def evaluate(self, sound):
stgrm = sg.generate_spectrogram(sound)
stgrm = numpy.asarray(stgrm, dtype=theano.config.floatX)
elements = [self.evaluate_model(x).tolist() for x in stgrm]
position = max(set(elements), key=elements.count)
return self.classes[position]
def evaluate(self, sound):
stgrm = sg.generate_spectrogram(sound)
stgrm = numpy.asarray(stgrm, dtype=theano.config.floatX)
elements = [self.evaluate_model(x).tolist() for x in stgrm]
position = max(set(elements), key=elements.count)
return self.classes[position]
def load_data(train_dir, test_dir):
train_input = [
f for f in os.listdir(train_dir) if isfile(join(train_dir, f))
]
test_input = [f for f in os.listdir(test_dir) if isfile(join(test_dir, f))]
classes = []
train_set = []
test_set = []
# Training data
for element in train_input:
elem_id = element.split(".")[0].split("_")[0]
try:
index = classes.index(elem_id)
except:
index = len(classes)
classes.append(elem_id)
stgrm = sg.generate_spectrogram(join(train_dir, element))
train_set.extend(_make_array(stgrm, index))
# Test data
for element in test_input:
elem_id = element.split(".")[0].split("_")[0]
if elem_id in classes:
index = classes.index(elem_id)
stgrm = sg.generate_spectrogram(join(test_dir, element))
test_set.extend(_make_array(stgrm, index))
train_min_nr = min([len(x[0]) for x in train_set])
test_min_nr = min([len(x[0]) for x in test_set])
train_data = [x[:train_min_nr] for x in train_set]
test_data = [x[:test_min_nr] for x in test_set]
return numpy.random.permutation(train_data), test_data, classes
def load_data(train_dir, test_dir):
train_input = [f for f in os.listdir(train_dir) if isfile(join(train_dir, f))]
test_input = [f for f in os.listdir(test_dir) if isfile(join(test_dir, f))]
classes = []
train_set = []
test_set = []
# Training data
for element in train_input:
elem_id = element.split(".")[0].split("_")[0]
try:
index = classes.index(elem_id)
except:
index = len(classes)
classes.append(elem_id)
stgrm = sg.generate_spectrogram(join(train_dir, element))
train_set.extend(_make_array(stgrm, index))
# Test data
for element in test_input:
elem_id = element.split(".")[0].split("_")[0]
if elem_id in classes:
index = classes.index(elem_id)
stgrm = sg.generate_spectrogram(join(test_dir, element))
test_set.extend(_make_array(stgrm, index))
train_min_nr = min([len(x[0]) for x in train_set])
test_min_nr = min([len(x[0]) for x in test_set])
train_data = [x[:train_min_nr] for x in train_set]
test_data = [x[:test_min_nr] for x in test_set]
return numpy.random.permutation(train_data), test_data, classes
async def analyze(request):
data = await request.form()
audio = await (data['file'].read())
uploadpath = os.path.join(path, 'uploads')
try:
os.makedirs(uploadpath)
filepath = os.path.join(uploadpath, 'test.wav')
with open(filepath, 'wb') as f:
f.write(audio)
img = open_image(generate_spectrogram(filepath))
finally:
shutil.rmtree(uploadpath)
return JSONResponse({'crying': learn.predict(img)[0]})
def load_data(train_dir, test_dir, noise_dir):
train_input = [f for f in os.listdir(train_dir) if isfile(join(train_dir, f))]
test_input = [f for f in os.listdir(test_dir) if isfile(join(test_dir, f))]
classes = []
train_set = []
test_set = []
# Training data
for element in train_input:
elem_id = element.split(".")[0].split("_")[0]
try:
index = classes.index(elem_id)
except:
index = len(classes)
classes.append(elem_id)
stgrm = sg.generate_spectrogram(join(train_dir, element))
train_set.append(_make_array(stgrm, index))
# Test data
for element in test_input:
elem_id = element.split(".")[0].split("_")[0]
test_set.append([join(test_dir,element),elem_id])
train_min_nr = min([len(x) for x in train_set])
train_data = []
for x in train_set:
indexes = numpy.random.choice(range(len(x)), train_min_nr, replace=False)
train_data.extend([x[i] for i in indexes])
return numpy.random.permutation(train_data), test_set, classes
def demo_load(train_dir):
train_input = [f for f in os.listdir(train_dir) if isfile(join(train_dir, f))]
classes = []
train_set = []
test_set = []
print 'loading data'
# Training data
ize = 1
for element in train_input:
elem_id = element.split(".")[0].split("_")[0]
print elem_id
try:
index = classes.index(elem_id)
except:
index = len(classes)
classes.append(elem_id)
stgrm = sg.generate_spectrogram(join(train_dir, element))
if ize == 3:
exit(1)
ize = ize + 1
train_set.extend(_make_array(stgrm, index))
train_min_nr = min([len(x[0]) for x in train_set])
train_data = [x[:train_min_nr] for x in train_set]
train_data = numpy.random.permutation(train_data)
test_data = train_data[:130]
train_data = train_data[1300:]
return train_data, test_data, classes
def evaluate(self, sound):
stgrm = sg.generate_spectrogram(sound)
elements = [self.evaluate_model(x).tolist() for x in stgrm]
position = max(set(elements), key=elements.count)
return self.classes[position]
def testGenerateSpectrogramReturnsCorrectShapeInCaseOfValidInput(self):
test = spectrogram.generate_spectrogram('FMEV_Sr10.wav')
self.assertEqual(test.shape[1], 601)
def evaluate(self, sound):
stgrm = sg.generate_spectrogram(sound)
elements = [self.evaluate_model(x).tolist() for x in stgrm]
position = max(set(elements), key=elements.count)
return self.classes[position]
def testGenerateSpectrogramReturnsCorrectShapeInCaseOfValidInput(self):
test = spectrogram.generate_spectrogram('FMEV_Sr10.wav')
self.assertEqual(test.shape[1], 601)