Fine-tuning Voice Activity Detection (VAD) models
**Is your feature request related to a problem? Please describe.**

After running diarization baselines, I wanted to fine-tune the component models (VAD + embedding) on my own labeled diarization dataset. For the embedding model this has gone well, and for VAD I have been able to set up training using Google Speech Recognition V2 dataset, as in the examples. 

Now that I try to create/adapt the Nemo manifests for VAD training on my dataset, I realise that the manifest format does not suffice for this: 

`{"audio_filepath": "<audio_path>", "duration": <full_file_duration>, "label": "speech", "text": "_", "offset": <start_of_speech_segment}`

If I understand correctly, this only allows for a single speech segment per audio file. This works for the Google dataset where each file is short and has a single utterance, but my (and I believe most) diarization dataset with RTTM labels defines many segments of speech per file. 

A workaround is technically possible where each file is cut into smaller segments, each with a single utterance, but this seems excessive. The requirement is also surprising, as other tools (e.g. PyAnnote) allow for training VAD models using the RTTM/diarization annotation format.

I'd love to know if I understand the situation correctly, or whether perhaps I've missed something.

Otherwise, thanks for a great framework! Especially the neural types is a feature that would enhance many other DL repositories.

**Describe the solution you'd like**

The ability to fine-tune VAD models using files with more than one utterance.

**Describe alternatives you've considered**

See workaround above

**Additional context**
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