





"""simple docstring"""
from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
       import importlib_metadata
else:
       import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
__UpperCamelCase    :	Tuple						=     '\\n'

__UpperCamelCase    :	Any						=     '\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

__UpperCamelCase    :	Optional[Any]						=     '\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

__UpperCamelCase    :	Tuple						=     ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
__UpperCamelCase    :	Dict						=     ''

try:
       from jiwer import transforms as tr

       __UpperCamelCase    :	Union[str, Any]						=     True
except ImportError:
       __UpperCamelCase    :	List[str]						=     False

if _jiwer_available and version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):







       class 			a       (      tr.AbstractTransform     ):



        def __init__(    self							,					_snake_case = " "    ):







         """simple docstring"""






         lowerCAmelCase													=   sentence_delimiter



        def       UpperCamelCase__     (    self							,					_snake_case    ):







         """simple docstring"""






         return list(A_    )



        def       UpperCamelCase__     (    self							,					_snake_case    ):







         """simple docstring"""






         lowerCAmelCase													=   []
         for sent_idx, sentence in enumerate(A_    ):
          chars.extend(self.process_string(A_    )    )
          if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(A_    ) - 1:
           chars.append(self.sentence_delimiter    )
         return chars


       __UpperCamelCase    :	List[Any]						=     tr.Compose(
           [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
       )
elif _jiwer_available:
       __UpperCamelCase    :	Tuple						=     tr.Compose(
           [
               tr.RemoveMultipleSpaces(),
               tr.Strip(),
               tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
               tr.ReduceToListOfListOfChars(),
           ]
       )
else:
       __UpperCamelCase    :	Dict						=     None

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						str     ,    _UpperCAmelCase				:						Union[str, Any] ):
 return float((preds == labels).mean() )

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Dict     ,    _UpperCAmelCase				:						List[str] ):
 return {
     "f1": float(fa_score(y_true=lowercase__     ,    y_pred=lowercase__     ,    average='macro' ) ),
     "accuracy": simple_accuracy(lowercase__     ,    lowercase__ ),
 }

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						str     ,    _UpperCAmelCase				:						Optional[Any]     ,    _UpperCAmelCase				:						List[str]="exp"     ,    _UpperCAmelCase				:						Dict=None     ,    _UpperCAmelCase				:						List[Any]=False     ,    _UpperCAmelCase				:						List[Any]=False     ,    _UpperCAmelCase				:						List[str]=None     ,    _UpperCAmelCase				:						Any=False     ,    ):
 # xtreme-s can only have one label
 lowerCAmelCase													=   [[label] for label in labels]
 lowerCAmelCase													=   list(lowercase__ )
 try:
  import sacrebleu as scb
 except ImportError:
  raise ValueError(
      'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
      'You can install it via `pip install sacrebleu`.' )

 if version.parse(scb.__version__ ) < version.parse('1.4.12' ):
  raise ImportWarning(
      'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
      'You can install it with `pip install \"sacrebleu>=1.4.12\"`.' )

 lowerCAmelCase													=   len(labels[0] )
 if any(len(lowercase__ ) != references_per_prediction for refs in labels ):
  raise ValueError('Sacrebleu requires the same number of references for each prediction' )
 lowerCAmelCase													=   [[refs[i] for refs in labels] for i in range(lowercase__ )]
 lowerCAmelCase													=   scb.corpus_bleu(
     lowercase__     ,    lowercase__     ,    smooth_method=lowercase__     ,    smooth_value=lowercase__     ,    force=lowercase__     ,    lowercase=lowercase__     ,    use_effective_order=lowercase__     ,    **({'tokenize': tokenize} if tokenize else {})     ,    )
 return {"bleu": output.score}

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Union[str, Any]     ,    _UpperCAmelCase				:						Union[str, Any]     ,    _UpperCAmelCase				:						Optional[int]     ,    _UpperCAmelCase				:						Tuple ):
 try:
  from jiwer import compute_measures
 except ImportError:
  raise ValueError(
      F'jiwer has to be installed in order to apply the wer metric for {config_name}.'
      'You can install it via `pip install jiwer`.' )

 if concatenate_texts:
  lowerCAmelCase													=   compute_measures(lowercase__     ,    lowercase__ )['wer']

  lowerCAmelCase													=   compute_measures(lowercase__     ,    lowercase__     ,    truth_transform=lowercase__     ,    hypothesis_transform=lowercase__ )['wer']
  return {"wer": wer, "cer": cer}
 else:

  def compute_score(_UpperCAmelCase				:						Optional[Any]     ,    _UpperCAmelCase				:						List[Any]     ,    _UpperCAmelCase				:						Dict="wer" ):
   lowerCAmelCase													=   0
   lowerCAmelCase													=   0
   for prediction, reference in zip(lowercase__     ,    lowercase__ ):
    if score_type == "wer":
     lowerCAmelCase													=   compute_measures(lowercase__     ,    lowercase__ )
    elif score_type == "cer":
     lowerCAmelCase													=   compute_measures(
         lowercase__     ,    lowercase__     ,    truth_transform=lowercase__     ,    hypothesis_transform=lowercase__ )
    incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
    total += measures["substitutions"] + measures["deletions"] + measures["hits"]
   return incorrect / total

  return {"wer": compute_score(lowercase__     ,    lowercase__     ,    'wer' ), "cer": compute_score(lowercase__     ,    lowercase__     ,    'cer' )}







