out.println("Model loaded successfully!");
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} catch (OrtException e) {
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System.err.println("Error initializing the VAD detector: " + e.getMessage());
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System.err.println("Failed to load model: " + e.getMessage());
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e.printStackTrace();
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return;
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}
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// Set audio format
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AudioFormat format = new AudioFormat(SAMPLE_RATE, 16, 1, true, false);
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DataLine.Info info = new DataLine.Info(TargetDataLine.class, format);
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// Get the target data line and open it with the specified format
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TargetDataLine targetDataLine;
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// Read WAV file
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float[] audioData;
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try {
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targetDataLine = (TargetDataLine) AudioSystem.getLine(info);
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targetDataLine.open(format);
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targetDataLine.start();
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} catch (LineUnavailableException e) {
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System.err.println("Error opening target data line: " + e.getMessage());
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System.out.println("\nReading audio file: " + AUDIO_FILE_PATH);
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audioData = readWavFileAsFloatArray(AUDIO_FILE_PATH);
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System.out.println("Audio file read successfully, samples: " + audioData.length);
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System.out.println("Audio duration: " + String.format("%.2f", (audioData.length / (float) SAMPLE_RATE)) + " seconds");
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} catch (Exception e) {
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System.err.println("Failed to read audio file: " + e.getMessage());
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e.printStackTrace();
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return;
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}
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// Main loop to continuously read data and apply Voice Activity Detection
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while (targetDataLine.isOpen()) {
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byte[] data = new byte[WINDOW_SIZE_SAMPLES];
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int numBytesRead = targetDataLine.read(data, 0, data.length);
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if (numBytesRead <= 0) {
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System.err.println("Error reading data from target data line.");
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continue;
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}
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// Apply the Voice Activity Detector to the data and get the result
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Map<String, Double> detectResult;
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try {
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detectResult = vadDetector.apply(data, true);
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} catch (Exception e) {
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System.err.println("Error applying VAD detector: " + e.getMessage());
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continue;
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}
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if (!detectResult.isEmpty()) {
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System.out.println(detectResult);
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}
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// Get speech timestamps (batch mode, consistent with Python's get_speech_timestamps)
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System.out.println("\nDetecting speech segments...");
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List<Map<String, Integer>> speechTimestamps;
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try {
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speechTimestamps = getSpeechTimestamps(
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audioData,
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model,
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THRESHOLD,
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SAMPLE_RATE,
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MIN_SPEECH_DURATION_MS,
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MIN_SILENCE_DURATION_MS,
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SPEECH_PAD_MS,
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NEG_THRESHOLD
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);
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} catch (OrtException e) {
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System.err.println("Failed to detect speech timestamps: " + e.getMessage());
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e.printStackTrace();
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return;
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}
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// Close the target data line to release audio resources
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targetDataLine.close();
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// Output detection results
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System.out.println("\nDetected speech timestamps (in samples):");
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for (Map<String, Integer> timestamp : speechTimestamps) {
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System.out.println(timestamp);
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}
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// Output summary
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System.out.println("\n" + "=".repeat(60));
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System.out.println("Detection completed!");
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System.out.println("Total detected " + speechTimestamps.size() + " speech segments");
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System.out.println("=".repeat(60));
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// Close model
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try {
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model.close();
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} catch (OrtException e) {
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System.err.println("Error closing model: " + e.getMessage());
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}
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}
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/**
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* Get speech timestamps
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* Implements the same logic as Python's get_speech_timestamps
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*
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* @param audio Audio data (float array)
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* @param model ONNX model
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* @param threshold Speech threshold
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* @param samplingRate Sampling rate
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* @param minSpeechDurationMs Minimum speech duration (milliseconds)
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* @param minSilenceDurationMs Minimum silence duration (milliseconds)
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* @param speechPadMs Speech padding (milliseconds)
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* @param negThreshold Negative threshold (used to determine speech end)
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* @return List of speech timestamps
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*/
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private static List<Map<String, Integer>> getSpeechTimestamps(
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float[] audio,
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SlieroVadOnnxModel model,
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float threshold,
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int samplingRate,
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int minSpeechDurationMs,
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int minSilenceDurationMs,
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int speechPadMs,
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float negThreshold) throws OrtException {
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// Reset model states
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model.resetStates();
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// Calculate parameters
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int minSpeechSamples = samplingRate * minSpeechDurationMs / 1000;
