use shifts 3 to avoid timeout

test_fuser.py

@@ -10,8 +10,8 @@ from evaluator.music_demixing import MusicDemixingPredictor
 
 
 fusion_weights = np.array([0.27203053, 0.73882418])
-fusion_weights_batch = np.array([[0.25, 0.27, 0.5, 0.6],
-                                 [0.75, 0.73, 0.5, 0.4]])
+fusion_weights_batch = np.array([[0.2, 0.3, 0.5, 0.6],
+                                 [0.8, 0.7, 0.5, 0.4]])
 fusion_weights_full = np.array(
     [[0.12421362233786719, 0.053170121998740195, -0.023004887368765208, -0.030820051344791908, 0.7362167118453092, -0.2011487819137626, -0.1254105898553318, -0.11682101700727994, 0.14540460690976179],
      [0.06420106209073237, 0.07177930713127706, 0.0035666737989309845, 0.0005502465774743552, -
@@ -47,7 +47,7 @@ def separate(
     unet_model,
     demucs_model,
     niter=0,
-    shifts=4
+    shifts=3
 ):
     unet_predict = unet_separate(audio, unet_model, niter=niter)
     demucs_predict = demucs_separate(audio, 44100, demucs_model, shifts=shifts)
@@ -60,8 +60,8 @@ def separate(
     # chunks = vad(estimates[3].sum(0), 44100, threshold=0.3)
     # for on, off in chunks:
     #     estimates[3, :, on:off] = 0
-    # gains = lstsq(estimates.reshape(4, -1), mix.ravel())
-    # estimates *= gains[:, None, None]
+    gains = lstsq(estimates.reshape(4, -1), mix.ravel())
+    estimates *= gains[:, None, None]
     return estimates