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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) -> float:
"""Calculate the average word error rate (WER).
Args:
wer_scores: word error rate scores
combined_ref_len: combined length of reference sentences
Returns:
average word error rate (float)
Usage:
>>> avg_wer([0.5, 0.5], 2)
0.5
"""
return float(sum(wer_scores)) / float(combined_ref_len)
def _levenshtein_distance(ref, hyp) -> int:
"""Levenshtein distance.
Args:
ref: reference sentence
hyp: hypothesis sentence
Returns:
distance: levenshtein distance between reference and hypothesis
Usage:
>>> _levenshtein_distance("hello", "helo")
2
"""
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 = " "
) -> tuple[float, int]:
"""Compute the levenshtein distance between reference sequence and
hypothesis sequence in word-level.
Args:
reference: The reference sentence.
hypothesis: The hypothesis sentence.
ignore_case: Whether case-sensitive or not.
delimiter: Delimiter of input sentences.
Returns:
Levenshtein distance and length of reference sentence.
Usage:
>>> word_errors("hello world", "hello")
1, 2
"""
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,
) -> tuple[float, int]:
"""Compute the levenshtein distance between reference sequence and
hypothesis sequence in char-level.
Args:
reference: The reference sentence.
hypothesis: The hypothesis sentence.
ignore_case: Whether case-sensitive or not.
remove_space: Whether remove internal space characters
Returns:
Levenshtein distance and length of reference sentence.
Usage:
>>> char_errors("hello world", "hello")
1, 10
"""
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=" ") -> float:
"""Calculate word error rate (WER). WER compares reference text and
hypothesis text in word-level.
WER is defined as:
WER = (Sw + Dw + Iw) / Nw
with:
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
Args:
reference: The reference sentence.
hypothesis: The hypothesis sentence.
ignore_case: Whether case-sensitive or not.
delimiter: Delimiter of input sentences.
Returns:
Word error rate (float)
Usage:
>>> wer("hello world", "hello")
0.5
"""
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:
CER = (Sc + Dc + Ic) / Nc
with
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
Args:
reference: The reference sentence.
hypothesis: The hypothesis sentence.
ignore_case: Whether case-sensitive or not.
remove_space: Whether remove internal space characters
Returns:
Character error rate (float)
Usage:
>>> cer("hello world", "hello")
0.2727272727272727
"""
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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