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"""Methods for determining the loss and scores of the model."""
import numpy as np
def avg_wer(wer_scores, combined_ref_len):
"""Calculate the average word error rate (WER) of the model."""
return float(sum(wer_scores)) / float(combined_ref_len)
def _levenshtein_distance(ref, hyp):
"""Levenshtein distance is a string metric for measuring the difference
between two sequences. Informally, the levenshtein disctance is defined as
the minimum number of single-character edits (substitutions, insertions or
deletions) required to change one word into the other. We can naturally
extend the edits to word level when calculate levenshtein disctance for
two sentences.
"""
len_ref = len(ref)
len_hyp = len(hyp)
# special case
if ref == hyp:
return 0
if len_ref == 0:
return len_hyp
if len_hyp == 0:
return len_ref
if len_ref < len_hyp:
ref, hyp = hyp, ref
len_ref, len_hyp = len_hyp, len_ref
# use O(min(m, n)) space
distance = np.zeros((2, len_hyp + 1), dtype=np.int32)
# initialize distance matrix
for j in range(0, len_hyp + 1):
distance[0][j] = j
# calculate levenshtein distance
for i in range(1, len_ref + 1):
prev_row_idx = (i - 1) % 2
cur_row_idx = i % 2
distance[cur_row_idx][0] = i
for j in range(1, len_hyp + 1):
if ref[i - 1] == hyp[j - 1]:
distance[cur_row_idx][j] = distance[prev_row_idx][j - 1]
else:
s_num = distance[prev_row_idx][j - 1] + 1
i_num = distance[cur_row_idx][j - 1] + 1
d_num = distance[prev_row_idx][j] + 1
distance[cur_row_idx][j] = min(s_num, i_num, d_num)
return distance[len_ref % 2][len_hyp]
def word_errors(
reference: str, hypothesis: str, ignore_case: bool = False, delimiter: str = " "
):
"""Compute the levenshtein distance between reference sequence and
hypothesis sequence in word-level.
:param reference: The reference sentence.
:type reference: basestring
:param hypothesis: The hypothesis sentence.
:type hypothesis: basestring
:param ignore_case: Whether case-sensitive or not.
:type ignore_case: bool
:param delimiter: Delimiter of input sentences.
:type delimiter: char
:return: Levenshtein distance and word number of reference sentence.
:rtype: list
"""
if ignore_case:
reference = reference.lower()
hypothesis = hypothesis.lower()
ref_words = reference.split(delimiter)
hyp_words = hypothesis.split(delimiter)
edit_distance = _levenshtein_distance(ref_words, hyp_words)
return float(edit_distance), len(ref_words)
def char_errors(
reference: str,
hypothesis: str,
ignore_case: bool = False,
remove_space: bool = False,
):
"""Compute the levenshtein distance between reference sequence and
hypothesis sequence in char-level.
:param reference: The reference sentence.
:type reference: basestring
:param hypothesis: The hypothesis sentence.
:type hypothesis: basestring
:param ignore_case: Whether case-sensitive or not.
:type ignore_case: bool
:param remove_space: Whether remove internal space characters
:type remove_space: bool
:return: Levenshtein distance and length of reference sentence.
:rtype: list
"""
if ignore_case:
reference = reference.lower()
hypothesis = hypothesis.lower()
join_char = " "
if remove_space:
join_char = ""
reference = join_char.join(filter(None, reference.split(" ")))
hypothesis = join_char.join(filter(None, hypothesis.split(" ")))
edit_distance = _levenshtein_distance(reference, hypothesis)
return float(edit_distance), len(reference)
def wer(reference: str, hypothesis: str, ignore_case=False, delimiter=" "):
"""Calculate word error rate (WER). WER compares reference text and
hypothesis text in word-level. WER is defined as:
.. math::
WER = (Sw + Dw + Iw) / Nw
where
.. code-block:: text
Sw is the number of words subsituted,
Dw is the number of words deleted,
Iw is the number of words inserted,
Nw is the number of words in the reference
We can use levenshtein distance to calculate WER. Please draw an attention
that empty items will be removed when splitting sentences by delimiter.
:param reference: The reference sentence.
:type reference: basestring
:param hypothesis: The hypothesis sentence.
:type hypothesis: basestring
:param ignore_case: Whether case-sensitive or not.
:type ignore_case: bool
:param delimiter: Delimiter of input sentences.
:type delimiter: char
:return: Word error rate.
:rtype: float
:raises ValueError: If word number of reference is zero.
"""
edit_distance, ref_len = word_errors(reference, hypothesis, ignore_case, delimiter)
if ref_len == 0:
raise ValueError("Reference's word number should be greater than 0.")
word_error_rate = float(edit_distance) / ref_len
return word_error_rate
def cer(reference, hypothesis, ignore_case=False, remove_space=False):
"""Calculate charactor error rate (CER). CER compares reference text and
hypothesis text in char-level. CER is defined as:
.. math::
CER = (Sc + Dc + Ic) / Nc
where
.. code-block:: text
Sc is the number of characters substituted,
Dc is the number of characters deleted,
Ic is the number of characters inserted
Nc is the number of characters in the reference
We can use levenshtein distance to calculate CER. Chinese input should be
encoded to unicode. Please draw an attention that the leading and tailing
space characters will be truncated and multiple consecutive space
characters in a sentence will be replaced by one space character.
:param reference: The reference sentence.
:type reference: basestring
:param hypothesis: The hypothesis sentence.
:type hypothesis: basestring
:param ignore_case: Whether case-sensitive or not.
:type ignore_case: bool
:param remove_space: Whether remove internal space characters
:type remove_space: bool
:return: Character error rate.
:rtype: float
:raises ValueError: If the reference length is zero.
"""
edit_distance, ref_len = char_errors(
reference, hypothesis, ignore_case, remove_space
)
if ref_len == 0:
raise ValueError("Length of reference should be greater than 0.")
char_error_rate = float(edit_distance) / ref_len
return char_error_rate
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