



"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A:   int								=		logging.get_logger(__name__)



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   str						=						['input_features']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=80      ,     _SCREAMING_SNAKE_CASE=16000      ,     _SCREAMING_SNAKE_CASE=160      ,     _SCREAMING_SNAKE_CASE=30      ,     _SCREAMING_SNAKE_CASE=400      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=False      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''simple docstring'''




    super().__init__(
        feature_size=_SCREAMING_SNAKE_CASE      ,     sampling_rate=_SCREAMING_SNAKE_CASE      ,     padding_value=_SCREAMING_SNAKE_CASE      ,     return_attention_mask=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )
    UpperCAmelCase	:					int	=	n_fft
    UpperCAmelCase	:					str	=	hop_length
    UpperCAmelCase	:					Tuple	=	chunk_length
    UpperCAmelCase	:					Optional[Any]	=	chunk_length * sampling_rate
    UpperCAmelCase	:					str	=	self.n_samples // hop_length
    UpperCAmelCase	:					Any	=	sampling_rate
    UpperCAmelCase	:					List[Any]	=	mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2      ,     num_mel_filters=_SCREAMING_SNAKE_CASE      ,     min_frequency=0.0      ,     max_frequency=8000.0      ,     sampling_rate=_SCREAMING_SNAKE_CASE      ,     norm="""slaney"""      ,     mel_scale="""slaney"""      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      np.ndarray:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	spectrogram(
        _SCREAMING_SNAKE_CASE      ,     window_function(self.n_fft      ,     """hann"""						)      ,     frame_length=self.n_fft      ,     hop_length=self.hop_length      ,     power=2.0      ,     mel_filters=self.mel_filters      ,     log_mel="""log10"""      ,     )
    UpperCAmelCase	:					List[str]	=	log_spec[:, :-1]
    UpperCAmelCase	:					Any	=	np.maximum(_SCREAMING_SNAKE_CASE      ,     log_spec.max() - 8.0						)
    UpperCAmelCase	:					List[Any]	=	(log_spec + 4.0) / 4.0
    return log_spec

   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 	SCREAMING_SNAKE_CASE     (   _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = 0.0						)  ->      List[np.ndarray]:





    '''simple docstring'''




    if attention_mask is not None:
     UpperCAmelCase	:					Optional[Any]	=	np.array(_SCREAMING_SNAKE_CASE      ,     np.intaa						)
     UpperCAmelCase	:					List[str]	=	[]

     for vector, length in zip(_SCREAMING_SNAKE_CASE      ,     attention_mask.sum(-1						)						):
      UpperCAmelCase	:					str	=	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7						)
      if length < normed_slice.shape[0]:
       UpperCAmelCase	:					Optional[Any]	=	padding_value

      normed_input_values.append(_SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					Dict	=	[(x - x.mean()) / np.sqrt(x.var() + 1E-7						) for x in input_values]

    return normed_input_values

   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = True      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = "max_length"      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      BatchFeature:





    '''simple docstring'''




    if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
      raise ValueError(
          F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
          F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
          F" was sampled with {self.sampling_rate} and not {sampling_rate}."						)
    else:
     logger.warning(
         """It is strongly recommended to pass the `sampling_rate` argument to this function. """
         """Failing to do so can result in silent errors that might be hard to debug."""						)

    UpperCAmelCase	:					Tuple	=	isinstance(_SCREAMING_SNAKE_CASE      ,     np.ndarray						) and len(raw_speech.shape						) > 1
    if is_batched_numpy and len(raw_speech.shape						) > 2:
     raise ValueError(F"Only mono-channel audio is supported for input to {self}"						)
    UpperCAmelCase	:					Optional[Any]	=	is_batched_numpy or (
        isinstance(_SCREAMING_SNAKE_CASE      ,     (list, tuple)						) and (isinstance(raw_speech[0]      ,     (np.ndarray, tuple, list)						))
    )

    if is_batched:
     UpperCAmelCase	:					Any	=	[np.asarray([speech]      ,     dtype=np.floataa						).T for speech in raw_speech]
    elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE      ,     np.ndarray						):
     UpperCAmelCase	:					List[str]	=	np.asarray(_SCREAMING_SNAKE_CASE      ,     dtype=np.floataa						)
    elif isinstance(_SCREAMING_SNAKE_CASE      ,     np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
     UpperCAmelCase	:					List[Any]	=	raw_speech.astype(np.floataa						)

