# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import torch
import Levenshtein as Lev
from typing import Tuple
[docs]class ErrorRate(object):
r"""
Provides inteface of error rate calcuation.
Note:
Do not use this class directly, use one of the sub classes.
"""
def __init__(self, tokenizer) -> None:
self.total_dist = 0.0
self.total_length = 0.0
self.tokenizer = tokenizer
def __call__(self, targets, y_hats):
r"""
Calculating error rate.
Args:
targets (torch.Tensor): set of ground truth
y_hats (torch.Tensor): predicted y values (y_hat) by the model
Returns:
- **cer**: character error rate
"""
dist, length = self._get_distance(targets, y_hats)
self.total_dist += dist
self.total_length += length
return self.total_dist / self.total_length
def _get_distance(self, targets: torch.Tensor, y_hats: torch.Tensor) -> Tuple[float, int]:
r"""
Provides total character distance between targets & y_hats
Args: targets, y_hats
targets (torch.Tensor): set of ground truth
y_hats (torch.Tensor): predicted y values (y_hat) by the model
Returns: total_dist, total_length
- **total_dist**: total distance between targets & y_hats
- **total_length**: total length of targets sequence
"""
total_dist = 0
total_length = 0
for (target, y_hat) in zip(targets, y_hats):
s1 = self.tokenizer.decode(target)
s2 = self.tokenizer.decode(y_hat)
dist, length = self.metric(s1, s2)
total_dist += dist
total_length += length
return total_dist, total_length
def metric(self, *args, **kwargs) -> Tuple[float, int]:
raise NotImplementedError
[docs]class CharacterErrorRate(ErrorRate):
r"""
Computes the Character Error Rate, defined as the edit distance between the
two provided sentences after tokenizing to characters.
"""
def __init__(self, tokenizer):
super(CharacterErrorRate, self).__init__(tokenizer)
[docs] def metric(self, s1: str, s2: str) -> Tuple[float, int]:
r"""
Computes the Character Error Rate, defined as the edit distance between the
two provided sentences after tokenizing to characters.
Args: s1, s2
s1 (string): space-separated sentence
s2 (string): space-separated sentence
Returns: dist, length
- **dist**: distance between target & y_hat
- **length**: length of target sequence
"""
s1 = s1.replace(' ', '')
s2 = s2.replace(' ', '')
# if '_' in sentence, means subword-unit, delete '_'
if '_' in s1:
s1 = s1.replace('_', '')
if '_' in s2:
s2 = s2.replace('_', '')
dist = Lev.distance(s2, s1)
length = len(s1.replace(' ', ''))
return dist, length
[docs]class WordErrorRate(ErrorRate):
r"""
Computes the Word Error Rate, defined as the edit distance between the
two provided sentences after tokenizing to words.
"""
def __init__(self, tokenizer):
super(WordErrorRate, self).__init__(tokenizer)
[docs] def metric(self, s1: str, s2: str) -> Tuple[float, int]:
r"""
Computes the Word Error Rate, defined as the edit distance between the
two provided sentences after tokenizing to words.
Args: s1, s2
s1 (string): space-separated sentence
s2 (string): space-separated sentence
Returns: dist, length
- **dist**: distance between target & y_hat
- **length**: length of target sequence
"""
b = set(s1.split() + s2.split())
word2char = dict(zip(b, range(len(b))))
# map the words to a char array (Levenshtein packages only accepts
# strings)
w1 = [chr(word2char[w]) for w in s1.split()]
w2 = [chr(word2char[w]) for w in s2.split()]
dist = Lev.distance(''.join(w1), ''.join(w2))
length = len(s1.split())
return dist, length