Speaker Diarization goes haywire due to small segments of audio
Please note that clustering based speaker diarization is a type of self-supervised machine learning system, not a rule-based software. Thus, speaker diarization can generate incorrect results and such behavior should be regarded as the limitation in accuracy, not a type of bug.

Especially, there is no guarantee that the speaker diarization system would generate the same speaker assignment to the truncated shorter audio clips from the original audio clips.

Closing since there is no clear ways to avoid this case.
**Describe the bug**

I have a long audio of around 3 hours that spans multiple speakers. The speaker diarization label a single speaker when this audio is passed. When I break down into this audio in parts and pass each part separately, some of the parts get assigned speakers correctly but the rest of the portion has the same bug. I identified some 1 min chunks that when added in this audio cause the model to behave this way. I'm seeking possible explanations or solutions to this behavior since I believe that the model should be resilient enough.

I think this might be related to having lots of overlap and a good number of speakers resulting in exceeding Nemo's limit of 20 max speakers.

**Steps/Code to reproduce bug**

Test Speaker Diarization on the [audio]( https://21133.mc.tritondigital.com/OMNY_DAVECHUCKTHEFREAKFULLSHOW_P/media-session/ba27cae7-9871-409f-b206-2183ad4628fe/d/clips/58a22c8d-1810-4cd1-bce2-a849004eeede/f545c88c-6f30-414d-b85a-aff10125b149/17c6c4c0-70cb-4c39-b16b-b18000f52f46/audio/direct/t1717081258/Thursday,_May_30th_2024_Dave_Chuck_the_Freak_Full_Show.mp3?t=1717081258&utm_source=Podcast%26in_playlist%3D9857b359-5905-4672-a201-aff10125b173)

**Expected behavior**

A clear and concise description of what you expected to happen.

**Environment overview (please complete the following information)**

 - Environment location: AWS
 - Method of NeMo install: pip install

**Environment details**

- AWS Linux 2
- PyTorch version: 2.3.1
- Python version: 3.10

**Additional context**

GPU model
