From: Mart Lubbers <mart@martlubbers.net>
Date: Tue, 16 May 2017 10:16:56 +0000 (+0200)
Subject: update data
X-Git-Url: https://git.martlubbers.net/?a=commitdiff_plain;h=ce6a177b6cb5dc726ea075bb47190b21ce0995d3;p=asr1617data.git

update data
---

diff --git a/.gitignore b/.gitignore
index 2146a79..a23102a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,4 @@ test.txt
 train.txt
 *.hdf5
 *.json
+class.wav
diff --git a/makevenv.sh b/makevenv.sh
index d75000f..0146460 100755
--- a/makevenv.sh
+++ b/makevenv.sh
@@ -3,5 +3,5 @@ deactivate || true
 virtualenv -p python3 --system-site-packages . 
 . bin/activate
 pip install --upgrade keras h5py python_speech_features pympi-ling scipy
-pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.1.0-cp35-none-linux_x86_64.whl
+#pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.1.0-cp36-none-linux_x86_64.whl
 deactivate
diff --git a/predict.py b/predict.py
index b561c65..18a84e7 100644
--- a/predict.py
+++ b/predict.py
@@ -1,6 +1,9 @@
-import numpy as np
 import sys
 
+if len(sys.argv) != 3:
+    print("Usage: {} MODEL.json AUDIO.wav".format(sys.argv[0]))
+    sys.exit(1)
+
 import pympi
 import scipy.io.wavfile as wav
 import numpy as np
@@ -30,16 +33,28 @@ data = mfcc(sig, rate, winlen, winstep, numcep=13, appendEnergy=True)
 tgob = pympi.TextGrid(xmax=winstep*len(data))
 tier = tgob.add_tier('lyrics')
 
-time = 0.0
-lastlabel = False
-lasttime = 0.0
-for i in model.predict(data, batch_size=32, verbose=0):
-#    print('{}\t{}'.format(time, i))
-    label = i > 0.5
-    if label != lastlabel and time-lasttime > 0.5:
-        tier.add_interval(lasttime, time, '*' if lastlabel else '')
-        lastlabel = label
-        lasttime = time
-
-    time += winstep
+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)
+
+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))])
+
+
+print('sr: ', int(1.0/winstep))
+print("len(wavdata): ", len(wavdata))
+print("len(x): ", len(x))
+wav.write('class.wav', int(1.0/winstep), wavdata)
+#for i in smoothed:
+#    print(int(i*255))
+
+
+
+
+
+
 tgob.to_file('/dev/stdout')