please the linters

1 files changed, 19 insertions, 17 deletions

diff --git a/swr2_asr/loss_scores.py b/swr2_asr/loss_scores.py
index 977462d..c49cc15 100644
--- a/swr2_asr/loss_scores.py
+++ b/swr2_asr/loss_scores.py
@@ -1,7 +1,9 @@
+"""Methods for determining the loss and scores of the model."""
 import numpy as np
 
 
 def avg_wer(wer_scores, combined_ref_len):
+    """Calculate the average word error rate (WER) of the model."""
     return float(sum(wer_scores)) / float(combined_ref_len)
 
 
@@ -13,34 +15,34 @@ def _levenshtein_distance(ref, hyp):
     extend the edits to word level when calculate levenshtein disctance for
     two sentences.
     """
-    m = len(ref)
-    n = len(hyp)
+    len_ref = len(ref)
+    len_hyp = len(hyp)
 
     # special case
     if ref == hyp:
         return 0
-    if m == 0:
-        return n
-    if n == 0:
-        return m
+    if len_ref == 0:
+        return len_hyp
+    if len_hyp == 0:
+        return len_ref
 
-    if m < n:
+    if len_ref < len_hyp:
         ref, hyp = hyp, ref
-        m, n = n, m
+        len_ref, len_hyp = len_hyp, len_ref
 
     # use O(min(m, n)) space
-    distance = np.zeros((2, n + 1), dtype=np.int32)
+    distance = np.zeros((2, len_hyp + 1), dtype=np.int32)
 
     # initialize distance matrix
-    for j in range(0, n + 1):
+    for j in range(0, len_hyp + 1):
         distance[0][j] = j
 
     # calculate levenshtein distance
-    for i in range(1, m + 1):
+    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, n + 1):
+        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:
@@ -49,7 +51,7 @@ def _levenshtein_distance(ref, hyp):
                 d_num = distance[prev_row_idx][j] + 1
                 distance[cur_row_idx][j] = min(s_num, i_num, d_num)
 
-    return distance[m % 2][n]
+    return distance[len_ref % 2][len_hyp]
 
 
 def word_errors(
@@ -143,8 +145,8 @@ def wer(reference: str, hypothesis: str, ignore_case=False, delimiter=" "):
     if ref_len == 0:
         raise ValueError("Reference's word number should be greater than 0.")
 
-    wer = float(edit_distance) / ref_len
-    return wer
+    word_error_rate = float(edit_distance) / ref_len
+    return word_error_rate
 
 
 def cer(reference, hypothesis, ignore_case=False, remove_space=False):
@@ -181,5 +183,5 @@ def cer(reference, hypothesis, ignore_case=False, remove_space=False):
     if ref_len == 0:
         raise ValueError("Length of reference should be greater than 0.")
 
-    cer = float(edit_distance) / ref_len
-    return cer
+    char_error_rate = float(edit_distance) / ref_len
+    return char_error_rate