code: 
  
  
  
  
  
  import argparse
  import os
  from dataclasses import dataclass
  
  import torch
  import torchaudio
  from tqdm import tqdm
  
  from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  int:
  							UpperCamelCase__         =	cuda
  							UpperCamelCase__         =	AutoConfig.from_pretrained(__lowerCAmelCase       )
  							UpperCamelCase__         =	AutoModelForCTC.from_pretrained(__lowerCAmelCase       )
  							self.model.eval()
  							if self.cuda:
  									self.model.to(device="""cuda"""       )
  							UpperCamelCase__         =	AutoProcessor.from_pretrained(__lowerCAmelCase       )
  							UpperCamelCase__         =	torchaudio.transforms.Resample(__lowerCAmelCase    ,      16_000       )
  							UpperCamelCase__         =	0
  							UpperCamelCase__         =	list(self.processor.tokenizer.get_vocab().keys()       )
  							for i in range(len(__lowerCAmelCase       )       ):
  									if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
  											UpperCamelCase__         =	i
  							print("""Blank Token id [PAD]/<pad>"""    ,      __lowerCAmelCase       )
  							UpperCamelCase__         =	blank_id
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  List[str]:
  							UpperCamelCase__  ,			UpperCamelCase__         =	torchaudio.load(__lowerCAmelCase       )
  							UpperCamelCase__         =	self.resampler(__lowerCAmelCase       ).squeeze().numpy()
  							return speech
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  List[str]:
  							UpperCamelCase__         =	self.blank_id
  							UpperCamelCase__         =	"""|""".join(item["""sent"""].split(""" """       )       )
  							if not os.path.isfile(item["""wav_path"""]       ):
  									print(item["""wav_path"""]    ,      """not found in wavs directory"""       )
  
  							UpperCamelCase__         =	self.speech_file_to_array_fn(item["""wav_path"""]       )
  							UpperCamelCase__         =	self.processor(__lowerCAmelCase    ,      sampling_rate=16_000    ,      return_tensors="""pt"""    ,      padding=__lowerCAmelCase       )
  							if self.cuda:
  									UpperCamelCase__         =	inputs.to(device="""cuda"""       )
  
  							with torch.no_grad():
  									UpperCamelCase__         =	self.model(inputs.input_values       ).logits
  
  							# get the emission probability at frame level
  							UpperCamelCase__         =	torch.log_softmax(__lowerCAmelCase    ,      dim=-1       )
  							UpperCamelCase__         =	emissions[0].cpu().detach()
  
  							# get labels from vocab
  							UpperCamelCase__         =	([""""""] + list(self.processor.tokenizer.get_vocab().keys()       ))[
  							    :-1
  							]  # logits don't align with the tokenizer's vocab
  
  							UpperCamelCase__         =	{c: i for i, c in enumerate(__lowerCAmelCase       )}
  							UpperCamelCase__         =	[]
  							for c in transcript:
  									if c in dictionary:
  											tokens.append(dictionary[c]       )
  
  							def get_trellis(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase=0       ):
  									UpperCamelCase__         =	emission.size(0       )
  									UpperCamelCase__         =	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.
  									UpperCamelCase__         =	torch.full((num_frames + 1, num_tokens + 1)    ,      -float("""inf"""       )       )
  									UpperCamelCase__         =	0
  									for t in range(__lowerCAmelCase       ):
  											UpperCamelCase__         =	torch.maximum(
  											    # Score for staying at the same token
  											    trellis[t, 1:] + emission[t, blank_id]    ,      trellis[t, :-1] + emission[t, tokens]    ,      )
  									return trellis
  
  							UpperCamelCase__         =	get_trellis(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  
  
  
  
  							@dataclass
  							class   _UpperCAmelCase	:
  												__A          =   42
  												__A          =   42
  												__A          =   42
  
  							def backtrack(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase=0       ):
  									UpperCamelCase__         =	trellis.size(1       ) - 1
  									UpperCamelCase__         =	torch.argmax(trellis[:, j]       ).item()
  
  									UpperCamelCase__         =	[]
  									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.
  											UpperCamelCase__         =	trellis[t - 1, j] + emission[t - 1, blank_id]
  											# Score for token changing from C-1 at T-1 to J at T.
  											UpperCamelCase__         =	trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]
  