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int speechPadSamples = samplingRate * speechPadMs / 1000;
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int minSilenceSamples = samplingRate * minSilenceDurationMs / 1000;
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int windowSizeSamples = samplingRate == 16000 ? 512 : 256;
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int audioLengthSamples = audio.length;
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// Calculate speech probabilities for all audio chunks
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List<Float> speechProbs = new ArrayList<>();
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for (int currentStart = 0; currentStart < audioLengthSamples; currentStart += windowSizeSamples) {
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float[] chunk = new float[windowSizeSamples];
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int chunkLength = Math.min(windowSizeSamples, audioLengthSamples - currentStart);
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System.arraycopy(audio, currentStart, chunk, 0, chunkLength);
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// Pad with zeros if chunk is shorter than window size
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if (chunkLength < windowSizeSamples) {
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for (int i = chunkLength; i < windowSizeSamples; i++) {
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chunk[i] = 0.0f;
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}
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}
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float speechProb = model.call(new float[][]{chunk}, samplingRate)[0];
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speechProbs.add(speechProb);
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}
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// Detect speech segments using the same algorithm as Python
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boolean triggered = false;
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List<Map<String, Integer>> speeches = new ArrayList<>();
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Map<String, Integer> currentSpeech = null;
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int tempEnd = 0;
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for (int i = 0; i < speechProbs.size(); i++) {
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float speechProb = speechProbs.get(i);
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// Reset temporary end if speech probability exceeds threshold
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if (speechProb >= threshold && tempEnd != 0) {
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tempEnd = 0;
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}
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// Detect speech start
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if (speechProb >= threshold && !triggered) {
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triggered = true;
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currentSpeech = new HashMap<>();
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currentSpeech.put("start", windowSizeSamples * i);
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continue;
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}
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// Detect speech end
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if (speechProb < negThreshold && triggered) {
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if (tempEnd == 0) {
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tempEnd = windowSizeSamples * i;
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}
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if (windowSizeSamples * i - tempEnd < minSilenceSamples) {
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continue;
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} else {
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currentSpeech.put("end", tempEnd);
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if (currentSpeech.get("end") - currentSpeech.get("start") > minSpeechSamples) {
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speeches.add(currentSpeech);
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}
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currentSpeech = null;
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tempEnd = 0;
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triggered = false;
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}
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}
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}
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// Handle the last speech segment
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if (currentSpeech != null &&
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(audioLengthSamples - currentSpeech.get("start")) > minSpeechSamples) {
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currentSpeech.put("end", audioLengthSamples);
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speeches.add(currentSpeech);
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}
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// Add speech padding - same logic as Python
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for (int i = 0; i < speeches.size(); i++) {
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Map<String, Integer> speech = speeches.get(i);
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if (i == 0) {
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speech.put("start", Math.max(0, speech.get("start") - speechPadSamples));
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}
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if (i != speeches.size() - 1) {
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int silenceDuration = speeches.get(i + 1).get("start") - speech.get("end");
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if (silenceDuration < 2 * speechPadSamples) {
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speech.put("end", speech.get("end") + silenceDuration / 2);
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speeches.get(i + 1).put("start",
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Math.max(0, speeches.get(i + 1).get("start") - silenceDuration / 2));
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} else {
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speech.put("end", Math.min(audioLengthSamples, speech.get("end") + speechPadSamples));
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speeches.get(i + 1).put("start",
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Math.max(0, speeches.get(i + 1).get("start") - speechPadSamples));
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}
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} else {
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speech.put("end", Math.min(audioLengthSamples, speech.get("end") + speechPadSamples));
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}
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}
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return speeches;
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}
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/**
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* Read WAV file and return as float array
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*
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* @param filePath WAV file path
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* @return Audio data as float array (normalized to -1.0 to 1.0)
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*/
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private static float[] readWavFileAsFloatArray(String filePath)
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throws UnsupportedAudioFileException, IOException {
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File audioFile = new File(filePath);
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AudioInputStream audioStream = AudioSystem.getAudioInputStream(audioFile);
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// Get audio format information
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AudioFormat format = audioStream.getFormat();
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System.out.println("Audio format: " + format);
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// Read all audio data
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byte[] audioBytes = audioStream.readAllBytes();
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audioStream.close();
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// Convert to float array
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float[] audioData = new float[audioBytes.length / 2];
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for (int i = 0; i < audioData.length; i++) {
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// 16-bit PCM: two bytes per sample (little-endian)
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short sample = (short) ((audioBytes[i * 2] & 0xff) | (audioBytes[i * 2 + 1] << 8));
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audioData[i] = sample / 32768.0f; // Normalize to -1.0 to 1.0
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}
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return audioData;
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}
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}
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