code: import argparse
  import os
  from dataclasses import dataclass
  
  import torch
  import torchaudio
  from tqdm import tqdm
  
  from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
  
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Tuple         =  cuda
         lowerCAmelCase_:       Tuple         =  AutoConfig.from_pretrained(lowerCamelCase__   )
         lowerCAmelCase_:       List[Any]         =  AutoModelForCTC.from_pretrained(lowerCamelCase__   )
         self.model.eval()
         if self.cuda:
            self.model.to(device="cuda"   )
         lowerCAmelCase_:       List[str]         =  AutoProcessor.from_pretrained(lowerCamelCase__   )
         lowerCAmelCase_:       str         =  torchaudio.transforms.Resample(lowerCamelCase__					,	16_000   )
         lowerCAmelCase_:       Any         =  0
         lowerCAmelCase_:       Optional[Any]         =  list(self.processor.tokenizer.get_vocab().keys()   )
         for i in range(len(lowerCamelCase__   )   ):
            if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
               lowerCAmelCase_:       Tuple         =  i
         print("Blank Token id [PAD]/<pad>"					,	lowerCamelCase__   )
         lowerCAmelCase_:       Union[str, Any]         =  blank_id
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       List[Any]         =  torchaudio.load(lowerCamelCase__   )
         lowerCAmelCase_:       List[str]         =  self.resampler(lowerCamelCase__   ).squeeze().numpy()
         return speech
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Optional[Any]         =  self.blank_id
         lowerCAmelCase_:       int         =  "|".join(item["sent"].split(" "   )   )
         if not os.path.isfile(item["wav_path"]   ):
            print(item["wav_path"]					,	"not found in wavs directory"   )
  
         lowerCAmelCase_:       Optional[int]         =  self.speech_file_to_array_fn(item["wav_path"]   )
         lowerCAmelCase_:       List[Any]         =  self.processor(lowerCamelCase__					,	sampling_rate=16_000					,	return_tensors="pt"					,	padding=lowerCamelCase__   )
         if self.cuda:
            lowerCAmelCase_:       Union[str, Any]         =  inputs.to(device="cuda"   )
  
         with torch.no_grad():
            lowerCAmelCase_:       Any         =  self.model(inputs.input_values   ).logits
  
         # get the emission probability at frame level
         lowerCAmelCase_:       List[str]         =  torch.log_softmax(lowerCamelCase__					,	dim=-1   )
         lowerCAmelCase_:       Optional[int]         =  emissions[0].cpu().detach()
  
         # get labels from vocab
         lowerCAmelCase_:       Any         =  ([""] + list(self.processor.tokenizer.get_vocab().keys()   ))[
             :-1
         ]  # logits don't align with the tokenizer's vocab
  
         lowerCAmelCase_:       Optional[Any]         =  {c: i for i, c in enumerate(lowerCamelCase__   )}
         lowerCAmelCase_:       Dict         =  []
         for c in transcript:
            if c in dictionary:
               tokens.append(dictionary[c]   )
  
         def get_trellis(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__=0   ):
            lowerCAmelCase_:       Union[str, Any]         =  emission.size(0   )
            lowerCAmelCase_:       Optional[Any]         =  len(lowerCamelCase__   )
  
            # 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_:       Tuple         =  torch.full((num_frames + 1, num_tokens + 1)					,	-float("inf"   )   )
            lowerCAmelCase_:       int         =  0
            for t in range(lowerCamelCase__   ):
               lowerCAmelCase_:       Dict         =  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_:       str         =  get_trellis(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
         @dataclass
         class 				_lowercase						:
  
  
  
  
  
  
             '''simple docstring'''
             SCREAMING_SNAKE_CASE:							int
             SCREAMING_SNAKE_CASE:							int
             SCREAMING_SNAKE_CASE:							float
  
         def backtrack(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__=0   ):
            lowerCAmelCase_:       List[str]         =  trellis.size(1   ) - 1
            lowerCAmelCase_:       Optional[Any]         =  torch.argmax(trellis[:, j]   ).item()
  
            lowerCAmelCase_:       List[Any]         =  []
            for t in range(lowerCamelCase__					,	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_:       Any         =  trellis[t - 1, j] + emission[t - 1, blank_id]
               # Score for token changing from C-1 at T-1 to J at T.
               lowerCAmelCase_:       str         =  trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]
  
