text: #!/usr/bin/env python3
  import argparse
  import re
  import unicodedata
  from typing import Dict
  
  import torch
  from datasets import Audio, Dataset, load_dataset, load_metric
  
  from transformers import AutoFeatureExtractor, pipeline
  
  
  def log_results(result: Dataset, args: Dict[str, str]):
      """DO NOT CHANGE. This function computes and logs the result metrics."""
  
      log_outputs = args.log_outputs
      dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
  
      # load metric
      wer = load_metric("wer")
      cer = load_metric("cer")
  
      # compute metrics
      wer_result = wer.compute(
          references=result["target"], predictions=result["prediction"]
      )
      cer_result = cer.compute(
          references=result["target"], predictions=result["prediction"]
      )
  
      # print & log results
      result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
      print(result_str)
  
      with open(f"{dataset_id}_eval_results.txt", "w") as f:
          f.write(result_str)
  
      # log all results in text file. Possibly interesting for analysis
      if log_outputs is not None:
          pred_file = f"log_{dataset_id}_predictions.txt"
          target_file = f"log_{dataset_id}_targets.txt"
  
          with open(pred_file, "w") as p, open(target_file, "w") as t:
  
              # mapping function to write output
              def write_to_file(batch, i):
                  p.write(f"{i}" + "\n")
                  p.write(batch["prediction"] + "\n")
                  t.write(f"{i}" + "\n")
                  t.write(batch["target"] + "\n")
  
              result.map(write_to_file, with_indices=True)
  
  
  def normalize_text(text: str) -> str:
      """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
  
      chars_to_ignore_regex = """[\,\?\.\!\-\;\:\"\“\%\‘\”\�\—\’\…\–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
      text = re.sub(chars_to_ignore_regex, "", text.lower())
      text = unicodedata.normalize("NFKC", text)
  
      # In addition, we can normalize the target text, e.g. removing new lines characters etc...
      # note that order is important here!
      token_sequences_to_ignore = ["\n\n", "\n", "   ", "  "]
  
      for t in token_sequences_to_ignore:
          text = " ".join(text.split(t))
  
      return text
  
  
  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)
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
  
      parser.add_argument(
          "--model_id",
          type=str,
          required=True,
          help="Model identifier. Should be loadable with 🤗 Transformers",
      )
      parser.add_argument(
          "--dataset",
          type=str,
          required=True,
          help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
      )
      parser.add_argument(
          "--config",
          type=str,
          required=True,
          help="Config of the dataset. *E.g.* `'en'`  for Common Voice",
      )
      parser.add_argument(
          "--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`"
      )
      parser.add_argument(
          "--chunk_length_s",
          type=float,
          default=None,
          help="Chunk length in seconds. Defaults to 5 seconds.",
      )
      parser.add_argument(
          "--stride_length_s",
          type=float,
          default=None,
          help="Stride of the audio chunks. Defaults to 1 second.",
      )
      parser.add_argument(
          "--log_outputs",
          action="store_true",
          help="If defined, write outputs to log file for analysis.",
      )
      parser.add_argument(
          "--device",
          type=int,
          default=None,
          help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
      )
      args = parser.parse_args()
  
      main(args)
  