chunk_content:  because int8 training requires autocasting. Then, pass a batch of examples to the model to evaluate. Get the decoded predictions and labels, and add them as a batch to the WER metric before calling compute to get the final WER score:
  
  
  	Copied
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  import numpy as np
  import gc
  
  eval_dataloader = DataLoader(common_voice["test"], batch_size=8, collate_fn=data_collator)
  
  model.eval()
  for st