def timeMain(filename, kit):
start_time = timeit.default_timer()
y,sr = librosa.load(filename, sr=None)
print("librosa.load: ")
print(timeit.default_timer() - start_time)
start_time = timeit.default_timer()
segments = segmentr.segment_audio(y, sr)
print("segmenter: ")
print(timeit.default_timer() - start_time)
start_time = timeit.default_timer()
model = klassifier.load_classifier()
print("Load Klassifier: ")
print(timeit.default_timer() - start_time)
samples = [s[0] for s in segments]
times = [s[1] for s in segments]
labels = []
i = 1
start_time = timeit.default_timer()
for seg in samples:
label = klassifier.use_classifier(model, seg)
labels.append(label)
i += 1
print("all classifications: ")
print(timeit.default_timer() - start_time)
start_time = timeit.default_timer()
quantized_times = quantize_times(y, sr, times)
print("Quantize_times: ")
print(timeit.default_timer() - start_time)
def get_model(self, button):
global model
model = klassifier.load_classifier()
button.config(state=Tkinter.NORMAL)
self.status.set("Waiting for user")
def main(file_path, kit):
time, quantized, labels, inputLength = quantize_and_classify(file_path, klassifier.load_classifier(), False)
print build_output(time, quantized, labels, kit, file_path, inputLength, False)
def main():
model = klassifier.load_classifier()
new_sample = listen_for_speech()
play_wav(new_sample)
new_features = klassifier.get_feature_from_mfcc(klassifier.get_mfcc(new_sample, 44100))
klassifier.use_classifier(model, new_features)
