






'''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:    Optional[int]				,   a:    Optional[Any]				,   a:    Optional[int]				,   a:    int)  ->Optional[int]:






								'''simple docstring'''


								a_				     =   cuda
								a_				     =   AutoConfig.from_pretrained(a)
								a_				     =   AutoModelForCTC.from_pretrained(a)
								self.model.eval()
								if self.cuda:
														self.model.to(device="cuda")
								a_				     =   AutoProcessor.from_pretrained(a)
								a_				     =   torchaudio.transforms.Resample(a				,   1_60_00)
								a_				     =   0
								a_				     =   list(self.processor.tokenizer.get_vocab().keys())
								for i in range(len(a)):
														if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																				a_				     =   i
								print("Blank Token id [PAD]/<pad>"				,   a)
								a_				     =   blank_id



		def _lowerCAmelCase     (    self:    Any				,   a:    int)  ->Optional[int]:






								'''simple docstring'''


								a_     ,       a_				     =   torchaudio.load(a)
								a_				     =   self.resampler(a).squeeze().numpy()
								return speech



		def _lowerCAmelCase     (    self:    Union[str, Any]				,   a:    Optional[Any])  ->int:






								'''simple docstring'''


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

								a_				     =   self.speech_file_to_array_fn(item["wav_path"])
								a_				     =   self.processor(a				,   sampling_rate=1_60_00				,   return_tensors="pt"				,   padding=a)
								if self.cuda:
														a_				     =   inputs.to(device="cuda")

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

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

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

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

								def get_trellis(a:    List[Any]				,   a:    Any				,   a:    int=0):
														a_				     =   emission.size(0)
														a_				     =   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.
														a_				     =   torch.full((num_frames + 1, num_tokens + 1)				,   -float("inf"))
														a_				     =   0
														for t in range(a):
																				a_				     =   torch.maximum(
																				    # Score for staying at the same token
																				    trellis[t, 1:] + emission[t, blank_id]				,   trellis[t, :-1] + emission[t, tokens]				,   )
														return trellis

								a_				     =   get_trellis(a				,   a				,   a)





								@dataclass
								class 		SCREAMING_SNAKE_CASE__   :
										_UpperCAmelCase         =42
										_UpperCAmelCase         =42
										_UpperCAmelCase         =42

								def backtrack(a:    Optional[int]				,   a:    List[str]				,   a:    Union[str, Any]				,   a:    Optional[Any]=0):
														a_				     =   trellis.size(1) - 1
														a_				     =   torch.argmax(trellis[:, j]).item()

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

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

								a_				     =   backtrack(a				,   a				,   a				,   a)





								@dataclass
								class 		SCREAMING_SNAKE_CASE__   :
										_UpperCAmelCase         =42
										_UpperCAmelCase         =42
										_UpperCAmelCase         =42
										_UpperCAmelCase         =42



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






																'''simple docstring'''


																return self.end - self.start

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

								a_				     =   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:    List[Any]				,   a:    Dict				,   a:    Tuple				,   a:    str)  ->Dict:






								'''simple docstring'''


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

								# load text file
								a_				     =   open(a				,   encoding="utf8").readlines()

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

														a_     ,       a_				     =   line.strip().split("\t")
														a_				     =   os.path.join(a				,   wav_name + ".wav")
														a_				     =   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 			__UpperCAmelCase				()		->				Optional[int]:

						'''simple docstring'''


						a_				     =   argparse.ArgumentParser()

						parser.add_argument(
						    "--model_name"	,type=lowercase__	,default="arijitx/wav2vec2-xls-r-300m-bengali"	,help="wav2vec model name"				)
						parser.add_argument("--wav_dir"	,type=lowercase__	,default="./wavs"	,help="directory containing wavs"				)
						parser.add_argument("--text_file"	,type=lowercase__	,default="script.txt"	,help="file containing text"				)
						parser.add_argument("--input_wavs_sr"	,type=lowercase__	,default=16000	,help="sampling rate of input audios"				)
						parser.add_argument(
						    "--output_dir"	,type=lowercase__	,default="./out_alignment"	,help="output directory containing the alignment files"				)
						parser.add_argument("--cuda"	,action="store_true"				)

						a_				     =   parser.parse_args()

						a_				     =   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 json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
							from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

							from .test_feature_extraction_speechta import floats_list


a_      = get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')





@require_torch
class 		SCREAMING_SNAKE_CASE__   (     unittest.TestCase    ):



		def _lowerCAmelCase     (    self:    str)  ->Optional[Any]:






