

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	:				List[str]     ,snake_case	:				Any     ,snake_case	:				Any     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=cuda
           SCREAMING_SNAKE_CASE 		=AutoConfig.from_pretrained(snake_case							)
           SCREAMING_SNAKE_CASE 		=AutoModelForCTC.from_pretrained(snake_case							)
           self.model.eval()
           if self.cuda:
                self.model.to(device='cuda'							)
           SCREAMING_SNAKE_CASE 		=AutoProcessor.from_pretrained(snake_case							)
           SCREAMING_SNAKE_CASE 		=torchaudio.transforms.Resample(snake_case     ,16000							)
           SCREAMING_SNAKE_CASE 		=0
           SCREAMING_SNAKE_CASE 		=list(self.processor.tokenizer.get_vocab().keys()							)
           for i in range(len(snake_case							)							):
                if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                     SCREAMING_SNAKE_CASE 		=i
           print('Blank Token id [PAD]/<pad>'     ,snake_case							)
           SCREAMING_SNAKE_CASE 		=blank_id
      def 			_lowerCAmelCase	( self	:				int     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=torchaudio.load(snake_case							)
           SCREAMING_SNAKE_CASE 		=self.resampler(snake_case							).squeeze().numpy()
           return speech
      def 			_lowerCAmelCase	( self	:				Optional[int]     ,snake_case	:				List[Any]							):
           SCREAMING_SNAKE_CASE 		=self.blank_id
           SCREAMING_SNAKE_CASE 		='|'.join(item['sent'].split(' '							)							)
           if not os.path.isfile(item['wav_path']							):
                print(item['wav_path']     ,'not found in wavs directory'							)

           SCREAMING_SNAKE_CASE 		=self.speech_file_to_array_fn(item['wav_path']							)
           SCREAMING_SNAKE_CASE 		=self.processor(snake_case     ,sampling_rate=16000     ,return_tensors='pt'     ,padding=snake_case							)
           if self.cuda:
                SCREAMING_SNAKE_CASE 		=inputs.to(device='cuda'							)

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

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

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

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

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

           SCREAMING_SNAKE_CASE 		=get_trellis(snake_case     ,snake_case     ,snake_case							)



           @dataclass
           class 						a_			:



                 """simple docstring"""







                 __UpperCAmelCase     						= 42
                 __UpperCAmelCase     						= 42
                 __UpperCAmelCase     						= 42

           def backtrack(snake_case	:				Dict     ,snake_case	:				str     ,snake_case	:				Optional[int]     ,snake_case	:				Tuple=0							):
                SCREAMING_SNAKE_CASE 		=trellis.size(1							) - 1
                SCREAMING_SNAKE_CASE 		=torch.argmax(trellis[:, j]							).item()

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

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

           SCREAMING_SNAKE_CASE 		=backtrack(snake_case     ,snake_case     ,snake_case     ,snake_case							)



           @dataclass
           class 						a_			:



                 """simple docstring"""







                 __UpperCAmelCase     						= 42
                 __UpperCAmelCase     						= 42
                 __UpperCAmelCase     						= 42
                 __UpperCAmelCase     						= 42
                 def __repr__( self	:				Tuple							):
                      return f'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'


                 @property
                 def 			_lowerCAmelCase	( self	:				List[str]							):
                      return self.end - self.start

           def merge_repeats(snake_case	:				str							):
                SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=0, 0
                SCREAMING_SNAKE_CASE 		=[]
                while ia < len(snake_case							):
                     while ia < len(snake_case							) and path[ia].token_index == path[ia].token_index:
                          ia += 1
                     SCREAMING_SNAKE_CASE 		=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     ,)							)
                     SCREAMING_SNAKE_CASE 		=ia
                return segments

           SCREAMING_SNAKE_CASE 		=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 			_lowerCAmelCase	( self	:				List[str]     ,snake_case	:				str     ,snake_case	:				List[Any]     ,snake_case	:				str							):
           if not os.path.exists(snake_case							):
                os.makedirs(snake_case							)

           # load text file
           SCREAMING_SNAKE_CASE 		=open(snake_case     ,encoding='utf8'							).readlines()

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

                SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=line.strip().split('\t'							)
                SCREAMING_SNAKE_CASE 		=os.path.join(snake_case     ,wav_name + '.wav'							)
                SCREAMING_SNAKE_CASE 		=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      snake_case__							(  ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=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'     )

     SCREAMING_SNAKE_CASE 		=parser.parse_args()

     SCREAMING_SNAKE_CASE 		=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()




from math import cos, sin







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=float(lowerCAmelCase_     )
     return [[scaling_factor * int(x == y     ) for x in range(2     )] for y in range(2     )]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     return [[c, -s], [s, c]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[c * c, cs], [cs, s * s]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f'    {scaling(5) = }')
print(f'  {rotation(45) = }')
print(f'{projection(45) = }')



print(f'{reflection(45) = }')