  											# 2. Store the path with frame-wise probability.
  											UpperCamelCase__         =	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]
  
  							UpperCamelCase__         =	backtrack(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  
  
  
  
  							@dataclass
  							class   _UpperCAmelCase	:
  												__A          =   42
  												__A          =   42
  												__A          =   42
  												__A          =   42
  
  
  
  
  												def __repr__(						self       )						->  Any:
  														return F"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""
  
  
  
  
  												@property
  												def 	snake_case_		(						self       )						->  str:
  														return self.end - self.start
  
  							def merge_repeats(__lowerCAmelCase       ):
  									UpperCamelCase__  ,			UpperCamelCase__         =	0, 0
  									UpperCamelCase__         =	[]
  									while ia < len(__lowerCAmelCase       ):
  											while ia < len(__lowerCAmelCase       ) and path[ia].token_index == path[ia].token_index:
  													ia += 1
  											UpperCamelCase__         =	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    ,      )       )
  											UpperCamelCase__         =	ia
  									return segments
  
  							UpperCamelCase__         =	merge_repeats(__lowerCAmelCase       )
  							with open(item["""out_path"""]    ,      """w"""       ) as out_align:
  									for seg in segments:
  											out_align.write(str(__lowerCAmelCase       ) + """\n"""       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Any:
  							if not os.path.exists(__lowerCAmelCase       ):
  									os.makedirs(__lowerCAmelCase       )
  
  							# load text file
  							UpperCamelCase__         =	open(__lowerCAmelCase    ,      encoding="""utf8"""       ).readlines()
  
  							UpperCamelCase__         =	[]
  							for line in lines:
  									if len(line.strip().split("""\t"""       )       ) != 2:
  											print("""Script must be in format: 00001  this is my sentence"""       )
  											exit()
  
  									UpperCamelCase__  ,			UpperCamelCase__         =	line.strip().split("""\t"""       )
  									UpperCamelCase__         =	os.path.join(__lowerCAmelCase    ,      wav_name + """.wav"""       )
  									UpperCamelCase__         =	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     a						(      )     ->Union[str, Any]:
  		UpperCamelCase__         =	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=1_6_0_0_0 ,						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"""       )
  
  		UpperCamelCase__         =	parser.parse_args()
  
  		UpperCamelCase__         =	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: 
  
  
  
  
  
  __lowerCAmelCase     =			{
      "0": 1,
      "1": 1,
      "2": 2,
      "3": 6,
      "4": 24,
      "5": 120,
      "6": 720,
      "7": 5_040,
      "8": 40_320,
      "9": 362_880,
  }
  
  __lowerCAmelCase     =			{145: 145}
  
  __lowerCAmelCase     =			{
      145: 0,
      169: 3,
      36_301: 3,
      1_454: 3,
      871: 2,
      45_361: 2,
      872: 2,
  }
  
  def     a						(      _lowercase   :     int       )     ->int:
  		if n in CACHE_SUM_DIGIT_FACTORIALS:
  				return CACHE_SUM_DIGIT_FACTORIALS[n]
  		UpperCamelCase__         =	sum(DIGIT_FACTORIALS[let] for let in str(_lowercase       )       )
  		UpperCamelCase__         =	ret
  		return ret
  
  def     a						(      _lowercase   :     int ,						_lowercase   :     set | None = None       )     ->int:
  		UpperCamelCase__         =	previous or set()
  		if n in CHAIN_LENGTH_CACHE:
  				return CHAIN_LENGTH_CACHE[n]
  		UpperCamelCase__         =	sum_digit_factorials(_lowercase       )
  		if next_number in previous:
  				UpperCamelCase__         =	0
  				return 0
  		else:
  				previous.add(_lowercase       )
  				UpperCamelCase__         =	1 + chain_length(_lowercase ,						_lowercase       )
  				UpperCamelCase__         =	ret
  				return ret
  
  def     a						(      _lowercase   :     int = 6_0 ,						_lowercase   :     int = 1_0_0_0_0_0_0       )     ->int:
  		return sum(1 for i in range(1 ,						_lowercase       ) if chain_length(_lowercase       ) == num_terms       )
  
  
  if __name__ == "__main__":
  				print(f"""{solution() = }""")
  
  
  
  
  
  