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,   _KWARGS_DESCRIPTION     )
class 			a       (      datasets.Metric     ):



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  if self.config_name not in _CONFIG_NAMES:
   raise KeyError(F'You should supply a configuration name selected in {_CONFIG_NAMES}'    )

  lowerCAmelCase													=   'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

  return datasets.MetricInfo(
      description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
          {'predictions': datasets.Value(A_    ), 'references': datasets.Value(A_    )}    )							,					codebase_urls=[]							,					reference_urls=[]							,					format='numpy'							,					)



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case=None							,					_snake_case=None    ):







  """simple docstring"""






  lowerCAmelCase													=   bleu_kwargs if bleu_kwargs is not None else {}
  lowerCAmelCase													=   wer_kwargs if wer_kwargs is not None else {}

  if self.config_name == "fleurs-lang_id":
   return {"accuracy": simple_accuracy(A_							,					A_    )}
  elif self.config_name == "minds14":
   return fa_and_simple_accuracy(A_							,					A_    )
  elif self.config_name == "covost2":
   lowerCAmelCase													=   bleu_kwargs.pop('smooth_method'							,					'exp'    )
   lowerCAmelCase													=   bleu_kwargs.pop('smooth_value'							,					A_    )
   lowerCAmelCase													=   bleu_kwargs.pop('force'							,					A_    )
   lowerCAmelCase													=   bleu_kwargs.pop('lowercase'							,					A_    )
   lowerCAmelCase													=   bleu_kwargs.pop('tokenize'							,					A_    )
   lowerCAmelCase													=   bleu_kwargs.pop('use_effective_order'							,					A_    )
   return bleu(
       preds=A_							,					labels=A_							,					smooth_method=A_							,					smooth_value=A_							,					force=A_							,					lowercase=A_							,					tokenize=A_							,					use_effective_order=A_							,					)
  elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
   lowerCAmelCase													=   wer_kwargs.pop('concatenate_texts'							,					A_    )
   return wer_and_cer(A_							,					A_							,					A_							,					self.config_name    )
  else:
   raise KeyError(F'You should supply a configuration name selected in {_CONFIG_NAMES}'    )












"""simple docstring"""
import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor







class 			a       :



 def __init__(    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   cuda
  lowerCAmelCase													=   AutoConfig.from_pretrained(_snake_case    )
  lowerCAmelCase													=   AutoModelForCTC.from_pretrained(_snake_case    )
  self.model.eval()
  if self.cuda:
   self.model.to(device='cuda'    )
  lowerCAmelCase													=   AutoProcessor.from_pretrained(_snake_case    )
  lowerCAmelCase													=   torchaudio.transforms.Resample(_snake_case							,					1_60_00    )
  lowerCAmelCase													=   0
  lowerCAmelCase													=   list(self.processor.tokenizer.get_vocab().keys()    )
  for i in range(len(_snake_case    )    ):
   if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
    lowerCAmelCase													=   i
  print('Blank Token id [PAD]/<pad>'							,					_snake_case    )
  lowerCAmelCase													=   blank_id



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase													=   torchaudio.load(_snake_case    )
  lowerCAmelCase													=   self.resampler(_snake_case    ).squeeze().numpy()
  return speech



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.blank_id
  lowerCAmelCase													=   '|'.join(item['sent'].split(' '    )    )
  if not os.path.isfile(item['wav_path']    ):
   print(item['wav_path']							,					'not found in wavs directory'    )

  lowerCAmelCase													=   self.speech_file_to_array_fn(item['wav_path']    )
  lowerCAmelCase													=   self.processor(_snake_case							,					sampling_rate=1_60_00							,					return_tensors='pt'							,					padding=_snake_case    )
  if self.cuda:
   lowerCAmelCase													=   inputs.to(device='cuda'    )

  with torch.no_grad():
   lowerCAmelCase													=   self.model(inputs.input_values    ).logits

  # get the emission probability at frame level
  lowerCAmelCase													=   torch.log_softmax(_snake_case							,					dim=-1    )
  lowerCAmelCase													=   emissions[0].cpu().detach()

  # get labels from vocab
  lowerCAmelCase													=   ([''] + list(self.processor.tokenizer.get_vocab().keys()    ))[
      :-1
  ]  # logits don't align with the tokenizer's vocab

  lowerCAmelCase													=   {c: i for i, c in enumerate(_snake_case    )}
  lowerCAmelCase													=   []
  for c in transcript:
   if c in dictionary:
    tokens.append(dictionary[c]    )

  def get_trellis(_snake_case							,					_snake_case							,					_snake_case=0    ):
   lowerCAmelCase													=   emission.size(0    )
   lowerCAmelCase													=   len(_snake_case    )

