conformer fine-tuning with conformer_ctc_char.yaml
Hi thanks for your great tutorial, 
I have an error `Interpolation key 'model.labels' not found` when I set up the training params model. I do the following steps:
1) I used `stt_en_conformer_ctc_small` as pre-trained model.
2) I load `conformer_ctc_char.yaml ` file.
3) I change the model.labels in this file manually to new language characters. 
4) I change the train test monifest path and lr.
5) fit the model.
I attach my code. so what is the problem? How can I fine-tune the conformer for another language in CTC char-based?

```
import nemo
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_ctc_small")

from omegaconf import OmegaConf, open_dict
params = OmegaConf.load("conformer_ctc_char.yaml")
print(OmegaConf.to_yaml(params))

train_manifest = "./train.json"
test_manifest = "./test.json"
params.model.train_ds.manifest_filepath = train_manifest
params.model.validation_ds.manifest_filepath = test_manifest

params.model.spec_augment.rect_masks = 0

import copy
new_opt = copy.deepcopy(params.model.optim)
new_opt.lr = 0.001

print(params.model.optim)

print(asr_model.decoder.vocabulary)

asr_model.setup_optimization(optim_config=new_opt)

asr_model.setup_training_data(train_data_config=params['model']['train_ds'])

asr_model.setup_validation_data(val_data_config=params['model']['validation_ds'])

asr_model.encoder.freeze()

import pytorch_lightning as pl
from pytorch_lightning.callbacks.early_stopping import EarlyStopping

early_stop_callback = EarlyStopping(
   monitor= 'val_wer',
   min_delta=0.05,
   patience=3,
   verbose=True,
   mode='min'
)

trainer = pl.Trainer(max_epochs=2,
                     accumulate_grad_batches=1,
                     checkpoint_callback=False,
                     logger=False,
                     log_every_n_steps=5,
                     check_val_every_n_epoch=1,               
                     callbacks=[early_stop_callback],
                     gpus=0, 
                     # accelerator='ddp',
                     # plugins='ddp_sharded'
                    )

asr_model.set_trainer(trainer)

trainer.fit(asr_model)
```

**Environment overview**

Environment location: Google Colab
Method of NeMo install: !pip install nemo_toolkit[asr]
NeMo branch version: main
Learning Rate: 1e-3

**Environment details**

OS version : "Ubuntu20.04.3 LTS"
PyTorch version : "1.10.0"
