


"""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				:   Union[str, Any]		,							UpperCAmelCase_				:   Optional[Any]		,							UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   int							)       ->int:



														"""simple docstring"""



														snake_case_     							=		cuda
														snake_case_     							=		AutoConfig.from_pretrained(a__							)
														snake_case_     							=		AutoModelForCTC.from_pretrained(a__							)
														self.model.eval()
														if self.cuda:
																					self.model.to(device="""cuda"""							)
														snake_case_     							=		AutoProcessor.from_pretrained(a__							)
														snake_case_     							=		torchaudio.transforms.Resample(a__		,							16_000							)
														snake_case_     							=		0
														snake_case_     							=		list(self.processor.tokenizer.get_vocab().keys()							)
														for i in range(len(a__							)							):
																					if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																												snake_case_     							=		i
														print("""Blank Token id [PAD]/<pad>"""		,							a__							)
														snake_case_     							=		blank_id



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   int							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		torchaudio.load(a__							)
														snake_case_     							=		self.resampler(a__							).squeeze().numpy()
														return speech



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Tuple							)       ->str:



														"""simple docstring"""



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

														snake_case_     							=		self.speech_file_to_array_fn(item["""wav_path"""]							)
														snake_case_     							=		self.processor(a__		,							sampling_rate=16_000		,							return_tensors="""pt"""		,							padding=a__							)
														if self.cuda:
																					snake_case_     							=		inputs.to(device="""cuda"""							)

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

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

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

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

														def get_trellis(UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   str=0							):
																					snake_case_     							=		emission.size(0							)
																					snake_case_     							=		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.
																					snake_case_     							=		torch.full((num_frames + 1, num_tokens + 1)		,							-float("""inf"""							)							)
																					snake_case_     							=		0
																					for t in range(a__							):
																												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

														snake_case_     							=		get_trellis(a__		,							a__		,							a__							)





														@dataclass
														class   __A  :





																					'''simple docstring'''

																					__lowercase:		Optional[Any]				   =     42
																					__lowercase:		Any				   =     42
																					__lowercase:		List[Any]				   =     42

														def backtrack(UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Optional[Any]		,							UpperCAmelCase_				:   Optional[int]=0							):
																					snake_case_     							=		trellis.size(1							) - 1
																					snake_case_     							=		torch.argmax(trellis[:, j]							).item()

																					snake_case_     							=		[]
																					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.
																												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.
																												snake_case_     							=		trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																												# 2. Store the path with frame-wise probability.
																												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		,							a__							)							)

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

														snake_case_     							=		backtrack(a__		,							a__		,							a__		,							a__							)





														@dataclass
														class   __A  :





																					'''simple docstring'''

																					__lowercase:		Any				   =     42
																					__lowercase:		Optional[Any]				   =     42
																					__lowercase:		Tuple				   =     42
																					__lowercase:		Dict				   =     42



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



																												"""simple docstring"""



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



																					@property
																					def 		lowerCAmelCase							(					self				:   Dict							)       ->Tuple:



																												"""simple docstring"""



																												return self.end - self.start

														def merge_repeats(UpperCAmelCase_				:   List[str]							):
																					snake_case_     							=		0, 0
																					snake_case_     							=		[]
																					while ia < len(a__							):
																												while ia < len(a__							) and path[ia].token_index == path[ia].token_index:
																																			ia += 1
																												snake_case_     							=		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__		,							)							)
																												snake_case_     							=		ia
																					return segments

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



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   List[Any]		,							UpperCAmelCase_				:   Dict							)       ->Optional[int]:



														"""simple docstring"""



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

														# load text file
														snake_case_     							=		open(a__		,							encoding="""utf8"""							).readlines()

														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()

																					snake_case_     							=		line.strip().split("""\t"""							)
																					snake_case_     							=		os.path.join(a__		,							wav_name + """.wav"""							)
																					snake_case_     							=		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					(   )     ->					Optional[Any]:
							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=16_000					,			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"""					)

							snake_case_     							=		parser.parse_args()

							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()







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a					:				Dict       =      logging.get_logger(__name__)

a					:				str       =      {
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hubert"""

       def __init__(      self						,    a__=32						,    a__=7_68						,    a__=12						,    a__=12						,    a__=30_72						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=0.1						,    a__=True						,    a__=0.0						,    a__=0.1						,    a__=0.1						,    a__=0.02						,    a__=1e-5						,    a__="group"						,    a__="gelu"						,    a__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)						,    a__=(5, 2, 2, 2, 2, 2, 2)						,    a__=(10, 3, 3, 3, 3, 2, 2)						,    a__=False						,    a__=1_28						,    a__=16						,    a__=False						,    a__=True						,    a__=0.05						,    a__=10						,    a__=2						,    a__=0.0						,    a__=10						,    a__=0						,    a__="sum"						,    a__=False						,    a__=False						,    a__=2_56						,    a__=0						,    a__=1						,    a__=2						,    **a__						,    ):






        '''simple docstring'''






        super().__init__(**a__						,    pad_token_id=a__						,    bos_token_id=a__						,    eos_token_id=a__       )
        snake_case :			List[str]							=   hidden_size
        snake_case :			List[str]							=   feat_extract_norm
        snake_case :			List[str]							=   feat_extract_activation
        snake_case :			Dict							=   list(a__       )
        snake_case :			Tuple							=   list(a__       )
        snake_case :			Dict							=   list(a__       )
        snake_case :			str							=   conv_bias
        snake_case :			Dict							=   num_conv_pos_embeddings
        snake_case :			Union[str, Any]							=   num_conv_pos_embedding_groups
        snake_case :			Optional[int]							=   len(self.conv_dim       )
        snake_case :			Tuple							=   num_hidden_layers
        snake_case :			Any							=   intermediate_size
        snake_case :			Optional[Any]							=   hidden_act
        snake_case :			str							=   num_attention_heads
        snake_case :			List[Any]							=   hidden_dropout
        snake_case :			Union[str, Any]							=   attention_dropout
        snake_case :			Dict							=   activation_dropout
        snake_case :			List[str]							=   feat_proj_layer_norm
        snake_case :			List[str]							=   feat_proj_dropout
        snake_case :			Union[str, Any]							=   final_dropout
        snake_case :			List[str]							=   layerdrop
        snake_case :			str							=   layer_norm_eps
        snake_case :			Tuple							=   initializer_range
        snake_case :			str							=   vocab_size
        snake_case :			Tuple							=   do_stable_layer_norm
        snake_case :			Dict							=   use_weighted_layer_sum
        snake_case :			Optional[Any]							=   classifier_proj_size

        if (
            (len(self.conv_stride       ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
            or (len(self.conv_dim       ) != self.num_feat_extract_layers)
        ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
             ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
             f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
             f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        snake_case :			Optional[int]							=   apply_spec_augment
        snake_case :			int							=   mask_time_prob
        snake_case :			Dict							=   mask_time_length
        snake_case :			Any							=   mask_time_min_masks
        snake_case :			Any							=   mask_feature_prob
        snake_case :			Union[str, Any]							=   mask_feature_length
        snake_case :			List[Any]							=   mask_feature_min_masks

        # ctc loss
        snake_case :			List[str]							=   ctc_loss_reduction
        snake_case :			Dict							=   ctc_zero_infinity



       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return functools.reduce(operator.mul						,    self.conv_stride						,    1       )






