



from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


__lowerCamelCase								=      logging.get_logger(__name__)


class 	snake_case_			(nn.Module	):




       """simple docstring"""






       _lowerCamelCase           =					42
       _lowerCamelCase           =					jnp.floataa



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						int					   =		self.config.text_config
             UpperCAmelCase_	:						List[str]					   =		self.config.vision_config

             UpperCAmelCase_	:						Dict					   =		self.config.projection_dim
             UpperCAmelCase_	:						Union[str, Any]					   =		text_config.hidden_size
             UpperCAmelCase_	:						List[str]					   =		vision_config.hidden_size

             UpperCAmelCase_	:						Union[str, Any]					   =		FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
             UpperCAmelCase_	:						Optional[int]					   =		FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__  ,_UpperCAmelCase).module_class

             UpperCAmelCase_	:						Any					   =		text_module(_UpperCAmelCase  ,dtype=self.dtype)
             UpperCAmelCase_	:						Optional[int]					   =		vision_module(_UpperCAmelCase  ,dtype=self.dtype)

             UpperCAmelCase_	:						str					   =		nn.Dense(
                 self.projection_dim  ,dtype=self.dtype  ,kernel_init=jax.nn.initializers.normal(0.02)  ,use_bias=_UpperCAmelCase  ,)
             UpperCAmelCase_	:						Tuple					   =		nn.Dense(
                 self.projection_dim  ,dtype=self.dtype  ,kernel_init=jax.nn.initializers.normal(0.02)  ,use_bias=_UpperCAmelCase  ,)
             UpperCAmelCase_	:						Any					   =		self.param("logit_scale"  ,jax.nn.initializers.ones  ,[])



       def __call__(  self  ,lowercase=None  ,lowercase=None  ,lowercase=None  ,lowercase=None  ,lowercase=None  ,lowercase = True  ,lowercase=None  ,lowercase=None  ,lowercase=None  ,):

             """simple docstring"""





             UpperCAmelCase_	:						str					   =		return_dict if return_dict is not None else self.config.return_dict

             UpperCAmelCase_	:						str					   =		self.vision_model(
                 pixel_values=_UpperCAmelCase  ,deterministic=_UpperCAmelCase  ,output_attentions=_UpperCAmelCase  ,output_hidden_states=_UpperCAmelCase  ,return_dict=_UpperCAmelCase  ,)

             UpperCAmelCase_	:						int					   =		self.text_model(
                 input_ids=_UpperCAmelCase  ,attention_mask=_UpperCAmelCase  ,token_type_ids=_UpperCAmelCase  ,position_ids=_UpperCAmelCase  ,deterministic=_UpperCAmelCase  ,output_attentions=_UpperCAmelCase  ,output_hidden_states=_UpperCAmelCase  ,return_dict=_UpperCAmelCase  ,)

             UpperCAmelCase_	:						List[Any]					   =		vision_outputs[1]
             UpperCAmelCase_	:						Optional[Any]					   =		self.visual_projection(_UpperCAmelCase)

             UpperCAmelCase_	:						Any					   =		text_outputs[1]
             UpperCAmelCase_	:						Any					   =		self.text_projection(_UpperCAmelCase)

             # normalized features
             UpperCAmelCase_	:						Optional[int]					   =		image_embeds / jnp.linalg.norm(_UpperCAmelCase  ,axis=-1  ,keepdims=_UpperCAmelCase)
             UpperCAmelCase_	:						Tuple					   =		text_embeds / jnp.linalg.norm(_UpperCAmelCase  ,axis=-1  ,keepdims=_UpperCAmelCase)

             # cosine similarity as logits
             UpperCAmelCase_	:						Any					   =		jnp.exp(self.logit_scale)
             UpperCAmelCase_	:						Tuple					   =		jnp.matmul(_UpperCAmelCase  ,image_embeds.T) * logit_scale
             UpperCAmelCase_	:						int					   =		logits_per_text.T

             if not return_dict:
                   return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

             return FlaxCLIPOutput(
                 logits_per_image=_UpperCAmelCase  ,logits_per_text=_UpperCAmelCase  ,text_embeds=_UpperCAmelCase  ,image_embeds=_UpperCAmelCase  ,text_model_output=_UpperCAmelCase  ,vision_model_output=_UpperCAmelCase  ,)


class 	snake_case_			(SCREAMING_SNAKE_CASE__	):




       """simple docstring"""






       _lowerCamelCase           =					HybridCLIPConfig
       _lowerCamelCase           =					FlaxHybridCLIPModule



       def __init__(  self  ,lowercase  ,lowercase = None  ,lowercase = 0  ,lowercase = jnp.floataa  ,**lowercase  ,):

             """simple docstring"""





             if input_shape is None:
                   UpperCAmelCase_	:						Tuple					   =		((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

