


'''simple docstring'''



from jiwer import compute_measures

import datasets


__A	='\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

__A	='\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

__A	='\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,   _KWARGS_DESCRIPTION	)
class 		_snake_case					(     datasets.Metric	):




					def 				snake_case__						(      self):
								return datasets.MetricInfo(
								    description=_DESCRIPTION							,							citation=_CITATION							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
								        {
								            """predictions""": datasets.Value("""string"""							,							id="""sequence"""),
								            """references""": datasets.Value("""string"""							,							id="""sequence"""),
								        })							,							codebase_urls=["""https://github.com/jitsi/jiwer/"""]							,							reference_urls=[
								        """https://en.wikipedia.org/wiki/Word_error_rate""",
								    ]							,							)




					def 				snake_case__						(      self							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=False):
								if concatenate_texts:
											return compute_measures(_lowerCamelCase							,							_lowerCamelCase)["wer"]
								else:
											UpperCAmelCase__  :					List[Any]        =     0
											UpperCAmelCase__  :					Optional[Any]        =     0
											for prediction, reference in zip(_lowerCamelCase							,							_lowerCamelCase):
														UpperCAmelCase__  :					str        =     compute_measures(_lowerCamelCase							,							_lowerCamelCase)
														incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
														total += measures["substitutions"] + measures["deletions"] + measures["hits"]
											return incorrect / total



'''simple docstring'''



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__A	=logging.get_logger(__name__)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :int   =					['''input_features''', '''is_longer''']




					def __init__(      self							,							_lowerCamelCase=64							,							_lowerCamelCase=4_8000							,							_lowerCamelCase=480							,							_lowerCamelCase=10							,							_lowerCamelCase=1024							,							_lowerCamelCase=0.0							,							_lowerCamelCase=False							,							_lowerCamelCase = 0							,							_lowerCamelCase = 1_4000							,							_lowerCamelCase = None							,							_lowerCamelCase = "fusion"							,							_lowerCamelCase = "repeatpad"							,							**_lowerCamelCase							,							):
								super().__init__(
								    feature_size=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							padding_value=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							**_lowerCamelCase							,							)
								UpperCAmelCase__  :					Dict        =     top_db
								UpperCAmelCase__  :					int        =     truncation
								UpperCAmelCase__  :					Optional[Any]        =     padding
								UpperCAmelCase__  :					Union[str, Any]        =     fft_window_size
								UpperCAmelCase__  :					List[Any]        =     (fft_window_size >> 1) + 1
								UpperCAmelCase__  :					Optional[int]        =     hop_length
								UpperCAmelCase__  :					int        =     max_length_s
								UpperCAmelCase__  :					Tuple        =     max_length_s * sampling_rate
								UpperCAmelCase__  :					List[Any]        =     sampling_rate
								UpperCAmelCase__  :					Tuple        =     frequency_min
								UpperCAmelCase__  :					Dict        =     frequency_max
								UpperCAmelCase__  :					Union[str, Any]        =     mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins							,							num_mel_filters=_lowerCamelCase							,							min_frequency=_lowerCamelCase							,							max_frequency=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							norm=_lowerCamelCase							,							mel_scale="""htk"""							,							)
								UpperCAmelCase__  :					Optional[int]        =     mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins							,							num_mel_filters=_lowerCamelCase							,							min_frequency=_lowerCamelCase							,							max_frequency=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							norm="""slaney"""							,							mel_scale="""slaney"""							,							)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     copy.deepcopy(self.__dict__)
								UpperCAmelCase__  :					Optional[int]        =     self.__class__.__name__
								if "mel_filters" in output:
											del output["mel_filters"]
								if "mel_filters_slaney" in output:
											del output["mel_filters_slaney"]
								return output




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					str        =     spectrogram(
								    _lowerCamelCase							,							window_function(self.fft_window_size							,							"""hann""")							,							frame_length=self.fft_window_size							,							hop_length=self.hop_length							,							power=2.0							,							mel_filters=_lowerCamelCase							,							log_mel="""dB"""							,							)
								return log_mel_spectrogram.T




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     np.array_split(list(range(0							,							total_frames - chunk_frames + 1))							,							3)
								if len(ranges[1]) == 0:
											# if the audio is too short, we just use the first chunk
											UpperCAmelCase__  :					List[Any]        =     [0]
								if len(ranges[2]) == 0:
											# if the audio is too short, we just use the first chunk
											UpperCAmelCase__  :					Union[str, Any]        =     [0]
								# randomly choose index for each part
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[0])
								UpperCAmelCase__  :					Optional[int]        =     np.random.choice(ranges[1])
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[2])

								UpperCAmelCase__  :					str        =     mel[idx_front : idx_front + chunk_frames, :]
								UpperCAmelCase__  :					Optional[int]        =     mel[idx_middle : idx_middle + chunk_frames, :]
								UpperCAmelCase__  :					Tuple        =     mel[idx_back : idx_back + chunk_frames, :]

								UpperCAmelCase__  :					int        =     torch.tensor(mel[None, None, :])
								UpperCAmelCase__  :					Optional[int]        =     torch.nn.functional.interpolate(
								    _lowerCamelCase							,							size=[chunk_frames, 64]							,							mode="""bilinear"""							,							align_corners=_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     mel_shrink[0][0].numpy()
								UpperCAmelCase__  :					Optional[Any]        =     np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]							,							axis=0)
								return mel_fusion




