
import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
class      A__       :





							def __init__(			self  :       Tuple					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       List[str]    )						->		Any:



											"""simple docstring"""


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





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       Optional[int]    )						->		Dict:



											"""simple docstring"""


											__lowercase		, __lowercase								= torchaudio.load(__SCREAMING_SNAKE_CASE    )
											__lowercase								= self.resampler(__SCREAMING_SNAKE_CASE    ).squeeze().numpy()
											return speech





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       int    )						->		str:



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

											def get_trellis(_UpperCAmelCase  :       Dict					,			_UpperCAmelCase  :       Union[str, Any]					,			_UpperCAmelCase  :       Optional[Any]=0    ):
															__lowercase								= emission.size(0    )
															__lowercase								= 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.
															__lowercase								= torch.full((num_frames + 1, num_tokens + 1)					,			-float('inf'    )    )
															__lowercase								= 0
															for t in range(__SCREAMING_SNAKE_CASE    ):
																			__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(__SCREAMING_SNAKE_CASE					,			__SCREAMING_SNAKE_CASE					,			__SCREAMING_SNAKE_CASE    )
											@dataclass
											class      A__       :
																		lowerCAmelCase__		:						int
																		lowerCAmelCase__		:						int
																		lowerCAmelCase__		:						float

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

															__lowercase								= []
															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.
																			__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					,			__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]

											__lowercase								= backtrack(__SCREAMING_SNAKE_CASE					,			__SCREAMING_SNAKE_CASE					,			__SCREAMING_SNAKE_CASE					,			__SCREAMING_SNAKE_CASE    )
											@dataclass
											class      A__       :
																		lowerCAmelCase__		:						str
																		lowerCAmelCase__		:						int
																		lowerCAmelCase__		:						int
																		lowerCAmelCase__		:						float





																		def __repr__(			self  :       Tuple    )						->		int:



																						"""simple docstring"""


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





																		@property
																		def       a__			(			self  :       List[Any]    )						->		str:



																						"""simple docstring"""


																						return self.end - self.start

											def merge_repeats(_UpperCAmelCase  :       Optional[Any]    ):
															__lowercase		, __lowercase								= 0, 0
															__lowercase								= []
															while ia < len(__SCREAMING_SNAKE_CASE    ):
																			while ia < len(__SCREAMING_SNAKE_CASE    ) and path[ia].token_index == path[ia].token_index:
																							ia += 1
																			__lowercase								= 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					,			)    )
																			__lowercase								= ia
															return segments

											__lowercase								= 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       a__			(			self  :       Dict					,			_UpperCAmelCase  :       Dict					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Optional[int]    )						->		Dict:



											"""simple docstring"""


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

											# load text file
											__lowercase								= open(__SCREAMING_SNAKE_CASE					,			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(__SCREAMING_SNAKE_CASE					,			wav_name + '.wav'    )
															__lowercase								= 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       (		)					->						Optional[Any]:
				__lowercase								= argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name'   ,							type=snake_case__   ,							default='arijitx/wav2vec2-xls-r-300m-bengali'   ,							help='wav2vec model name'      )
				parser.add_argument('--wav_dir'   ,							type=snake_case__   ,							default='./wavs'   ,							help='directory containing wavs'      )
				parser.add_argument('--text_file'   ,							type=snake_case__   ,							default='script.txt'   ,							help='file containing text'      )
				parser.add_argument('--input_wavs_sr'   ,							type=snake_case__   ,							default=16000   ,							help='sampling rate of input audios'      )
				parser.add_argument(
				    '--output_dir'   ,							type=snake_case__   ,							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()







from __future__ import annotations

from enum import Enum
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Dict         =							24
							lowerCAmelCase__		:						Dict         =							21
							lowerCAmelCase__		:						Union[str, Any]         =							18
							lowerCAmelCase__		:						Dict         =							15
							lowerCAmelCase__		:						Any         =							12
							lowerCAmelCase__		:						Union[str, Any]         =							9
							lowerCAmelCase__		:						Any         =							6
							lowerCAmelCase__		:						Optional[Any]         =							3
							lowerCAmelCase__		:						Union[str, Any]         =							2
							lowerCAmelCase__		:						List[Any]         =							1
							lowerCAmelCase__		:						Optional[Any]         =							-1
							lowerCAmelCase__		:						Tuple         =							-2
							lowerCAmelCase__		:						Dict         =							-3
							lowerCAmelCase__		:						Union[str, Any]         =							-6
							lowerCAmelCase__		:						Any         =							-9
							lowerCAmelCase__		:						Union[str, Any]         =							-12
							lowerCAmelCase__		:						str         =							-15
							lowerCAmelCase__		:						Optional[int]         =							-18
							lowerCAmelCase__		:						Any         =							-21
							lowerCAmelCase__		:						Any         =							-24






class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							8
							lowerCAmelCase__		:						Union[str, Any]         =							7
							lowerCAmelCase__		:						Optional[Any]         =							6
							lowerCAmelCase__		:						Tuple         =							5
							lowerCAmelCase__		:						Optional[int]         =							4
							lowerCAmelCase__		:						Optional[int]         =							3
							lowerCAmelCase__		:						Optional[int]         =							2
							lowerCAmelCase__		:						Any         =							1






def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float   ,							SCREAMING_SNAKE_CASE	:				str | SIUnit   ,							SCREAMING_SNAKE_CASE	:				str | SIUnit   ,							)					->						float:
				if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								__lowercase								= SIUnit[known_prefix.lower()]
				if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								__lowercase								= SIUnit[unknown_prefix.lower()]
				__lowercase								= known_amount * (
				    10 ** (known_prefix.value - unknown_prefix.value)
				)
				return unknown_amount




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float   ,							SCREAMING_SNAKE_CASE	:				str | BinaryUnit   ,							SCREAMING_SNAKE_CASE	:				str | BinaryUnit   ,							)					->						float:
				if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								__lowercase								= BinaryUnit[known_prefix.lower()]
				if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								__lowercase								= BinaryUnit[unknown_prefix.lower()]
				__lowercase								= known_amount * (
				    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
				)
				return unknown_amount


if __name__ == "__main__":
							import doctest

							doctest.testmod()





