





"""simple docstring"""
from __future__ import annotations

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						list[int] ):
 if not list_of_ints:
  return []

 if min(_UpperCAmelCase ) < 0:
  raise ValueError('All numbers must be positive' )

 lowerCAmelCase													=   max(len(bin(_UpperCAmelCase )[2:] ) for x in list_of_ints )
 return _msd_radix_sort(_UpperCAmelCase     ,    _UpperCAmelCase )

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						list[int]     ,    _UpperCAmelCase				:						int ):
 if bit_position == 0 or len(_UpperCAmelCase ) in [0, 1]:
  return list_of_ints

 lowerCAmelCase													=   []
 lowerCAmelCase													=   []
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(_UpperCAmelCase )
  else:
   # number has a zero at bit bit_position
   zeros.append(_UpperCAmelCase )

    # recursively split both lists further
 lowerCAmelCase													=   _msd_radix_sort(_UpperCAmelCase     ,    bit_position - 1 )
 lowerCAmelCase													=   _msd_radix_sort(_UpperCAmelCase     ,    bit_position - 1 )

 # recombine lists
 lowerCAmelCase													=   zeros
 res.extend(_UpperCAmelCase )

 return res

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						list[int] ):
 lowerCAmelCase													=   len(_UpperCAmelCase )
 if not list_of_ints or length == 1:
  return

 if min(_UpperCAmelCase ) < 0:
  raise ValueError('All numbers must be positive' )

 lowerCAmelCase													=   max(len(bin(_UpperCAmelCase )[2:] ) for x in list_of_ints )
 _msd_radix_sort_inplace(_UpperCAmelCase     ,    _UpperCAmelCase     ,    0     ,    _UpperCAmelCase )

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						list[int]     ,    _UpperCAmelCase				:						int     ,    _UpperCAmelCase				:						int     ,    _UpperCAmelCase				:						int ):
 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 lowerCAmelCase													=   begin_index
 lowerCAmelCase													=   end_index - 1
 while i <= j:
  lowerCAmelCase													=   False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   lowerCAmelCase													=   True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   lowerCAmelCase													=   True

  if changed:
   continue

  lowerCAmelCase							,lowerCAmelCase													=   list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(_UpperCAmelCase     ,    _UpperCAmelCase     ,    _UpperCAmelCase     ,    _UpperCAmelCase )
 _msd_radix_sort_inplace(_UpperCAmelCase     ,    _UpperCAmelCase     ,    _UpperCAmelCase     ,    _UpperCAmelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()












"""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       :



 def __init__(    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






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



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase													=   torchaudio.load(_snake_case    )
  lowerCAmelCase													=   self.resampler(_snake_case    ).squeeze().numpy()
  return speech



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






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

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

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

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

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

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

  def get_trellis(_snake_case							,					_snake_case							,					_snake_case=0    ):
   lowerCAmelCase													=   emission.size(0    )
   lowerCAmelCase													=   len(_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.
   lowerCAmelCase													=   torch.full((num_frames + 1, num_tokens + 1)							,					-float('inf'    )    )
   lowerCAmelCase													=   0
   for t in range(_snake_case    ):
    lowerCAmelCase													=   torch.maximum(
        # Score for staying at the same token
        trellis[t, 1:] + emission[t, blank_id]							,					trellis[t, :-1] + emission[t, tokens]							,					)
   return trellis

  lowerCAmelCase													=   get_trellis(_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42

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

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

    # 2. Store the path with frame-wise probability.
    lowerCAmelCase													=   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							,					_snake_case    )    )

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

  lowerCAmelCase													=   backtrack(_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42



   def __repr__(    self    ):







    """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    ):







    """simple docstring"""






    return self.end - self.start

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

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



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






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

  # load text file
  lowerCAmelCase													=   open(_snake_case							,					encoding='utf8'    ).readlines()

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

   lowerCAmelCase							,lowerCAmelCase													=   line.strip().split('\t'    )
   lowerCAmelCase													=   os.path.join(_snake_case							,					wav_name + '.wav'    )
   lowerCAmelCase													=   os.path.join(_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(_snake_case    )    )

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


def 		_SCREAMING_SNAKE_CASE ():
 lowerCAmelCase													=   argparse.ArgumentParser()

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

 lowerCAmelCase													=   parser.parse_args()

 lowerCAmelCase													=   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()





