Support for FastConformer-CTC models in Diarization+ASR with timestamps
State: closed | Pull request: yes
Labels: name: ASR

name: Run CICD

# 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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**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
* Related to #8438 


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
```