               # 2. Store the path with frame-wise probability.
               lowerCAmelCase_:       Optional[int]         =  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					,	lowerCamelCase__   )   )
  
               # 3. Update the token
               if changed > stayed:
                  j -= 1
                  if j == 0:
                     break
            else:
               raise ValueError("Failed to align"   )
            return path[::-1]
  
         lowerCAmelCase_:       str         =  backtrack(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
         @dataclass
         class 				_lowercase						:
  
  
  
  
  
  
             '''simple docstring'''
             SCREAMING_SNAKE_CASE:							str
             SCREAMING_SNAKE_CASE:							int
             SCREAMING_SNAKE_CASE:							int
             SCREAMING_SNAKE_CASE:							float
  
  
  
  
             def __repr__(						self   ):
                return F'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''
  
  
  
  
             @property
             def     _a						(						self   ):
                return self.end - self.start
  
         def merge_repeats(lowerCamelCase__   ):
            lowerCAmelCase_     ,  lowerCAmelCase_:       List[Any]         =  0, 0
            lowerCAmelCase_:       Union[str, Any]         =  []
            while ia < len(lowerCamelCase__   ):
               while ia < len(lowerCamelCase__   ) and path[ia].token_index == path[ia].token_index:
                  ia += 1
               lowerCAmelCase_:       Dict         =  sum(path[k].score for k in range(lowerCamelCase__					,	lowerCamelCase__   )   ) / (ia - ia)
               segments.append(
                   Segment(
                       transcript[path[ia].token_index]					,	path[ia].time_index					,	path[ia - 1].time_index + 1					,	lowerCamelCase__					,	)   )
               lowerCAmelCase_:       Optional[int]         =  ia
            return segments
  
         lowerCAmelCase_:       Dict         =  merge_repeats(lowerCamelCase__   )
         with open(item["out_path"]					,	"w"   ) as out_align:
            for seg in segments:
               out_align.write(str(lowerCamelCase__   ) + "\n"   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         if not os.path.exists(lowerCamelCase__   ):
            os.makedirs(lowerCamelCase__   )
  
         # load text file
         lowerCAmelCase_:       Dict         =  open(lowerCamelCase__					,	encoding="utf8"   ).readlines()
  
         lowerCAmelCase_:       int         =  []
         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_:       int         =  line.strip().split("\t"   )
            lowerCAmelCase_:       List[Any]         =  os.path.join(lowerCamelCase__					,	wav_name + ".wav"   )
            lowerCAmelCase_:       List[str]         =  os.path.join(lowerCamelCase__					,	wav_name + ".txt"   )
  
            items.append({"sent": sentence, "wav_path": wav_path, "out_path": out_path}   )
         print("Number of samples found in script file"					,	len(lowerCamelCase__   )   )
  
         for item in tqdm(lowerCamelCase__   ):
            self.align_single_sample(lowerCamelCase__   )
  
  
  
  
  
  def 		snake_case__  (		):
     lowerCAmelCase_:       List[Any]         =  argparse.ArgumentParser()
  
     parser.add_argument(
         "--model_name"       ,							type=lowercase       ,							default="arijitx/wav2vec2-xls-r-300m-bengali"       ,							help="wav2vec model name"				)
     parser.add_argument("--wav_dir"       ,							type=lowercase       ,							default="./wavs"       ,							help="directory containing wavs"				)
     parser.add_argument("--text_file"       ,							type=lowercase       ,							default="script.txt"       ,							help="file containing text"				)
     parser.add_argument("--input_wavs_sr"       ,							type=lowercase       ,							default=16000       ,							help="sampling rate of input audios"				)
     parser.add_argument(
         "--output_dir"       ,							type=lowercase       ,							default="./out_alignment"       ,							help="output directory containing the alignment files"				)
     parser.add_argument("--cuda"       ,							action="store_true"				)
  
     lowerCAmelCase_:       str         =  parser.parse_args()
  
     lowerCAmelCase_:       int         =  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()
style_context: from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  a     :			Optional[Any]					    =						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   a     :			Optional[int]					    =						[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   a     :			Any					    =						[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   a     :			int					    =						[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   a     :			Dict					    =						[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Optional[Any]					    =						[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   a     :			Any					    =						[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   a     :			Dict					    =						[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
   import sys
  
   a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)