def test_learn():
FeatureExtractor(store=True, overwrite=True).extract(
audio_file=os.path.join('material', 'test.wav'),
extractor_name='mfcc',
extractor_params={
'mfcc': {
'n_mfcc': 10
}
},
storage_paths={
'mfcc': os.path.join('material', 'test.mfcc.cpickle')
}
)
feature_container = FeatureContainer(filename=os.path.join('material', 'test.mfcc.cpickle'))
data = {
'file1.wav': feature_container,
'file2.wav': feature_container,
}
annotations = {
'file1.wav': MetaDataItem(
{
'file': 'file1.wav',
'scene_label': 'scene1',
}
),
'file2.wav': MetaDataItem(
{
'file': 'file2.wav',
'scene_label': 'scene2',
}
),
}
sc = SceneClassifierGMM(
method='gmm',
class_labels=['scene1', 'scene2'],
params={
'n_components': 6,
'covariance_type': 'diag',
'tol': 0.001,
'reg_covar': 0,
'max_iter': 40,
'n_init': 1,
'init_params': 'kmeans',
'random_state': 0,
},
filename=os.path.join('material', 'test.model.cpickle'),
disable_progress_bar=True,
)
sc.learn(data=data, annotations=annotations)
# Test model count
nose.tools.eq_(len(sc.model), 2)
# Test model dimensions
nose.tools.eq_(sc.model['scene1'].means_.shape[0], 6)
def test_predict():
FeatureExtractor(store=True, overwrite=True).extract(
audio_file=os.path.join('material', 'test.wav'),
extractor_name='mfcc',
extractor_params={
'mfcc': {
'n_mfcc': 10
}
},
storage_paths={
'mfcc': os.path.join('material', 'test.mfcc.cpickle')
}
)
feature_container = FeatureContainer(filename=os.path.join('material', 'test.mfcc.cpickle'))
data = {
'file1.wav': feature_container,
'file2.wav': feature_container,
}
annotations = {
'file1.wav': MetaDataItem(
{
'file': 'file1.wav',
'scene_label': 'scene1',
}
),
'file2.wav': MetaDataItem(
{
'file': 'file2.wav',
'scene_label': 'scene2',
}
),
}
sc = SceneClassifierGMM(
method='gmm',
class_labels=['scene1', 'scene2'],
params={
'n_components': 6,
'covariance_type': 'diag',
'tol': 0.001,
'reg_covar': 0,
'max_iter': 40,
'n_init': 1,
'init_params': 'kmeans',
'random_state': 0,
},
filename=os.path.join('material', 'test.model.cpickle'),
disable_progress_bar=True,
)
sc.learn(data=data, annotations=annotations)
recognizer_params = {
'frame_accumulation': {
'enable': True,
'type': 'sum',
},
'frame_binarization': {
'enable': False,
},
'decision_making': {
'enable': True,
'type': 'maximum',
}
}
result = sc.predict(
feature_data=feature_container,
recognizer_params=recognizer_params
)
# Test result
nose.tools.eq_(result, 'scene1')
# Test errors
recognizer_params['frame_accumulation']['type'] = 'test'
nose.tools.assert_raises(AssertionError, sc.predict, feature_container, recognizer_params)
recognizer_params['frame_accumulation']['type'] = 'sum'
recognizer_params['decision_making']['type'] = 'test'
nose.tools.assert_raises(AssertionError, sc.predict, feature_container, recognizer_params)