


import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class   lowerCamelCase						:

       def __init__(      self						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






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

       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        snake_case	,       snake_case :			Any							=   torchaudio.load(a__       )
        snake_case :			Optional[int]							=   self.resampler(a__       ).squeeze().numpy()
        return speech

       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






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

        snake_case :			str							=   self.speech_file_to_array_fn(item['''wav_path''']       )
        snake_case :			Any							=   self.processor(a__						,    sampling_rate=1_60_00						,    return_tensors='''pt'''						,    padding=a__       )
        if self.cuda:
         snake_case :			Tuple							=   inputs.to(device='''cuda'''       )

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

        # get the emission probability at frame level
        snake_case :			Optional[int]							=   torch.log_softmax(a__						,    dim=-1       )
        snake_case :			Tuple							=   emissions[0].cpu().detach()

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

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

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

        snake_case :			Optional[int]							=   get_trellis(a__						,    a__						,    a__       )

        @dataclass
        class   lowerCamelCase						:
               _UpperCamelCase     		=   42
               _UpperCamelCase     		=   42
               _UpperCamelCase     		=   42

        def backtrack(a__						,    a__						,    a__						,    a__=0       ):
         snake_case :			Any							=   trellis.size(1       ) - 1
         snake_case :			Union[str, Any]							=   torch.argmax(trellis[:, j]       ).item()

         snake_case :			int							=   []
         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.
          snake_case :			Union[str, Any]							=   trellis[t - 1, j] + emission[t - 1, blank_id]
          # Score for token changing from C-1 at T-1 to J at T.
          snake_case :			Any							=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

          # 2. Store the path with frame-wise probability.
          snake_case :			Any							=   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]

        snake_case :			Tuple							=   backtrack(a__						,    a__						,    a__						,    a__       )

        @dataclass
        class   lowerCamelCase						:
               _UpperCamelCase     		=   42
               _UpperCamelCase     		=   42
               _UpperCamelCase     		=   42
               _UpperCamelCase     		=   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(a__       ):
         snake_case	,       snake_case :			List[Any]							=   0, 0
         snake_case :			Tuple							=   []
         while ia < len(a__       ):
          while ia < len(a__       ) and path[ia].token_index == path[ia].token_index:
           ia += 1
          snake_case :			Dict							=   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__						,    )       )
          snake_case :			Dict							=   ia
         return segments

        snake_case :			Optional[int]							=   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__       ):






        '''simple docstring'''






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

        # load text file
        snake_case :			Tuple							=   open(a__						,    encoding='''utf8'''       ).readlines()

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

         snake_case	,       snake_case :			Tuple							=   line.strip().split('''\t'''       )
         snake_case :			Optional[int]							=   os.path.join(a__						,    wav_name + '''.wav'''       )
         snake_case :			int							=   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						( )		->Optional[Any]:


 '''simple docstring'''
 snake_case :			int							=   argparse.ArgumentParser()

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

 snake_case :			Dict							=   parser.parse_args()

 snake_case :			List[str]							=   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()










a					:				Tuple       =      [0.4_85, 0.4_56, 0.4_06]
a					:				Optional[int]       =      [0.2_29, 0.2_24, 0.2_25]
a					:				List[Any]       =      [0.5, 0.5, 0.5]
a					:				Tuple       =      [0.5, 0.5, 0.5]
a					:				Optional[Any]       =      [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
a					:				int       =      [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]






