






import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


__snake_case     :     Optional[Any]       =  logging.get_logger(__name__)

__snake_case     :     List[Any]       =  {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

__snake_case     :     Optional[int]       =  {
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

__snake_case     :     Dict       =  {
    """openai-gpt""": 5_12,
}







def 						_UpperCamelCase (     UpperCamelCase_							:       int		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    text.strip()
						if not text:
												return []
						lowerCAmelCase__         =    text.split()
						return tokens


class      __SCREAMING_SNAKE_CASE       ( __lowercase):






						def __init__(						self  ,    _UpperCamelCase=True  ,    _UpperCamelCase=None  ,    _UpperCamelCase=True  ,    _UpperCamelCase=None  ,    _UpperCamelCase=True  ,    ):






												"""simple docstring"""
												if never_split is None:
																		lowerCAmelCase__         =    []
												lowerCAmelCase__         =    do_lower_case
												lowerCAmelCase__         =    set(_UpperCamelCase )
												lowerCAmelCase__         =    tokenize_chinese_chars
												lowerCAmelCase__         =    strip_accents
												lowerCAmelCase__         =    do_split_on_punc






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






												"""simple docstring"""
												lowerCAmelCase__         =    self.never_split.union(set(_UpperCamelCase ) ) if never_split else self.never_split
												lowerCAmelCase__         =    self._clean_text(_UpperCamelCase )

												# This was added on November 1st, 2018 for the multilingual and Chinese
												# models. This is also applied to the English models now, but it doesn't
												# matter since the English models were not trained on any Chinese data
												# and generally don't have any Chinese data in them (there are Chinese
												# characters in the vocabulary because Wikipedia does have some Chinese
												# words in the English Wikipedia.).
												if self.tokenize_chinese_chars:
																		lowerCAmelCase__         =    self._tokenize_chinese_chars(_UpperCamelCase )
												# prevents treating the same character with different unicode codepoints as different characters
												lowerCAmelCase__         =    unicodedata.normalize('NFC'  ,    _UpperCamelCase )
												lowerCAmelCase__         =    whitespace_tokenize(_UpperCamelCase )
												lowerCAmelCase__         =    []
												for token in orig_tokens:
																		if token not in never_split:
																								if self.do_lower_case:
																														lowerCAmelCase__         =    token.lower()
																														if self.strip_accents is not False:
																																				lowerCAmelCase__         =    self._run_strip_accents(_UpperCamelCase )
																								elif self.strip_accents:
																														lowerCAmelCase__         =    self._run_strip_accents(_UpperCamelCase )
																		split_tokens.extend(self._run_split_on_punc(_UpperCamelCase  ,    _UpperCamelCase ) )

												lowerCAmelCase__         =    whitespace_tokenize(' '.join(_UpperCamelCase ) )
												return output_tokens






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    unicodedata.normalize('NFD'  ,    _UpperCamelCase )
												lowerCAmelCase__         =    []
												for char in text:
																		lowerCAmelCase__         =    unicodedata.category(_UpperCamelCase )
																		if cat == "Mn":
																								continue
																		output.append(_UpperCamelCase )
												return "".join(_UpperCamelCase )






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






												"""simple docstring"""
												if not self.do_split_on_punc or (never_split is not None and text in never_split):
																		return [text]
												lowerCAmelCase__         =    list(_UpperCamelCase )
												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    True
												lowerCAmelCase__         =    []
												while i < len(_UpperCamelCase ):
																		lowerCAmelCase__         =    chars[i]
																		if _is_punctuation(_UpperCamelCase ):
																								output.append([char] )
																								lowerCAmelCase__         =    True
																		else:
																								if start_new_word:
																														output.append([] )
																								lowerCAmelCase__         =    False
																								output[-1].append(_UpperCamelCase )
																		i += 1

												return ["".join(_UpperCamelCase ) for x in output]






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    []
												for char in text:
																		lowerCAmelCase__         =    ord(_UpperCamelCase )
																		if self._is_chinese_char(_UpperCamelCase ):
																								output.append(' ' )
																								output.append(_UpperCamelCase )
																								output.append(' ' )
																		else:
																								output.append(_UpperCamelCase )
												return "".join(_UpperCamelCase )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												# This defines a "chinese character" as anything in the CJK Unicode block:
												#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
												#
												# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
												# despite its name. The modern Korean Hangul alphabet is a different block,
												# as is Japanese Hiragana and Katakana. Those alphabets are used to write
												# space-separated words, so they are not treated specially and handled
												# like the all of the other languages.
												if (
												    (cp >= 0X4E00 and cp <= 0X9FFF)
												    or (cp >= 0X3400 and cp <= 0X4DBF)  #
												    or (cp >= 0X20000 and cp <= 0X2A6DF)  #
												    or (cp >= 0X2A700 and cp <= 0X2B73F)  #
												    or (cp >= 0X2B740 and cp <= 0X2B81F)  #
												    or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
												    or (cp >= 0XF900 and cp <= 0XFAFF)
												    or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
												):  #
																		return True

