code: 
  import argparse
  import os
  from dataclasses import dataclass
  
  import torch
  import torchaudio
  from tqdm import tqdm
  
  from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:Optional[int]      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:int )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   cuda
  				SCREAMING_SNAKE_CASE    	=   AutoConfig.from_pretrained(lowercase )
  				SCREAMING_SNAKE_CASE    	=   AutoModelForCTC.from_pretrained(lowercase )
  				self.model.eval()
  				if self.cuda:
  							self.model.to(device='''cuda''' )
  				SCREAMING_SNAKE_CASE    	=   AutoProcessor.from_pretrained(lowercase )
  				SCREAMING_SNAKE_CASE    	=   torchaudio.transforms.Resample(lowercase      ,    1_6_0_0_0 )
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   list(self.processor.tokenizer.get_vocab().keys() )
  				for i in range(len(lowercase ) ):
  							if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
  										SCREAMING_SNAKE_CASE    	=   i
  				print('''Blank Token id [PAD]/<pad>'''      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   blank_id
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:int )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   torchaudio.load(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.resampler(lowercase ).squeeze().numpy()
  				return speech
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Optional[Any] )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.blank_id
  				SCREAMING_SNAKE_CASE    	=   '''|'''.join(item['''sent'''].split(''' ''' ) )
  				if not os.path.isfile(item['''wav_path'''] ):
  							print(item['''wav_path''']      ,    '''not found in wavs directory''' )
  
  				SCREAMING_SNAKE_CASE    	=   self.speech_file_to_array_fn(item['''wav_path'''] )
  				SCREAMING_SNAKE_CASE    	=   self.processor(lowercase      ,    sampling_rate=1_6_0_0_0      ,    return_tensors='''pt'''      ,    padding=lowercase )
  				if self.cuda:
  							SCREAMING_SNAKE_CASE    	=   inputs.to(device='''cuda''' )
  
  				with torch.no_grad():
  							SCREAMING_SNAKE_CASE    	=   self.model(inputs.input_values ).logits
  
  				# get the emission probability at frame level
  				SCREAMING_SNAKE_CASE    	=   torch.log_softmax(lowercase      ,    dim=-1 )
  				SCREAMING_SNAKE_CASE    	=   emissions[0].cpu().detach()
  
  				# get labels from vocab
  				SCREAMING_SNAKE_CASE    	=   ([''''''] + list(self.processor.tokenizer.get_vocab().keys() ))[
  				    :-1
  				]  # logits don't align with the tokenizer's vocab
  
  				SCREAMING_SNAKE_CASE    	=   {c: i for i, c in enumerate(lowercase )}
  				SCREAMING_SNAKE_CASE    	=   []
  				for c in transcript:
  							if c in dictionary:
  										tokens.append(dictionary[c] )
  
  				def get_trellis(lowercase			:List[Any]      ,    lowercase			:Any      ,    lowercase			:int=0 ):
  							SCREAMING_SNAKE_CASE    	=   emission.size(0 )
  							SCREAMING_SNAKE_CASE    	=   len(lowercase )
  
  							# 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.
  							SCREAMING_SNAKE_CASE    	=   torch.full((num_frames + 1, num_tokens + 1)      ,    -float('''inf''' ) )
  							SCREAMING_SNAKE_CASE    	=   0
  							for t in range(lowercase ):
  										SCREAMING_SNAKE_CASE    	=   torch.maximum(
  										    # Score for staying at the same token
  										    trellis[t, 1:] + emission[t, blank_id]      ,    trellis[t, :-1] + emission[t, tokens]      ,    )
  							return trellis
  
  				SCREAMING_SNAKE_CASE    	=   get_trellis(lowercase      ,    lowercase      ,    lowercase )
  
  
  
  
  
  				@dataclass
  				class 						lowerCamelCase			:
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				float
  
  				def backtrack(lowercase			:Optional[int]      ,    lowercase			:List[str]      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Any]=0 ):
  							SCREAMING_SNAKE_CASE    	=   trellis.size(1 ) - 1
  							SCREAMING_SNAKE_CASE    	=   torch.argmax(trellis[:, j] ).item()
  
  							SCREAMING_SNAKE_CASE    	=   []
  							for t in range(lowercase      ,    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.
  										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j] + emission[t - 1, blank_id]
  										# Score for token changing from C-1 at T-1 to J at T.
  										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]
  
  										# 2. Store the path with frame-wise probability.
  										SCREAMING_SNAKE_CASE    	=   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      ,    lowercase ) )
  
  										# 3. Update the token
  										if changed > stayed:
  													j -= 1
  													if j == 0:
  																break
  							else:
  										raise ValueError('''Failed to align''' )
  							return path[::-1]
  
  				SCREAMING_SNAKE_CASE    	=   backtrack(lowercase      ,    lowercase      ,    lowercase      ,    lowercase )
  
  
  
  
  
