

import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor



class     lowercase						:



 '''simple docstring'''


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






  """simple docstring"""





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




 def   UpperCamelCase__							(self       ,       __a ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__				,    UpperCAmelCase__							=				torchaudio.load(__a )
  UpperCAmelCase__							=				self.resampler(__a ).squeeze().numpy()
  return speech




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






  """simple docstring"""





  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(__a       ,       sampling_rate=16000       ,       return_tensors='pt'       ,       padding=__a )
  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(__a       ,       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(__a )}
  UpperCAmelCase__							=				[]
  for c in transcript:
   if c in dictionary:
    tokens.append(dictionary[c] )

  def get_trellis(__a       ,       __a       ,       __a=0 ):
   UpperCAmelCase__							=				emission.size(0 )
   UpperCAmelCase__							=				len(__a )

   # 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(__a ):
    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(__a       ,       __a       ,       __a )



  @dataclass
  class     lowercase						:



   '''simple docstring'''


   __SCREAMING_SNAKE_CASE												=				42
   __SCREAMING_SNAKE_CASE												=				42
   __SCREAMING_SNAKE_CASE												=				42

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

   UpperCAmelCase__							=				[]
   for t in range(__a       ,       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       ,       __a ) )

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

  UpperCAmelCase__							=				backtrack(__a       ,       __a       ,       __a       ,       __a )



  @dataclass
  class     lowercase						:



   '''simple docstring'''


   __SCREAMING_SNAKE_CASE												=				42
   __SCREAMING_SNAKE_CASE												=				42
   __SCREAMING_SNAKE_CASE												=				42
   __SCREAMING_SNAKE_CASE												=				42




   def __repr__(self ) ->					List[Any]:






    """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 ) ->					Optional[Any]:






    """simple docstring"""





    return self.end - self.start

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

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




 def   UpperCamelCase__							(self       ,       __a       ,       __a       ,       __a ) ->					Optional[Any]:






  """simple docstring"""





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

  # load text file
  UpperCAmelCase__							=				open(__a       ,       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(__a       ,       wav_name + '.wav' )
   UpperCAmelCase__							=				os.path.join(__a       ,       wav_name + '.txt' )

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

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







def 						UpperCamelCase_(			)       -> List[str]:
 UpperCAmelCase__							=				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=1_60_00  ,    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'   )

 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()









from typing import Any





class 		A__				:






	def __init__(	self						)      ->   int:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							[]
						__magic_name__					:							str		   =							0






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




						"""simple docstring"""






						__magic_name__					:							str		   =							'''<''' + str(self.stack						)[1:-1] + '''>'''
						return printed






	def  lowercase      (	self				,     lowerCamelCase						)      ->   None:




						"""simple docstring"""






						self.stack.append(lowerCamelCase						)
						__magic_name__					:							Tuple		   =							self.length + 1






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						self.rotate(1						)
						__magic_name__					:							Union[str, Any]		   =							self.stack[self.length - 1]
						__magic_name__					:							Union[str, Any]		   =							self.stack[:-1]
						self.rotate(self.length - 1						)
						__magic_name__					:							List[Any]		   =							self.length - 1
						return dequeued






	def  lowercase      (	self				,     lowerCamelCase						)      ->   None:




						"""simple docstring"""






						for _ in range(lowerCamelCase						):
											__magic_name__					:							str		   =							self.stack[0]
											__magic_name__					:							Optional[Any]		   =							self.stack[1:]
											self.put(lowerCamelCase						)
											__magic_name__					:							List[Any]		   =							self.length - 1






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							self.get()
						self.put(lowerCamelCase						)
						self.rotate(self.length - 1						)
						return front






	def  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						return self.length



