[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

tools/speech_data_explorer/data_explorer.py

@@ -121,10 +121,7 @@ def parse_args():
         help='field name for which you want to see statistics (optional). Example: pred_text_contextnet.',
     )
     parser.add_argument(
-        '--gpu',
-        '-gpu',
-        action='store_true',
-        help='use GPU-acceleration',
+        '--gpu', '-gpu', action='store_true', help='use GPU-acceleration',
     )
     args = parser.parse_args()
 
@@ -490,7 +487,7 @@ def plot_histogram(data, key, label, gpu_acceleration=False):
         data_frame = data[key].to_list()
     else:
         data_frame = [item[key] for item in data]
-    
+
     fig = px.histogram(
         data_frame=data_frame,
         nbins=50,
@@ -504,10 +501,10 @@ def plot_histogram(data, key, label, gpu_acceleration=False):
     return fig
 
 
-def plot_word_accuracy(vocabulary_data):            
+def plot_word_accuracy(vocabulary_data):
     labels = ['Unrecognized', 'Sometimes recognized', 'Always recognized']
     counts = [0, 0, 0]
-    
+
     if args.gpu:
         counts[0] = (vocabulary_data['Accuracy'] == 0).sum()
         counts[1] = (vocabulary_data['Accuracy'] < 100).sum()
@@ -576,24 +573,27 @@ def absolute_audio_filepath(audio_filepath, audio_base_path):
 if args.gpu:
     if args.names_compared is not None:
         raise Exception(f"Currently comparision mode is not available with gpu acceleation.")
-    
+
     hypothesis_fields = ["pred_text"]
     if args.show_statistics is not None:
         hypothesis_fields = [args.show_statistics]
-        
+
     enable_plk = True
     if args.disable_caching_metrics:
         enable_plk = False
-    
+
     cu_df = cuDF()
 
-    dataset = Dataset(manifest_filepath = args.manifest, data_engine = cu_df,
-                      hypothesis_fields = hypothesis_fields, 
-                      estimate_audio_metrics = args.estimate_audio_metrics,
-                      enable_plk = enable_plk)
-
+    dataset = Dataset(
+        manifest_filepath=args.manifest,
+        data_engine=cu_df,
+        hypothesis_fields=hypothesis_fields,
+        estimate_audio_metrics=args.estimate_audio_metrics,
+        enable_plk=enable_plk,
+    )
+
     dataset = dataset.process()
-    
+
     data = dataset.samples_data
     num_hours = dataset.duration
     vocabulary = dataset.vocabulary_data
@@ -602,8 +602,8 @@ def absolute_audio_filepath(audio_filepath, audio_base_path):
     metrics_available = len(dataset.hypotheses) != 0
     if metrics_available:
         wer = dataset.hypotheses[hypothesis_fields[0]].wer
-        cer = dataset.hypotheses[hypothesis_fields[0]].cer 
-        wmr = dataset.hypotheses[hypothesis_fields[0]].wmr 
+        cer = dataset.hypotheses[hypothesis_fields[0]].cer
+        wmr = dataset.hypotheses[hypothesis_fields[0]].wmr
         mwa = dataset.hypotheses[hypothesis_fields[0]].mwa
 else:
     if not comparison_mode:
@@ -706,7 +706,7 @@ def absolute_audio_filepath(audio_filepath, audio_base_path):
         figure_word_acc = plot_word_accuracy(vocabulary_data)
     else:
         figure_word_acc = plot_word_accuracy(vocabulary)
-        
+
 stats_layout = [
     dbc.Row(dbc.Col(html.H5(children='Global Statistics'), class_name='text-secondary'), class_name='mt-3'),
     dbc.Row(
@@ -827,7 +827,7 @@ def absolute_audio_filepath(audio_filepath, audio_base_path):
 
 wordstable_columns = [{'name': 'Word', 'id': 'Word'}, {'name': 'Count', 'id': 'Amount'}]
 
-if args.gpu:    
+if args.gpu:
     vocabulary_columns = vocabulary.columns
 else:
     vocabulary_columns = vocabulary[0].keys()
@@ -910,22 +910,22 @@ def update_wordstable(page_current, sort_by, filter_query):
         if op in ('eq', 'ne', 'lt', 'le', 'gt', 'ge'):
             if args.gpu:
                 vocabulary_view = vocabulary_view.loc[getattr(operator, op)(vocabulary_view[col_name], filter_value)]
-            else:    
+            else:
                 vocabulary_view = [x for x in vocabulary_view if getattr(operator, op)(x[col_name], filter_value)]
         elif op == 'contains':
             vocabulary_view = [x for x in vocabulary_view if filter_value in str(x[col_name])]
 
     if len(sort_by):
         col = sort_by[0]['column_id']
         ascending = sort_by[0]['direction'] != 'desc'
-        
+
         if args.gpu:
             vocabulary_view = vocabulary_view.sort_values(col, ascending=ascending)
         else:
             vocabulary_view = sorted(vocabulary_view, key=lambda x: x[col], reverse=descending)
     if page_current * DATA_PAGE_SIZE >= len(vocabulary_view):
         page_current = len(vocabulary_view) // DATA_PAGE_SIZE
-    
+
     if args.gpu:
         return [
             vocabulary_view[page_current * DATA_PAGE_SIZE : (page_current + 1) * DATA_PAGE_SIZE].to_dict('records'),
@@ -937,6 +937,7 @@ def update_wordstable(page_current, sort_by, filter_query):
             math.ceil(len(vocabulary_view) / DATA_PAGE_SIZE),
         ]
 
+
 if args.gpu:
     col_names = data.columns
 else:
@@ -1564,22 +1565,22 @@ def update_datatable(page_current, sort_by, filter_query):
         if op in ('eq', 'ne', 'lt', 'le', 'gt', 'ge'):
             if args.gpu:
                 data_view = data_view.loc[getattr(operator, op)(data_view[col_name], filter_value)]
-            else: 
+            else:
                 data_view = [x for x in data_view if getattr(operator, op)(x[col_name], filter_value)]
         elif op == 'contains':
             data_view = [x for x in data_view if filter_value in str(x[col_name])]
 
     if len(sort_by):
         col = sort_by[0]['column_id']
         ascending = sort_by[0]['direction'] != 'desc'
-        
+
         if args.gpu:
             data_view = data_view.sort_values(col, ascending=ascending)
         else:
             data_view = sorted(data_view, key=lambda x: x[col], reverse=descending)
     if page_current * DATA_PAGE_SIZE >= len(data_view):
         page_current = len(data_view) // DATA_PAGE_SIZE
-        
+
     if args.gpu:
         return [
             data_view[page_current * DATA_PAGE_SIZE : (page_current + 1) * DATA_PAGE_SIZE].to_dict('records'),