

from numpy import exp, pi, sqrt
def     UpperCAmelCase_       (						_A				,		_A = 0.0				,		_A = 1.0    ):





     '''simple docstring'''




     return 1 / sqrt(2 * pi * sigma**2    ) * exp(-((x - mu) ** 2) / (2 * sigma**2)    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()





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     UpperCAmelCase_       (						_A				,		_A    ):





     '''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(_A    )

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

     # 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(_A				,		'''w'''    ) as p, open(_A				,		'''w'''    ) as t:
               # mapping function to write output
               def write_to_file(_A				,		_A    ):
                    p.write(F'''{i}''' + '''\n'''    )
                    p.write(batch['''prediction'''] + '''\n'''    )
                    t.write(F'''{i}''' + '''\n'''    )
                    t.write(batch['''target'''] + '''\n'''    )

               result.map(_A				,		with_indices=_A    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE__   =   re.sub(_A				,		''''''				,		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(_A    )    )

     return text
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   load_dataset(args.dataset				,		args.config				,		split=args.split				,		use_auth_token=_A    )

     # 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=_A    )    )

     # 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(_A    ):
          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(_A				,		remove_columns=dataset.column_names    )

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  List[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.''',
       )
       _SCREAMING_SNAKE_CASE					:  List[Any]             =      parser.parse_args()

       main(args)