  				@dataclass
  				class 						lowerCamelCase			:
  					UpperCamelCase_     :				str
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				float
  
  
  
  
  
  
  
  					def __repr__(    self			:Optional[Any] )		->			str:
  
  
  
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return f"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""
  
  
  
  
  
  
  
  					@property
  					def    snake_case__    (    self			:List[str] )		->			Any:
  
  
  
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.end - self.start
  
  				def merge_repeats(lowercase			:List[str] ):
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   0, 0
  							SCREAMING_SNAKE_CASE    	=   []
  							while ia < len(lowercase ):
  										while ia < len(lowercase ) and path[ia].token_index == path[ia].token_index:
  													ia += 1
  										SCREAMING_SNAKE_CASE    	=   sum(path[k].score for k in range(lowercase      ,    lowercase ) ) / (ia - ia)
  										segments.append(
  										    Segment(
  										        transcript[path[ia].token_index]      ,    path[ia].time_index      ,    path[ia - 1].time_index + 1      ,    lowercase      ,    ) )
  										SCREAMING_SNAKE_CASE    	=   ia
  							return segments
  
  				SCREAMING_SNAKE_CASE    	=   merge_repeats(lowercase )
  				with open(item['''out_path''']      ,    '''w''' ) as out_align:
  							for seg in segments:
  										out_align.write(str(lowercase ) + '''\n''' )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    lowercase			:Dict      ,    lowercase			:Tuple      ,    lowercase			:str )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if not os.path.exists(lowercase ):
  							os.makedirs(lowercase )
  
  				# load text file
  				SCREAMING_SNAKE_CASE    	=   open(lowercase      ,    encoding='''utf8''' ).readlines()
  
  				SCREAMING_SNAKE_CASE    	=   []
  				for line in lines:
  							if len(line.strip().split('''\t''' ) ) != 2:
  										print('''Script must be in format: 00001  this is my sentence''' )
  										exit()
  
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   line.strip().split('''\t''' )
  							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.wav''' )
  							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.txt''' )
  
  							items.append({'''sent''': sentence, '''wav_path''': wav_path, '''out_path''': out_path} )
  				print('''Number of samples found in script file'''      ,    len(lowercase ) )
  
  				for item in tqdm(lowercase ):
  							self.align_single_sample(lowercase )
  
  
  
  def       a			(   )    ->Optional[int]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   argparse.ArgumentParser()
  
  			parser.add_argument(
  			    '''--model_name'''		,	type=a		,	default='''arijitx/wav2vec2-xls-r-300m-bengali'''		,	help='''wav2vec model name'''  )
  			parser.add_argument('''--wav_dir'''		,	type=a		,	default='''./wavs'''		,	help='''directory containing wavs'''  )
  			parser.add_argument('''--text_file'''		,	type=a		,	default='''script.txt'''		,	help='''file containing text'''  )
  			parser.add_argument('''--input_wavs_sr'''		,	type=a		,	default=1_6000		,	help='''sampling rate of input audios'''  )
  			parser.add_argument(
  			    '''--output_dir'''		,	type=a		,	default='''./out_alignment'''		,	help='''output directory containing the alignment files'''  )
  			parser.add_argument('''--cuda'''		,	action='''store_true'''  )
  
  			SCREAMING_SNAKE_CASE    	=   parser.parse_args()
  
  			SCREAMING_SNAKE_CASE    	=   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: 
  import subprocess
  from argparse import ArgumentParser
  from typing import List, Union
  
  from huggingface_hub.hf_api import HfFolder, create_repo, whoami
  from requests.exceptions import HTTPError
  
  from . import BaseTransformersCLICommand
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	@staticmethod
  	def    snake_case__    (    lowercase			:ArgumentParser )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''login'''      ,    help='''Log in using the same credentials as on huggingface.co''' )
  				login_parser.set_defaults(func=lambda lowercase : LoginCommand(lowercase ) )
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''whoami'''      ,    help='''Find out which huggingface.co account you are logged in as.''' )
  				whoami_parser.set_defaults(func=lambda lowercase : WhoamiCommand(lowercase ) )
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''logout'''      ,    help='''Log out''' )
  				logout_parser.set_defaults(func=lambda lowercase : LogoutCommand(lowercase ) )
  
  				# new system: git-based repo system
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser(
  				    '''repo'''      ,    help='''Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.'''      ,    )
  				SCREAMING_SNAKE_CASE    	=   repo_parser.add_subparsers(
  				    help='''Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands''' )
  				SCREAMING_SNAKE_CASE    	=   repo_subparsers.add_parser(
  				    '''create'''      ,    help='''Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co''' )
  				repo_create_parser.add_argument(
  				    '''name'''      ,    type=lowercase      ,    help='''Name for your model\'s repo. Will be namespaced under your username to build the model id.'''      ,    )
  				repo_create_parser.add_argument('''--organization'''      ,    type=lowercase      ,    help='''Optional: organization namespace.''' )
  				repo_create_parser.add_argument('''-y'''      ,    '''--yes'''      ,    action='''store_true'''      ,    help='''Optional: answer Yes to the prompt''' )
  				repo_create_parser.set_defaults(func=lambda lowercase : RepoCreateCommand(lowercase ) )
  
