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138 lines
4.3 KiB
138 lines
4.3 KiB
# -- * -- coding: utf-8 -- * --
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import numpy as np
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def levenshtein_distance(ref, hyp):
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ref_len = len(ref)
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hyp_len = len(hyp)
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# special case
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if ref == hyp:
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return 0
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if ref_len == 0:
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return hyp_len
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if hyp_len == 0:
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return ref_len
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distance = np.zeros((ref_len + 1, hyp_len + 1), dtype=np.int64)
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# initialization distance matrix
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for j in xrange(hyp_len + 1):
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distance[0][j] = j
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for i in xrange(ref_len + 1):
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distance[i][0] = i
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# calculate levenshtein distance
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for i in xrange(1, ref_len + 1):
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for j in xrange(1, hyp_len + 1):
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if ref[i - 1] == hyp[j - 1]:
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distance[i][j] = distance[i - 1][j - 1]
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else:
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s_num = distance[i - 1][j - 1] + 1
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i_num = distance[i][j - 1] + 1
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d_num = distance[i - 1][j] + 1
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distance[i][j] = min(s_num, i_num, d_num)
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return distance[ref_len][hyp_len]
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def wer(reference, hypophysis, delimiter=' ', filter_none=True):
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"""
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Calculate word error rate (WER). WER is a popular evaluation metric used
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in speech recognition. It compares a reference with an hypophysis and
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is defined like this:
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.. math::
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WER = (Sw + Dw + Iw) / Nw
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where
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.. code-block:: text
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Sw is the number of words subsituted,
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Dw is the number of words deleted,
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Iw is the number of words inserted,
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Nw is the number of words in the reference
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We can use levenshtein distance to calculate WER. Please draw an attention
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that this function will truncate the beginning and ending delimiter for
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reference and hypophysis sentences before calculating WER.
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:param reference: The reference sentence.
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:type reference: str
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:param hypophysis: The hypophysis sentence.
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:type reference: str
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:param delimiter: Delimiter of input sentences.
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:type delimiter: char
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:param filter_none: Whether to remove None value when splitting sentence.
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:type filter_none: bool
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:return: WER
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:rtype: float
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"""
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if len(reference.strip(delimiter)) == 0:
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raise ValueError("Reference's word number should be greater than 0.")
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if filter_none == True:
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ref_words = filter(None, reference.strip(delimiter).split(delimiter))
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hyp_words = filter(None, hypophysis.strip(delimiter).split(delimiter))
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else:
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ref_words = reference.strip(delimiter).split(delimiter)
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hyp_words = reference.strip(delimiter).split(delimiter)
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edit_distance = levenshtein_distance(ref_words, hyp_words)
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wer = float(edit_distance) / len(ref_words)
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return wer
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def cer(reference, hypophysis, squeeze=True, ignore_case=False, strip_char=''):
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"""
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Calculate charactor error rate (CER). CER will compare reference text and
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hypophysis text in char-level. CER is defined as:
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.. math::
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CER = (Sc + Dc + Ic) / Nc
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where
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.. code-block:: text
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Sc is the number of character substituted,
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Dc is the number of deleted,
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Ic is the number of inserted
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Nc is the number of characters in the reference
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We can use levenshtein distance to calculate CER. Chinese input should be
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encoded to unicode.
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:param reference: The reference sentence.
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:type reference: str
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:param hypophysis: The hypophysis sentence.
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:type reference: str
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:param squeeze: If set true, consecutive space character
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will be squeezed to one
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:type squeeze: bool
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:param ignore_case: Whether case-sensitive or not.
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:type ignore_case: bool
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:param strip_char: If not set to '', strip_char in beginning and ending of
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sentence will be truncated.
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:type strip_char: char
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:return: CER
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:rtype: float
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"""
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if ignore_case == True:
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reference = reference.lower()
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hypophysis = hypophysis.lower()
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if strip_char != '':
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reference = reference.strip(strip_char)
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hypophysis = hypophysis.strip(strip_char)
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if squeeze == True:
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reference = ' '.join(filter(None, reference.split(' ')))
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hypophysis = ' '.join(filter(None, hypophysis.split(' ')))
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if len(reference) == 0:
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raise ValueError("Length of reference should be greater than 0.")
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edit_distance = levenshtein_distance(reference, hypophysis)
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cer = float(edit_distance) / len(reference)
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return cer
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