c6e474fb13be0362c6f239f81c128f6d4c751da4,pyannote/audio/labeling/tasks/resegmentation.py,Resegmentation,apply,#Resegmentation#Any#Any#,247

Before Change

            best_speaker_indices = np.argmax(scores[:, 1:], axis=1) + 1

            // reconstruct annotation
            new_hypothesis = one_hot_decoding(
                best_speaker_indices, window, labels=labels)

            // revert non-speech regions back to original
            speech = hypothesis.get_timeline().support()
            new_hypothesis = new_hypothesis.crop(speech)

        else:

            // sequence of most likely class index (including 0=non-speech)

After Change

        // ensemble scores
        scores = SlidingWindowFeature(
            np.mean([s.data for s in scores], axis=0),
            scores[-1].sliding_window)

        // speaker labels
        labels = batch_generator.specifications["y"]["classes"][1:]

Instances