code: def main():
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
  
      parser = HfArgumentParser(
          (ModelArguments, DataTrainingArguments, TrainingArguments)
      )
  
      (
          model_args,
          data_args,
          training_args,
      ) = parser.parse_args_into_dataclasses()
      configure_logger(model_args, training_args)
  
      orthography = Orthography.from_name(data_args.orthography.lower())
      processor = orthography.create_processor(model_args)
      model = Wav2Vec2ForCTC.from_pretrained(
          model_args.model_name_or_path,
          cache_dir=model_args.cache_dir,
          gradient_checkpointing=training_args.gradient_checkpointing,
          vocab_size=len(processor.tokenizer),
      )
  
      train_dataset = datasets.load_dataset(
          data_args.dataset_name,
          data_args.dataset_config_name,
          split=data_args.train_split_name,
      )
      val_dataset = datasets.load_dataset(
          data_args.dataset_name,
          data_args.dataset_config_name,
          split=data_args.validation_split_name,
      )
  
      wer_metric = datasets.load_metric("wer")
      target_sr = (
          processor.feature_extractor.sampling_rate
          if data_args.target_feature_extractor_sampling_rate
          else None
      )
      vocabulary_chars_str = "".join(
          t
          for t in processor.tokenizer.get_vocab().keys()
          if len(t) == 1
      )
      vocabulary_text_cleaner = re.compile(  # remove characters not in vocabulary
          f"[^\s{re.escape(vocabulary_chars_str)}]",  # allow space in addition to chars in vocabulary
          flags=re.IGNORECASE
          if processor.tokenizer.do_lower_case
          else 0,
      )
      text_updates = []
  
      def prepare_example(
          example,
      ):  # TODO(elgeish) make use of multiprocessing?
          example["speech"], example["sampling_rate"] = librosa.load(
              example[data_args.speech_file_column], sr=target_sr
          )
          if data_args.max_duration_in_seconds is not None:
              example["duration_in_seconds"] = (
                  len(example["speech"]) / example["sampling_rate"]
              )
          # Normalize and clean up text; order matters!
          updated_text = orthography.preprocess_for_training(
              example[data_args.target_text_column]
          )
          updated_text = vocabulary_text_cleaner.sub("", updated_text)
          if updated_text != example[data_args.target_text_column]:
              text_updates.append(
                  (example[data_args.target_text_column], updated_text)
              )
              example[data_args.target_text_column] = updated_text
          return example
  
      train_dataset = train_dataset.map(
          prepare_example, remove_columns=[data_args.speech_file_column]
      )
      val_dataset = val_dataset.map(
          prepare_example, remove_columns=[data_args.speech_file_column]
      )
  
      if data_args.max_duration_in_seconds is not None:
  
          def filter_by_max_duration(example):
              return (
                  example["duration_in_seconds"]
                  <= data_args.max_duration_in_seconds
              )
  
          old_train_size = len(train_dataset)
          old_val_size = len(val_dataset)
          train_dataset = train_dataset.filter(
              filter_by_max_duration,
              remove_columns=["duration_in_seconds"],
          )
          val_dataset = val_dataset.filter(
              filter_by_max_duration,
              remove_columns=["duration_in_seconds"],
          )
          if len(train_dataset) > old_train_size:
              logger.warning(
                  f"Filtered out {len(train_dataset) - old_train_size} train example(s) longer than"
                  f" {data_args.max_duration_in_seconds} second(s)."
              )
          if len(val_dataset) > old_val_size:
              logger.warning(
                  f"Filtered out {len(val_dataset) - old_val_size} validation example(s) longer than"
                  f" {data_args.max_duration_in_seconds} second(s)."
              )
      logger.info(
          f"Split sizes: {len(train_dataset)} train and {len(val_dataset)} validation."
      )
  
      logger.warning(
          f"Updated {len(text_updates)} transcript(s) using '{data_args.orthography}' orthography rules."
      )
      if logger.isEnabledFor(logging.DEBUG):
          for original_text, updated_text in text_updates:
              logger.debug(
                  f'Updated text: "{original_text}" -> "{updated_text}"'
              )
      text_updates = None
  
      def prepare_dataset(batch):
          # check that all files have the correct sampling rate
          assert (
              len(set(batch["sampling_rate"])) == 1
          ), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."
  
          processed_batch = processor(
              audio=batch["speech"],
              text=batch[data_args.target_text_column],
              sampling_rate=batch["sampling_rate"][0],
          )
          batch.update(processed_batch)
          return batch
  
      train_dataset = train_dataset.map(
          prepare_dataset,
          batch_size=training_args.per_device_train_batch_size,
          batched=True,
          num_proc=data_args.preprocessing_num_workers,
      )
      val_dataset = val_dataset.map(
          prepare_dataset,
          batch_size=training_args.per_device_train_batch_size,
          batched=True,
          num_proc=data_args.preprocessing_num_workers,
      )
  
      data_collator = DataCollatorCTCWithPadding(
          processor=processor, padding=True
      )
  
      def compute_metrics(pred):
          pred_logits = pred.predictions
          pred_ids = np.argmax(pred_logits, axis=-1)
  
          pred.label_ids[
              pred.label_ids == -100
          ] = processor.tokenizer.pad_token_id
  
          pred_str = processor.batch_decode(pred_ids)
          # we do not want to group tokens when computing the metrics
          label_str = processor.batch_decode(
              pred.label_ids, group_tokens=False
          )
          if logger.isEnabledFor(logging.DEBUG):
              for reference, predicted in zip(label_str, pred_str):
                  logger.debug(f'reference: "{reference}"')
                  logger.debug(f'predicted: "{predicted}"')
                  if orthography.untransliterator is not None:
                      logger.debug(
                          f'reference (untransliterated): "{orthography.untransliterator(reference)}"'
                      )
                      logger.debug(
                          f'predicted (untransliterated): "{orthography.untransliterator(predicted)}"'
                      )
  
          wer = wer_metric.compute(
              predictions=pred_str, references=label_str
          )
  
          return {"wer": wer}
  
      if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()
  
      trainer = CTCTrainer(
          model=model,
          data_collator=data_collator,
          args=training_args,
          compute_metrics=compute_metrics,
          train_dataset=train_dataset,
          eval_dataset=val_dataset,
          tokenizer=processor.feature_extractor,
      )
  
      trainer.train()
  
imports: import logging
  import pathlib
  import re
  import sys
  from dataclasses import dataclass, field
  from typing import Any, Callable, Dict, List, Optional, Set, Union
  import datasets
  import numpy
  import torch
  from packaging import version
  from torch import nn
  import librosa
  from lang_trans import arabic
  from transformers import HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC, Wav2Vec2Processor, is_apex_available, trainer_utils
label_desc: Use image node_id 2 for calling a global function with example usage: main() and returns: example, batch, dict, unknown