From: Mart Lubbers <mart@martlubbers.net>
Date: Sun, 30 Apr 2017 19:36:28 +0000 (+0200)
Subject: add prediction
X-Git-Url: https://git.martlubbers.net/?a=commitdiff_plain;h=8fdbbd451426bebb71e686f19bcf146769d02968;p=asr1617data.git

add prediction
---

diff --git a/experiments.py b/experiments.py
index 699c68c..911de6b 100644
--- a/experiments.py
+++ b/experiments.py
@@ -126,7 +126,7 @@ if __name__ == '__main__':
         for winlen, winstep in ((0.025, 0.01), (0.1, 0.04), (0.2, 0.08)):
             for name, model in (('simple', simplemodel), ('bottle', bottlemodel)):
                 m = run('mfcc', winlen, winstep, model, name)
-                fproot = 'model_{}_{}_{}.json'.format(winlen, winstep, name)
+                fproot = 'model_{}_{}_{}'.format(winlen, winstep, name)
                 with open('{}.json'.format(fproot), 'w') as f:
                     f.write(m.to_json())
                 m.save_weights('{}.hdf5'.format(fproot))
diff --git a/predict.py b/predict.py
index 962b9fd..b561c65 100644
--- a/predict.py
+++ b/predict.py
@@ -1,26 +1,45 @@
 import numpy as np
 import sys
 
+import pympi
 import scipy.io.wavfile as wav
 import numpy as np
-from python_speech_featuresimport mfcc
+from python_speech_features import mfcc
 
 from keras.models import model_from_json
 
 modelfile = sys.argv[1]
-hdf5file = '{}.hdf5'.format(modelfile[-4:])
+hdf5file = '{}.hdf5'.format(modelfile[:-5])
 
-with open(modelfile, 'r') as f:
+with open(modelfile, 'r', encoding='utf-8') as f:
     json = f.read()
 model = model_from_json(json)
 model.load_weights(hdf5file)
 
+(_, winlen, winstep, _) = modelfile.split('_')
+winlen = float(winlen)
+winstep = float(winstep)
+
 model.compile(
     loss='binary_crossentropy',
     optimizer='rmsprop',
     metrics=['accuracy'])
 
-(rate, sig) = wav.read(sys.stdin.buffer)
-data = mfcc(sig, rate, winlen
-for i in model.predict(dat, batch_size=32, verbose=0):
-    print(i[0])
+(rate, sig) = wav.read(sys.argv[2], mmap=True)
+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
+tgob.to_file('/dev/stdout')