






"""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  __lowerCamelCase     :

   def __init__(       self			,					snake_case_			,					snake_case_			,					snake_case_   )		->							Tuple:
       UpperCamelCase__								=     cuda
       UpperCamelCase__								=     AutoConfig.from_pretrained(snake_case_   )
       UpperCamelCase__								=     AutoModelForCTC.from_pretrained(snake_case_   )
       self.model.eval()
       if self.cuda:
           self.model.to(device='cuda'   )
       UpperCamelCase__								=     AutoProcessor.from_pretrained(snake_case_   )
       UpperCamelCase__								=     torchaudio.transforms.Resample(snake_case_			,					1_6000   )
       UpperCamelCase__								=     0
       UpperCamelCase__								=     list(self.processor.tokenizer.get_vocab().keys()   )
       for i in range(len(snake_case_   )   ):
           if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
               UpperCamelCase__								=     i
       print('Blank Token id [PAD]/<pad>'			,					snake_case_   )
       UpperCamelCase__								=     blank_id

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Tuple:
       UpperCamelCase__							,    UpperCamelCase__								=     torchaudio.load(snake_case_   )
       UpperCamelCase__								=     self.resampler(snake_case_   ).squeeze().numpy()
       return speech

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[int]:
       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(snake_case_			,					sampling_rate=1_6000			,					return_tensors='pt'			,					padding=snake_case_   )
       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(snake_case_			,					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(snake_case_   )}
       UpperCamelCase__								=     []
       for c in transcript:
           if c in dictionary:
               tokens.append(dictionary[c]   )

       def get_trellis(snake_case_			,					snake_case_			,					snake_case_=0   ):
           UpperCamelCase__								=     emission.size(0   )
           UpperCamelCase__								=     len(snake_case_   )

           # 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(snake_case_   ):
               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(snake_case_			,					snake_case_			,					snake_case_   )
       @dataclass
       class  __lowerCamelCase     :
          a   :					int
          a   :					int
          a   :					float

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

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

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

       UpperCamelCase__								=     backtrack(snake_case_			,					snake_case_			,					snake_case_			,					snake_case_   )
       @dataclass
       class  __lowerCamelCase     :
          a   :					str
          a   :					int
          a   :					int
          a   :					float

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







          @property
          def     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, Any]:
              return self.end - self.start

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

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







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_   )		->							str:
       if not os.path.exists(snake_case_   ):
           os.makedirs(snake_case_   )

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

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

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







def 	lowerCAmelCase_(			)       ->						List[Any]:
    """simple docstring"""

    UpperCamelCase__								=     argparse.ArgumentParser()

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





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__       :			Union[str, Any]=						logging.get_logger(__name__)

A__       :			Dict=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    ="""rembert"""

   def __init__(       self			,					snake_case_=25_0300			,					snake_case_=1152			,					snake_case_=32			,					snake_case_=18			,					snake_case_=256			,					snake_case_=1664			,					snake_case_=4608			,					snake_case_="gelu"			,					snake_case_=0.0			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=512			,					snake_case_=2			,					snake_case_=0.02			,					snake_case_=1E-12			,					snake_case_=True			,					snake_case_=0			,					snake_case_=312			,					snake_case_=313			,					**snake_case_			,					)		->							str:
       super().__init__(pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_			,					**snake_case_   )

       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     input_embedding_size
       UpperCamelCase__								=     output_embedding_size
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     classifier_dropout_prob
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     type_vocab_size
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     use_cache
       UpperCamelCase__								=     False







class  __lowerCamelCase     (	_a	):

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           UpperCamelCase__								=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
           UpperCamelCase__								=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]   )







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							float:
       return 1E-4




