Add catt/xer.py

catt/xer.py

@@ -0,0 +1,76 @@
+"""
+    @author
+          ______         _                  _
+         |  ____|       (_)           /\   | |
+         | |__ __ _ _ __ _ ___       /  \  | | __ _ ___ _ __ ___   __ _ _ __ _   _
+         |  __/ _` | '__| / __|     / /\ \ | |/ _` / __| '_ ` _ \ / _` | '__| | | |
+         | | | (_| | |  | \__ \    / ____ \| | (_| \__ \ | | | | | (_| | |  | |_| |
+         |_|  \__,_|_|  |_|___/   /_/    \_\_|\__,_|___/_| |_| |_|\__,_|_|   \__, |
+                                                                              __/ |
+                                                                             |___/
+            Email: farisalasmary@gmail.com
+            Date:  Mar 15, 2022
+"""
+
+# pip install git+https://github.com/pzelasko/kaldialign.git
+
+from kaldialign import edit_distance
+
+
+def cer(ref, hyp):
+    """
+    Computes the Character Error Rate, defined as the edit distance.
+
+    Arguments:
+        ref (string): a space-separated ground truth string
+        hyp (string): a space-separated hypothesis
+    """
+    ref, hyp, = ref.replace(' ', '').strip(), hyp.replace(' ', '').strip()
+    info = edit_distance(ref, hyp)
+    distance = info['total']
+    ref_length = float(len(ref))
+    
+    data = {
+                'insertions': info['ins'],
+                'deletions': info['del'],
+                'substitutions': info['sub'],
+                'distance': distance,
+                'ref_length': ref_length,
+                'Error Rate': (distance / ref_length) * 100
+           }
+    
+    return data
+
+
+def wer(ref, hyp):
+    """
+    Computes the Word Error Rate, defined as the edit distance between the
+    two provided sentences after tokenizing to words.
+    Arguments:
+        ref (string): a space-separated ground truth string
+        hyp (string): a space-separated hypothesis
+    """
+    
+    # build mapping of words to integers
+    b = set(ref.split() + hyp.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 ref.split()]
+    w2 = [chr(word2char[w]) for w in hyp.split()]
+    
+    info = edit_distance(''.join(w1), ''.join(w2))
+    distance = info['total']
+    ref_length = float(len(w1))
+    
+    data = {
+                'insertions': info['ins'],
+                'deletions': info['del'],
+                'substitutions': info['sub'],
+                'distance': distance,
+                'ref_length': ref_length,
+                'Error Rate': (distance / ref_length) * 100
+           }
+    
+    return data 
+