please the linters

3 files changed, 35 insertions, 31 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
diff --git a/swr2_asr/tokenizer.py b/swr2_asr/tokenizer.py
index 79d6727..a665159 100644
--- a/swr2_asr/tokenizer.py
+++ b/swr2_asr/tokenizer.py
@@ -63,15 +63,15 @@ class CharTokenizer:
         else:
             splits = [split]
 
-        chars = set()
-        for sp in splits:
+        chars: set = set()
+        for s_plit in splits:
             transcript_path = os.path.join(
-                dataset_path, language, sp, "transcripts.txt"
+                dataset_path, language, s_plit, "transcripts.txt"
             )
 
             # check if dataset is downloaded, download if not
             if download and not os.path.exists(transcript_path):
-                MultilingualLibriSpeech(dataset_path, language, sp, download=True)
+                MultilingualLibriSpeech(dataset_path, language, s_plit, download=True)
 
             with open(
                 transcript_path,
@@ -82,7 +82,7 @@ class CharTokenizer:
             lines = [line.split(" ", 1)[1] for line in lines]
             lines = [line.strip() for line in lines]
 
-            for line in tqdm(lines, desc=f"Training tokenizer on {sp} split"):
+            for line in tqdm(lines, desc=f"Training tokenizer on {s_plit} split"):
                 chars.update(line)
         offset = len(self.char_map)
         for i, char in enumerate(chars):
@@ -205,10 +205,12 @@ def train_bpe_tokenizer(
 
     lines = []
 
-    for sp in splits:
-        transcripts_path = os.path.join(dataset_path, language, sp, "transcripts.txt")
+    for s_plit in splits:
+        transcripts_path = os.path.join(
+            dataset_path, language, s_plit, "transcripts.txt"
+        )
         if download and not os.path.exists(transcripts_path):
-            MultilingualLibriSpeech(dataset_path, language, sp, download=True)
+            MultilingualLibriSpeech(dataset_path, language, s_plit, download=True)
 
         with open(
             transcripts_path,
diff --git a/swr2_asr/train.py b/swr2_asr/train.py
index 8943f71..6af1e80 100644
--- a/swr2_asr/train.py
+++ b/swr2_asr/train.py
@@ -83,7 +83,7 @@ class CNNLayerNorm(nn.Module):
     """Layer normalization built for cnns input"""
 
     def __init__(self, n_feats: int):
-        super(CNNLayerNorm, self).__init__()
+        super().__init__()
         self.layer_norm = nn.LayerNorm(n_feats)
 
     def forward(self, data):
@@ -105,7 +105,7 @@ class ResidualCNN(nn.Module):
         dropout: float,
         n_feats: int,
     ):
-        super(ResidualCNN, self).__init__()
+        super().__init__()
 
         self.cnn1 = nn.Conv2d(
             in_channels, out_channels, kernel, stride, padding=kernel // 2
@@ -147,7 +147,7 @@ class BidirectionalGRU(nn.Module):
         dropout: float,
         batch_first: bool,
     ):
-        super(BidirectionalGRU, self).__init__()
+        super().__init__()
 
         self.bi_gru = nn.GRU(
             input_size=rnn_dim,
@@ -181,7 +181,7 @@ class SpeechRecognitionModel(nn.Module):
         stride: int = 2,
         dropout: float = 0.1,
     ):
-        super(SpeechRecognitionModel, self).__init__()
+        super().__init__()
         n_feats //= 2
         self.cnn = nn.Conv2d(1, 32, 3, stride=stride, padding=3 // 2)
         # n residual cnn layers with filter size of 32
@@ -227,7 +227,7 @@ class SpeechRecognitionModel(nn.Module):
         return data
 
 
-class IterMeter(object):
+class IterMeter:
     """keeps track of total iterations"""
 
     def __init__(self):
@@ -381,7 +381,7 @@ def run(
     ).to(device)
 
     print(
-        "Num Model Parameters", sum([param.nelement() for param in model.parameters()])
+        "Num Model Parameters", sum((param.nelement() for param in model.parameters()))
     )
     optimizer = optim.AdamW(model.parameters(), hparams["learning_rate"])
     criterion = nn.CTCLoss(blank=28).to(device)