


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]       ,    _a					: int       ,    _a					: List[Any]       ,    _a					: List[str]				)    ->			Dict:
      UpperCAmelCase						:				Tuple		  =			cuda
      UpperCAmelCase						:				Tuple		  =			AutoConfig.from_pretrained(lowerCamelCase__				)
      UpperCAmelCase						:				List[Any]		  =			AutoModelForCTC.from_pretrained(lowerCamelCase__				)
      self.model.eval()
      if self.cuda:
           self.model.to(device="cuda"				)
      UpperCAmelCase						:				List[str]		  =			AutoProcessor.from_pretrained(lowerCamelCase__				)
      UpperCAmelCase						:				str		  =			torchaudio.transforms.Resample(lowerCamelCase__       ,    1_6_0_0_0				)
      UpperCAmelCase						:				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						:				Tuple		  =			i
      print("Blank Token id [PAD]/<pad>"       ,    lowerCamelCase__				)
      UpperCAmelCase						:				Union[str, Any]		  =			blank_id
 def   _UpperCamelCase    (			self					: int       ,    _a					: Optional[int]				)    ->			Any:
      UpperCAmelCase						:				List[Any]		  =			torchaudio.load(lowerCamelCase__				)
      UpperCAmelCase						:				List[str]		  =			self.resampler(lowerCamelCase__				).squeeze().numpy()
      return speech
 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: List[Any]				)    ->			Any:
      UpperCAmelCase						:				Optional[Any]		  =			self.blank_id
      UpperCAmelCase						:				int		  =			"|".join(item["sent"].split(" "				)				)
      if not os.path.isfile(item["wav_path"]				):
           print(item["wav_path"]       ,    "not found in wavs directory"				)

      UpperCAmelCase						:				Optional[int]		  =			self.speech_file_to_array_fn(item["wav_path"]				)
      UpperCAmelCase						:				List[Any]		  =			self.processor(lowerCamelCase__       ,    sampling_rate=1_6_0_0_0       ,    return_tensors="pt"       ,    padding=lowerCamelCase__				)
      if self.cuda:
           UpperCAmelCase						:				Union[str, Any]		  =			inputs.to(device="cuda"				)

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

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

      # get labels from vocab
      UpperCAmelCase						:				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(_a					: int       ,    _a					: Union[str, Any]       ,    _a					: Tuple=0				):
           UpperCAmelCase						:				Union[str, Any]		  =			emission.size(0				)
           UpperCAmelCase						:				Optional[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						:				Tuple		  =			torch.full((num_frames + 1, num_tokens + 1)       ,    -float("inf"				)				)
           UpperCAmelCase						:				int		  =			0
           for t in range(lowerCamelCase__				):
                UpperCAmelCase						:				Dict		  =			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						:				str		  =			get_trellis(lowerCamelCase__       ,    lowerCamelCase__       ,    lowerCamelCase__				)




      @dataclass
      class    A_   :







       """simple docstring"""





       _UpperCamelCase				: int
       _UpperCamelCase				: int
       _UpperCamelCase				: float

      def backtrack(_a					: str       ,    _a					: List[str]       ,    _a					: List[str]       ,    _a					: str=0				):
           UpperCAmelCase						:				List[str]		  =			trellis.size(1				) - 1
           UpperCAmelCase						:				Optional[Any]		  =			torch.argmax(trellis[:, j]				).item()

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

                # 2. Store the path with frame-wise probability.
                UpperCAmelCase						:				Optional[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       ,    lowerCamelCase__				)				)

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

      UpperCAmelCase						:				str		  =			backtrack(lowerCamelCase__       ,    lowerCamelCase__       ,    lowerCamelCase__       ,    lowerCamelCase__				)




      @dataclass
      class    A_   :







       """simple docstring"""





       _UpperCamelCase				: str
       _UpperCamelCase				: int
       _UpperCamelCase				: int
       _UpperCamelCase				: float
       def __repr__(			self					: Union[str, Any]				)    ->			Tuple:
            return f'{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'

