





"""simple docstring"""




from jiwer import compute_measures

import datasets


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

a		:					Optional[int]	      =     '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The 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.

This 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.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

a		:					int	      =     '''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''


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






							def        __a  (  self )      ->					Tuple:
								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        __a  (  self ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=False )      ->					Union[str, Any]:
								if concatenate_texts:
									return compute_measures(lowerCAmelCase__ ,   lowerCAmelCase__ )["wer"]
								else:
									a :						Any      =							0
									a :						Optional[int]      =							0
									for prediction, reference in zip(lowerCAmelCase__ ,   lowerCAmelCase__ ):
										a :						Optional[Any]      =							compute_measures(lowerCAmelCase__ ,   lowerCAmelCase__ )
										incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
										total += measures["substitutions"] + measures["deletions"] + measures["hits"]
									return incorrect / total









"""simple docstring"""




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ..utils.dummy_pt_objects import *  # noqa F403
else:
		from .scheduling_consistency_models import CMStochasticIterativeScheduler
		from .scheduling_ddim import DDIMScheduler
		from .scheduling_ddim_inverse import DDIMInverseScheduler
		from .scheduling_ddim_parallel import DDIMParallelScheduler
		from .scheduling_ddpm import DDPMScheduler
		from .scheduling_ddpm_parallel import DDPMParallelScheduler
		from .scheduling_deis_multistep import DEISMultistepScheduler
		from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
		from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
		from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
		from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
		from .scheduling_euler_discrete import EulerDiscreteScheduler
		from .scheduling_heun_discrete import HeunDiscreteScheduler
		from .scheduling_ipndm import IPNDMScheduler
		from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
		from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
		from .scheduling_karras_ve import KarrasVeScheduler
		from .scheduling_pndm import PNDMScheduler
		from .scheduling_repaint import RePaintScheduler
		from .scheduling_sde_ve import ScoreSdeVeScheduler
		from .scheduling_sde_vp import ScoreSdeVpScheduler
		from .scheduling_unclip import UnCLIPScheduler
		from .scheduling_unipc_multistep import UniPCMultistepScheduler
		from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
		from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ..utils.dummy_flax_objects import *  # noqa F403
else:
		from .scheduling_ddim_flax import FlaxDDIMScheduler
		from .scheduling_ddpm_flax import FlaxDDPMScheduler
		from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
		from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
		from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
		from .scheduling_pndm_flax import FlaxPNDMScheduler
		from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
		from .scheduling_utils_flax import (
		    FlaxKarrasDiffusionSchedulers,
		    FlaxSchedulerMixin,
		    FlaxSchedulerOutput,
		    broadcast_to_shape_from_left,
		)


try:
		if not (is_torch_available() and is_scipy_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
		from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
		if not (is_torch_available() and is_torchsde_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
		from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler


