class_docstr: CTC-based grapheme-to-phoneme model.
class_docstr_tok: ["CTC-based","grapheme-to-phoneme","model","."]
code: def validation_step(
      self, batch, batch_idx, dataloader_idx=0, split="val"
  ):
      (
          input_ids,
          attention_mask,
          input_len,
          targets,
          target_lengths,
      ) = batch
  
      log_probs, greedy_predictions, encoded_len = self.forward(
          input_ids=input_ids,
          attention_mask=attention_mask,
          input_len=input_len,
      )
      val_loss = self.loss(
          log_probs=log_probs,
          targets=targets,
          input_lengths=encoded_len,
          target_lengths=target_lengths,
      )
  
      self.wer.update(
          predictions=log_probs,
          targets=targets,
          targets_lengths=target_lengths,
          predictions_lengths=encoded_len,
      )
      wer, wer_num, wer_denom = self.wer.compute()
      self.wer.reset()
  
      self.per.update(
          predictions=log_probs,
          targets=targets,
          targets_lengths=target_lengths,
          predictions_lengths=encoded_len,
      )
      per, per_num, per_denom = self.per.compute()
      self.per.reset()
  
      self.log(f"{split}_loss", val_loss)
      loss = {
          f"{split}_loss": val_loss,
          f"{split}_wer_num": wer_num,
          f"{split}_wer_denom": wer_denom,
          f"{split}_wer": wer,
          f"{split}_per_num": per_num,
          f"{split}_per_denom": per_denom,
          f"{split}_per": per,
      }
  
      if split == "val":
          if (
              type(self.trainer.val_dataloaders) == list
              and len(self.trainer.val_dataloaders) > 1
          ):
              self.validation_step_outputs[dataloader_idx].append(loss)
          else:
              self.validation_step_outputs.append(loss)
      elif split == "test":
          if (
              type(self.trainer.test_dataloaders) == list
              and len(self.trainer.test_dataloaders) > 1
          ):
              self.test_step_outputs[dataloader_idx].append(loss)
          else:
              self.test_step_outputs.append(loss)
  
      return loss
  
imports: import os
  import string
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Union
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from transformers import AutoConfig, AutoModel, AutoTokenizer
  from nemo.collections.tts.g2p.data.ctc import CTCG2PBPEDataset
  from nemo.collections.tts.models.base import G2PModel
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 7 for calling the CTCG2PModel obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx, dataloader_idx, split) and returns: loss