sclass TrainVAD {

  replace Recognizer with F1<BWImage, Bool>.

  new Best<Recognizer> best;

  LPair<BWImage, Bool> trainingList;

  float stepSizeRatio = 0.25f;

  int clipLength;

  Recognizer go() {

    final BWImage img1 = frequencyImage(voiceMegaMix());

    final BWImage img2 = frequencyImage(nonVoiceMegaMix());

    clipLength = iround(2.0*spectro_pixelsPerSecond());

    int stepSize = max(1, iround(clipLength*stepSizeRatio));

    print(+clipLength);

    print(+stepSize);

    L<BWImage> images1 = map(func(IntRange r) -> BWImage { bwHorizontalClip(img1, r) }, stepIntRange(clipLength, intRange(0, img1.getWidth()), stepSize));

    L<BWImage> images2 = map(func(IntRange r) -> BWImage { bwHorizontalClip(img2, r) }, stepIntRange(clipLength, intRange(0, img2.getWidth()), stepSize));

    print(allImageSizes(concatLists(images1, images2)));

    print(l(images1) + " + " + l(images2) + " images");

    trainingList = trueFalseBPairs(images1, images2);

    // dummy recognizers first

    test(func(BWImage img) -> bool { false });

    test(func(BWImage img) -> bool { true });

    new AIStrategy_RandomWithVariation<Recognizer> strategy;

    strategy.best = best;

    strategy.submit = f1_scorePreciseRecognizer(trainingList);

    strategy.random = func -> Recognizer {

      nu(VAD_1,

        streakLength := random(1, clipLength/2),

        threshold := randomFloat(),

        band := audio_randomBand())

    };

    strategy.vary = func(Recognizer r) -> Recognizer {

      if (r cast VAD_1)

        ret nu(VAD_1,

          streakLength := varyInt(r.streakLength),

          threshold := varyFloat(r.threshold),

          band := audio_varyBand(r.band));

      null;

    };

    runStrategyWhileImprovement(best, strategy);

    /*for (int streakLength = 1; streakLength <= clipLength/2; streakLength++)

      test(nu(VAD_1, +streakLength));*/

    ret best!;

  }

  void test(Recognizer r) {

    scorePreciseRecognizer(best, r, trainingList);

  }

  void showErrors(Recognizer r) {

    if (r == null) ret;

    new L<BWImage> errorImages;

    for (Pair<BWImage, Bool> p : trainingList)

      if (r.get(p.a) != p.b)

        errorImages.add(bwAutoContrast(p.a));

    if (nempty(errorImages))

      showImage("Errors", mergeBWImagesVertically(errorImages));

  }

}