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

      def merge_repeats(_a					: Optional[int]				):
           UpperCAmelCase						:				List[Any]		  =			0, 0
           UpperCAmelCase						:				Union[str, Any]		  =			[]
           while ia < len(lowerCamelCase__				):
                while ia < len(lowerCamelCase__				) and path[ia].token_index == path[ia].token_index:
                     ia += 1
                UpperCAmelCase						:				Dict		  =			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						:				Optional[int]		  =			ia
           return segments

      UpperCAmelCase						:				Dict		  =			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					: Optional[Any]       ,    _a					: Union[str, Any]       ,    _a					: List[str]       ,    _a					: str				)    ->			Optional[Any]:
      if not os.path.exists(lowerCamelCase__				):
           os.makedirs(lowerCamelCase__				)

      # load text file
      UpperCAmelCase						:				Dict		  =			open(lowerCamelCase__       ,    encoding="utf8"				).readlines()

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

           UpperCAmelCase						:				int		  =			line.strip().split("\t"				)
           UpperCAmelCase						:				List[Any]		  =			os.path.join(lowerCamelCase__       ,    wav_name + ".wav"				)
           UpperCAmelCase						:				List[str]		  =			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   (		):
     UpperCAmelCase						:				List[Any]		  =			argparse.ArgumentParser()

     parser.add_argument(
         "--model_name"		,		type=A__		,		default="arijitx/wav2vec2-xls-r-300m-bengali"		,		help="wav2vec model name" )
     parser.add_argument("--wav_dir"		,		type=A__		,		default="./wavs"		,		help="directory containing wavs" )
     parser.add_argument("--text_file"		,		type=A__		,		default="script.txt"		,		help="file containing text" )
     parser.add_argument("--input_wavs_sr"		,		type=A__		,		default=1_6_0_0_0		,		help="sampling rate of input audios" )
     parser.add_argument(
         "--output_dir"		,		type=A__		,		default="./out_alignment"		,		help="output directory containing the alignment files" )
     parser.add_argument("--cuda"		,		action="store_true" )

     UpperCAmelCase						:				str		  =			parser.parse_args()

     UpperCAmelCase						:				int		  =			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 __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_snake_case   = TypeVar("T")
_snake_case   = TypeVar("U")




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 def __init__(			self					: List[Any]       ,    _a					: T | None       ,    _a					: U | None				)    ->			Dict:
      UpperCAmelCase						:				str		  =			key
      UpperCAmelCase						:				Dict		  =			val
      UpperCAmelCase						:				int		  =			0
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None

 def __repr__(			self					: List[str]				)    ->			str:
      return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
          self.key       ,    self.val       ,    self.freq       ,    self.next is not None       ,    self.prev is not None				)




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 def __init__(			self					: Any				)    ->			None:
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase  , UpperCAmelCase						:				Optional[int]		  =			self.rear, self.head
 def __repr__(			self					: int				)    ->			str:
      UpperCAmelCase						:				Union[str, Any]		  =			["DoubleLinkedList"]
      UpperCAmelCase						:				int		  =			self.head
      while node.next is not None:
           rep.append(str(_a				)				)
           UpperCAmelCase						:				Union[str, Any]		  =			node.next
      rep.append(str(self.rear				)				)
      return ",\n    ".join(_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			None:
      UpperCAmelCase						:				str		  =			self.rear.prev

      # All nodes other than self.head are guaranteed to have non-None previous
      assert previous is not None

      UpperCAmelCase						:				Any		  =			node
      UpperCAmelCase						:				Optional[int]		  =			previous
      UpperCAmelCase						:				Optional[int]		  =			node
      UpperCAmelCase						:				Union[str, Any]		  =			self.rear
      node.freq += 1
      self._position_node(_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			None:

      while node.prev is not None and node.prev.freq > node.freq:
           # swap node with previous node
           UpperCAmelCase						:				List[str]		  =			node.prev

