Fine-tune stt conformer transducer EncDecRNNTBPEModel model
I'm trying to fine-tine stt_en_conformer_transducer_large model on a custom dataset of 300H. I have a problem with setting up the training configuration. 

In [this ](https://github.com/NVIDIA/NeMo/blob/8c912f515a77efb0e16b1433030da23e488960d5/tutorials/asr/ASR_with_Transducers.ipynb) example you reported how to train a model from scratch, but I understood from the model card (https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_small) that we can fine-tune it.

I tried to load the model, extract the config file, modify it and re-assign it to the mode, but did not work.

Here is what I did:

# Loading the model
```
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="stt_en_conformer_transducer_large", map_location='cpu')
```

# Loading the model configurations:
```
import copy
from omegaconf import DictConfig

modelConfig = DictConfig(asr_model.cfg)
modelConfig

```

Set the training data:

```
modelConfig['train_ds']['manifest_filepath'] = TRAIN_MANIFEST
modelConfig['validation_ds']['manifest_filepath'] = TEST_MANIFEST
```

Assigned it to the model:
`asr_model.setup_training_data(modelConfig['train_ds'])`

Here is the error I get:


```
[NeMo I 2022-01-18 18:44:40 collections:173] Dataset loaded with 1503 files totalling 2.81 hours
[NeMo I 2022-01-18 18:44:40 collections:174] 0 files were filtered totalling 0.00 hours
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_3295870/3465286351.py in <module>
----> 1 asr_model.setup_training_data(modelConfig['train_ds'])

~/anaconda3/envs/nemo/lib/python3.7/site-packages/nemo/collections/asr/models/rnnt_models.py in setup_training_data(self, train_data_config)
    499             # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches,
    500             # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0).
--> 501             if isinstance(self._trainer.limit_train_batches, float):
    502                 self._trainer.limit_train_batches = int(
    503                     self._trainer.limit_train_batches

AttributeError: 'NoneType' object has no attribute 'limit_train_batches'
```


Thanks!