


'''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  lowercase_					:


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


    def        __a							(					self  , a   ):
          UpperCamelCase__ ,     UpperCamelCase__  =    torchaudio.load(UpperCamelCase__   )
          UpperCamelCase__  =    self.resampler(UpperCamelCase__   ).squeeze().numpy()
          return speech


    def        __a							(					self  , a   ):
          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(UpperCamelCase__  , sampling_rate=1_60_00  , return_tensors="pt"  , padding=UpperCamelCase__   )
          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(UpperCamelCase__  , 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(UpperCamelCase__   )}
          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(UpperCamelCase__   )

                # 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(UpperCamelCase__   ):
                      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(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__   )
          @dataclass
          class  lowercase_					:
              __UpperCAmelCase             =     42
              __UpperCAmelCase             =     42
              __UpperCAmelCase             =     42

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

                UpperCamelCase__  =    []
                for t in range(UpperCamelCase__  , 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  , UpperCamelCase__   )   )

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

          UpperCamelCase__  =    backtrack(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__   )
          @dataclass
          class  lowercase_					:
              __UpperCAmelCase             =     42
              __UpperCAmelCase             =     42
              __UpperCAmelCase             =     42
              __UpperCAmelCase             =     42


              def __repr__(					self   ):
                    return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''


              @property
              def        __a							(					self   ):
                    return self.end - self.start

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

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


    def        __a							(					self  , a  , a  , a   ):






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

          # load text file
          UpperCamelCase__  =    open(UpperCamelCase__  , 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(UpperCamelCase__  , wav_name + ".wav"   )
                UpperCamelCase__  =    os.path.join(UpperCamelCase__  , wav_name + ".txt"   )

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

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







def _UpperCamelCase	(					)							->		Optional[int]:


      '''simple docstring'''



      UpperCamelCase__  =    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=16000				,      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"   )

      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()







'''simple docstring'''







def _UpperCamelCase	(					__A   )							->		int:


      '''simple docstring'''



      if number != int(__A   ):
            raise ValueError("factorial() only accepts integral values"   )
      if number < 0:
            raise ValueError("factorial() not defined for negative values"   )
      UpperCamelCase__  =    1
      for i in range(1				,      number + 1   ):
            value *= i
      return value







def _UpperCamelCase	(					__A   )							->		int:


      '''simple docstring'''



      if not isinstance(__A				,      __A   ):
            raise ValueError("factorial() only accepts integral values"   )
      if n < 0:
            raise ValueError("factorial() not defined for negative values"   )
      return 1 if n == 0 or n == 1 else n * factorial(n - 1   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a__  : str				=	int(input('Enter a positive integer: ').strip() or 0)
  print(F"""factorial{n} is {factorial(n)}""")





