Interpretation of Conformer CTC training loss and WER
**Describe the bug**

The Val loss keeps increasing while the val_wer keeps getting better.

**Steps/Code to reproduce bug**

I am trying to fine tune conformer CTC large on a custom dataset and my val loss Keeps getting worse but val wer keeps getting better. Please have a look at graph images below. 

![Screenshot 2023-06-29 at 11 05 21](https://github.com/NVIDIA/NeMo/assets/32937046/9cfaef19-de40-40f8-a1df-370739ff3a0e)
![Screenshot 2023-06-29 at 11 07 28](https://github.com/NVIDIA/NeMo/assets/32937046/b69d1ba5-2fa4-4609-a777-54d5dfbc4d21)

![Screenshot 2023-06-29 at 11 07 44](https://github.com/NVIDIA/NeMo/assets/32937046/dcdffa04-1600-494b-8a46-237795ca7acd)

![Screenshot 2023-06-29 at 11 07 56](https://github.com/NVIDIA/NeMo/assets/32937046/296d114c-18b6-4c67-9c9d-07a23585d1ab)

Run command: 

model.optim.lr=0.01 model.optim.weight_decay=1e-4  \
+init_from_nemo_model=stt_en_conformer_ctc_large.nemo \
model.train_ds.batch_size=8 \
trainer.devices=4  trainer.max_epochs=1000 model.optim.sched.warmup_steps=100 trainer.accumulate_grad_batches=4 ++exp_manager.use_datetime_version=False

Configuration:

```
name: "Conformer-CTC-BPE-finetune-OptumRX"

model:
  sample_rate: 16000
  log_prediction: true # enables logging sample predictions in the output during training
  ctc_reduction: 'mean_batch'
  skip_nan_grad: false

  train_ds:
    manifest_filepath: ???
    sample_rate: ${model.sample_rate}
    batch_size: 16 # you may increase batch_size if your memory allows
    shuffle: true
    num_workers: 8
    pin_memory: true
    use_start_end_token: false
    trim_silence: false
    max_duration: 21 # it is set for LibriSpeech, you may need to update it for your dataset
    min_duration: 0.1
    # tarred datasets
    is_tarred: false
    tarred_audio_filepaths: null
    shuffle_n: 2048
    # bucketing params
    bucketing_strategy: "synced_randomized"
    bucketing_batch_size: null

  validation_ds:
    manifest_filepath: ???
    sample_rate: ${model.sample_rate}
    batch_size: 2 # you may increase batch_size if your memory allows
    shuffle: false
    num_workers: 8
    pin_memory: true
    use_start_end_token: false

  test_ds:
    manifest_filepath: null
    sample_rate: ${model.sample_rate}
    batch_size: 16 # you may increase batch_size if your memory allows
    shuffle: false
    num_workers: 8
    pin_memory: true
    use_start_end_token: false

  tokenizer:
    dir: ???  # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe)
    type: bpe  # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)

  preprocessor:
    _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
    sample_rate: ${model.sample_rate}
    normalize: "per_feature"
    window_size: 0.025
    window_stride: 0.01
    window: "hann"
    features: 80
    n_fft: 512
    log: true
    frame_splicing: 1
    dither: 0.00001
    pad_to: 0
    pad_value: 0.0

  spec_augment:
    _target_: nemo.collections.asr.modules.SpectrogramAugmentation
    freq_masks: 2 # set to zero to disable it
    # you may use lower time_masks for smaller models to have a faster convergence
    time_masks: 10 # set to zero to disable it
    freq_width: 27
    time_width: 0.05

  encoder:
    _target_: nemo.collections.asr.modules.ConformerEncoder
    feat_in: ${model.preprocessor.features}
    feat_out: -1 # you may set it if you need different output size other than the default d_model
    n_layers: 18
    d_model: 512

    # Sub-sampling params
    subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding
    subsampling_factor: 4 # must be power of 2 for striding and vggnet
    subsampling_conv_channels: -1 # -1 sets it to d_model
    causal_downsampling: false

    # Feed forward module's params
    ff_expansion_factor: 4

    # Multi-headed Attention Module's params
    self_attention_model: rel_pos # rel_pos or abs_pos
    n_heads: 8 # may need to be lower for smaller d_models
    # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
    att_context_size: [-1, -1] # -1 means unlimited context
    att_context_style: regular # regular or chunked_limited
    xscaling: true # scales up the input embeddings by sqrt(d_model)
    untie_biases: true # unties the biases of the TransformerXL layers
    pos_emb_max_len: 5000

    # Convolution module's params
    conv_kernel_size: 31
    conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
    # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size
    # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0]
    conv_context_size: null

    ### regularization
    dropout: 0.1 # The dropout used in most of the Conformer Modules
    dropout_pre_encoder: 0.1 # The dropout used before the encoder
    dropout_emb: 0.0 # The dropout used for embeddings
    dropout_att: 0.1 # The dropout for multi-headed attention modules

  decoder:
    _target_: nemo.collections.asr.modules.ConvASRDecoder
    feat_in: null
    num_classes: -1
    vocabulary: []

  optim:
    name: adamw
    lr: 0.1
    # optimizer arguments
    betas: [0.9, 0.98]
    # less necessity for weight_decay as we already have large augmentations with SpecAug
    # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
    # weight decay of 0.0 with lr of 2.0 also works fine
    weight_decay: 1e-3

    # scheduler setup
    sched:
      name: NoamAnnealing
      d_model: ${model.encoder.d_model}
      # scheduler config override
      warmup_steps: 10000
      warmup_ratio: null
      min_lr: 1e-6

trainer:
  devices: -1 # number of GPUs, -1 would use all available GPUs
  num_nodes: 1
  max_epochs: 1000
  max_steps: -1 # computed at runtime if not set
  val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
  accelerator: auto
  strategy: ddp
  accumulate_grad_batches: 1
  gradient_clip_val: 0.0
  precision: 16 # Should be set to 16 for O1 and O2 to enable the AMP.
  log_every_n_steps: 20  # Interval of logging.
  enable_progress_bar: True
  resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
  num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
  check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
  sync_batchnorm: true
  enable_checkpointing: False  # Provided by exp_manager
  logger: false  # Provided by exp_manager
  benchmark: false # needs to be false for models with variable-length speech input as it slows down training

exp_manager:
  exp_dir: null
  name: ${name}-lr-${model.optim.lr}-wd-${model.optim.weight_decay}
  create_tensorboard_logger: true
  create_checkpoint_callback: true
  checkpoint_callback_params:
    # in case of multiple validation sets, first one is used
    monitor: "val_wer"
    mode: "min"
    save_top_k: 2
    always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints

  # you need to set these two to True to continue the training
  resume_if_exists: true
  resume_ignore_no_checkpoint: true

  # You may use this section to create a W&B logger
  create_wandb_logger: false
  wandb_logger_kwargs:
    name: null
    project: null
  
hydra:
  run:
    dir: .
  sweep:
    dir: .
    subdir: .

```

**Expected behavior**

Successful fine tuning on our dataset.

**Environment overview (please complete the following information)**

 - Environment location: Docker
 - Method of NeMo install: official Memo docker file

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version

**Additional context**

Add any other context about the problem here.
Example: GPU model
