
'''simple docstring'''



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				args.log_outputs
   _SCREAMING_SNAKE_CASE      =				"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

   # load metric
   _SCREAMING_SNAKE_CASE      =				load_metric("""wer"""						)
   _SCREAMING_SNAKE_CASE      =				load_metric("""cer"""						)

   # compute metrics
   _SCREAMING_SNAKE_CASE      =				wer.compute(references=result["""target"""]    ,      predictions=result["""prediction"""]						)
   _SCREAMING_SNAKE_CASE      =				cer.compute(references=result["""target"""]    ,      predictions=result["""prediction"""]						)

   # print & log results
   _SCREAMING_SNAKE_CASE      =				F"WER: {wer_result}\nCER: {cer_result}"
   print(SCREAMING_SNAKE_CASE_						)

   with open(F"{dataset_id}_eval_results.txt"    ,      """w"""						) as f:
      f.write(SCREAMING_SNAKE_CASE_						)

   # log all results in text file. Possibly interesting for analysis
   if log_outputs is not None:
      _SCREAMING_SNAKE_CASE      =				F"log_{dataset_id}_predictions.txt"
      _SCREAMING_SNAKE_CASE      =				F"log_{dataset_id}_targets.txt"

      with open(SCREAMING_SNAKE_CASE_    ,      """w"""						) as p, open(SCREAMING_SNAKE_CASE_    ,      """w"""						) as t:
         # mapping function to write output
         def write_to_file(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
            p.write(F"{i}" + """\n"""						)
            p.write(batch["""prediction"""] + """\n"""						)
            t.write(F"{i}" + """\n"""						)
            t.write(batch["""target"""] + """\n"""						)

         result.map(SCREAMING_SNAKE_CASE_    ,      with_indices=SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

   _SCREAMING_SNAKE_CASE      =				re.sub(SCREAMING_SNAKE_CASE_    ,      """"""    ,      text.lower()						)

   # In addition, we can normalize the target text, e.g. removing new lines characters etc...
   # note that order is important here!
   _SCREAMING_SNAKE_CASE      =				["""\n\n""", """\n""", """   """, """  """]

   for t in token_sequences_to_ignore:
      _SCREAMING_SNAKE_CASE      =				""" """.join(text.split(SCREAMING_SNAKE_CASE_						)						)

   return text
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   # load dataset
   _SCREAMING_SNAKE_CASE      =				load_dataset(args.dataset    ,      args.config    ,      split=args.split    ,      use_auth_token=SCREAMING_SNAKE_CASE_						)

   # for testing: only process the first two examples as a test
   # dataset = dataset.select(range(10))

   # load processor
   _SCREAMING_SNAKE_CASE      =				AutoFeatureExtractor.from_pretrained(args.model_id						)
   _SCREAMING_SNAKE_CASE      =				feature_extractor.sampling_rate

   # resample audio
   _SCREAMING_SNAKE_CASE      =				dataset.cast_column("""audio"""    ,      Audio(sampling_rate=SCREAMING_SNAKE_CASE_						)						)

   # load eval pipeline
   if args.device is None:
      _SCREAMING_SNAKE_CASE      =				0 if torch.cuda.is_available() else -1
   _SCREAMING_SNAKE_CASE      =				pipeline("""automatic-speech-recognition"""    ,      model=args.model_id    ,      device=args.device						)

   # map function to decode audio
   def map_to_pred(SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				asr(
          batch["""audio"""]["""array"""]    ,      chunk_length_s=args.chunk_length_s    ,      stride_length_s=args.stride_length_s						)

      _SCREAMING_SNAKE_CASE      =				prediction["""text"""]
      _SCREAMING_SNAKE_CASE      =				normalize_text(batch["""sentence"""]						)
      return batch

   # run inference on all examples
   _SCREAMING_SNAKE_CASE      =				dataset.map(SCREAMING_SNAKE_CASE_    ,      remove_columns=dataset.column_names						)

   # compute and log_results
   # do not change function below
   log_results(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
     UpperCamelCase__      :		str       							=  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.",
     )
     UpperCamelCase__      :		List[str]       							=  parser.parse_args()

     main(args)








'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   return "".join([hex(SCREAMING_SNAKE_CASE_						)[2:].zfill(2						).upper() for byte in list(SCREAMING_SNAKE_CASE_						)]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     bytes:




   """simple docstring"""
   # Check data validity, following RFC3548
   # https://www.ietf.org/rfc/rfc3548.txt
   if (len(SCREAMING_SNAKE_CASE_						) % 2) != 0:
      raise ValueError(
          """Base16 encoded data is invalid:
Data does not have an even number of hex digits."""						)
   # Check the character set - the standard base16 alphabet
   # is uppercase according to RFC3548 section 6
   if not set(SCREAMING_SNAKE_CASE_						) <= set("""0123456789ABCDEF"""						):
      raise ValueError(
          """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""						)
   # For every two hexadecimal digits (= a byte), turn it into an integer.
   # Then, string the result together into bytes, and return it.
   return bytes(int(data[i] + data[i + 1]    ,      16						) for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)    ,      2						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






