





'''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 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )							->							Optional[Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Any:







				'''simple docstring'''





				__lowercase  ,						__lowercase   =							torchaudio.load(_lowerCamelCase       )
				__lowercase   =							self.resampler(_lowerCamelCase       ).squeeze().numpy()
				return speech


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[Any]:







				'''simple docstring'''





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

				__lowercase   =							self.speech_file_to_array_fn(item['''wav_path''']       )
				__lowercase   =							self.processor(_lowerCamelCase   ,sampling_rate=16000   ,return_tensors='''pt'''   ,padding=_lowerCamelCase       )
				if self.cuda:
							__lowercase   =							inputs.to(device='''cuda'''       )

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

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

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

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

				def get_trellis(_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase=0       ):
							__lowercase   =							emission.size(0       )
							__lowercase   =							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.
							__lowercase   =							torch.full((num_frames + 1, num_tokens + 1)   ,-float('''inf'''       )       )
							__lowercase   =							0
							for t in range(_lowerCamelCase       ):
										__lowercase   =							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   =							get_trellis(_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )






				@dataclass
				class 							__lowercase				:







					'''simple docstring'''

					a			: int
					a			: int
					a			: float

				def backtrack(_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase=0       ):
							__lowercase   =							trellis.size(1       ) - 1
							__lowercase   =							torch.argmax(trellis[:, j]       ).item()

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

										# 2. Store the path with frame-wise probability.
										__lowercase   =							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]

				__lowercase   =							backtrack(_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )






				@dataclass
				class 							__lowercase				:







					'''simple docstring'''

					a			: str
					a			: int
					a			: int
					a			: float


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







								'''simple docstring'''





								return self.end - self.start

				def merge_repeats(_lowerCamelCase       ):
							__lowercase  ,						__lowercase   =							0, 0
							__lowercase   =							[]
							while ia < len(_lowerCamelCase       ):
										while ia < len(_lowerCamelCase       ) and path[ia].token_index == path[ia].token_index:
													ia += 1
										__lowercase   =							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   ,)       )
										__lowercase   =							ia
							return segments

				__lowercase   =							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   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )							->							Union[str, Any]:







				'''simple docstring'''





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

				# load text file
				__lowercase   =							open(_lowerCamelCase   ,encoding='''utf8'''       ).readlines()

				__lowercase   =							[]
				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   =							line.strip().split('''\t'''       )
							__lowercase   =							os.path.join(_lowerCamelCase   ,wav_name + '''.wav'''       )
							__lowercase   =							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       _lowerCAmelCase  ( ):
			__lowercase   =							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_6_0_0_0						,				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   =							parser.parse_args()

			__lowercase   =							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 csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_SCREAMING_SNAKE_CASE    =			'''\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
'''

# TODO: Add description of the dataset here
# You can copy an official description
_SCREAMING_SNAKE_CASE    =			'''\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
'''

# TODO: Add a link to an official homepage for the dataset here
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add the licence for the dataset here if you can find it
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_SCREAMING_SNAKE_CASE    =			{
    '''first_domain''': '''https://huggingface.co/great-new-dataset-first_domain.zip''',
    '''second_domain''': '''https://huggingface.co/great-new-dataset-second_domain.zip''',
}






class 							__lowercase				(					datasets.GeneratorBasedBuilder  ):







	'''simple docstring'''

	a			: Tuple      =				datasets.Version("1.1.0"  )

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	a			: List[Any]      =				[
	    datasets.BuilderConfig(name="first_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a first domain"  ),
	    datasets.BuilderConfig(name="second_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a second domain"  ),
	]

	a			: Dict      =				"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option1''': datasets.Value('''string'''       ),
							        '''answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option2''': datasets.Value('''string'''       ),
							        '''second_domain_answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				return datasets.DatasetInfo(
				    # This is the description that will appear on the datasets page.
				    description=_DESCRIPTION   ,features=_lowerCamelCase   ,homepage=_HOMEPAGE   ,license=_LICENSE   ,citation=_CITATION   ,)


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				__lowercase   =							_URLS[self.config.name]
				__lowercase   =							dl_manager.download_and_extract(_lowerCamelCase       )
				return [
				    datasets.SplitGenerator(
				        name=datasets.Split.TRAIN   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''train.jsonl'''       ),
				            '''split''': '''train''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.VALIDATION   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''dev.jsonl'''       ),
				            '''split''': '''dev''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.TEST   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''test.jsonl'''       ),
				            '''split''': '''test'''
				        }   ,),
				]


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				with open(_lowerCamelCase   ,encoding='''utf-8'''       ) as f:
							for key, row in enumerate(_lowerCamelCase       ):
										__lowercase   =							json.loads(_lowerCamelCase       )
										if self.config.name == "first_domain":
													# Yields examples as (key, example) tuples
													yield key, {
													    "sentence": data["sentence"],
													    "option1": data["option1"],
													    "answer": "" if split == "test" else data["answer"],
													}
										else:
													yield key, {
													    "sentence": data["sentence"],
													    "option2": data["option2"],
													    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
													}





