Support for FastConformer-CTC models in Diarization+ASR with timestamps
This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days. Eval numbers on CH109:
Running `NeMo/examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_with_asr_infer.py` using `stt_en_fastconformer_ctc_large` as the ASR model gives the following performance on  CallHome-109 dataset-
```
    DER                : 0.0474
    FA                 : 0.0111
    MISS               : 0.0285
    CER                : 0.0078
    Spk. counting acc. : 0.9174
    cpWER              : 0.5659
    WER                : 0.3142
```
# What does this PR do ?

Adds support for FastConformer-CTC models in Diarization+ASR with timestamps

**Collection**: [Note which collection this PR will affect]
ASR, Speaker Tasks

# Changelog 
Add configuration of FastConformer in `ASRDecoderTimeStamps` class of  `nemo/collections/asr/parts/utils/decoder_timestamps_utils.py` 

# Usage
To perform diarization with ASR using a pretrained FastConformer CTC model you can use the following code snippet

```python

python  NeMo/examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_with_asr_infer.py --config-path='../conf/inference' --config-name='diar_infer_telephonic.yaml' \
    diarizer.manifest_filepath=<add your manifest here> \
    diarizer.oracle_vad=False \
    diarizer.clustering.parameters.oracle_num_speakers=False\
    diarizer.out_dir=<your output folder to save predictions and intermediate embeddings> \
    diarizer.speaker_embeddings.model_path="titanet_large"\
    diarizer.asr.parameters.asr_based_vad=False \
    diarizer.asr.parameters.colored_text=True \
    diarizer.asr.parameters.fix_word_ts_with_VAD=False \
    diarizer.asr.parameters.break_lines=False \
    diarizer.asr.parameters.word_ts_anchor_pos='start'\
    diarizer.asr.model_path="stt_en_fastconformer_ctc_large.nemo"  
    # diarizer.asr.parameters.decoder_delay_in_sec=0.08  
    # diarizer.asr.parameters.word_ts_anchor_offset=0.15 
```

Note that for most applications word_ts_anchor_offset of 0.12 secs works best and hence was added as the default value, 0.15 is also a promising candidate can tried if the default values don't work well for your use case.

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# Additional Information
* Related to #8438 