   # Trellis has extra diemsions for both time axis and tokens.
   # The extra dim for tokens represents <SoS> (start-of-sentence)
   # The extra dim for time axis is for simplification of the code.
   lowerCAmelCase													=   torch.full((num_frames + 1, num_tokens + 1)							,					-float('inf'    )    )
   lowerCAmelCase													=   0
   for t in range(_snake_case    ):
    lowerCAmelCase													=   torch.maximum(
        # Score for staying at the same token
        trellis[t, 1:] + emission[t, blank_id]							,					trellis[t, :-1] + emission[t, tokens]							,					)
   return trellis

  lowerCAmelCase													=   get_trellis(_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42

  def backtrack(_snake_case							,					_snake_case							,					_snake_case							,					_snake_case=0    ):
   lowerCAmelCase													=   trellis.size(1    ) - 1
   lowerCAmelCase													=   torch.argmax(trellis[:, j]    ).item()

   lowerCAmelCase													=   []
   for t in range(_snake_case							,					0							,					-1    ):
    # 1. Figure out if the current position was stay or change
    # Note (again):
    # `emission[J-1]` is the emission at time frame `J` of trellis dimension.
    # Score for token staying the same from time frame J-1 to T.
    lowerCAmelCase													=   trellis[t - 1, j] + emission[t - 1, blank_id]
    # Score for token changing from C-1 at T-1 to J at T.
    lowerCAmelCase													=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

    # 2. Store the path with frame-wise probability.
    lowerCAmelCase													=   emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
    # Return token index and time index in non-trellis coordinate.
    path.append(Point(j - 1							,					t - 1							,					_snake_case    )    )

    # 3. Update the token
    if changed > stayed:
     j -= 1
     if j == 0:
      break
   else:
    raise ValueError('Failed to align'    )
   return path[::-1]

  lowerCAmelCase													=   backtrack(_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42



   def __repr__(    self    ):







    """simple docstring"""






    return F'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'



   @property
   def       UpperCamelCase__     (    self    ):







    """simple docstring"""






    return self.end - self.start

  def merge_repeats(_snake_case    ):
   lowerCAmelCase							,lowerCAmelCase													=   0, 0
   lowerCAmelCase													=   []
   while ia < len(_snake_case    ):
    while ia < len(_snake_case    ) and path[ia].token_index == path[ia].token_index:
     ia += 1
    lowerCAmelCase													=   sum(path[k].score for k in range(_snake_case							,					_snake_case    )    ) / (ia - ia)
    segments.append(
        Segment(
            transcript[path[ia].token_index]							,					path[ia].time_index							,					path[ia - 1].time_index + 1							,					_snake_case							,					)    )
    lowerCAmelCase													=   ia
   return segments

  lowerCAmelCase													=   merge_repeats(_snake_case    )
  with open(item['out_path']							,					'w'    ) as out_align:
   for seg in segments:
    out_align.write(str(_snake_case    ) + '\n'    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  if not os.path.exists(_snake_case    ):
   os.makedirs(_snake_case    )

  # load text file
  lowerCAmelCase													=   open(_snake_case							,					encoding='utf8'    ).readlines()

  lowerCAmelCase													=   []
  for line in lines:
   if len(line.strip().split('\t'    )    ) != 2:
    print('Script must be in format: 00001  this is my sentence'    )
    exit()

   lowerCAmelCase							,lowerCAmelCase													=   line.strip().split('\t'    )
   lowerCAmelCase													=   os.path.join(_snake_case							,					wav_name + '.wav'    )
   lowerCAmelCase													=   os.path.join(_snake_case							,					wav_name + '.txt'    )

   items.append({'sent': sentence, 'wav_path': wav_path, 'out_path': out_path}    )
  print('Number of samples found in script file'							,					len(_snake_case    )    )

  for item in tqdm(_snake_case    ):
   self.align_single_sample(_snake_case    )


def 		_SCREAMING_SNAKE_CASE ():
 lowerCAmelCase													=   argparse.ArgumentParser()

 parser.add_argument(
     '--model_name'     ,    type=_UpperCAmelCase     ,    default='arijitx/wav2vec2-xls-r-300m-bengali'     ,    help='wav2vec model name' )
 parser.add_argument('--wav_dir'     ,    type=_UpperCAmelCase     ,    default='./wavs'     ,    help='directory containing wavs' )
 parser.add_argument('--text_file'     ,    type=_UpperCAmelCase     ,    default='script.txt'     ,    help='file containing text' )
 parser.add_argument('--input_wavs_sr'     ,    type=_UpperCAmelCase     ,    default=1_6000     ,    help='sampling rate of input audios' )
 parser.add_argument(
     '--output_dir'     ,    type=_UpperCAmelCase     ,    default='./out_alignment'     ,    help='output directory containing the alignment files' )
 parser.add_argument('--cuda'     ,    action='store_true' )

 lowerCAmelCase													=   parser.parse_args()

 lowerCAmelCase													=   WavaVecaAligner(args.model_name     ,    args.input_wavs_sr     ,    args.cuda )
 aligner.align_data(args.wav_dir     ,    args.text_file     ,    args.output_dir )


if __name__ == "__main__":
       main()





