class_docstr: HiFi-GAN model (https://arxiv.org/abs/2010.05646) that is used to generate audio from mel spectrogram.
class_docstr_tok: ["HiFi-GAN","model","(","https",":","\/\/arxiv.org\/abs\/2010.05646",")","that","is","used","to","generate","audio","from","mel","spectrogram","."]
code: def validation_step(self, batch, batch_idx):
      audio, audio_len, audio_mel, audio_mel_len = self._process_batch(
          batch
      )
  
      audio_pred = self(spec=audio_mel)
  
      if self.input_as_mel:
          gt_mel, gt_mel_len = self.audio_to_melspec_precessor(
              audio, audio_len
          )
      audio_pred_mel, _ = self.audio_to_melspec_precessor(
          audio_pred.squeeze(1), audio_len
      )
      loss_mel = F.l1_loss(audio_mel, audio_pred_mel)
  
      self.log_dict(
          {"val_loss": loss_mel}, on_epoch=True, sync_dist=True
      )
  
      # Plot audio once per epoch
      if (
          self.log_audio
          and batch_idx == 0
          and isinstance(self.logger, WandbLogger)
          and HAVE_WANDB
      ):
          # Perform bias denoising
          pred_denoised = self._bias_denoise(
              audio_pred, audio_mel
          ).squeeze(1)
          pred_denoised_mel, _ = self.audio_to_melspec_precessor(
              pred_denoised, audio_len
          )
  
          clips = []
          specs = []
          for i in range(min(5, audio.shape[0])):
              clips += [
                  wandb.Audio(
                      audio[i, : audio_len[i]].data.cpu().numpy(),
                      caption=f"real audio {i}",
                      sample_rate=self.sample_rate,
                  ),
                  wandb.Audio(
                      audio_pred[i, 0, : audio_len[i]]
                      .data.cpu()
                      .numpy()
                      .astype("float32"),
                      caption=f"generated audio {i}",
                      sample_rate=self.sample_rate,
                  ),
                  wandb.Audio(
                      pred_denoised[i, : audio_len[i]]
                      .data.cpu()
                      .numpy(),
                      caption=f"denoised audio {i}",
                      sample_rate=self.sample_rate,
                  ),
              ]
              specs += [
                  wandb.Image(
                      plot_spectrogram_to_numpy(
                          audio_mel[i, :, : audio_mel_len[i]]
                          .data.cpu()
                          .numpy()
                      ),
                      caption=f"input mel {i}",
                  ),
                  wandb.Image(
                      plot_spectrogram_to_numpy(
                          audio_pred_mel[i, :, : audio_mel_len[i]]
                          .data.cpu()
                          .numpy()
                      ),
                      caption=f"output mel {i}",
                  ),
                  wandb.Image(
                      plot_spectrogram_to_numpy(
                          pred_denoised_mel[i, :, : audio_mel_len[i]]
                          .data.cpu()
                          .numpy()
                      ),
                      caption=f"denoised mel {i}",
                  ),
              ]
              if self.input_as_mel:
                  specs += [
                      wandb.Image(
                          plot_spectrogram_to_numpy(
                              gt_mel[i, :, : audio_mel_len[i]]
                              .data.cpu()
                              .numpy()
                          ),
                          caption=f"gt mel {i}",
                      ),
                  ]
  
          self.logger.experiment.log({"audio": clips, "specs": specs})
  
imports: import itertools
  from pathlib import Path
  import torch
  import torch.nn.functional
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning.loggers.wandb import WandbLogger
  from nemo.collections.tts.losses.hifigan_losses import DiscriminatorLoss, FeatureMatchingLoss, GeneratorLoss
  from nemo.collections.tts.models.base import Vocoder
  from nemo.collections.tts.modules.hifigan_modules import MultiPeriodDiscriminator, MultiScaleDiscriminator
  from nemo.collections.tts.parts.utils.callbacks import LoggingCallback
  from nemo.collections.tts.parts.utils.helpers import get_batch_size, get_num_workers, plot_spectrogram_to_numpy
  from nemo.core.classes import Exportable
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types.elements import AudioSignal, MelSpectrogramType
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.core.optim.lr_scheduler import compute_max_steps, prepare_lr_scheduler
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 10 for calling the HifiGanModel obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) without return types