phone_errors.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Edit distances between Unicode International Phonetic Alphabet strings. 
This is a Hugging Face wrapper around the panphon library's distance module. 
"""

import evaluate
import datasets
import numpy as np
import panphon.distance


_CITATION = """\
@inproceedings{Mortensen-et-al:2016,
  author    = {David R. Mortensen and
               Patrick Littell and
               Akash Bharadwaj and
               Kartik Goyal and
               Chris Dyer and
               Lori S. Levin},
  title     = {PanPhon: {A} Resource for Mapping {IPA} Segments to Articulatory Feature Vectors},
  booktitle = {Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  pages     = {3475--3484},
  publisher = {{ACL}},
  year      = {2016}
}
"""

_DESCRIPTION = """
Error rates in terms of distance between articulatory phonological features can help understand differences 
between strings in the International Phonetic Alphabet (IPA) in a linguistically motivated way. 
This is useful when evaluating speech recognition or orthographic to IPA conversion tasks.
"""


_KWARGS_DESCRIPTION = """
Calculates the following measures of difference that rely on phonetic features:
 - Phone error rate (PER) gives edit distance in terms of phones, rather than Unicode characters, since phones can consist of\ 
multiple characters. It is normalized by the number of phones of the reference string.
 - Phone feature error rate (PFER) is Levenshtein distance between strings where distance between individual phones\
is computed using Hamming distance between phonetic features. By default it is a metric that obeys the triangle\ 
equality, but can also be normalized by number of phones. 
- Feature error rate (FER) is the edit distance in terms of articulatory features normalized by the number of phones in the reference. 


Each measure is given for each prediction, reference pair along with the mean value across all pairs. 

Args:
    predictions: list of predictions to score. Each predictions should be a string of unicode characters.
    references: list of reference for each prediction. Each reference should be a string with of unicode characters.
    feature_model: string to set which panphon.distance.Distance feature parsing model is used, choose from "strict", "permissive", "segment". Defaults to "segment".
    is_normalize_pfer: bool, set to True to normalize PFER by the largest number of phones in the prediction, reference pair
Returns:
    phone_error_rates: list of floats giving PER for each prediction, reference pair
    mean_phone_error_rate: float, average PER across all examples
    phone_feature_error_rates: list of floats giving PFER for each prediction, reference pair
    mean_phone_feature_error_rate: float, average PFER across all examples
    feature_error_rates: list of floats giving FER for each prediction, reference pair
    mean_feature_error_rate: float, average FER across all examples

Examples:
    Compare articulatory differences in voicing in "bob" vs. "pop" and different pronunciations of "the":
>>> phone_distance = evaluate.load("ginic/phone_errors")
>>> phone_distance.compute(predictions=["bob", "ði"], references=["pop", "ðə"])
{'phone_error_rates': [0.6666666666666666, 0.5], 'mean_phone_error_rate': 0.5833333333333333, 'phone_feature_error_rates': [0.08333333333333333, 0.125], 'mean_phone_feature_error_rate': 0.10416666666666666, 'feature_error_rates': [0.027777777777777776, 0.0625], 'mean_feature_error_rate': 0.04513888888888889}

    Normalize PFER by the length of string with largest number of phones: 
>>> phone_distance = evaluate.load("ginic/phone_errors")
>>> phone_distance.compute(predictions=["bob", "ði"], references=["pop", "ðə"], is_normalize_pfer=True)

"""

def phone_error_rate(prediction:str, reference: str, distance_computer:panphon.distance.Distance):
    """Computes phone error rates. This is similar to the Distance.phoneme_error_rate function, but 
    is more efficient and fixes some bugs related to normalization in the original function.

    Args:
        distance_computer (panphon.distance.Distance): computes edit distance and resolves characters to phones

    Returns:
        float:  the phone error rate

    >>> phone_error_rate("bob", "po", panphon.distance.Distance())
    1.0
    >>> phone_error_rate("ði", "ðə", panphon.distance.Distance())
    0.5
    """
    if reference: # Can only be computed when the length of the reference greater than 0 
        pred_phones = distance_computer.fm.ipa_segs(prediction)
        ref_phones = distance_computer.fm.ipa_segs(reference)

        phone_edits = distance_computer.min_edit_distance(
            lambda x: 1, # deletion cost
            lambda x: 1, # insertion cost
            lambda x, y: 0 if x == y else 1, # substitution cost,
            [[]], 
            pred_phones, 
            ref_phones
        )

        return phone_edits / len(ref_phones)
    else: 
        raise ValueError("one or more references are empty strings")
        

@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class PhoneDistance(evaluate.Metric):
    """Class for computing distance between Unicode IPA strings """

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features=datasets.Features({
                'predictions': datasets.Value('string', id="sequence"),
                'references': datasets.Value('string', id="sequence"),
            }),
            # Additional links to the codebase or references
            codebase_urls=["https://github.com/dmort27/panphon", "https://huggingface.co/spaces/ginic/phone_distance/tree/main"],
            reference_urls=["https://pypi.org/project/panphon/", "https://arxiv.org/abs/2308.03917"]
        )

    def _compute(self, predictions:list[str], references:list[str], feature_model:str="segment", is_normalize_pfer:bool=False):
        """Computes phoneme error rates, phone feature error rate (Hamming feature edit distance) and feature error rates between prediction and reference strings 

        Args:
            predictions (list[str]): Predicted transcriptions.
            references (list[str]): Reference transcriptions.
            feature_model (str, optional): panphon.distance.Distance feature parsing model to be used, choose from "strict", "permissive", "segment". Defaults to "segment".
            is_normalize_pfer (bool, optional): Set to true to normalize phone feature error rates by maximum length (measure won't be a true metric). Defaults to False.

        Returns:
            dict:  {"phone_error_rates": list[float], "mean_phone_error_rate": float, "phone_feature_error_rates": list[float], "mean_phone_feature_error_rates": float, 
                    "feature_error_rates": list[float], "mean_feature_error_rate": float}
        """
        distance_computer = panphon.distance.Distance(feature_model=feature_model)
        phone_error_rates = []
        feature_error_rates = []
        hamming_distances = []
        for p, r in zip(predictions, references):
            if is_normalize_pfer:
                hd = distance_computer.hamming_feature_edit_distance_div_maxlen(p, r)
            else:
                hd = distance_computer.hamming_feature_edit_distance(p, r)
            hamming_distances.append(hd)
            per = phone_error_rate(p, r, distance_computer)
            phone_error_rates.append(per)
            fer = distance_computer.feature_error_rate(p, r)
            feature_error_rates.append(fer)
        
        return {
            "phone_error_rates": phone_error_rates,
            "mean_phone_error_rate": np.mean(phone_error_rates),
            "phone_feature_error_rates": hamming_distances, 
            "mean_phone_feature_error_rate": np.mean(hamming_distances), 
            "feature_error_rates": feature_error_rates, 
            "mean_feature_error_rate": np.mean(feature_error_rates)
        }