  
  
  
  
  class 						lowerCamelCase			:
  	UpperCamelCase_     :				List[str]								=  '\u001b[1m'
  	UpperCamelCase_     :				Union[str, Any]								=  '\u001b[31m'
  	UpperCamelCase_     :				Any								=  '\u001b[90m'
  	UpperCamelCase_     :				List[str]								=  '\u001b[0m'
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:List[Any]      ,    lowercase			:Any )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return f"""{cls._bold}{s}{cls._reset}"""
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:Optional[Any]      ,    lowercase			:str )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return f"""{cls._bold}{cls._red}{s}{cls._reset}"""
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:List[str]      ,    lowercase			:Any )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return f"""{cls._gray}{s}{cls._reset}"""
  
  
  
  def       a			(   a		,	a  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   [max(len(str(a  )  ) for x in col  ) for col in zip(*a		,	a  )]
  			SCREAMING_SNAKE_CASE    	=   ('''{{:{}}} ''' * len(a  )).format(*a  )
  			SCREAMING_SNAKE_CASE    	=   []
  			lines.append(row_format.format(*a  )  )
  			lines.append(row_format.format(*['''-''' * w for w in col_widths]  )  )
  			for row in rows:
  						lines.append(row_format.format(*a  )  )
  			return "\n".join(a  )
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:List[str]      ,    lowercase			:Optional[Any] )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   args
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''ERROR! `huggingface-cli login` uses an outdated login mechanism '''
  				        '''that is not compatible with the Hugging Face Hub backend anymore. '''
  				        '''Please use `huggingface-cli login instead.''' ) )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use '''
  				        '''`huggingface-cli whoami` instead.''' ) )
  				SCREAMING_SNAKE_CASE    	=   HfFolder.get_token()
  				if token is None:
  							print('''Not logged in''' )
  							exit()
  				try:
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   whoami(lowercase )
  							print(lowercase )
  							if orgs:
  										print(ANSI.bold('''orgs: ''' )      ,    ''','''.join(lowercase ) )
  				except HTTPError as e:
  							print(lowercase )
  							print(ANSI.red(e.response.text ) )
  							exit(1 )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''ERROR! `transformers-cli logout` uses an outdated logout mechanism '''
  				        '''that is not compatible with the Hugging Face Hub backend anymore. '''
  				        '''Please use `huggingface-cli logout instead.''' ) )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''WARNING! Managing repositories through transformers-cli is deprecated. '''
  				        '''Please use `huggingface-cli` instead.''' ) )
  				SCREAMING_SNAKE_CASE    	=   HfFolder.get_token()
  				if token is None:
  							print('''Not logged in''' )
  							exit(1 )
  				try:
  							SCREAMING_SNAKE_CASE    	=   subprocess.check_output(['''git''', '''--version'''] ).decode('''utf-8''' )
  							print(ANSI.gray(stdout.strip() ) )
  				except FileNotFoundError:
  							print('''Looks like you do not have git installed, please install.''' )
  
  				try:
  							SCREAMING_SNAKE_CASE    	=   subprocess.check_output(['''git-lfs''', '''--version'''] ).decode('''utf-8''' )
  							print(ANSI.gray(stdout.strip() ) )
  				except FileNotFoundError:
  							print(
  							    ANSI.red(
  							        '''Looks like you do not have git-lfs installed, please install.'''
  							        ''' You can install from https://git-lfs.github.com/.'''
  							        ''' Then run `git lfs install` (you only have to do this once).''' ) )
  				print('''''' )
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   whoami(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.args.organization if self.args.organization is not None else user
  				SCREAMING_SNAKE_CASE    	=   f"""{namespace}/{self.args.name}"""
  				print(f"""You are about to create {ANSI.bold(lowercase )}""" )
  
  				if not self.args.yes:
  							SCREAMING_SNAKE_CASE    	=   input('''Proceed? [Y/n] ''' ).lower()
  							if not (choice == "" or choice == "y" or choice == "yes"):
  										print('''Abort''' )
  										exit()
  				try:
  							SCREAMING_SNAKE_CASE    	=   create_repo(lowercase      ,    name=self.args.name      ,    organization=self.args.organization )
  				except HTTPError as e:
  							print(lowercase )
  							print(ANSI.red(e.response.text ) )
  							exit(1 )
  				print('''\nYour repo now lives at:''' )
  				print(f"""  {ANSI.bold(lowercase )}""" )
  				print('''\nYou can clone it locally with the command below, and commit/push as usual.''' )
  				print(f"""\n  git clone {url}""" )
  				print('''''' )