             UpperCAmelCase_	:						str					   =		self.module_class(config=_UpperCAmelCase  ,dtype=_UpperCAmelCase  ,**_UpperCAmelCase)
             super().__init__(_UpperCAmelCase  ,_UpperCAmelCase  ,input_shape=_UpperCAmelCase  ,seed=_UpperCAmelCase  ,dtype=_UpperCAmelCase)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_	:						Union[str, Any]					   =		jnp.zeros(input_shape[0]  ,dtype="i4")
             UpperCAmelCase_	:						List[Any]					   =		jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_UpperCAmelCase).shape[-1])  ,input_shape[0])
             UpperCAmelCase_	:						Union[str, Any]					   =		jnp.ones_like(_UpperCAmelCase)
             UpperCAmelCase_	:						Optional[Any]					   =		jnp.ones_like(_UpperCAmelCase)

             UpperCAmelCase_	:						List[str]					   =		jax.random.normal(_UpperCAmelCase  ,input_shape[1])

             UpperCAmelCase_	:						str					   =		jax.random.split(_UpperCAmelCase)
             UpperCAmelCase_	:						str					   =		{"params": params_rng, "dropout": dropout_rng}

             return self.module.init(_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase)["params"]



       def __call__(  self  ,lowercase  ,lowercase  ,lowercase=None  ,lowercase=None  ,lowercase=None  ,lowercase = None  ,lowercase = None  ,lowercase = False  ,lowercase = None  ,lowercase = None  ,lowercase = None  ,):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[int]					   =		output_attentions if output_attentions is not None else self.config.output_attentions
             UpperCAmelCase_	:						Optional[int]					   =		(
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             UpperCAmelCase_	:						Dict					   =		return_dict if return_dict is not None else self.config.return_dict

             if position_ids is None:
                   UpperCAmelCase_	:						List[str]					   =		jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_UpperCAmelCase).shape[-1])  ,input_ids.shape)

             if token_type_ids is None:
                   UpperCAmelCase_	:						Dict					   =		jnp.zeros_like(_UpperCAmelCase)

             if attention_mask is None:
                   UpperCAmelCase_	:						Dict					   =		jnp.ones_like(_UpperCAmelCase)

             # Handle any PRNG if needed
             UpperCAmelCase_	:						str					   =		{}
             if dropout_rng is not None:
                   UpperCAmelCase_	:						Union[str, Any]					   =		dropout_rng

             return self.module.apply(
                 {"params": params or self.params}  ,jnp.array(_UpperCAmelCase  ,dtype="i4")  ,jnp.array(_UpperCAmelCase  ,dtype=jnp.floataa)  ,jnp.array(_UpperCAmelCase  ,dtype="i4")  ,jnp.array(_UpperCAmelCase  ,dtype="i4")  ,jnp.array(_UpperCAmelCase  ,dtype="i4")  ,not train  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,rngs=_UpperCAmelCase  ,)



       def 			A_ (  self  ,lowercase  ,lowercase=None  ,lowercase=None  ,lowercase=None  ,lowercase = None  ,lowercase = None  ,lowercase=False  ,):

             """simple docstring"""





             if position_ids is None:
                   UpperCAmelCase_	:						int					   =		jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_UpperCAmelCase).shape[-1])  ,input_ids.shape)

             if token_type_ids is None:
                   UpperCAmelCase_	:						int					   =		jnp.zeros_like(_UpperCAmelCase)

             if attention_mask is None:
                   UpperCAmelCase_	:						Any					   =		jnp.ones_like(_UpperCAmelCase)

             # Handle any PRNG if needed
             UpperCAmelCase_	:						str					   =		{}
             if dropout_rng is not None:
                   UpperCAmelCase_	:						Dict					   =		dropout_rng

             def _get_features(lowercase  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,lowercase):
                   UpperCAmelCase_	:						Any					   =		module.text_model(
                       input_ids=_UpperCAmelCase  ,attention_mask=_UpperCAmelCase  ,position_ids=_UpperCAmelCase  ,token_type_ids=_UpperCAmelCase  ,deterministic=_UpperCAmelCase  ,)
                   UpperCAmelCase_	:						Any					   =		text_outputs[1]
                   UpperCAmelCase_	:						Optional[Any]					   =		module.text_projection(_UpperCAmelCase)
                   return text_features

             return self.module.apply(
                 {"params": params or self.params}  ,jnp.array(_UpperCAmelCase  ,dtype="i4")  ,jnp.array(_UpperCAmelCase  ,dtype="i4")  ,jnp.array(_UpperCAmelCase  ,dtype="i4")  ,jnp.array(_UpperCAmelCase  ,dtype="i4")  ,not train  ,method=_get_features  ,rngs=_UpperCAmelCase  ,)