												return False







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    []
												for char in text:
																		lowerCAmelCase__         =    ord(_UpperCamelCase )
																		if cp == 0 or cp == 0XFFFD or _is_control(_UpperCamelCase ):
																								continue
																		if _is_whitespace(_UpperCamelCase ):
																								output.append(' ' )
																		else:
																								output.append(_UpperCamelCase )
												return "".join(_UpperCamelCase )







def 						_UpperCamelCase (     UpperCamelCase_							:       int		)	-> Dict:

						"""simple docstring"""



						lowerCAmelCase__         =    set()
						lowerCAmelCase__         =    word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)		)
												lowerCAmelCase__         =    char
						return pairs







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]		)	-> int:

						"""simple docstring"""



						lowerCAmelCase__         =    text.replace('—'     ,							'-'		)
						lowerCAmelCase__         =    text.replace('–'     ,							'-'		)
						lowerCAmelCase__         =    text.replace('―'     ,							'-'		)
						lowerCAmelCase__         =    text.replace('…'     ,							'...'		)
						lowerCAmelCase__         =    text.replace('´'     ,							'\''		)
						lowerCAmelCase__         =    re.sub(r'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'     ,							r' \1 '     ,							UpperCamelCase_		)
						lowerCAmelCase__         =    re.sub(r'\s*\n\s*'     ,							' \n '     ,							UpperCamelCase_		)
						lowerCAmelCase__         =    re.sub(r'[^\S\n]+'     ,							' '     ,							UpperCamelCase_		)
						return text.strip()


class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Dict				=       VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE						:							Tuple				=       PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE						:							Tuple				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE						:							List[str]				=       ['''input_ids''', '''attention_mask''']






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase="<unk>"  ,    **_UpperCamelCase ):






												"""simple docstring"""
												super().__init__(unk_token=_UpperCamelCase  ,    **_UpperCamelCase )

												try:
																		import ftfy
																		from spacy.lang.en import English

																		lowerCAmelCase__         =    English()
																		lowerCAmelCase__         =    _nlp.tokenizer
																		lowerCAmelCase__         =    ftfy.fix_text
												except ImportError:
																		logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.' )
																		lowerCAmelCase__         =    BasicTokenizer(do_lower_case=_UpperCamelCase )
																		lowerCAmelCase__         =    None

												with open(_UpperCamelCase  ,    encoding='utf-8' ) as vocab_handle:
																		lowerCAmelCase__         =    json.load(_UpperCamelCase )
												lowerCAmelCase__         =    {v: k for k, v in self.encoder.items()}
												with open(_UpperCamelCase  ,    encoding='utf-8' ) as merges_handle:
																		lowerCAmelCase__         =    merges_handle.read().split('\n' )[1:-1]
												lowerCAmelCase__         =    [tuple(merge.split() ) for merge in merges]
												lowerCAmelCase__         =    dict(zip(_UpperCamelCase  ,    range(len(_UpperCamelCase ) ) ) )
												lowerCAmelCase__         =    {}






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return True






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return len(self.encoder )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return dict(self.encoder  ,    **self.added_tokens_encoder )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    tuple(token[:-1] ) + (token[-1] + '</w>',)
												if token in self.cache:
																		return self.cache[token]
												lowerCAmelCase__         =    get_pairs(_UpperCamelCase )

												if not pairs:
																		return token + "</w>"

												while True:
																		lowerCAmelCase__         =    min(_UpperCamelCase  ,    key=lambda _UpperCamelCase : self.bpe_ranks.get(_UpperCamelCase  ,    float('inf' ) ) )
																		if bigram not in self.bpe_ranks:
																								break
																		lowerCAmelCase__    ,							lowerCAmelCase__         =    bigram
																		lowerCAmelCase__         =    []
																		lowerCAmelCase__         =    0
																		while i < len(_UpperCamelCase ):
																								try:
																														lowerCAmelCase__         =    word.index(_UpperCamelCase  ,    _UpperCamelCase )
																								except ValueError:
																														new_word.extend(word[i:] )
																														break
																								else:
																														new_word.extend(word[i:j] )
																														lowerCAmelCase__         =    j

