Add error rate calculation script.

error_rate.py

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