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 load_samples():
result = load_pickle('samples.p')
table_of_contents = load_pickle('toc.p')
samples_dir = sickBeetz.relative_path('samples/unparsed-samples')
for filename in os.listdir(samples_dir):
filetype = filename.split('.')[-1]
if filetype != 'wav':
continue
if filename in table_of_contents:
continue
full_filename = sickBeetz.relative_path(os.path.join('samples/unparsed-samples', filename))
if not os.path.isfile(full_filename):
continue
label_toks = filename.split('-')
label = '-'.join(label_toks[i] for i in range(len(label_toks)-1))
y, sr = librosa.load(full_filename, sr=None)
segments = segmentr.segment_audio(y, sr)
print filename + ' - ' + str(len(segments))
for segment in segments:
result.append((segment[0], label, sr))
table_of_contents.append(filename)
save_pickle(result, 'samples.p')
save_pickle(table_of_contents, 'toc.p')
return result
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)