    # always return batch
    if not is_batched:
     UpperCAmelCase	:					Optional[Any]	=	[np.asarray([raw_speech]						).T]

    UpperCAmelCase	:					Union[str, Any]	=	BatchFeature({"""input_features""": raw_speech}						)

    # convert into correct format for padding

    UpperCAmelCase	:					Optional[int]	=	self.pad(
        _SCREAMING_SNAKE_CASE      ,     padding=_SCREAMING_SNAKE_CASE      ,     max_length=max_length if max_length else self.n_samples      ,     truncation=_SCREAMING_SNAKE_CASE      ,     pad_to_multiple_of=_SCREAMING_SNAKE_CASE      ,     return_attention_mask=return_attention_mask or do_normalize      ,     )

    # zero-mean and unit-variance normalization
    if do_normalize:
     UpperCAmelCase	:					Any	=	self.zero_mean_unit_var_norm(
         padded_inputs["""input_features"""]      ,     attention_mask=padded_inputs["""attention_mask"""]      ,     padding_value=self.padding_value      ,     )
     UpperCAmelCase	:					Tuple	=	np.stack(padded_inputs["""input_features"""]      ,     axis=0						)

    # make sure list is in array format
    UpperCAmelCase	:					Optional[int]	=	padded_inputs.get("""input_features"""						).transpose(2      ,     0      ,     1						)

    UpperCAmelCase	:					int	=	[self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE						) for waveform in input_features[0]]

    if isinstance(input_features[0]      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Any	=	[np.asarray(_SCREAMING_SNAKE_CASE      ,     dtype=np.floataa						) for feature in input_features]
    else:
     UpperCAmelCase	:					Dict	=	input_features

    if return_attention_mask:
     # rescale from sample (48000) to feature (3000)
     UpperCAmelCase	:					Tuple	=	padded_inputs["""attention_mask"""][:, :: self.hop_length]

    if return_tensors is not None:
     UpperCAmelCase	:					str	=	padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE						)

    return padded_inputs




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	copy.deepcopy(self.__dict__						)
    UpperCAmelCase	:					Optional[Any]	=	self.__class__.__name__
    if "mel_filters" in output:
     del output["mel_filters"]
    return output





"""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       _snake_case						(						UpperCamelCase						:		Dataset			,		UpperCamelCase						:		Dict[str, str]						):
 UpperCAmelCase	:					Any	=	args.log_outputs
 UpperCAmelCase	:					Any	=	"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

 # load metric
 UpperCAmelCase	:					List[Any]	=	load_metric("""wer"""						)
 UpperCAmelCase	:					Any	=	load_metric("""cer"""						)

 # compute metrics
 UpperCAmelCase	:					int	=	wer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)
 UpperCAmelCase	:					str	=	cer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)

 # print & log results
 UpperCAmelCase	:					Tuple	=	F"WER: {wer_result}\nCER: {cer_result}"
 print(UpperCamelCase						)

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

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

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

   result.map(UpperCamelCase			,		with_indices=UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					List[str]	=	"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 UpperCAmelCase	:					Dict	=	re.sub(UpperCamelCase			,		""""""			,		text.lower()						)

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

 for t in token_sequences_to_ignore:
  UpperCAmelCase	:					Optional[Any]	=	""" """.join(text.split(UpperCamelCase						)						)

 return text

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 # load dataset
 UpperCAmelCase	:					Union[str, Any]	=	load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=UpperCamelCase						)

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

 # load processor
 UpperCAmelCase	:					Optional[int]	=	AutoFeatureExtractor.from_pretrained(args.model_id						)
 UpperCAmelCase	:					Any	=	feature_extractor.sampling_rate

 # resample audio
 UpperCAmelCase	:					List[str]	=	dataset.cast_column("""audio"""			,		Audio(sampling_rate=UpperCamelCase						)						)

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

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

  UpperCAmelCase	:					Tuple	=	prediction["""text"""]
  UpperCAmelCase	:					List[str]	=	normalize_text(batch["""sentence"""]						)
  return batch

 # run inference on all examples
 UpperCAmelCase	:					int	=	dataset.map(UpperCamelCase			,		remove_columns=dataset.column_names						)

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


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

      main(args)
