code: def main(args):
      # load dataset
      dataset = load_dataset(
          args.dataset,
          args.config,
          split=args.split,
          use_auth_token=True,
      )
  
      # for testing: only process the first two examples as a test
      # dataset = dataset.select(range(10))
  
      # load processor
      feature_extractor = AutoFeatureExtractor.from_pretrained(
          args.model_id
      )
      sampling_rate = feature_extractor.sampling_rate
  
      # resample audio
      dataset = dataset.cast_column(
          "audio", Audio(sampling_rate=sampling_rate)
      )
  
      # load eval pipeline
      if args.device is None:
          args.device = 0 if torch.cuda.is_available() else -1
      asr = pipeline(
          "automatic-speech-recognition",
          model=args.model_id,
          device=args.device,
      )
  
      # map function to decode audio
      def map_to_pred(batch):
          prediction = asr(
              batch["audio"]["array"],
              chunk_length_s=args.chunk_length_s,
              stride_length_s=args.stride_length_s,
          )
  
          batch["prediction"] = prediction["text"]
          batch["target"] = normalize_text(batch["sentence"])
          return batch
  
      # run inference on all examples
      result = dataset.map(
          map_to_pred, remove_columns=dataset.column_names
      )
  
      # compute and log_results
      # do not change function below
      log_results(result, args)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/robust-speech-event/eval.py
imports: import argparse
  import re
  from typing import Dict
  import torch
  from datasets import Audio, Dataset, load_dataset, load_metric
  from transformers import AutoFeatureExtractor, pipeline
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) and returns: batch