
"""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 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					cuda
										__lowercase						      =					AutoConfig.from_pretrained(lowerCamelCase__					)
										__lowercase						      =					AutoModelForCTC.from_pretrained(lowerCamelCase__					)
										self.model.eval()
										if self.cuda:
													self.model.to(device="cuda"					)
										__lowercase						      =					AutoProcessor.from_pretrained(lowerCamelCase__					)
										__lowercase						      =					torchaudio.transforms.Resample(lowerCamelCase__     ,						16_000					)
										__lowercase						      =					0
										__lowercase						      =					list(self.processor.tokenizer.get_vocab().keys()					)
										for i in range(len(lowerCamelCase__					)					):
													if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																__lowercase						      =					i
										print("Blank Token id [PAD]/<pad>"     ,						lowerCamelCase__					)
										__lowercase						      =					blank_id
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					torchaudio.load(lowerCamelCase__					)
										__lowercase						      =					self.resampler(lowerCamelCase__					).squeeze().numpy()
										return speech
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       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(lowerCamelCase__     ,						sampling_rate=16_000     ,						return_tensors="pt"     ,						padding=lowerCamelCase__					)
										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(lowerCamelCase__     ,						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(lowerCamelCase__					)}
										__lowercase						      =					[]
										for c in transcript:
													if c in dictionary:
																tokens.append(dictionary[c]					)

										def get_trellis(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=0					):
													__lowercase						      =					emission.size(0					)
													__lowercase						      =					len(lowerCamelCase__					)

													# 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(lowerCamelCase__					):
																__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(lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					)

										@dataclass
										class 				__a    :



																	'''simple docstring'''







																	_lowerCamelCase				: int
																	_lowerCamelCase				: int
																	_lowerCamelCase				: float

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

													__lowercase						      =					[]
													for t in range(lowerCamelCase__     ,						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     ,						lowerCamelCase__					)					)

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

										__lowercase						      =					backtrack(lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					)

										@dataclass
										class 				__a    :



																	'''simple docstring'''







																	_lowerCamelCase				: str
																	_lowerCamelCase				: int
																	_lowerCamelCase				: int
																	_lowerCamelCase				: float
																	def __repr__(    self					) ->       Optional[Any]:




																				'''simple docstring'''
																				return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''





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




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

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

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





							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       int:




										'''simple docstring'''
										if not os.path.exists(lowerCamelCase__					):
													os.makedirs(lowerCamelCase__					)

										# load text file
										__lowercase						      =					open(lowerCamelCase__     ,						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						      =					line.strip().split("\t"					)
													__lowercase						      =					os.path.join(lowerCamelCase__     ,						wav_name + ".wav"					)
													__lowercase						      =					os.path.join(lowerCamelCase__     ,						wav_name + ".txt"					)

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

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





def     lowerCAmelCase__ (		)	->Any:
			__lowercase						      =					argparse.ArgumentParser()

			parser.add_argument(
			    "--model_name" ,      type=__magic_name__ ,      default="arijitx/wav2vec2-xls-r-300m-bengali" ,      help="wav2vec model name"			)
			parser.add_argument("--wav_dir" ,      type=__magic_name__ ,      default="./wavs" ,      help="directory containing wavs"			)
			parser.add_argument("--text_file" ,      type=__magic_name__ ,      default="script.txt" ,      help="file containing text"			)
			parser.add_argument("--input_wavs_sr" ,      type=__magic_name__ ,      default=1_6_0_0_0 ,      help="sampling rate of input audios"			)
			parser.add_argument(
			    "--output_dir" ,      type=__magic_name__ ,      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()







"""simple docstring"""






class 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					data
										__lowercase						      =					None
										__lowercase						      =					None





							def __str__(    self					) ->       str:




										'''simple docstring'''
										return f'''{self.data}'''





class 				__a    :



							'''simple docstring'''







							def __init__(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					None
										__lowercase						      =					None
							def __iter__(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					self.head
										while node:
													yield node.data
													__lowercase						      =					node.next
							def __str__(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										return "->".join([str(_lowerCamelCase					) for item in self]					)
							def __len__(    self					) ->       Dict:




										'''simple docstring'''
										return sum(1 for _ in self					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Union[str, Any]:




										'''simple docstring'''
										self.insert_at_nth(0     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       List[str]:




										'''simple docstring'''
										self.insert_at_nth(len(self					)     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       List[Any]:




										'''simple docstring'''
										__lowercase						      =					len(self					)

										if not 0 <= index <= length:
													raise IndexError("list index out of range"					)
										__lowercase						      =					Node(_lowerCamelCase					)
										if self.head is None:
													__lowercase						      =					__lowercase						      =					new_node
										elif index == 0:
													__lowercase						      =					new_node
													__lowercase						      =					self.head
													__lowercase						      =					new_node
										elif index == length:
													__lowercase						      =					new_node
													__lowercase						      =					self.tail
													__lowercase						      =					new_node
										else:
													__lowercase						      =					self.head
													for _ in range(0     ,						_lowerCamelCase					):
																__lowercase						      =					temp.next
													__lowercase						      =					new_node
													__lowercase						      =					temp.previous
													__lowercase						      =					temp
													__lowercase						      =					new_node
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										return self.delete_at_nth(0					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										return self.delete_at_nth(len(self					) - 1					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					len(self					)

										if not 0 <= index <= length - 1:
													raise IndexError("list index out of range"					)
										__lowercase						      =					self.head  # default first node
										if length == 1:
													__lowercase						      =					__lowercase						      =					None
										elif index == 0:
													__lowercase						      =					self.head.next
													__lowercase						      =					None
										elif index == length - 1:
													__lowercase						      =					self.tail
													__lowercase						      =					self.tail.previous
													__lowercase						      =					None
										else:
													__lowercase						      =					self.head
													for _ in range(0     ,						_lowerCamelCase					):
																__lowercase						      =					temp.next
													__lowercase						      =					temp
													__lowercase						      =					temp.previous
													__lowercase						      =					temp.next
										return delete_node.data
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					self.head

										while current.data != data:  # Find the position to delete
													if current.next:
																__lowercase						      =					current.next
													else:  # We have reached the end an no value matches
																raise ValueError("No data matching given value"					)

										if current == self.head:
													self.delete_head()

										elif current == self.tail:
													self.delete_tail()

										else:  # Before: 1 <--> 2(current) <--> 3
													__lowercase						      =					current.next  # 1 --> 3
													__lowercase						      =					current.previous  # 1 <--> 3
										return data





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




										'''simple docstring'''
										return len(self					) == 0





def     lowerCAmelCase__ (		)	->None:
			__lowercase						      =					DoublyLinkedList()
			assert linked_list.is_empty() is True
			assert str(__magic_name__			) == ""

			try:
						linked_list.delete_head()
						raise AssertionError  # This should not happen.
			except IndexError:
						assert True  # This should happen.

			try:
						linked_list.delete_tail()
						raise AssertionError  # This should not happen.
			except IndexError:
						assert True  # This should happen.

			for i in range(1_0			):
						assert len(__magic_name__			) == i
						linked_list.insert_at_nth(__magic_name__ ,      i + 1			)
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(1 ,      1_1			)			)

			linked_list.insert_at_head(0			)
			linked_list.insert_at_tail(1_1			)
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(0 ,      1_2			)			)

			assert linked_list.delete_head() == 0
			assert linked_list.delete_at_nth(9			) == 1_0
			assert linked_list.delete_tail() == 1_1
			assert len(__magic_name__			) == 9
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(1 ,      1_0			)			)


if __name__ == "__main__":
					from doctest import testmod

					testmod()





