diff --git a/kws/bin/export_jit.py b/kws/bin/export_jit.py
index 7c1846e..81f48f9 100644
--- a/kws/bin/export_jit.py
+++ b/kws/bin/export_jit.py
@@ -56,8 +56,7 @@ def main():
     # Export quantized jit torch script model
     if args.output_quant_file:
         quantized_model = torch.quantization.quantize_dynamic(
-            model, {torch.nn.Linear}, dtype=torch.qint8
-        )
+            model, {torch.nn.Linear}, dtype=torch.qint8)
         print(quantized_model)
         script_quant_model = torch.jit.script(quantized_model)
         script_quant_model.save(args.output_quant_file)
diff --git a/kws/bin/train.py b/kws/bin/train.py
index 55c87ec..00c367b 100644
--- a/kws/bin/train.py
+++ b/kws/bin/train.py
@@ -135,7 +135,8 @@ def main():
                                 num_workers=args.num_workers,
                                 prefetch_factor=args.prefetch)
 
-    input_dim = configs['dataset_conf']['feature_extraction_conf']['num_mel_bins']
+    input_dim = configs['dataset_conf']['feature_extraction_conf'][
+        'num_mel_bins']
     output_dim = args.num_keywords
 
     # Write model_dir/config.yaml for inference and export
@@ -161,8 +162,8 @@ def main():
     # Try to export the model by script, if fails, we should refine
     # the code to satisfy the script export requirements
     #if args.rank == 0:
-        #script_model = torch.jit.script(model)
-        #script_model.save(os.path.join(args.model_dir, 'init.zip'))
+    #script_model = torch.jit.script(model)
+    #script_model.save(os.path.join(args.model_dir, 'init.zip'))
     executor = Executor()
     # If specify checkpoint, load some info from checkpoint
     if args.checkpoint is not None:
diff --git a/kws/dataset/processor.py b/kws/dataset/processor.py
index 9108b23..0fd1d84 100644
--- a/kws/dataset/processor.py
+++ b/kws/dataset/processor.py
@@ -126,6 +126,7 @@ def speed_perturb(data, speeds=None):
 
         yield sample
 
+
 def compute_mfcc(
     data,
     feature_type='mfcc',
@@ -164,6 +165,7 @@ def compute_mfcc(
         )
         yield dict(key=sample['key'], label=sample['label'], feat=mat)
 
+
 def compute_fbank(data,
                   feature_type='fbank',
                   num_mel_bins=23,
diff --git a/tools/compute_cmvn_stats.py b/tools/compute_cmvn_stats.py
index be8df99..95b7e0c 100755
--- a/tools/compute_cmvn_stats.py
+++ b/tools/compute_cmvn_stats.py
@@ -32,7 +32,8 @@ class CollateFunc(object):
             value = item[1].strip().split(",")
             assert len(value) == 3 or len(value) == 1
             wav_path = value[0]
-            sample_rate = torchaudio.backend.sox_io_backend.info(wav_path).sample_rate
+            sample_rate = torchaudio.backend.sox_io_backend.info(
+                wav_path).sample_rate
             resample_rate = sample_rate
             # len(value) == 3 means segmented wav.scp,
             # len(value) == 1 means original wav.scp
@@ -53,18 +54,19 @@ class CollateFunc(object):
                     orig_freq=sample_rate, new_freq=resample_rate)(waveform)
             if self.feat_type == 'fbank':
                 mat = kaldi.fbank(waveform,
-                              num_mel_bins=self.feat_dim,
-                              dither=0.0,
-                              energy_floor=0.0,
-                              sample_frequency=resample_rate)
+                                  num_mel_bins=self.feat_dim,
+                                  dither=0.0,
+                                  energy_floor=0.0,
+                                  sample_frequency=resample_rate)
             elif self.feat_type == 'mfcc':
-                mat = kaldi.mfcc(waveform,
-                        num_ceps=self.feat_dim,
-                        num_mel_bins=self.feat_dim,
-                        dither=0.0,
-                        energy_floor=0.0,
-                        sample_frequency=resample_rate,
-        )
+                mat = kaldi.mfcc(
+                    waveform,
+                    num_ceps=self.feat_dim,
+                    num_mel_bins=self.feat_dim,
+                    dither=0.0,
+                    energy_floor=0.0,
+                    sample_frequency=resample_rate,
+                )
             mean_stat += torch.sum(mat, axis=0)
             var_stat += torch.sum(torch.square(mat), axis=0)
             number += mat.shape[0]
@@ -104,11 +106,14 @@ if __name__ == '__main__':
 
     with open(args.train_config, 'r') as fin:
         configs = yaml.load(fin, Loader=yaml.FullLoader)
-    feat_dim = configs['dataset_conf']['feature_extraction_conf']['num_mel_bins']
-    feat_type = configs['dataset_conf']['feature_extraction_conf']['feature_type']
+    feat_dim = configs['dataset_conf']['feature_extraction_conf'][
+        'num_mel_bins']
+    feat_type = configs['dataset_conf']['feature_extraction_conf'][
+        'feature_type']
     resample_rate = 0
     if 'resample_conf' in configs['dataset_conf']:
-        resample_rate = configs['dataset_conf']['resample_conf']['resample_rate']
+        resample_rate = configs['dataset_conf']['resample_conf'][
+            'resample_rate']
         print('using resample and new sample rate is {}'.format(resample_rate))
 
     collate_func = CollateFunc(feat_dim, feat_type, resample_rate)
diff --git a/tools/wav2dur.py b/tools/wav2dur.py
index 1bcc1b6..b53a7fe 100755
--- a/tools/wav2dur.py
+++ b/tools/wav2dur.py
@@ -4,6 +4,7 @@
 import sys
 
 import torchaudio
+
 torchaudio.set_audio_backend("sox_io")
 
 scp = sys.argv[1]