how to properly checkpoint for incremental optimization of the ASR model
I am using [this code](https://github.com/NVIDIA/NeMo/blob/v1.0.2/tutorials/asr/01_ASR_with_NeMo.ipynb) and a VCTK corpus for training. Due to the large data-set size I split it up in chunks and use checkpoint to save the model, and then read the checkpoint model for training on further data records. The issue is that when I read in the checkpoint the inference does not deliver the expected results. So I suspect the way I handle the checkpoint is the culprit:

In [this notebook](https://github.com/joepareti54/joepareti54/blob/main/NB_3_chkpt.ipynb) I train the model, save a checkpoint and do some inference. The results appear reasonable.

In [this notebook ](https://github.com/joepareti54/joepareti54/blob/main/NB_3a_chkpt.ipynb) I read in the checkpoint from the step above, train the model on just a few lines and do inferences: the results are different, and it looks like the model was not properly saved or restored.

My overall assumption is that the NeMo model lends itself to transfer learning, so that I can use the public version and refine it incrementally with my own data: is this a valid assumption?