								'''simple docstring'''


								a_				     =   tempfile.mkdtemp()

								a_				     =   SpeechTaTokenizer(a)
								tokenizer.save_pretrained(self.tmpdirname)

								a_				     =   {
								    "feature_size": 1,
								    "padding_value": 0.0,
								    "sampling_rate": 1_60_00,
								    "do_normalize": False,
								    "num_mel_bins": 80,
								    "hop_length": 16,
								    "win_length": 64,
								    "win_function": "hann_window",
								    "fmin": 80,
								    "fmax": 76_00,
								    "mel_floor": 1e-10,
								    "reduction_factor": 2,
								    "return_attention_mask": True,
								}

								a_				     =   os.path.join(self.tmpdirname				,   a)
								with open(self.feature_extraction_file				,   "w"				,   encoding="utf-8") as fp:
														fp.write(json.dumps(a) + "\n")



		def _lowerCAmelCase     (    self:    Any				,   **a:    List[Any])  ->Optional[Any]:






								'''simple docstring'''


								return SpeechTaTokenizer.from_pretrained(self.tmpdirname				,   **a)



		def _lowerCAmelCase     (    self:    Union[str, Any]				,   **a:    int)  ->str:






								'''simple docstring'''


								return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname				,   **a)



		def _lowerCAmelCase     (    self:    int)  ->Optional[Any]:






								'''simple docstring'''


								shutil.rmtree(self.tmpdirname)



		def _lowerCAmelCase     (    self:    Any)  ->str:






								'''simple docstring'''


								a_				     =   self.get_tokenizer()
								a_				     =   self.get_feature_extractor()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								processor.save_pretrained(self.tmpdirname)
								a_				     =   SpeechTaProcessor.from_pretrained(self.tmpdirname)

								self.assertEqual(processor.tokenizer.get_vocab()				,   tokenizer.get_vocab())
								self.assertIsInstance(processor.tokenizer				,   a)

								self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor.to_json_string())
								self.assertIsInstance(processor.feature_extractor				,   a)



		def _lowerCAmelCase     (    self:    List[Any])  ->Optional[int]:






								'''simple docstring'''


								a_				     =   SpeechTaProcessor(tokenizer=self.get_tokenizer()				,   feature_extractor=self.get_feature_extractor())
								processor.save_pretrained(self.tmpdirname)

								a_				     =   self.get_tokenizer(bos_token="(BOS)"				,   eos_token="(EOS)")
								a_				     =   self.get_feature_extractor(do_normalize=a				,   padding_value=1.0)

								a_				     =   SpeechTaProcessor.from_pretrained(
								    self.tmpdirname				,   bos_token="(BOS)"				,   eos_token="(EOS)"				,   do_normalize=a				,   padding_value=1.0)

								self.assertEqual(processor.tokenizer.get_vocab()				,   tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.tokenizer				,   a)

								self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.feature_extractor				,   a)



		def _lowerCAmelCase     (    self:    Any)  ->List[Any]:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   floats_list((3, 10_00))

								a_				     =   feature_extractor(audio=a				,   return_tensors="np")
								a_				     =   processor(audio=a				,   return_tensors="np")

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()				,   input_processor[key].sum()				,   delta=1e-2)



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






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   floats_list((3, 10_00))

								a_				     =   feature_extractor(audio_target=a				,   return_tensors="np")
								a_				     =   processor(audio_target=a				,   return_tensors="np")

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()				,   input_processor[key].sum()				,   delta=1e-2)



		def _lowerCAmelCase     (    self:    str)  ->Dict:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   "This is a test string"

								a_				     =   processor(text=a)
								a_				     =   tokenizer(a)

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]				,   encoded_processor[key])



		def _lowerCAmelCase     (    self:    List[Any])  ->Any:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   "This is a test string"

								a_				     =   processor(text_target=a)
								a_				     =   tokenizer(a)

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]				,   encoded_processor[key])



		def _lowerCAmelCase     (    self:    List[Any])  ->Any:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								a_				     =   processor.batch_decode(a)
								a_				     =   tokenizer.batch_decode(a)

								self.assertListEqual(a				,   a)




		def _lowerCAmelCase     (    self:    Optional[int])  ->Optional[int]:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								self.assertListEqual(
								    processor.model_input_names				,   feature_extractor.model_input_names				,   msg="`processor` and `feature_extractor` model input names do not match"				,   )



