






"""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_						)						->str:







        '''simple docstring'''


        _a					      =						cuda
        _a					      =						AutoConfig.from_pretrained(_UpperCAmelCase						)
        _a					      =						AutoModelForCTC.from_pretrained(_UpperCAmelCase						)
        self.model.eval()
        if self.cuda:
          self.model.to(device="cuda"						)
        _a					      =						AutoProcessor.from_pretrained(_UpperCAmelCase						)
        _a					      =						torchaudio.transforms.Resample(_UpperCAmelCase  ,  1_6_0_0_0						)
        _a					      =						0
        _a					      =						list(self.processor.tokenizer.get_vocab().keys()						)
        for i in range(len(_UpperCAmelCase						)						):
          if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
            _a					      =						i
        print("Blank Token id [PAD]/<pad>"  ,  _UpperCAmelCase						)
        _a					      =						blank_id






      def       lowerCamelCase__     (     self  ,  a_						)						->Union[str, Any]:







        '''simple docstring'''


        _a					      =						torchaudio.load(_UpperCAmelCase						)
        _a					      =						self.resampler(_UpperCAmelCase						).squeeze().numpy()
        return speech






      def       lowerCamelCase__     (     self  ,  a_						)						->int:







        '''simple docstring'''


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

        _a					      =						self.speech_file_to_array_fn(item["wav_path"]						)
        _a					      =						self.processor(_UpperCAmelCase  ,  sampling_rate=1_6_0_0_0  ,  return_tensors="pt"  ,  padding=_UpperCAmelCase						)
        if self.cuda:
          _a					      =						inputs.to(device="cuda"						)

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

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

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

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

        def get_trellis(a_  ,  a_  ,  a_=0						):
          _a					      =						emission.size(0						)
          _a					      =						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.
          _a					      =						torch.full((num_frames + 1, num_tokens + 1)  ,  -float("inf"						)						)
          _a					      =						0
          for t in range(_UpperCAmelCase						):
            _a					      =						torch.maximum(
                # Score for staying at the same token
                trellis[t, 1:] + emission[t, blank_id]  ,  trellis[t, :-1] + emission[t, tokens]  ,  )
          return trellis

        _a					      =						get_trellis(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase						)




        @dataclass
        class  lowercase_     :
              A__						:		Optional[int]							      =					42
              A__						:		List[str]							      =					42
              A__						:		str							      =					42

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

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

            # 2. Store the path with frame-wise probability.
            _a					      =						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]

        _a					      =						backtrack(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase						)




        @dataclass
        class  lowercase_     :
              A__						:		Union[str, Any]							      =					42
              A__						:		Tuple							      =					42
              A__						:		List[Any]							      =					42
              A__						:		Tuple							      =					42






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







                '''simple docstring'''


                return f'''{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'''






              @property
              def       lowerCamelCase__     (     self						)						->Tuple:







                '''simple docstring'''


                return self.end - self.start

        def merge_repeats(a_						):
          _a					      =						0, 0
          _a					      =						[]
          while ia < len(_UpperCAmelCase						):
            while ia < len(_UpperCAmelCase						) and path[ia].token_index == path[ia].token_index:
              ia += 1
            _a					      =						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  ,  )						)
            _a					      =						ia
          return segments

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






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_						)						->str:







        '''simple docstring'''


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

        # load text file
        _a					      =						open(_UpperCAmelCase  ,  encoding="utf8"						).readlines()

        _a					      =						[]
        for line in lines:
          if len(line.strip().split("\t"						)						) != 2:
            print("Script must be in format: 00001  this is my sentence"						)
            exit()

          _a					      =						line.strip().split("\t"						)
          _a					      =						os.path.join(_UpperCAmelCase  ,  wav_name + ".wav"						)
          _a					      =						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 							lowerCAmelCase			(   )							->				List[Any]:







  '''simple docstring'''







  _a					      =						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"       )

  _a					      =						parser.parse_args()

  _a					      =						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'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				@register_to_config
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    )      ->			Tuple:
							super().__init__()

							__UpperCamelCase :     Optional[int]										=  nn.Linear(_UpperCAmelCase					,    _UpperCAmelCase					,    bias=_UpperCAmelCase     )

							__UpperCamelCase :     str										=  nn.Embedding(_UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  False

							__UpperCamelCase :     Union[str, Any]										=  nn.Dropout(p=_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  TaConfig(
							    d_model=_UpperCAmelCase					,    num_heads=_UpperCAmelCase					,    d_kv=_UpperCAmelCase					,    d_ff=_UpperCAmelCase					,    feed_forward_proj=_UpperCAmelCase					,    dropout_rate=_UpperCAmelCase					,    is_decoder=_UpperCAmelCase					,    is_encoder_decoder=_UpperCAmelCase					,    )
							__UpperCamelCase :     Tuple										=  nn.ModuleList()
							for lyr_num in range(_UpperCAmelCase     ):
										__UpperCamelCase :     List[str]										=  TaBlock(_UpperCAmelCase     )
										self.encoders.append(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  TaLayerNorm(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  nn.Dropout(p=_UpperCAmelCase     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Any										=  self.input_proj(_UpperCAmelCase     )

							# terminal relative positional encodings
							__UpperCamelCase :     Optional[Any]										=  encoder_inputs.shape[1]
							__UpperCamelCase :     Optional[int]										=  torch.arange(_UpperCAmelCase					,    device=encoder_inputs.device     )

							__UpperCamelCase :     Tuple										=  encoder_inputs_mask.sum(-1     )
							__UpperCamelCase :     Optional[Any]										=  torch.roll(input_positions.unsqueeze(0     )					,    tuple(seq_lens.tolist()     )					,    dims=0     )
							x += self.position_encoding(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  self.dropout_pre(_UpperCAmelCase     )

							# inverted the attention mask
							__UpperCamelCase :     Union[str, Any]										=  encoder_inputs.size()
							__UpperCamelCase :     Optional[Any]										=  self.get_extended_attention_mask(_UpperCAmelCase					,    _UpperCAmelCase     )

							for lyr in self.encoders:
										__UpperCamelCase :     List[Any]										=  lyr(_UpperCAmelCase					,    _UpperCAmelCase     )[0]
							__UpperCamelCase :     Union[str, Any]										=  self.layer_norm(_UpperCAmelCase     )

							return self.dropout_post(_UpperCAmelCase     ), encoder_inputs_mask