																								if word[i] == first and i < len(_UpperCamelCase ) - 1 and word[i + 1] == second:
																														new_word.append(first + second )
																														i += 2
																								else:
																														new_word.append(word[i] )
																														i += 1
																		lowerCAmelCase__         =    tuple(_UpperCamelCase )
																		lowerCAmelCase__         =    new_word
																		if len(_UpperCamelCase ) == 1:
																								break
																		else:
																								lowerCAmelCase__         =    get_pairs(_UpperCamelCase )
												lowerCAmelCase__         =    ' '.join(_UpperCamelCase )
												if word == "\n  </w>":
																		lowerCAmelCase__         =    '\n</w>'
												lowerCAmelCase__         =    word
												return word






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    []
												if self.fix_text is None:
																		# Using BERT's BasicTokenizer
																		lowerCAmelCase__         =    self.nlp.tokenize(_UpperCamelCase )
																		for token in text:
																								split_tokens.extend(list(self.bpe(_UpperCamelCase ).split(' ' ) ) )
												else:
																		# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
																		lowerCAmelCase__         =    self.nlp(text_standardize(self.fix_text(_UpperCamelCase ) ) )
																		for token in text:
																								split_tokens.extend(list(self.bpe(token.text.lower() ).split(' ' ) ) )
												return split_tokens






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												return self.encoder.get(_UpperCamelCase  ,    self.encoder.get(self.unk_token ) )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												return self.decoder.get(_UpperCamelCase  ,    self.unk_token )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    ''.join(_UpperCamelCase ).replace('</w>'  ,    ' ' ).strip()
												return out_string







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






												"""simple docstring"""
												if not os.path.isdir(_UpperCamelCase ):
																		logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
																		return
												lowerCAmelCase__         =    os.path.join(
												    _UpperCamelCase  ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
												lowerCAmelCase__         =    os.path.join(
												    _UpperCamelCase  ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

												with open(_UpperCamelCase  ,    'w'  ,    encoding='utf-8' ) as f:
																		f.write(json.dumps(self.encoder  ,    indent=2  ,    sort_keys=_UpperCamelCase  ,    ensure_ascii=_UpperCamelCase ) + '\n' )

												lowerCAmelCase__         =    0
												with open(_UpperCamelCase  ,    'w'  ,    encoding='utf-8' ) as writer:
																		writer.write('#version: 0.2\n' )
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,    key=lambda _UpperCamelCase : kv[1] ):
																								if index != token_index:
																														logger.warning(
																														    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																														    ' Please check that the tokenizer is not corrupted!' )
																														lowerCAmelCase__         =    token_index
																								writer.write(' '.join(_UpperCamelCase ) + '\n' )
																								index += 1

												return vocab_file, merge_file













import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor


class      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






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






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__         =    torchaudio.load(_UpperCamelCase )
												lowerCAmelCase__         =    self.resampler(_UpperCamelCase ).squeeze().numpy()
												return speech






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""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(_UpperCamelCase  ,    sampling_rate=1_60_00  ,    return_tensors='pt'  ,    padding=_UpperCamelCase )
												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(_UpperCamelCase  ,    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(_UpperCamelCase )}
												lowerCAmelCase__         =    []
												for c in transcript:
																		if c in dictionary:
																								tokens.append(dictionary[c] )

												def get_trellis(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase=0 ):
																		lowerCAmelCase__         =    emission.size(0 )
																		lowerCAmelCase__         =    len(_UpperCamelCase )

																		# 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(_UpperCamelCase ):
																								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(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )


												@dataclass
												class      __SCREAMING_SNAKE_CASE       :
																		_SCREAMING_SNAKE_CASE						:							int
																		_SCREAMING_SNAKE_CASE						:							int
																		_SCREAMING_SNAKE_CASE						:							float

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

																		lowerCAmelCase__         =    []
																		for t in range(_UpperCamelCase  ,    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  ,    _UpperCamelCase ) )

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

												lowerCAmelCase__         =    backtrack(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )


												@dataclass
												class      __SCREAMING_SNAKE_CASE       :
																		_SCREAMING_SNAKE_CASE						:							str
																		_SCREAMING_SNAKE_CASE						:							int
																		_SCREAMING_SNAKE_CASE						:							int
																		_SCREAMING_SNAKE_CASE						:							float






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

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







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if not os.path.exists(_UpperCamelCase ):
																		os.makedirs(_UpperCamelCase )

												# load text file
												lowerCAmelCase__         =    open(_UpperCamelCase  ,    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(_UpperCamelCase  ,    wav_name + '.wav' )
																		lowerCAmelCase__         =    os.path.join(_UpperCamelCase  ,    wav_name + '.txt' )

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

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







def 						_UpperCamelCase (     )	-> Tuple:

						"""simple docstring"""



						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()





