






"""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  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   Any:




          '''simple docstring'''




          lowercase__:							Tuple         =     cuda
          lowercase__:							int         =     AutoConfig.from_pretrained(UpperCAmelCase_)
          lowercase__:							Union[str, Any]         =     AutoModelForCTC.from_pretrained(UpperCAmelCase_)
          self.model.eval()
          if self.cuda:
                 self.model.to(device="cuda")
          lowercase__:							int         =     AutoProcessor.from_pretrained(UpperCAmelCase_)
          lowercase__:							Optional[int]         =     torchaudio.transforms.Resample(UpperCAmelCase_					, 16_000)
          lowercase__:							Tuple         =     0
          lowercase__:							List[str]         =     list(self.processor.tokenizer.get_vocab().keys())
          for i in range(len(UpperCAmelCase_)):
                 if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                        lowercase__:							str         =     i
          print("Blank Token id [PAD]/<pad>"					, UpperCAmelCase_)
          lowercase__:							int         =     blank_id





   def __lowercase							(       self					, UpperCAmelCase_)			->   Tuple:




          '''simple docstring'''




          lowercase__    ,      lowercase__:							Any         =     torchaudio.load(UpperCAmelCase_)
          lowercase__:							Any         =     self.resampler(UpperCAmelCase_).squeeze().numpy()
          return speech





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




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

          lowercase__:							int         =     self.speech_file_to_array_fn(item["wav_path"])
          lowercase__:							int         =     self.processor(UpperCAmelCase_					, sampling_rate=16_000					, return_tensors="pt"					, padding=UpperCAmelCase_)
          if self.cuda:
                 lowercase__:							Union[str, Any]         =     inputs.to(device="cuda")

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

          # get the emission probability at frame level
          lowercase__:							Dict         =     torch.log_softmax(UpperCAmelCase_					, dim=-1)
          lowercase__:							Optional[Any]         =     emissions[0].cpu().detach()

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

          lowercase__:							Union[str, Any]         =     {c: i for i, c in enumerate(UpperCAmelCase_)}
          lowercase__:							Optional[Any]         =     []
          for c in transcript:
                 if c in dictionary:
                        tokens.append(dictionary[c])

          def get_trellis(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=0):
                 lowercase__:							Any         =     emission.size(0)
                 lowercase__:							List[str]         =     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.
                 lowercase__:							str         =     torch.full((num_frames + 1, num_tokens + 1)					, -float("inf"))
                 lowercase__:							str         =     0
                 for t in range(UpperCAmelCase_):
                        lowercase__:							Optional[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

          lowercase__:							str         =     get_trellis(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)







          @dataclass
          class 						_a  :





             '''simple docstring'''






             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42

          def backtrack(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=0):
                 lowercase__:							List[str]         =     trellis.size(1) - 1
                 lowercase__:							Dict         =     torch.argmax(trellis[:, j]).item()

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

                        # 2. Store the path with frame-wise probability.
                        lowercase__:							int         =     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]

          lowercase__:							Optional[Any]         =     backtrack(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)







          @dataclass
          class 						_a  :





             '''simple docstring'''






             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42





             def __repr__(       self)			->   Optional[int]:




                    '''simple docstring'''




                    return F"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""





             @property
             def __lowercase							(       self)			->   Any:




                    '''simple docstring'''




                    return self.end - self.start

          def merge_repeats(UpperCAmelCase_):
                 lowercase__    ,      lowercase__:							Tuple         =     0, 0
                 lowercase__:							Union[str, Any]         =     []
                 while ia < len(UpperCAmelCase_):
                        while ia < len(UpperCAmelCase_) and path[ia].token_index == path[ia].token_index:
                               ia += 1
                        lowercase__:							List[Any]         =     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_					, ))
                        lowercase__:							Optional[int]         =     ia
                 return segments

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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   List[str]:




          '''simple docstring'''