       def 			A_ (  self  ,lowercase  ,lowercase = None  ,lowercase = None  ,lowercase=False):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[Any]					   =		{}
             if dropout_rng is not None:
                   UpperCAmelCase_	:						List[Any]					   =		dropout_rng

             def _get_features(lowercase  ,lowercase  ,lowercase):
                   UpperCAmelCase_	:						Dict					   =		module.vision_model(pixel_values=_UpperCAmelCase  ,deterministic=_UpperCAmelCase)
                   UpperCAmelCase_	:						Any					   =		vision_outputs[1]  # pooled_output
                   UpperCAmelCase_	:						List[str]					   =		module.visual_projection(_UpperCAmelCase)
                   return image_features

             return self.module.apply(
                 {"params": params or self.params}  ,jnp.array(_UpperCAmelCase  ,dtype=jnp.floataa)  ,not train  ,method=_get_features  ,rngs=_UpperCAmelCase  ,)



       @classmethod
       def 			A_ (  cls  ,lowercase = None  ,lowercase = None  ,*lowercase  ,**lowercase  ,):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[int]					   =		{
                 argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_")
             }

             UpperCAmelCase_	:						Tuple					   =		{
                 argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_")
             }

             # remove text, vision kwargs from kwargs
             for key in kwargs_text.keys():
                   del kwargs["text_" + key]
             for key in kwargs_vision.keys():
                   del kwargs["vision_" + key]

             # Load and initialize the text and vision model
             UpperCAmelCase_	:						List[Any]					   =		kwargs_text.pop("model"  ,_UpperCAmelCase)
             if text_model is None:
                   assert (
                       text_model_name_or_path is not None
                   ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
                   from transformers import FlaxAutoModel

                   if "config" not in kwargs_text:
                         from transformers import AutoConfig

                         UpperCAmelCase_	:						Optional[int]					   =		AutoConfig.from_pretrained(_UpperCAmelCase)
                         UpperCAmelCase_	:						Union[str, Any]					   =		text_config

                   UpperCAmelCase_	:						int					   =		FlaxAutoModel.from_pretrained(_UpperCAmelCase  ,*_UpperCAmelCase  ,**_UpperCAmelCase)

             UpperCAmelCase_	:						Dict					   =		kwargs_vision.pop("model"  ,_UpperCAmelCase)
             if vision_model is None:
                   assert (
                       vision_model_name_or_path is not None
                   ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
                   from transformers import FlaxAutoModel

                   if "config" not in kwargs_vision:
                         from transformers import AutoConfig

                         UpperCAmelCase_	:						List[Any]					   =		AutoConfig.from_pretrained(_UpperCAmelCase)
                         UpperCAmelCase_	:						Union[str, Any]					   =		vision_config

                   UpperCAmelCase_	:						Optional[Any]					   =		FlaxAutoModel.from_pretrained(_UpperCAmelCase  ,*_UpperCAmelCase  ,**_UpperCAmelCase)

             # instantiate config with corresponding kwargs
             UpperCAmelCase_	:						List[Any]					   =		kwargs.pop("dtype"  ,jnp.floataa)
             UpperCAmelCase_	:						Union[str, Any]					   =		HybridCLIPConfig.from_text_vision_configs(text_model.config  ,vision_model.config  ,**_UpperCAmelCase)

             # init model
             UpperCAmelCase_	:						Union[str, Any]					   =		cls(_UpperCAmelCase  ,*_UpperCAmelCase  ,dtype=_UpperCAmelCase  ,**_UpperCAmelCase)

             if vision_config.model_type == "clip":
                   UpperCAmelCase_	:						Tuple					   =		vision_model.params["vision_model"]
                   UpperCAmelCase_	:						Dict					   =		vision_model.params["visual_projection"]["kernel"]
             else:
                   UpperCAmelCase_	:						Union[str, Any]					   =		vision_model.params

             UpperCAmelCase_	:						List[str]					   =		text_model.params

             return model






'''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    A						:




				'''simple docstring'''





				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			str:
							__UpperCamelCase :     str										=  cuda
							__UpperCamelCase :     Union[str, Any]										=  AutoConfig.from_pretrained(_UpperCAmelCase     )
							__UpperCamelCase :     List[str]										=  AutoModelForCTC.from_pretrained(_UpperCAmelCase     )
							self.model.eval()
							if self.cuda:
										self.model.to(device="cuda"     )
							__UpperCamelCase :     Any										=  AutoProcessor.from_pretrained(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  torchaudio.transforms.Resample(_UpperCAmelCase					,    1_6_0_0_0     )
							__UpperCamelCase :     List[Any]										=  0
							__UpperCamelCase :     Dict										=  list(self.processor.tokenizer.get_vocab().keys()     )
							for i in range(len(_UpperCAmelCase     )     ):
										if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
													__UpperCamelCase :     Union[str, Any]										=  i
							print("Blank Token id [PAD]/<pad>"					,    _UpperCAmelCase     )
							__UpperCamelCase :     Tuple										=  blank_id
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Union[str, Any]:
							__UpperCamelCase						,   __UpperCamelCase :     str										=  torchaudio.load(_UpperCAmelCase     )
							__UpperCamelCase :     Dict										=  self.resampler(_UpperCAmelCase     ).squeeze().numpy()
							return speech
				def 			a_			(self					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Union[str, Any]										=  self.blank_id
							__UpperCamelCase :     Any										=  "|".join(item["sent"].split(" "     )     )
							if not os.path.isfile(item["wav_path"]     ):
										print(item["wav_path"]					,    "not found in wavs directory"     )

