def test_task_simple_chord_absent(SR, HOP_LENGTH):
jam = jams.JAMS(file_metadata=dict(duration=4.0))
trans = pumpp.task.SimpleChordTransformer(name='chord_s')
output = trans.transform(jam)
# Mask should be false since we have no matching namespace
assert not np.any(output['chord_s/_valid'])
# Check the shape
assert output['chord_s/pitch'].shape == (1, 4 * (SR // HOP_LENGTH), 12)
# Make sure it's empty
assert not np.any(output['chord_s/pitch'])
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_beatpos_absent(SR, HOP_LENGTH, MAX_DIVISIONS, SPARSE):
jam = jams.JAMS(file_metadata=dict(duration=4.0))
trans = pumpp.task.BeatPositionTransformer(name='beat',
max_divisions=MAX_DIVISIONS,
sr=SR,
hop_length=HOP_LENGTH,
sparse=SPARSE)
output = trans.transform(jam)
assert not np.any(output['beat/_valid'])
Y_pred = trans.encoder.inverse_transform(output['beat/position'][0])
assert all([_ == 'X' for _ in Y_pred])
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_beat_absent():
# Construct a jam
jam = jams.JAMS(file_metadata=dict(duration=4.0))
# One second = one frame
T = crema.task.BeatTransformer()
output = T.transform(jam)
# Make sure we have the mask
eq_(output['mask_beat'], False)
eq_(output['mask_downbeat'], False)
# Check the shape: 4 seconds at 2 samples per second
assert np.allclose(output['output_beat'].shape, [4 * N_REPEAT, 1])
assert np.allclose(output['output_downbeat'].shape, [4 * N_REPEAT, 1])
assert not np.any(output['output_beat'])
assert not np.any(output['output_downbeat'])
def test_task_tslabel_absent():
labels = ['alpha', 'beta', 'psycho', 'aqua', 'disco']
jam = jams.JAMS(file_metadata=dict(duration=4.0))
T = crema.task.TimeSeriesLabelTransformer(namespace='tag_open',
name='madeup',
labels=labels)
output = T.transform(jam)
# Mask should be false since we have no matching namespace
eq_(output['mask_madeup'], False)
y = output['output_madeup']
# Check the shape
assert np.allclose(y.shape, [4 * N_REPEAT, len(labels)])
# Make sure it's empty
assert not np.any(y)
def test_task_glabel_absent():
labels = ['alpha', 'beta', 'psycho', 'aqua', 'disco']
jam = jams.JAMS(file_metadata=dict(duration=4.0))
T = crema.task.GlobalLabelTransformer(namespace='tag_open',
name='madeup',
labels=labels)
output = T.transform(jam)
# Mask should be false since we have no matching namespace
eq_(output['mask_madeup'], False)
# Check the shape
eq_(output['output_madeup'].ndim, 1)
eq_(output['output_madeup'].shape[0], len(labels))
# Make sure it's empty
assert not np.any(output['output_madeup'])
def test_task_beat_nometer(SR, HOP_LENGTH):
# Construct a jam
jam = jams.JAMS(file_metadata=dict(duration=4.0))
ann = jams.Annotation(namespace='beat')
ann.append(time=0, duration=0.0)
ann.append(time=1, duration=0.0)
ann.append(time=2, duration=0.0)
ann.append(time=3, duration=0.0)
jam.annotations.append(ann)
# One second = one frame
trans = pumpp.task.BeatTransformer(name='beat')
output = trans.transform(jam)
# Make sure we have the mask
assert np.all(
output['beat/_valid'] == [0, 4 * trans.sr // trans.hop_length])
assert not output['beat/mask_downbeat']
# Check the shape: 4 seconds at 2 samples per second
assert output['beat/beat'].shape == (1, 4 * (SR // HOP_LENGTH), 1)
assert output['beat/downbeat'].shape == (1, 4 * (SR // HOP_LENGTH), 1)
# Ideal vectors:
# a beat every second (two samples)
# no downbeats
beat_true = np.asarray([1, 1, 1, 1])
downbeat_true = np.asarray([0, 0, 0, 0])
assert np.all(output['beat/beat'][0, ::(SR // HOP_LENGTH)] == beat_true)
assert np.all(output['beat/downbeat'][0, ::(SR //
HOP_LENGTH)] == downbeat_true)
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_key_present(SR, HOP_LENGTH, SPARSE):
# Create jams with key annotation
jam = jams.JAMS(file_metadata=dict(duration=12.0))
ann = jams.Annotation(namespace='key_mode')
ann.append(time=0, duration=2.0, value='C:minor')
ann.append(time=2, duration=2.0, value='N')
ann.append(time=4, duration=2.0, value='Eb')
ann.append(time=8, duration=2.0, value='D:major')
ann.append(time=10, duration=2.0, value='D:lydian')
jam.annotations.append(ann)
trans = pumpp.task.KeyTransformer(name='key',
sr=SR,
hop_length=HOP_LENGTH,
sparse=SPARSE)
output = trans.transform(jam)
# Make sure we have the mask
assert np.all(
output['key/_valid'] == [0, 12 * trans.sr // trans.hop_length])
# Ideal vectors:
# pcp = Cmin, N, Eb, N, D, D_lyd
pcp_true = np.array([
_encode_key_str('C:minor', SPARSE)[0],
_encode_key_str('N', SPARSE)[0],
_encode_key_str('Eb:major', SPARSE)[0],
