From: Mart Lubbers <mart@martlubbers.net>
Date: Wed, 7 Jun 2017 14:37:35 +0000 (+0200)
Subject: predict smoothing
X-Git-Url: https://git.martlubbers.net/?a=commitdiff_plain;h=9e366e76ba14b20a9d153e96337ef3b307f4065d;p=asr1617data.git

predict smoothing
---

diff --git a/predict.py b/predict.py
index de1abc2..3e0e2f7 100644
--- a/predict.py
+++ b/predict.py
@@ -38,13 +38,13 @@ tier = tgob.add_tier('lyrics')
 window_len = int(1.0/winstep)
 
 x = model.predict(data, batch_size=32, verbose=0)
-#s = np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]
-#w = np.hanning(window_len)
+s = np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]
+w = np.hanning(window_len)
 #
-#smoothed = np.convolve(w/w.sum(), s[:,0], mode='valid')
-#wavdata = np.uint8(list(map(int,
-#    smoothed*255))[int(window_len/2):-1*(int(window_len/2))])
-wavdata = np.uint8(x*255)
+smoothed = np.convolve(w/w.sum(), s[:,0], mode='valid')
+wavdata = np.uint8(list(map(int,
+    smoothed*255))[int(window_len/2):-1*(int(window_len/2))])
+#wavdata = np.uint8(x*255)
 
 print('sr: ', int(1.0/winstep))
 print("len(wavdata): ", len(wavdata))