



import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor







class    A_    :



						"""simple docstring"""






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






						def 			UpperCAmelCase__  (      self				:Any     ,						lowercase_				:int      )       ->    int:
												UpperCAmelCase   ,					UpperCAmelCase    =				torchaudio.load(lowercase_      )
												UpperCAmelCase    =				self.resampler(lowercase_      ).squeeze().numpy()
												return speech






						def 			UpperCAmelCase__  (      self				:int     ,						lowercase_				:Union[str, Any]      )       ->    List[Any]:
												UpperCAmelCase    =				self.blank_id
												UpperCAmelCase    =				'|'.join(item['sent'].split(' '      )      )
												if not os.path.isfile(item['wav_path']      ):
																		print(item['wav_path']     ,						'not found in wavs directory'      )

												UpperCAmelCase    =				self.speech_file_to_array_fn(item['wav_path']      )
												UpperCAmelCase    =				self.processor(lowercase_     ,						sampling_rate=1_60_00     ,						return_tensors='pt'     ,						padding=lowercase_      )
												if self.cuda:
																		UpperCAmelCase    =				inputs.to(device='cuda'      )

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

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

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

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

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

												UpperCAmelCase    =				get_trellis(lowercase_     ,						lowercase_     ,						lowercase_      )







												@dataclass
												class    A_    :



																		"""simple docstring"""
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42

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

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

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

												UpperCAmelCase    =				backtrack(lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_      )







												@dataclass
												class    A_    :



																		"""simple docstring"""
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42






																		def __repr__(      self				:int      )       ->    Optional[Any]:
																								return f"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""






																		@property
																		def 			UpperCAmelCase__  (      self				:Tuple      )       ->    Any:
																								return self.end - self.start

												def merge_repeats(lowercase_				:Optional[int]      ):
																		UpperCAmelCase   ,					UpperCAmelCase    =				0, 0
																		UpperCAmelCase    =				[]
																		while ia < len(lowercase_      ):
																								while ia < len(lowercase_      ) and path[ia].token_index == path[ia].token_index:
																														ia += 1
																								UpperCAmelCase    =				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_     ,						)      )
																								UpperCAmelCase    =				ia
																		return segments

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






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						lowercase_				:Any     ,						lowercase_				:int     ,						lowercase_				:Tuple      )       ->    Union[str, Any]:
												if not os.path.exists(lowercase_      ):
																		os.makedirs(lowercase_      )

												# load text file
												UpperCAmelCase    =				open(lowercase_     ,						encoding='utf8'      ).readlines()

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

																		UpperCAmelCase   ,					UpperCAmelCase    =				line.strip().split('\t'      )
																		UpperCAmelCase    =				os.path.join(lowercase_     ,						wav_name + '.wav'      )
																		UpperCAmelCase    =				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 				_lowerCAmelCase   (     ):
						UpperCAmelCase    =				argparse.ArgumentParser()

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

						UpperCAmelCase    =				parser.parse_args()

						UpperCAmelCase    =				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"""


from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

snake_case_         = TypeVar("""T""", bound=bool)







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:str     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				value
												UpperCAmelCase    =				None
												UpperCAmelCase    =				None






						@property
						def 			UpperCAmelCase__  (      self				:Any      )       ->    T:
												return self._value






						@staticmethod
						def 			UpperCAmelCase__  (      lowercase_				:RandomizedHeapNode[T] | None     ,						lowercase_				:RandomizedHeapNode[T] | None      )       ->    RandomizedHeapNode[T] | None:
												if not roota:
																		return roota

												if not roota:
																		return roota

												if roota.value > roota.value:
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota, roota

												if random.choice([True, False]      ):
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota.right, roota.left

												UpperCAmelCase    =				RandomizedHeapNode.merge(roota.left     ,						lowercase_      )

												return roota







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:Optional[Any]     ,						lowercase_				:Iterable[T] | None = ()      )       ->    None:
												UpperCAmelCase    =				None

												if data:
																		for item in data:
																								self.insert(lowercase_      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root     ,						RandomizedHeapNode(lowercase_      )      )






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    T | None:
												UpperCAmelCase    =				self.top()

												if self._root is None:
																		return None

												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root.left     ,						self._root.right      )

												return result






						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    T:
												if not self._root:
																		raise IndexError('Can\'t get top element for the empty heap.'      )
												return self._root.value






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    None:
												UpperCAmelCase    =				None






						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    list[Any]:
												UpperCAmelCase    =				[]
												while self:
																		result.append(self.pop()      )

												return result






						def __bool__(      self				:Optional[Any]      )       ->    bool:
												return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





