scope trainVAD.

sclass TrainVAD {
  replace Recognizer with F1<BWImage, Bool>.
    
  new Best<Recognizer> best;
  LPair<BWImage, Bool> trainingList;
    
  *() {
    final BWImage img1 = frequencyImage(voiceMegaMix());
    final BWImage img2 = frequencyImage(nonVoiceMegaMix());
    int clipLength = iround(2.0*spectro_pixelsPerSecond());
    int stepSize = clipLength/4;
    print(+clipLength);
    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 });
    
    // now an actual one
    test(Rec1());
  }
  
  void test(Recognizer r) {
    scorePreciseRecognizer(best, r, trainingList);
  }
}

sclass #Rec1 extends F1<BWImage, Bool> {
  public Bool get(BWImage img) {
    ret hasStreaksLongerThan(5, audio_streaksUsingBand_v1(img,
      audio_bestBandForEntryPoints()));
  }
}

static F1<BWImage, Bool> trainVAD() {
  ret TrainVAD().best!;
}

end scope