


import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class    __magic_name__						:


     def __init__(   self				:  str				,						lowerCamelCase__				:  List[Any]				,						lowerCamelCase__				:  Dict				,						lowerCamelCase__				:  int      )	->   Dict:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= cuda
      UpperCamelCase__       :     Tuple 							= AutoConfig.from_pretrained(lowerCamelCase__      )
      UpperCamelCase__       :     Any 							= AutoModelForCTC.from_pretrained(lowerCamelCase__      )
      self.model.eval()
      if self.cuda:
       self.model.to(device='''cuda'''      )
      UpperCamelCase__       :     Optional[Any] 							= AutoProcessor.from_pretrained(lowerCamelCase__      )
      UpperCamelCase__       :     Optional[int] 							= torchaudio.transforms.Resample(lowerCamelCase__				,						16000      )
      UpperCamelCase__       :     Union[str, Any] 							= 0
      UpperCamelCase__       :     Optional[Any] 							= list(self.processor.tokenizer.get_vocab().keys()      )
      for i in range(len(lowerCamelCase__      )      ):
       if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
        UpperCamelCase__       :     Dict 							= i
      print('''Blank Token id [PAD]/<pad>'''				,						lowerCamelCase__      )
      UpperCamelCase__       :     Optional[Any] 							= blank_id


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  List[str]      )	->   Dict:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     Dict 							= torchaudio.load(lowerCamelCase__      )
      UpperCamelCase__       :     str 							= self.resampler(lowerCamelCase__      ).squeeze().numpy()
      return speech


     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  Optional[int]      )	->   int:





      '''simple docstring'''






      UpperCamelCase__       :     str 							= self.blank_id
      UpperCamelCase__       :     Optional[Any] 							= '''|'''.join(item['''sent'''].split(''' '''      )      )
      if not os.path.isfile(item['''wav_path''']      ):
       print(item['''wav_path''']				,						'''not found in wavs directory'''      )

      UpperCamelCase__       :     Any 							= self.speech_file_to_array_fn(item['''wav_path''']      )
      UpperCamelCase__       :     int 							= self.processor(lowerCamelCase__				,						sampling_rate=16000				,						return_tensors='''pt'''				,						padding=lowerCamelCase__      )
      if self.cuda:
       UpperCamelCase__       :     Optional[Any] 							= inputs.to(device='''cuda'''      )

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

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

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

      UpperCamelCase__       :     Optional[Any] 							= {c: i for i, c in enumerate(lowerCamelCase__      )}
      UpperCamelCase__       :     Dict 							= []
      for c in transcript:
       if c in dictionary:
        tokens.append(dictionary[c]      )

      def get_trellis(lowerCamelCase__				:  Optional[Any]				,						lowerCamelCase__				:  Optional[Any]				,						lowerCamelCase__				:  Tuple=0      ):
       UpperCamelCase__       :     str 							= emission.size(0      )
       UpperCamelCase__       :     Union[str, Any] 							= len(lowerCamelCase__      )

       # 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__       :     Union[str, Any] 							= torch.full((num_frames + 1, num_tokens + 1)				,						-float('''inf'''      )      )
       UpperCamelCase__       :     List[str] 							= 0
       for t in range(lowerCamelCase__      ):
        UpperCamelCase__       :     Any 							= 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__       :     Union[str, Any] 							= get_trellis(lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__      )

      @dataclass
      class    __magic_name__						:
           A:		int
           A:		int
           A:		float

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

       UpperCamelCase__       :     Union[str, Any] 							= []
       for t in range(lowerCamelCase__				,						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__       :     Tuple 							= trellis[t - 1, j] + emission[t - 1, blank_id]
        # Score for token changing from C-1 at T-1 to J at T.
        UpperCamelCase__       :     List[Any] 							= trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

        # 2. Store the path with frame-wise probability.
        UpperCamelCase__       :     Tuple 							= 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				,						lowerCamelCase__      )      )

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

      UpperCamelCase__       :     Optional[Any] 							= backtrack(lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__      )

      @dataclass
      class    __magic_name__						:
           A:		str
           A:		int
           A:		int
           A:		float


           def __repr__(   self				:  List[Any]      )	->   Union[str, Any]:





            '''simple docstring'''






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


           @property
           def   UpperCAmelCase__						(   self				:  Dict      )	->   int:





            '''simple docstring'''






            return self.end - self.start

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

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


     def   UpperCAmelCase__						(   self				:  List[Any]				,						lowerCamelCase__				:  Optional[int]				,						lowerCamelCase__				:  Union[str, Any]				,						lowerCamelCase__				:  Any      )	->   int:





      '''simple docstring'''






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

      # load text file
      UpperCamelCase__       :     List[str] 							= open(lowerCamelCase__				,						encoding='''utf8'''      ).readlines()

      UpperCamelCase__       :     Dict 							= []
      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__       :     Union[str, Any] 							= line.strip().split('''\t'''      )
       UpperCamelCase__       :     Any 							= os.path.join(lowerCamelCase__				,						wav_name + '''.wav'''      )
       UpperCamelCase__       :     Union[str, Any] 							= os.path.join(lowerCamelCase__				,						wav_name + '''.txt'''      )

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

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



def       _a	(					):







 """simple docstring"""



 UpperCamelCase__       :     Optional[int] 							= argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name'''     ,			type=UpperCAmelCase_     ,			default='''arijitx/wav2vec2-xls-r-300m-bengali'''     ,			help='''wav2vec model name'''  )
 parser.add_argument('''--wav_dir'''     ,			type=UpperCAmelCase_     ,			default='''./wavs'''     ,			help='''directory containing wavs'''  )
 parser.add_argument('''--text_file'''     ,			type=UpperCAmelCase_     ,			default='''script.txt'''     ,			help='''file containing text'''  )
 parser.add_argument('''--input_wavs_sr'''     ,			type=UpperCAmelCase_     ,			default=16000     ,			help='''sampling rate of input audios'''  )
 parser.add_argument(
     '''--output_dir'''     ,			type=UpperCAmelCase_     ,			default='''./out_alignment'''     ,			help='''output directory containing the alignment files'''  )
 parser.add_argument('''--cuda'''     ,			action='''store_true'''  )

 UpperCamelCase__       :     str 							= parser.parse_args()

 UpperCamelCase__       :     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()








# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_    :       int       =							{
    '''agents''': ['''Agent''', '''AzureOpenAiAgent''', '''HfAgent''', '''LocalAgent''', '''OpenAiAgent'''],
    '''base''': ['''PipelineTool''', '''RemoteTool''', '''Tool''', '''launch_gradio_demo''', '''load_tool'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_    :       int       =							['''DocumentQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''ImageCaptioningTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''ImageQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       str       =							['''ImageSegmentationTool''']
 SCREAMING_SNAKE_CASE_    :       Any       =							['''SpeechToTextTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''TextClassificationTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''TextQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TextSummarizationTool''']
 SCREAMING_SNAKE_CASE_    :       Optional[int]       =							['''TextToSpeechTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TranslationTool''']

if TYPE_CHECKING:
 from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
 from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .document_question_answering import DocumentQuestionAnsweringTool
  from .image_captioning import ImageCaptioningTool
  from .image_question_answering import ImageQuestionAnsweringTool
  from .image_segmentation import ImageSegmentationTool
  from .speech_to_text import SpeechToTextTool
  from .text_classification import TextClassificationTool
  from .text_question_answering import TextQuestionAnsweringTool
  from .text_summarization import TextSummarizationTool
  from .text_to_speech import TextToSpeechTool
  from .translation import TranslationTool
else:
 import sys

 SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






