alignment using ctc_segmentation fails with Hybrid RNNT-CTC models
@erastorgueva-nv can you look at this issue pls.  Hi @Jesteinbe, thanks for bringing this to our attention. You are correct, unfortunately NeMo CTC Segmentation does not currently support Hybrid models. 

Your fix of "`mdl.cfg.aux_ctc.decoder.vocabulary`" is a reasonable partial fix for this. The remaining issue of `list indices must be integers or slices, not tuple` can be fixed if you add the following lines in `run_ctc_segmentation.py` after `asr_model` is instantiated:

```
    if isinstance(asr_model, nemo_asr.models.EncDecHybridRNNTCTCModel):
        asr_model.change_decoding_strategy(decoder_type="ctc")
```
 Thanks @erastorgueva-nv ! I figured that out too. 

I'm also seeing that I get the same results no matter what window size I use when calling `run_ctc_segmentation.py`. I've tried values ranging from 10 (which I thought should result in bad alignment if not break it), 8000, 16000, 32000, and 48000. i am using single channel audio with limited amounts of silence so i didn't expect a bigger window size was necessary but I was surprised to find identical results. I saw this behavior on both 2 recordings, one English and the other Spanish, that were about ~5min long. it seems like the window size isn't getting update properly but the log/segment file names are all showing the expected values. i haven't tested on a larger amount of audio yet so this could be an anomaly but thought it was worth mentioning.  This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days. @erastorgueva-nv could we close this issue? 
Following the steps outlined in [CTC_Segmentation_Tutorial.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/CTC_Segmentation_Tutorial.ipynb) I'm trying to align text and audio. If I use a CTC-only model like [stt_en_fastconformer_ctc_large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_ctc_large) then things work fine. However, if I try to use a EncDecHybridRNNTCTCBPEModel model like [stt_en_fastconformer_hybrid_large_pc](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_pc) then things break. As far as I can tell, there are at least two problems. 

The first issue is that hybrid models' vocabularies are `mdl.cfg.aux_ctc.decoder.vocabulary`, not `mdl.cfg.decoder.vocabulary` which is what is used by [prepare_data.py](https://github.com/NVIDIA/NeMo/blob/main/tools/ctc_segmentation/scripts/prepare_data.py) and [run_ctc_segmentation.py](https://github.com/NVIDIA/NeMo/blob/main/tools/ctc_segmentation/scripts/run_ctc_segmentation.py). Once I fixed this, prepare_data.py seems to work fine but the segmentation still fails.

I'm not sure what the second issue is yet but the alignment fails and I just get a generic error like:

```
INFO:root:Processing 1st_call.wav...
Transcribing: 100%|███████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.21s/it]
ERROR:root:list indices must be integers or slices, not tuple
ERROR:root:Skipping 1st_call.wav
```

I'm guessing the call to the forward pass of the ASR decoder is different for the hybrid RNNT-CTC models.

As for expected behavior, the ctc_segmentation should work regardless of which CTC-based model you use. In this case, the bug shouldn't have anything to do with my environment but I'm running on bare metal (Ubuntu 20.04) using a conda environment in which i installed Nemo v.1.22.0 via pip.

