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 quantize_and_classify(filename, model, quantized=False):
# load and segment audio signal
y, sr = librosa.load(filename, sr=None)
segments = segmentr.segment_audio(y, sr)
samples = [s[0] for s in segments]
times = [s[1] for s in segments]
labels = []
for seg in samples:
label = klassifier.use_classifier(model, seg)
labels.append(label)
# quantize onset times to estimated tempo
if quantized:
quantized_times = quantize_times(y, sr, times)
else:
quantized_times = times
return times, quantized_times, labels, len(y)
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)
