




'''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 	SCREAMING_SNAKE_CASE_		:



 def __init__(	self ,		A ,		A ,		A			):






        '''simple docstring'''




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



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase,  lowercase	:		List[Any]    =   torchaudio.load(A			)
        lowercase	:		Union[str, Any]    =   self.resampler(A			).squeeze().numpy()
        return speech



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




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

        lowercase	:		int    =   self.speech_file_to_array_fn(item['''wav_path''']			)
        lowercase	:		Tuple    =   self.processor(A ,		sampling_rate=16000 ,		return_tensors='''pt''' ,		padding=A			)
        if self.cuda:
               lowercase	:		Dict    =   inputs.to(device='''cuda'''			)

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

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

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

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

        def get_trellis(A ,		A ,		A=0			):
               lowercase	:		int    =   emission.size(0			)
               lowercase	:		Any    =   len(A			)

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

        lowercase	:		List[str]    =   get_trellis(A ,		A ,		A			)

        @dataclass
        class 	SCREAMING_SNAKE_CASE_		:
         __snake_case						:  int
         __snake_case						:  int
         __snake_case						:  float

        def backtrack(A ,		A ,		A ,		A=0			):
               lowercase	:		Tuple    =   trellis.size(1			) - 1
               lowercase	:		List[str]    =   torch.argmax(trellis[:, j]			).item()

               lowercase	:		int    =   []
               for t in range(A ,		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.
                      lowercase	:		Optional[Any]    =   trellis[t - 1, j] + emission[t - 1, blank_id]
                      # Score for token changing from C-1 at T-1 to J at T.
                      lowercase	:		str    =   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                      # 2. Store the path with frame-wise probability.
                      lowercase	:		List[Any]    =   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 ,		A			)			)

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

        lowercase	:		List[Any]    =   backtrack(A ,		A ,		A ,		A			)

        @dataclass
        class 	SCREAMING_SNAKE_CASE_		:
         __snake_case						:  str
         __snake_case						:  int
         __snake_case						:  int
         __snake_case						:  float



         def __repr__(	self			):






                '''simple docstring'''




                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			):






                '''simple docstring'''




                return self.end - self.start

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

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





 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




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

        # load text file
        lowercase	:		Tuple    =   open(A ,		encoding='''utf8'''			).readlines()

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

               lowercase,  lowercase	:		int    =   line.strip().split('''\t'''			)
               lowercase	:		Optional[Any]    =   os.path.join(A ,		wav_name + '''.wav'''			)
               lowercase	:		Union[str, Any]    =   os.path.join(A ,		wav_name + '''.txt'''			)

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

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






def 		a_(			)					->Dict:






       '''simple docstring'''




       lowercase	:		Union[str, Any]    =   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=1_6000 ,					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'''   )

       lowercase	:		Any    =   parser.parse_args()

       lowercase	:		str    =   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 os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
      from .testing_utils import (
          floats_tensor,
          load_hf_numpy,
          load_image,
          load_numpy,
          load_pt,
          nightly,
          parse_flag_from_env,
          print_tensor_test,
          require_torch_a,
          require_torch_gpu,
          skip_mps,
          slow,
          torch_all_close,
          torch_device,
      )
      from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowercase						:   Tuple      						=							get_logger(__name__)




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       if version.parse(lowerCamelCase   ) < version.parse(lowerCamelCase   ):
              if "dev" in min_version:
                     lowercase	:		str    =   (
                         '''This example requires a source install from HuggingFace diffusers (see '''
                         '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                     )
              else:
                     lowercase	:		List[str]    =   f"""This example requires a minimum version of {min_version},"""
              error_message += f""" but the version found is {__version__}.\n"""
              raise ImportError(lowerCamelCase   )