							__UpperCamelCase :     Optional[Any]										=  self.speech_file_to_array_fn(item["wav_path"]     )
							__UpperCamelCase :     Tuple										=  self.processor(_UpperCAmelCase					,    sampling_rate=1_6_0_0_0					,    return_tensors="pt"					,    padding=_UpperCAmelCase     )
							if self.cuda:
										__UpperCamelCase :     str										=  inputs.to(device="cuda"     )

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

							# get the emission probability at frame level
							__UpperCamelCase :     List[Any]										=  torch.log_softmax(_UpperCAmelCase					,    dim=-1     )
							__UpperCamelCase :     Tuple										=  emissions[0].cpu().detach()

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

							__UpperCamelCase :     Tuple										=  {c: i for i, c in enumerate(_UpperCAmelCase     )}
							__UpperCamelCase :     List[str]										=  []
							for c in transcript:
										if c in dictionary:
													tokens.append(dictionary[c]     )

							def get_trellis(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase=0     ):
										__UpperCamelCase :     List[Any]										=  emission.size(0     )
										__UpperCamelCase :     Optional[Any]										=  len(_UpperCAmelCase     )

										# 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 :     Optional[int]										=  torch.full((num_frames + 1, num_tokens + 1)					,    -float("inf"     )     )
										__UpperCamelCase :     Union[str, Any]										=  0
										for t in range(_UpperCAmelCase     ):
													__UpperCamelCase :     Optional[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 :     Optional[Any]										=  get_trellis(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )



							@dataclass
							class    A						:




											'''simple docstring'''





											A       =							42
											A       =							42
											A       =							42

							def backtrack(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase=0     ):
										__UpperCamelCase :     Dict										=  trellis.size(1     ) - 1
										__UpperCamelCase :     List[str]										=  torch.argmax(trellis[:, j]     ).item()

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

													# 2. Store the path with frame-wise probability.
													__UpperCamelCase :     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					,    _UpperCAmelCase     )     )

													# 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(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )



							@dataclass
							class    A						:




											'''simple docstring'''





											A       =							42
											A       =							42
											A       =							42
											A       =							42
											def __repr__(self     )      ->			Optional[int]:
														return f"{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"





											@property
											def 			a_			(self     )      ->			Tuple:
														return self.end - self.start

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

							__UpperCamelCase :     Optional[Any]										=  merge_repeats(_UpperCAmelCase     )
							with open(item["out_path"]					,    "w"     ) as out_align:
										for seg in segments:
													out_align.write(str(_UpperCAmelCase     ) + "\n"     )





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

							# load text file
							__UpperCamelCase :     Optional[int]										=  open(_UpperCAmelCase					,    encoding="utf8"     ).readlines()

							__UpperCamelCase :     str										=  []
							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 :     List[Any]										=  line.strip().split("\t"     )
										__UpperCamelCase :     List[Any]										=  os.path.join(_UpperCAmelCase					,    wav_name + ".wav"     )
										__UpperCamelCase :     Tuple										=  os.path.join(_UpperCAmelCase					,    wav_name + ".txt"     )

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

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







def      __lowerCAmelCase   (       ):
			__UpperCamelCase :     Union[str, Any]										=  argparse.ArgumentParser()

			parser.add_argument(
			    "--model_name"   , type=snake_case__   , default="arijitx/wav2vec2-xls-r-300m-bengali"   , help="wav2vec model name"		)
			parser.add_argument("--wav_dir"   , type=snake_case__   , default="./wavs"   , help="directory containing wavs"		)
			parser.add_argument("--text_file"   , type=snake_case__   , default="script.txt"   , help="file containing text"		)
			parser.add_argument("--input_wavs_sr"   , type=snake_case__   , default=16_000   , help="sampling rate of input audios"		)
			parser.add_argument(
			    "--output_dir"   , type=snake_case__   , default="./out_alignment"   , help="output directory containing the alignment files"		)
			parser.add_argument("--cuda"   , action="store_true"		)

			__UpperCamelCase :     Any										=  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()



