chunk_content:  remove_columns=common_voice.column_names["train"], num_proc=2)
  Finally, create a DataCollator class to pad the labels in each batch to the maximum length, and replace padding with -100 so they’re ignored by the loss function. Then initialize an instance of the data collator:
  
  
  	Copied
  import torch
  
  from dataclasses import dataclass
  from typing import Any, Dict, List, Union
  
  
  @dataclass
  class DataCollatorSpeechSeq2SeqWithPadding:
      processor: