fine tuning in korean conformer-transducer
State: closed | Pull request: no
Labels: name: feature

How can I get a space(blank) it self in tokenizer vocab? 
I am trying to finetune the stt_en_conformer_transducer_large model with grapheme in korean. I changed some code to load labels with chardataset. 

in finetuning the prediction log shows blank in a unk tag. (??)

```
[NeMo I 2022-05-23 06:02:11 rnnt_wer_bpe:231] reference :그래서 ⁇ 내가 ⁇ 오글 ⁇ 막 ⁇ 이랬는데
[NeMo I 2022-05-23 06:02:11 rnnt_wer_bpe:232] predicted :그래서 ⁇ 다 ⁇ 얼 ⁇ 말인데
```

this is my tokenizer vocab. 
```
<unk>	0
<b>	0
▁	-0.693147
ᄌ	-4.18965
ᄀ	-4.8828
ᄁ	-4.8828
ᄂ	-4.8828
ᄃ	-4.8828
ᄄ	-4.8828
ᄅ	-4.8828
ᄆ	-4.8828
ᄇ	-4.8828
ᄈ	-4.8828
ᄉ	-4.8828
ᄊ	-4.8828
ᄋ	-4.8828
ᄍ	-4.8828
ᄎ	-4.8828
ᄏ	-4.8828
ᄐ	-4.8828
ᄑ	-4.8828
ᄒ	-4.8828
ᅡ	-4.8828
ᅢ	-4.8828
ᅣ	-4.8828
ᅤ	-4.8828
ᅥ	-4.8828
ᅦ	-4.8828
ᅧ	-4.8828
ᅨ	-4.8828
ᅩ	-4.8828
ᅪ	-4.8828
ᅫ	-4.8828
ᅬ	-4.8828
ᅭ	-4.8828
ᅮ	-4.8828
ᅯ	-4.8828
ᅰ	-4.8828
ᅱ	-4.8828
ᅲ	-4.8828
ᅳ	-4.8828
ᅴ	-4.8828
ᅵ	-4.8828
ᆨ	-4.8828
ᆩ	-4.8828
ᆪ	-4.8828
ᆫ	-4.8828
ᆬ	-4.8828
ᆭ	-4.8828
ᆮ	-4.8828
ᆯ	-4.8828
ᆰ	-4.8828
ᆱ	-4.8828
ᆲ	-4.8828
ᆴ	-4.8828
ᆶ	-4.8828
ᆷ	-4.8828
ᆸ	-4.8828
ᆹ	-4.8828
ᆺ	-4.8828
ᆻ	-4.8828
ᆼ	-4.8828
ᆽ	-4.8828
ᆾ	-4.8828
ᆿ	-4.8828
ᇀ	-4.8828
ᇁ	-4.8828
ᇂ	-4.8828
```


this is my config.
```
name: "Conformer-Transducer-BPE"
init_from_nemo_model:
  model0:
    path: ??
    exclude: ["joint", "decoder"]
    include: ["encoder"]

model:
  sample_rate: &sample_rate 16000
  compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
  log_prediction: true # enables logging sample predictions in the output during training
  skip_nan_grad: false
  change_vocab: True # added
  use_cer: True # added

  model_defaults:
    enc_hidden: ${model.encoder.d_model}
    pred_hidden: 640
    joint_hidden: 640

  train_ds:
    manifest_filepath: ???
    sample_rate: 16000 #${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: True
    trim_silence: false
    max_duration: 16.7 # 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
    use_char: true
    unk_index: 0

  validation_ds:
    manifest_filepath: ???
    sample_rate: 16000 #${model.sample_rate}
    batch_size: 16
    shuffle: false
    num_workers: 8
    pin_memory: true
    use_char: true
    unk_index: 0
    
    # use_start_end_token: True

  test_ds:
    manifest_filepath: null
    sample_rate: 16000 #${model.sample_rate}
    batch_size: 16
    shuffle: false
    num_workers: 8
    pin_memory: true
    use_char: true

    # use_start_end_token: True

  # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
  tokenizer:
    dir: ???  # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
    type: bpe  # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)

  preprocessor:
    _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
    sample_rate: *sample_rate
    normalize: "per_feature"
    window_size: 0.025
    window_stride: 0.01
    window: "hann"
    features: 80
    n_fft: 512
    frame_splicing: 1
    dither: 0.00001
    pad_to: 0

  spec_augment:
    _target_: nemo.collections.asr.modules.SpectrogramAugmentation
    freq_masks: 2 # set to zero to disable it
    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: 17
    d_model: 512

    # Sub-sampling params
    subsampling: striding # vggnet or striding
    subsampling_factor: 4 # must be power of 2
    subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model

    # 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
    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

    ### regularization
    dropout: 0.1 # The dropout used in most of the Conformer Modules
    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.RNNTDecoder
    normalization_mode: null # Currently only null is supported for export.
    random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf
    blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference.
    
    prednet:
      pred_hidden: ${model.model_defaults.pred_hidden}
      pred_rnn_layers: 1
      t_max: null
      dropout: 0.1

  joint:
    _target_: nemo.collections.asr.modules.RNNTJoint
    log_softmax: null  # 'null' would set it automatically according to CPU/GPU device
    preserve_memory: false  # dramatically slows down training, but might preserve some memory

    # Fuses the computation of prediction net + joint net + loss + WER calculation
    # to be run on sub-batches of size `fused_batch_size`.
    # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size.
    # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss.
    # Using small values here will preserve a lot of memory during training, but will make training slower as well.
    # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1.
    # However, to preserve memory, this ratio can be 1:8 or even 1:16.
    # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow.
    fuse_loss_wer: true
    fused_batch_size: 16

    jointnet:
      joint_hidden: ${model.model_defaults.joint_hidden}
      activation: "relu"
      dropout: 0.1

  decoding:
    strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd.

    # greedy strategy config
    greedy:
      max_symbols: 30

    # beam strategy config
    beam:
      beam_size: 2
      return_best_hypothesis: False
      score_norm: true
      tsd_max_sym_exp: 50  # for Time Synchronous Decoding
      alsd_max_target_len: 2.0  # for Alignment-Length Synchronous Decoding

  loss:
    loss_name: "default"

    warprnnt_numba_kwargs:
      # FastEmit regularization: https://arxiv.org/abs/2010.11148
      # You may enable FastEmit to reduce the latency of the model for streaming
      fastemit_lambda: 0.0  # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start.
      clamp: -1.0  # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only.

  # Adds Gaussian noise to the gradients of the decoder to avoid overfitting
  variational_noise:
    start_step: 0
    std: 0.0

  optim:
    name: adamw
    lr: 5.0
    # optimizer arguments
    betas: [0.9, 0.98]
    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: 500
  max_steps: null # 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: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
  log_every_n_steps: 50  # Interval of logging.
  progress_bar_refresh_rate: 10
  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


exp_manager:
  exp_dir: null
  name: ${name}
  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: 1
    always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
  resume_if_exists: False
  resume_ignore_no_checkpoint: false

  create_wandb_logger: false
  wandb_logger_kwargs:
    name: null
    project: null
```