           UpperCAmelCase						:				Any		  =			previous_node.prev
           UpperCAmelCase						:				int		  =			node.prev
           UpperCAmelCase						:				Tuple		  =			previous_node
           UpperCAmelCase						:				Optional[int]		  =			node

 def   _UpperCamelCase    (			self					: str       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			DoubleLinkedListNode[T, U] | None:

      if node.prev is None or node.next is None:
           return None

      UpperCAmelCase						:				Dict		  =			node.next
      UpperCAmelCase						:				Optional[Any]		  =			node.prev
      UpperCAmelCase						:				List[Any]		  =			None
      UpperCAmelCase						:				int		  =			None
      return node




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 _UpperCamelCase				: dict[Callable[[T], U], LFUCache[T, U]]      =					{}
 def __init__(			self					: List[Any]       ,    _a					: int				)    ->			int:
      UpperCAmelCase						:				DoubleLinkedList[T, U]		  =			DoubleLinkedList()
      UpperCAmelCase						:				Any		  =			capacity
      UpperCAmelCase						:				Union[str, Any]		  =			0
      UpperCAmelCase						:				Any		  =			0
      UpperCAmelCase						:				Dict		  =			0
      UpperCAmelCase						:				dict[T, DoubleLinkedListNode[T, U]]		  =			{}
 def __repr__(			self					: Union[str, Any]				)    ->			str:
      return (
          f'CacheInfo(hits={self.hits}, misses={self.miss}, '
          f'capacity={self.capacity}, current_size={self.num_keys})'
      )
 def __contains__(			self					: Optional[int]       ,    _a					: T				)    ->			bool:
      return key in self.cache
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: T				)    ->			U | None:

      if key in self.cache:
           self.hits += 1
           UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			self.cache[key]
           UpperCAmelCase						:				Optional[int]		  =			self.list.remove(self.cache[key]				)
           assert node == value_node

           # node is guaranteed not None because it is in self.cache
           assert node is not None
           self.list.add(_a				)
           return node.val
      self.miss += 1
      return None
 def   _UpperCamelCase    (			self					: str       ,    _a					: T       ,    _a					: U				)    ->			None:

      if key not in self.cache:
           if self.num_keys >= self.capacity:
                # delete first node when over capacity
                UpperCAmelCase						:				Any		  =			self.list.head.next

                # guaranteed to have a non-None first node when num_keys > 0
                # explain to type checker via assertions
                assert first_node is not None
                assert first_node.key is not None
                assert self.list.remove(_a				) is not None
                # first_node guaranteed to be in list

                del self.cache[first_node.key]
                self.num_keys -= 1
           UpperCAmelCase						:				Optional[Any]		  =			DoubleLinkedListNode(_a       ,    _a				)
           self.list.add(self.cache[key]				)
           self.num_keys += 1

      else:
           UpperCAmelCase						:				Any		  =			self.list.remove(self.cache[key]				)
           assert node is not None  # node guaranteed to be in list
           UpperCAmelCase						:				str		  =			value
           self.list.add(_a				)

 @classmethod
 def   _UpperCamelCase    (			cls					: type[LFUCache[T, U]]       ,    _a					: int = 1_2_8				)    ->			Callable[[Callable[[T], U]], Callable[..., U]]:

      def cache_decorator_inner(_a					: Callable[[T], U]				) -> Callable[..., U]:
           def cache_decorator_wrapper(*_a					: T				) -> U:
                if func not in cls.decorator_function_to_instance_map:
                     UpperCAmelCase						:				List[Any]		  =			LFUCache(_a				)

                UpperCAmelCase						:				List[Any]		  =			cls.decorator_function_to_instance_map[func].get(args[0]				)
                if result is None:
                     UpperCAmelCase						:				List[Any]		  =			func(*_a				)
                     cls.decorator_function_to_instance_map[func].put(args[0]       ,    _a				)
                return result

           def cache_info() -> LFUCache[T, U]:
                return cls.decorator_function_to_instance_map[func]

           setattr(_a       ,    "cache_info"       ,    _a				)  # noqa: B010

           return cache_decorator_wrapper

      return cache_decorator_inner


if __name__ == "__main__":
      import doctest

      doctest.testmod()



