

"""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 	_lowerCAmelCase    :

							def __init__( self       :    Any ,		a       :    Union[str, Any] ,		a       :    int ,		a       :    Optional[int]    )   ->    str:

									"""simple docstring"""
									lowercase										=							cuda
									lowercase										=							AutoConfig.from_pretrained(_UpperCamelCase    )
									lowercase										=							AutoModelForCTC.from_pretrained(_UpperCamelCase    )
									self.model.eval()
									if self.cuda:
											self.model.to(device='''cuda'''    )
									lowercase										=							AutoProcessor.from_pretrained(_UpperCamelCase    )
									lowercase										=							torchaudio.transforms.Resample(_UpperCamelCase ,		16000    )
									lowercase										=							0
									lowercase										=							list(self.processor.tokenizer.get_vocab().keys()    )
									for i in range(len(_UpperCamelCase    )    ):
											if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
													lowercase										=							i
									print('''Blank Token id [PAD]/<pad>''' ,		_UpperCamelCase    )
									lowercase										=							blank_id

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    Any    )   ->    Tuple:

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

							def 					_lowerCAmelCase  ( self       :    Union[str, Any] ,		a       :    Union[str, Any]    )   ->    List[Any]:

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

									lowercase										=							self.speech_file_to_array_fn(item['''wav_path''']    )
									lowercase										=							self.processor(_UpperCamelCase ,		sampling_rate=16000 ,		return_tensors='''pt''' ,		padding=_UpperCamelCase    )
									if self.cuda:
											lowercase										=							inputs.to(device='''cuda'''    )

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

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

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

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

									def get_trellis(a       :    int ,		a       :    Optional[int] ,		a       :    Dict=0    ):
											lowercase										=							emission.size(0    )
											lowercase										=							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.
											lowercase										=							torch.full((num_frames + 1, num_tokens + 1) ,		-float('''inf'''    )    )
											lowercase										=							0
											for t in range(_UpperCamelCase    ):
													lowercase										=							torch.maximum(
													    # Score for staying at the same token
													    trellis[t, 1:] + emission[t, blank_id] ,		trellis[t, :-1] + emission[t, tokens] ,		)
											return trellis

									lowercase										=							get_trellis(_UpperCamelCase ,		_UpperCamelCase ,		_UpperCamelCase    )
									@dataclass
									class 	_lowerCAmelCase    :
																__lowerCAmelCase					:    int
																__lowerCAmelCase					:    int
																__lowerCAmelCase					:    float

									def backtrack(a       :    List[Any] ,		a       :    List[Any] ,		a       :    List[Any] ,		a       :    int=0    ):
											lowercase										=							trellis.size(1    ) - 1
											lowercase										=							torch.argmax(trellis[:, j]    ).item()

											lowercase										=							[]
											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.
													lowercase										=							trellis[t - 1, j] + emission[t - 1, blank_id]
													# Score for token changing from C-1 at T-1 to J at T.
													lowercase										=							trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

													# 2. Store the path with frame-wise probability.
													lowercase										=							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]

									lowercase										=							backtrack(_UpperCamelCase ,		_UpperCamelCase ,		_UpperCamelCase ,		_UpperCamelCase    )
									@dataclass
									class 	_lowerCAmelCase    :
																__lowerCAmelCase					:    str
																__lowerCAmelCase					:    int
																__lowerCAmelCase					:    int
																__lowerCAmelCase					:    float

																def __repr__( self       :    Optional[int]    )   ->    Optional[int]:

																		"""simple docstring"""
																		return f"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""

																@property
																def 					_lowerCAmelCase  ( self       :    Any    )   ->    Tuple:

																		"""simple docstring"""
																		return self.end - self.start

									def merge_repeats(a       :    Union[str, Any]    ):
											lowercase					,  lowercase										=							0, 0
											lowercase										=							[]
											while ia < len(_UpperCamelCase    ):
													while ia < len(_UpperCamelCase    ) and path[ia].token_index == path[ia].token_index:
															ia += 1
													lowercase										=							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 ,		)    )
													lowercase										=							ia
											return segments

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

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    Dict ,		a       :    Tuple ,		a       :    Union[str, Any]    )   ->    Optional[Any]:

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

									# load text file
									lowercase										=							open(_UpperCamelCase ,		encoding='''utf8'''    ).readlines()

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

											lowercase					,  lowercase										=							line.strip().split('''\t'''    )
											lowercase										=							os.path.join(_UpperCamelCase ,		wav_name + '''.wav'''    )
											lowercase										=							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 		A_	(   ):
		lowercase										=							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_60_00       ,	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'''				)

		lowercase										=							parser.parse_args()

		lowercase										=							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()







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()





