# keras
from keras.models import Sequential
-from keras.layers import Dense, Dropout # , Activation
+from keras.layers import Dense # , Dropout # , Activation
from keras import backend, utils
# Testset ratio
testset = 0.10
samplerate = 16000
-verbosity = 1
+verbosity = 0
+
def get_datafiles():
files = glob.glob(os.path.join(os.getcwd(), 'textgrid', '*.TextGrid'))
num = re.match('^.*/(\\d+).TextGrid$', tg).group(1)
yield (tg, 'wav/{:02d}.wav'.format(int(num)), int(num))
+
def label_from_annotation(ann):
return 0 if ann.strip() == '' else 1
+
def features_from_wav(tg, wavp, typ='mfcc', winlen=0.025, winstep=0.01):
# Load textgrid
tgob = pympi.TextGrid(tg)
data = mfcc(sig, rate, winlen=winlen, winstep=winstep, numcep=13,
appendEnergy=True)
elif typ == 'fbank':
- (data, energy) = logfbank(sig, rate, winlen=winlen, winstep=winstep, nfilt=26)
+ (data, energy) = logfbank(
+ sig, rate, winlen=winlen, winstep=winstep, nfilt=26)
else:
raise ValueError("No such type")
i += 1
return (data, labels)
+
def singerfun(num, l):
if l == 1:
if 0 <= num <= 11:
else:
return 0
+
def run(typ, winlen, winstep, modelfun, modelname, multiclass=False):
datas = []
labels = []
else:
labels.append(l)
-
datas = np.concatenate(datas)
labels = np.concatenate(labels)
print(np.unique(labels, return_counts=True))
if multiclass:
labels = utils.to_categorical(labels, num_classes=4)
-
rng_state = np.random.get_state()
np.random.shuffle(datas)
np.random.set_state(rng_state)
model = modelfun(traindata)
- #Train
+ # Train
model.fit(traindata, trainlabels, epochs=10, batch_size=32, shuffle=False,
verbose=verbosity)
- #Test
+ # Test
loss, acc = model.evaluate(testdata, testlabels, batch_size=32,
verbose=verbosity)
print('{}\t{}\t{}\t{}\t{}\n'.format(
winlen, winstep, modelname, loss, acc))
return model
-def bottlemodel(d):
- model = Sequential()
- model.add(Dense(13, activation='relu', input_shape=(d.shape[1],)))
- model.add(Dense(1, activation='sigmoid'))
+
+def bottlemodel(layers):
+ def fun(d):
+ model = Sequential()
+ model.add(Dense(layers, activation='relu', input_shape=(d.shape[1],)))
+ model.add(Dense(1, activation='sigmoid'))
# 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',
- loss='binary_crossentropy',
- metrics=['accuracy'])
- return model
-
-def multimodel(d):
- model = Sequential()
-# model.add(Dense(d.shape[1]*2, input_shape=(d.shape[1],), activation='relu'))
- model.add(Dense(13, activation='relu', input_shape=(d.shape[1],)))
- model.add(Dense(4, activation='softmax'))
- model.compile(optimizer='rmsprop',
- loss='categorical_crossentropy',
- metrics=['accuracy'])
- return model
-
+ model.compile(optimizer='rmsprop',
+ loss='binary_crossentropy',
+ metrics=['accuracy'])
+ return model
+ return fun
+
+
+def multimodel(layers):
+ def fun(d):
+ model = Sequential()
+# model.add(Dense(d.shape[1]*2, input_shape=(d.shape[1],), activation='relu'))
+ model.add(Dense(layers, activation='relu', input_shape=(d.shape[1],)))
+ model.add(Dense(4, activation='softmax'))
+ model.compile(optimizer='rmsprop',
+ loss='categorical_crossentropy',
+ metrics=['accuracy'])
+ return model
+ return fun
+
+
+models = [
+ ('bottle3', bottlemodel(3), False),
+ ('bottle5', bottlemodel(5), False),
+ ('bottle8', bottlemodel(8), False),
+ ('bottle13', bottlemodel(13), False),
+ ('multi3', multimodel(3), True),
+ ('multi5', multimodel(5), True),
+ ('multi8', multimodel(8), True),
+ ('multi13', multimodel(13), True)]
if __name__ == '__main__':
print('winlen\twinstep\tmodel\tloss\taccuracy\n')
with backend.get_session():
for winlen, winstep in ((0.025, 0.01), (0.1, 0.04), (0.2, 0.08)):
- for name, model, multi in reversed((('bottle', bottlemodel, False), ('multi', multimodel, True))):
+ for name, model, multi in models:
m = run('mfcc', winlen, winstep, model, name, multi)
fproot = 'model_{}_{}_{}'.format(winlen, winstep, name)
+ print(fproot);
with open('{}.json'.format(fproot), 'w') as f:
f.write(m.to_json())
m.save_weights('{}.hdf5'.format(fproot))