format the code

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)

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

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,

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
@@ -58,7 +59,8 @@ class CollateFunc(object):
                                   energy_floor=0.0,
                                   sample_frequency=resample_rate)
             elif self.feat_type == 'mfcc':
-                mat = kaldi.mfcc(waveform,
+                mat = kaldi.mfcc(
+                    waveform,
                     num_ceps=self.feat_dim,
                     num_mel_bins=self.feat_dim,
                     dither=0.0,
@@ -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)

tools/wav2dur.py

@@ -4,6 +4,7 @@
 import sys
 
 import torchaudio
+
 torchaudio.set_audio_backend("sox_io")
 
 scp = sys.argv[1]