refactor: improve using suggestions from pycharm

AIModels.py

@@ -14,13 +14,13 @@ class NeuralASR(ModelInterfaces.IASRModel):
 
     def getTranscript(self) -> str:
         """Get the transcripts of the process audio"""
-        assert(self.audio_transcript != None,
+        assert(self.audio_transcript is not None,
                'Can get audio transcripts without having processed the audio')
         return self.audio_transcript
 
     def getWordLocations(self) -> list:
         """Get the pair of words location from audio"""
-        assert(self.word_locations_in_samples != None,
+        assert(self.word_locations_in_samples is not None,
                'Can get word locations without having processed the audio')
 
         return self.word_locations_in_samples

WordMetrics.py

@@ -11,9 +11,10 @@ def edit_distance_python2(a, b):
     if len(b) == 0:  # Can deal with empty sequences faster
         return len(a)
     # Only two rows are really needed: the one currently filled in, and the previous
-    distances = []
-    distances.append([i for i in range(len(b)+1)])
-    distances.append([0 for _ in range(len(b)+1)])
+    distances = [
+        [i for i in range(len(b) + 1)],
+        [0 for _ in range(len(b) + 1)]
+    ]
     # We can prefill the first row:
     costs = [0 for _ in range(3)]
     for i, a_token in enumerate(a, start=1):
@@ -27,7 +28,7 @@ def edit_distance_python2(a, b):
         distances[0][:] = distances[1][:]
     return distances[1][len(b)]
 
-#https://stackabuse.com/levenshtein-distance-and-text-similarity-in-python/
+# https://stackabuse.com/levenshtein-distance-and-text-similarity-in-python/
 def edit_distance_python(seq1, seq2):
     size_x = len(seq1) + 1
     size_y = len(seq2) + 1
@@ -52,4 +53,4 @@ def edit_distance_python(seq1, seq2):
                     matrix[x,y-1] + 1
                 )
     #print (matrix)
-    return (matrix[size_x - 1, size_y - 1])
+    return matrix[size_x - 1, size_y - 1]

lambdaGetSample.py

@@ -7,7 +7,7 @@ import random
 import pickle
 
 
-class TextDataset():
+class TextDataset:
     def __init__(self, table, language='-'):
         self.table_dataframe = table
         self.number_of_samples = len(table)
@@ -52,7 +52,7 @@ def lambda_handler(event, context):
 
     sample_in_category = False
 
-    while(not sample_in_category):
+    while not sample_in_category:
         valid_sequence = False
         while not valid_sequence:
             try:
@@ -85,5 +85,5 @@ def getSentenceCategory(sentence) -> int:
     number_of_words = len(sentence.split())
     categories_word_limits = [0, 8, 20, 100000]
     for category in range(len(categories_word_limits)-1):
-        if number_of_words > categories_word_limits[category] and number_of_words <= categories_word_limits[category+1]:
+        if categories_word_limits[category] < number_of_words <= categories_word_limits[category + 1]:
             return category+1

lambdaSpeechToScore.py

@@ -12,9 +12,10 @@ import numpy as np
 from torchaudio.transforms import Resample
 
 
-trainer_SST_lambda = {}
-trainer_SST_lambda['de'] = pronunciationTrainer.getTrainer("de")
-trainer_SST_lambda['en'] = pronunciationTrainer.getTrainer("en")
+trainer_SST_lambda = {
+    'de': pronunciationTrainer.getTrainer("de"),
+    'en': pronunciationTrainer.getTrainer("en")
+}
 
 transform = Resample(orig_freq=48000, new_freq=16000)
 

unitTests.py

@@ -15,7 +15,7 @@ def test_category(category: int, threshold_min: int, threshold_max: int):
         response_dict = json.loads(response)
         number_of_words = len(
             response_dict['real_transcript'][0].split())
-        length_valid = number_of_words > threshold_min and number_of_words <= threshold_max
+        length_valid = threshold_min < number_of_words <= threshold_max
         if not length_valid:
             print('Category ', category,
                   ' had a sentence with length ', number_of_words)
@@ -65,8 +65,7 @@ class TestPhonemConverter(unittest.TestCase):
             phonem_converter, 'Hallo, das ist ein Test', 'haloː, dɑːs ɪst ain tɛst'))
 
 
-trainer_SST_lambda = {}
-trainer_SST_lambda['de'] = pronunciationTrainer.getTrainer("de")
+trainer_SST_lambda = {'de': pronunciationTrainer.getTrainer("de")}
 
 
 class TestScore(unittest.TestCase):