# Testset ratio
testset = 0.10
samplerate = 16000
+verbosity = 1
def get_datafiles():
files = glob.glob(os.path.join(os.getcwd(), 'textgrid', '*.TextGrid'))
labels = []
for tg, wavp in get_datafiles():
- (d, l) = features_from_wav(tg, wavp, winlen=winlen, winstep=winstep, typ=typ)
+ (d, l) = features_from_wav(
+ tg, wavp, winlen=winlen, winstep=winstep, typ=typ)
datas.append(d)
labels.append(l)
#Train
model.fit(traindata, trainlabels, epochs=10, batch_size=32, shuffle=False,
- verbose=0)
+ verbose=verbosity)
#Test
- loss, acc = model.evaluate(testdata, testlabels, batch_size=32, verbose=0)
+ loss, acc = model.evaluate(testdata, testlabels, batch_size=32,
+ verbose=verbosity)
print('{}\t{}\t{}\t{}\t{}\n'.format(
winlen, winstep, modelname, loss, acc))
def simplemodel(d):
model = Sequential()
- model.add(Dense(d.shape[1]*2, input_shape=(d.shape[1],), activation='relu'))
+ model.add(
+ Dense(d.shape[1]*2, input_shape=(d.shape[1],), activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
def bottlemodel(d):
model = Sequential()
- model.add(Dense(d.shape[1]*2, input_shape=(d.shape[1],), activation='relu'))
+ model.add(
+ Dense(d.shape[1]*2, input_shape=(d.shape[1],), activation='relu'))
model.add(Dense(13, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',