



'''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  :							Dict      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      cuda
					SCREAMING_SNAKE_CASE_						:int										=      AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      AutoModelForCTC.from_pretrained(SCREAMING_SNAKE_CASE	)
					self.model.eval()
					if self.cuda:
							self.model.to(device='cuda'	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      torchaudio.transforms.Resample(SCREAMING_SNAKE_CASE      ,       16_000	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      0
					SCREAMING_SNAKE_CASE_						:List[str]										=      list(self.processor.tokenizer.get_vocab().keys()	)
					for i in range(len(SCREAMING_SNAKE_CASE	)	):
							if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
									SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      i
					print('Blank Token id [PAD]/<pad>'      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      blank_id






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      torchaudio.load(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:int										=      self.resampler(SCREAMING_SNAKE_CASE	).squeeze().numpy()
					return speech






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.blank_id
					SCREAMING_SNAKE_CASE_						:Any										=      '|'.join(item['sent'].split(' '	)	)
					if not os.path.isfile(item['wav_path']	):
							print(item['wav_path']      ,       'not found in wavs directory'	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.speech_file_to_array_fn(item['wav_path']	)
					SCREAMING_SNAKE_CASE_						:int										=      self.processor(SCREAMING_SNAKE_CASE      ,       sampling_rate=16_000      ,       return_tensors='pt'      ,       padding=SCREAMING_SNAKE_CASE	)
					if self.cuda:
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      inputs.to(device='cuda'	)

					with torch.no_grad():
							SCREAMING_SNAKE_CASE_						:List[str]										=      self.model(inputs.input_values	).logits

					# get the emission probability at frame level
					SCREAMING_SNAKE_CASE_						:Dict										=      torch.log_softmax(SCREAMING_SNAKE_CASE      ,       dim=-1	)
					SCREAMING_SNAKE_CASE_						:int										=      emissions[0].cpu().detach()

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

					SCREAMING_SNAKE_CASE_						:List[Any]										=      {c: i for i, c in enumerate(SCREAMING_SNAKE_CASE	)}
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      []
					for c in transcript:
							if c in dictionary:
									tokens.append(dictionary[c]	)

					def get_trellis(SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=0	):
							SCREAMING_SNAKE_CASE_						:Any										=      emission.size(0	)
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      len(SCREAMING_SNAKE_CASE	)

							# 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_						:str										=      torch.full((num_frames + 1, num_tokens + 1)      ,       -float('inf'	)	)
							SCREAMING_SNAKE_CASE_						:Dict										=      0
							for t in range(SCREAMING_SNAKE_CASE	):
									SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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_						:List[Any]										=      get_trellis(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)




					@dataclass
					class 				__lowerCAmelCase:
								__snake_case  :		int
								__snake_case  :		int
								__snake_case  :		float

					def backtrack(SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=0	):
							SCREAMING_SNAKE_CASE_						:List[str]										=      trellis.size(1	) - 1
							SCREAMING_SNAKE_CASE_						:Any										=      torch.argmax(trellis[:, j]	).item()

							SCREAMING_SNAKE_CASE_						:List[Any]										=      []
							for t in range(SCREAMING_SNAKE_CASE      ,       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_						:Any										=      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_						:int										=      trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

									# 2. Store the path with frame-wise probability.
									SCREAMING_SNAKE_CASE_						: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      ,       SCREAMING_SNAKE_CASE	)	)

									# 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_						:List[str]										=      backtrack(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)




					@dataclass
					class 				__lowerCAmelCase:
								__snake_case  :		str
								__snake_case  :		int
								__snake_case  :		int
								__snake_case  :		float






								def __repr__(   self  :							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 				_lowercase	(   self  :							List[str]	):




										"""simple docstring"""







										return self.end - self.start

					def merge_repeats(SCREAMING_SNAKE_CASE  :							Tuple	):
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Any										=      0, 0
							SCREAMING_SNAKE_CASE_						:List[Any]										=      []
							while ia < len(SCREAMING_SNAKE_CASE	):
									while ia < len(SCREAMING_SNAKE_CASE	) and path[ia].token_index == path[ia].token_index:
											ia += 1
									SCREAMING_SNAKE_CASE_						:int										=      sum(path[k].score for k in range(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)	) / (ia - ia)
									segments.append(
									    Segment(
									        transcript[path[ia].token_index]      ,       path[ia].time_index      ,       path[ia - 1].time_index + 1      ,       SCREAMING_SNAKE_CASE      ,       )	)
									SCREAMING_SNAKE_CASE_						:str										=      ia
							return segments

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






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







					if not os.path.exists(SCREAMING_SNAKE_CASE	):
							os.makedirs(SCREAMING_SNAKE_CASE	)

					# load text file
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      open(SCREAMING_SNAKE_CASE      ,       encoding='utf8'	).readlines()

					SCREAMING_SNAKE_CASE_						:List[Any]										=      []
					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_						:Union[str, Any]										=      line.strip().split('\t'	)
							SCREAMING_SNAKE_CASE_						:Tuple										=      os.path.join(SCREAMING_SNAKE_CASE      ,       wav_name + '.wav'	)
							SCREAMING_SNAKE_CASE_						:Dict										=      os.path.join(SCREAMING_SNAKE_CASE      ,       wav_name + '.txt'	)

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

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







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Dict										=      argparse.ArgumentParser()

		parser.add_argument(
		    '--model_name' ,		type=SCREAMING_SNAKE_CASE ,		default='arijitx/wav2vec2-xls-r-300m-bengali' ,		help='wav2vec model name'     )
		parser.add_argument('--wav_dir' ,		type=SCREAMING_SNAKE_CASE ,		default='./wavs' ,		help='directory containing wavs'     )
		parser.add_argument('--text_file' ,		type=SCREAMING_SNAKE_CASE ,		default='script.txt' ,		help='file containing text'     )
		parser.add_argument('--input_wavs_sr' ,		type=SCREAMING_SNAKE_CASE ,		default=1_6000 ,		help='sampling rate of input audios'     )
		parser.add_argument(
		    '--output_dir' ,		type=SCREAMING_SNAKE_CASE ,		default='./out_alignment' ,		help='output directory containing the alignment files'     )
		parser.add_argument('--cuda' ,		action='store_true'     )

		SCREAMING_SNAKE_CASE_						:Optional[int]										=      parser.parse_args()

		SCREAMING_SNAKE_CASE_						:List[str]										=      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()





'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


SCREAMING_SNAKE_CASE__     : Dict				=   logging.get_logger(__name__)




class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			def __init__(   self  :							List[Any]      ,       *SCREAMING_SNAKE_CASE  :							int      ,       **SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					warnings.warn(
					    'The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use MaskFormerImageProcessor instead.'      ,       SCREAMING_SNAKE_CASE      ,       )
					super().__init__(*SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)
