diff --git a/inference.py b/inference.py
index e520f72..62211b2 100644
--- a/inference.py
+++ b/inference.py
@@ -10,7 +10,6 @@ import time
 from tqdm import tqdm
 
 from lib import dataset
-from lib import nets
 from lib import spec_utils
 from lib.model_param_init import ModelParameters
 
@@ -97,18 +96,31 @@ def main():
     p.add_argument('--gpu', '-g', type=int, default=-1)
     p.add_argument('--pretrained_model', '-P', type=str, default='models/baseline.pth')
     p.add_argument('--input', '-i', required=True)
+    p.add_argument('--nn_architecture', '-n', type=str, default='default')
     p.add_argument('--model_params', '-m', type=str, default='')
-    p.add_argument('--window_size', '-w', type=int, default=320)
+    p.add_argument('--window_size', '-w', type=int, default=512)
     p.add_argument('--output_image', '-I', action='store_true')
     p.add_argument('--postprocess', '-p', action='store_true')
     p.add_argument('--tta', '-t', action='store_true')
     p.add_argument('--high_end_process', '-H', type=str, choices=['none', 'bypass', 'correlation'], default='none')
-    p.add_argument('--aggressiveness', '-A', type=float, default=0.1)
+    p.add_argument('--aggressiveness', '-A', type=float, default=0.09)
     args = p.parse_args()
     
+    if args.nn_architecture == 'default':
+            from lib import nets
+    if args.nn_architecture == '33966KB':
+            from lib import nets_33966KB as nets
+    if args.nn_architecture == '123821KB':
+            from lib import nets_123821KB as nets
+    if args.nn_architecture == '129605KB':
+            from lib import nets_129605KB as nets
+    
+    #if '' == args.model_params:
+    #    mp = ModelParameters(args.pretrained_model)
+    #else:
     mp = ModelParameters(args.model_params)    
     
-    start = time.time()
+    start_time = time.time()
 
     print('loading model...', end=' ')
 
@@ -139,7 +151,7 @@ def main():
         else: # lower bands
             X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
 
-        X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'])
+        X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
         
         if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
             input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start'])
@@ -175,7 +187,7 @@ def main():
    
     if args.high_end_process == 'bypass':
         wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end)        
-    elif args.high_end_process == 'correlation': # It needs improvement
+    elif args.high_end_process == 'correlation':       
         for i in range(input_high_end.shape[2]):            
             for c in range(2):
                 X_mag_max = np.amax(input_high_end[c, :, i])    
@@ -193,10 +205,12 @@ def main():
     model_name = os.path.splitext(os.path.basename(args.pretrained_model))[0]
     sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['inst'])), wave, mp.param['sr'])
 
-    print('inverse stft of {}...'.format(stems['vocals']), end=' ')
-    wave = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
-    print('done')
-    sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['vocals'])), wave, mp.param['sr'])
+    if True:
+        print('inverse stft of {}...'.format(stems['vocals']), end=' ')
+        #v_spec_m = X_spec_m - y_spec_m
+        wave = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
+        print('done')
+        sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['vocals'])), wave, mp.param['sr'])
 
     if args.output_image:
         with open('{}_{}.jpg'.format(basename, stems['inst']), mode='wb') as f:
@@ -208,7 +222,7 @@ def main():
             _, bin_image = cv2.imencode('.jpg', image)
             bin_image.tofile(f)
 
-    print('Runtime: {0:.{1}f}s'.format(time.time() - start, 1))
+    print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
 
 if __name__ == '__main__':
     main()