action='store',
help='path to output segment file')
return vars(parser.parse_args(sys.argv[1:]))
if __name__ == '__main__':
parameters = process_arguments()
# Load the features
print('- ', os.path.basename(parameters['input_song']))
X, Y, beats = features(parameters['input_song'])
# Load the transformation
W = segmenter.load_transform(parameters['transform'])
print('\tapplying transformation...')
X = W.dot(X)
# Find the segment boundaries
print('\tpredicting segments...')
if parameters['gnostic']:
S = segmenter.get_segments(X, kmin=len(Y) - 1, kmax=len(Y))
elif parameters['dynamic']:
kmin, kmax = get_num_segs(beats[-1])
S = segmenter.get_segments(X, kmin=kmin, kmax=kmax)
else:
S = segmenter.get_segments(X)
# Output lab file
print('\tsaving output to ', parameters['output_file'])
make_metadata(annotation)
for tag, bounds in zip(labels, levels):
add_level(annotation, tag, make_intervals(bounds, beats))
return J
if __name__ == '__main__':
parameters = segmenter.process_arguments()
# Load the features
print '- ', os.path.basename(parameters['input_song'])
X, beats = segmenter.features(parameters['input_song'])
# Load the boundary transformation
W_bound = segmenter.load_transform(parameters['transform_boundary'])
print '\tapplying boundary transformation...'
X_bound = W_bound.dot(X)
# Find the segment boundaries
print '\tpredicting segments...'
kmin, kmax = segmenter.get_num_segs(beats[-1])
levels, labels = get_all_levels(X_bound, kmin, kmax)
J = make_jams(levels, labels, beats)
with open(parameters['output_file'], 'w') as f:
json.dump(J, f, indent=2)
parser.add_argument( 'output_file',
action = 'store',
help = 'path to output segment file')
return vars(parser.parse_args(sys.argv[1:]))
if __name__ == '__main__':
parameters = process_arguments()
# Load the features
print('- ', os.path.basename(parameters['input_song']))
X, Y, beats = features(parameters['input_song'])
# Load the transformation
W = segmenter.load_transform(parameters['transform'])
print('\tapplying transformation...')
X = W.dot(X)
# Find the segment boundaries
print('\tpredicting segments...')
if parameters['gnostic']:
S = segmenter.get_segments(X, kmin=len(Y)-1, kmax=len(Y))
elif parameters['dynamic']:
kmin, kmax = get_num_segs(beats[-1])
S = segmenter.get_segments(X, kmin=kmin, kmax=kmax)
else:
S = segmenter.get_segments(X)
# Output lab file
print('\tsaving output to ', parameters['output_file'])