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								if waveform.shape[0] > max_length:
											if truncation == "rand_trunc":
														UpperCAmelCase__  :					List[str]        =     True
														# random crop to max_length (for compatibility) -> this should be handled by self.pad
														UpperCAmelCase__  :					List[Any]        =     len(_lowerCamelCase) - max_length
														UpperCAmelCase__  :					Optional[Any]        =     np.random.randint(0							,							overflow + 1)
														UpperCAmelCase__  :					int        =     waveform[idx : idx + max_length]
														UpperCAmelCase__  :					Tuple        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters_slaney)[None, :]
											elif truncation == "fusion":
														UpperCAmelCase__  :					Any        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters)
														UpperCAmelCase__  :					int        =     max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
														UpperCAmelCase__  :					int        =     mel.shape[0]
														if chunk_frames == total_frames:
																	# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																	# In this case, we just use the whole audio.
																	UpperCAmelCase__  :					Optional[Any]        =     np.stack([mel, mel, mel, mel]							,							axis=0)
																	UpperCAmelCase__  :					List[Any]        =     False
														else:
																	UpperCAmelCase__  :					Tuple        =     self._random_mel_fusion(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
																	UpperCAmelCase__  :					str        =     True
											else:
														raise NotImplementedError(f'''data_truncating {truncation} not implemented''')

								else:
											UpperCAmelCase__  :					str        =     False
											# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
											if waveform.shape[0] < max_length:
														if padding == "repeat":
																	UpperCAmelCase__  :					int        =     int(max_length / len(_lowerCamelCase))
																	UpperCAmelCase__  :					int        =     np.stack(np.tile(_lowerCamelCase							,							n_repeat + 1))[:max_length]
														if padding == "repeatpad":
																	UpperCAmelCase__  :					str        =     int(max_length / len(_lowerCamelCase))
																	UpperCAmelCase__  :					str        =     np.stack(np.tile(_lowerCamelCase							,							_lowerCamelCase))
														UpperCAmelCase__  :					Optional[Any]        =     np.pad(_lowerCamelCase							,							(0, max_length - waveform.shape[0])							,							mode="""constant"""							,							constant_values=0)

											if truncation == "fusion":
														UpperCAmelCase__  :					List[Any]        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters)
														UpperCAmelCase__  :					Dict        =     np.stack([input_mel, input_mel, input_mel, input_mel]							,							axis=0)
											else:
														UpperCAmelCase__  :					int        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters_slaney)[None, :]

								return input_mel, longer




					def __call__(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Dict        =     truncation if truncation is not None else self.truncation
								UpperCAmelCase__  :					List[Any]        =     padding if padding else self.padding

								if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
														raise ValueError(
														    f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
														    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
														    f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''')
								else:
											logger.warning(
											    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
											    """Failing to do so can result in silent errors that might be hard to debug.""")

								UpperCAmelCase__  :					int        =     isinstance(_lowerCamelCase							,							np.ndarray) and len(raw_speech.shape) > 1
								if is_batched_numpy and len(raw_speech.shape) > 2:
											raise ValueError(f'''Only mono-channel audio is supported for input to {self}''')
								UpperCAmelCase__  :					List[str]        =     is_batched_numpy or (
								    isinstance(_lowerCamelCase							,							(list, tuple)) and (isinstance(raw_speech[0]							,							(np.ndarray, tuple, list)))
								)

								if is_batched:
											UpperCAmelCase__  :					str        =     [np.asarray(_lowerCamelCase							,							dtype=np.floataa) for speech in raw_speech]
								elif not is_batched and not isinstance(_lowerCamelCase							,							np.ndarray):
											UpperCAmelCase__  :					Optional[int]        =     np.asarray(_lowerCamelCase							,							dtype=np.floataa)
								elif isinstance(_lowerCamelCase							,							np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
											UpperCAmelCase__  :					str        =     raw_speech.astype(np.floataa)

								# always return batch
								if not is_batched:
											UpperCAmelCase__  :					int        =     [np.asarray(_lowerCamelCase)]

								# convert to mel spectrogram, truncate and pad if needed.
								UpperCAmelCase__  :					int        =     [
								    self._get_input_mel(_lowerCamelCase							,							max_length if max_length else self.nb_max_samples							,							_lowerCamelCase							,							_lowerCamelCase)
								    for waveform in raw_speech
								]

								UpperCAmelCase__  :					int        =     []
								UpperCAmelCase__  :					List[str]        =     []
								for mel, longer in padded_inputs:
											input_mel.append(_lowerCamelCase)
											is_longer.append(_lowerCamelCase)

								if truncation == "fusion" and sum(_lowerCamelCase) == 0:
											# if no audio is longer than 10s, then randomly select one audio to be longer
											UpperCAmelCase__  :					Optional[int]        =     np.random.randint(0							,							len(_lowerCamelCase))
											UpperCAmelCase__  :					Tuple        =     True

								if isinstance(input_mel[0]							,							_lowerCamelCase):
											UpperCAmelCase__  :					str        =     [np.asarray(_lowerCamelCase							,							dtype=np.floataa) for feature in input_mel]

								# is_longer is a list of bool
								UpperCAmelCase__  :					str        =     [[longer] for longer in is_longer]

								UpperCAmelCase__  :					Optional[int]        =     {"""input_features""": input_mel, """is_longer""": is_longer}
								UpperCAmelCase__  :					Optional[int]        =     BatchFeature(_lowerCamelCase)

								if return_tensors is not None:
											UpperCAmelCase__  :					str        =     input_features.convert_to_tensors(_lowerCamelCase)

								return input_features