Vladislav A. Proskurov

We need to be able to re-save the dataset by cutting out the silence sections, so that when working with such datasets in the future we can be sure that...

No, unfortunately the problem does not boil down to the description you suggested. Because it does not take into account the need to refine the silence intervals inside the supervisions....

> Again, why can this not be done by appending the cuts corresponding to the supervisions? Could you give a concrete example of how exactly we can override one single...

``` 1 2 34 5 6 7 8 9 ┌─────────────────────────────────────────────────────────────────────┐ │ Cut with Supervision │ │ ◁───────────────.───────.▷ │ | ◁───────.───────.───────.─────▷ │─┐ │ . . . ◁─────.─────────────────▷ | │ └─────────────────────.───────.───────.───────.───────────────────────┘ │...

How can we describe this with the procedure you suggest?

How can such a resulting Cut be described? Is there any way to guarantee that when loading an audio track with load_audio, the numpy array will be shorter than the...

Yes //// means that we trimmed the silence and refined the supervisory intervals. In this PR, I implemented the required operations using IntervalTree to achieve the desired result. Since functionality...

Yes, of course, I am ready to implement such functionality in Cut, SupervisionSet, etc. But we need to strictly agree on how to test this functionality, and in which code...

I think that the main purpose of the silence detector is to remove silence from the supervised segment of audio. All of the proposed alternative approaches to full track resaving...