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

          # load text file
          lowercase__:							List[str]         =     open(UpperCAmelCase_					, encoding="utf8").readlines()

          lowercase__:							Optional[Any]         =     []
          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__:							str         =     line.strip().split("\t")
                 lowercase__:							List[Any]         =     os.path.join(UpperCAmelCase_					, wav_name + ".wav")
                 lowercase__:							List[Any]         =     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 							A(    ):
       """simple docstring"""





       lowercase__:							List[Any]         =     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=16000		,  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"						)

       lowercase__:							Any         =     parser.parse_args()

       lowercase__:							Any         =     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"""





from dataclasses import dataclass

UpperCamelCase					=     """0.1"""
UpperCamelCase					=     """Lucia Harcekova"""







@dataclass
class 						_a  :





   '''simple docstring'''






   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42





   def __repr__(       self)			->   str:




          '''simple docstring'''




          return F"""({self.offset}, {self.length}, {self.indicator})"""







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_ = 13					, UpperCAmelCase_ = 6)			->   None:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     window_size
          lowercase__:							Any         =     lookahead_buffer_size
          lowercase__:							Optional[Any]         =     self.window_size - self.lookahead_buffer_size





   def __lowercase							(       self					, UpperCAmelCase_)			->   list[Token]:




          '''simple docstring'''




          lowercase__:							Any         =     []
          lowercase__:							Union[str, Any]         =     ""

          # while there are still characters in text to compress
          while text:
                 # find the next encoding phrase
                 # - triplet with offset, length, indicator (the next encoding character)
                 lowercase__:							Optional[Any]         =     self._find_encoding_token(UpperCAmelCase_					, UpperCAmelCase_)

                 # update the search buffer:
                 # - add new characters from text into it
                 # - check if size exceed the max search buffer size, if so, drop the
                 #   oldest elements
                 search_buffer += text[: token.length + 1]
                 if len(UpperCAmelCase_) > self.search_buffer_size:
                        lowercase__:							List[Any]         =     search_buffer[-self.search_buffer_size :]

                 # update the text
                 lowercase__:							List[str]         =     text[token.length + 1 :]

                 # append the token to output
                 output.append(UpperCAmelCase_)

          return output





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							int         =     ""

          for token in tokens:
                 for _ in range(token.length):
                        output += output[-token.offset]
                 output += token.indicator

          return output





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Token:




          '''simple docstring'''




          if not text:
                 raise ValueError("We need some text to work with.")

          # Initialise result parameters to default values
          lowercase__    ,      lowercase__:							Optional[int]         =     0, 0

          if not search_buffer:
                 return Token(UpperCAmelCase_					, UpperCAmelCase_					, text[length])

          for i, character in enumerate(UpperCAmelCase_):
                 lowercase__:							Union[str, Any]         =     len(UpperCAmelCase_) - i
                 if character == text[0]:
                        lowercase__:							Optional[Any]         =     self._match_length_from_index(UpperCAmelCase_					, UpperCAmelCase_					, 0					, UpperCAmelCase_)
                        # if the found length is bigger than the current or if it's equal,
                        # which means it's offset is smaller: update offset and length
                        if found_length >= length:
                               lowercase__    ,      lowercase__:							Any         =     found_offset, found_length

          return Token(UpperCAmelCase_					, UpperCAmelCase_					, text[length])





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   int:




          '''simple docstring'''




          if not text or text[text_index] != window[window_index]:
                 return 0
          return 1 + self._match_length_from_index(
              UpperCAmelCase_					, window + text[text_index]					, text_index + 1					, window_index + 1)


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  # Initialize compressor class
  UpperCamelCase					=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)

  # Example
  UpperCamelCase					=     """cabracadabrarrarrad"""
  UpperCamelCase					=     lzaa_compressor.compress(TEXT)
  print(lzaa_compressor.compress("""ababcbababaa"""))
  UpperCamelCase					=     lzaa_compressor.decompress(compressed_text)
  assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."






