




'''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        SCREAMING_SNAKE_CASE:

       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					Optional[int]:

           """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__					,		1_6000     )
           __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     snake_case__			(    self					,		lowerCamelCase__     )    ->					Any:

           """simple docstring"""



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

       def     snake_case__			(    self					,		lowerCamelCase__     )    ->					Dict:

           """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=1_6000					,		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        SCREAMING_SNAKE_CASE:
                  snake_case_ :     int
                  snake_case_ :     int
                  snake_case_ :     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        SCREAMING_SNAKE_CASE:
                  snake_case_ :     str
                  snake_case_ :     int
                  snake_case_ :     int
                  snake_case_ :     float

                  def __repr__(    self     )    ->					Dict:

                      """simple docstring"""



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




                  @property
                  def     snake_case__			(    self     )    ->					Optional[int]:

                      """simple docstring"""



                      return self.end - self.start

           def merge_repeats(lowerCamelCase__     ):
               __lowercase				,__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     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				,__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 				snake_case_  (   ):



    """simple docstring"""




    __lowercase							=						argparse.ArgumentParser()

    parser.add_argument(
        """--model_name"""    ,type=a__    ,default="""arijitx/wav2vec2-xls-r-300m-bengali"""    ,help="""wav2vec model name"""			)
    parser.add_argument("""--wav_dir"""    ,type=a__    ,default="""./wavs"""    ,help="""directory containing wavs"""			)
    parser.add_argument("""--text_file"""    ,type=a__    ,default="""script.txt"""    ,help="""file containing text"""			)
    parser.add_argument("""--input_wavs_sr"""    ,type=a__    ,default=1_60_00    ,help="""sampling rate of input audios"""			)
    parser.add_argument(
        """--output_dir"""    ,type=a__    ,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'''
def 				snake_case_  (   a__		:	int			):



    """simple docstring"""




    if not isinstance(a__    ,a__			) or number < 0:
        raise ValueError("""Input must be a non-negative integer"""			)

    __lowercase							=						number & ~(number - 1)
    __lowercase							=						0
    while intermediate:
        intermediate >>= 1
        index += 1
    return index - 1


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)