_encode_key_str('N', SPARSE)[0],
_encode_key_str('D', SPARSE)[0],
_encode_key_str('D:lydian', SPARSE)[0]
])
assert np.all(output['key/pitch_profile'] == np.repeat(
pcp_true, (SR * 2 // HOP_LENGTH), axis=0))
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_beat_present(SR, HOP_LENGTH):
# Construct a jam
jam = jams.JAMS(file_metadata=dict(duration=4.0))
ann = jams.Annotation(namespace='beat')
ann.append(time=0, duration=0.0, value=1)
ann.append(time=1, duration=0.0, value=2)
ann.append(time=2, duration=0.0, value=3)
ann.append(time=3, duration=0.0, value=1)
jam.annotations.append(ann)
trans = pumpp.task.BeatTransformer(name='beat')
output = trans.transform(jam)
# Make sure we have the masks
assert np.all(
output['beat/_valid'] == [0, 4 * trans.sr // trans.hop_length])
assert output['beat/mask_downbeat']
# The first channel measures beats
# The second channel measures downbeats
assert output['beat/beat'].shape == (1, 4 * (SR // HOP_LENGTH), 1)
assert output['beat/downbeat'].shape == (1, 4 * (SR // HOP_LENGTH), 1)
# Ideal vectors:
# a beat every second (two samples)
# a downbeat every three seconds (6 samples)
beat_true = np.asarray([[1, 1, 1, 1]]).T
downbeat_true = np.asarray([[1, 0, 0, 1]]).T
assert np.all(output['beat/beat'][0, ::(SR // HOP_LENGTH)] == beat_true)
assert np.all(output['beat/downbeat'][0, ::(SR //
HOP_LENGTH)] == downbeat_true)
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_dlabel_auto(SR, HOP_LENGTH):
jam = jams.JAMS(file_metadata=dict(duration=4.0))
trans = pumpp.task.DynamicLabelTransformer(name='genre',
namespace='tag_gtzan')
output = trans.transform(jam)
# Mask should be false since we have no matching namespace
assert not np.any(output['genre/_valid'])
y = output['genre/tags']
# Check the shape
assert y.shape == (1, 4 * (SR // HOP_LENGTH), 10)
# Make sure it's empty
assert not np.any(y)
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_slabel_auto():
jam = jams.JAMS(file_metadata=dict(duration=4.0))
trans = pumpp.task.StaticLabelTransformer(name='genre',
namespace='tag_gtzan')
output = trans.transform(jam)
# Mask should be false since we have no matching namespace
assert not np.any(output['genre/_valid'])
# Check the shape
assert output['genre/tags'].ndim == 2
assert output['genre/tags'].shape[1] == 10
# Make sure it's empty
assert not np.any(output['genre/tags'])
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_load_jam_audio():
def __test(jam_in, audio_file):
jam = muda.load_jam_audio(jam_in, audio_file)
assert hasattr(jam.sandbox, 'muda')
eq_(jam.file_metadata.duration,
librosa.get_duration(**jam.sandbox.muda._audio))
# Add an empty jams test for missing duration
yield __test, jams.JAMS(), 'data/fixture.wav'
yield __test, 'data/fixture.jams', 'data/fixture.wav'
yield __test, jams.load('data/fixture.jams'), 'data/fixture.wav'
with open('data/fixture.jams', 'r') as fdesc:
yield __test, fdesc, 'data/fixture.wav'
def create_JAMS(in_dir, metadata, out_file):
"""Creates a JAMS file given the path to a SALAMI track.
Parameters
----------
in_dir : str
Path to the input directory
metadata : str
Metadata read from the CSV file
out_file : str
Output JAMS file
"""
path = os.path.join(
in_dir,
"annotations",
metadata[0],
)
# Sanity check
if not os.path.exists(path):
logging.warning("Path not found %s", path)
return
# New JAMS and annotation
jam = jams.JAMS()
# Create Annotations if they exist
# Maximum 3 annotations per file
for annotation_id in range(1, 4):
if os.path.isfile(
os.path.join(path, "textfile" + str(annotation_id) + ".txt")):
create_annotations(jam, path, annotation_id, metadata)
# Get the duration from the annotations
dur = get_duration(jam)
# Global file metadata
fill_global_metadata(jam, metadata, dur)
# Save JAMS
jam.save(out_file)
def test_jams_trim_valid():
# For a valid scenario, ensure everything behaves as expected
jam = jams.JAMS()
jam.file_metadata.duration = 15
namespace = 'tag_open'
data = dict(time=[5.0, 5.0, 10.0],
duration=[2.0, 4.0, 4.0],
value=['one', 'two', 'three'],
confidence=[0.9, 0.9, 0.9])
ann = jams.Annotation(namespace, data=data, time=5.0, duration=10.0)
for _ in range(5):
jam.annotations.append(ann)
ann_copy = jams.Annotation(namespace, data=data, time=5.0, duration=10.0)
ann_trim = ann_copy.trim(0, 10, strict=False)
jam_trim = jam.trim(0, 10, strict=False)
for ann in jam_trim.annotations:
assert ann.data == ann_trim.data
assert jam_trim.file_metadata.duration == jam.file_metadata.duration
assert jam_trim.sandbox.trim == [{'start_time': 0, 'end_time': 10}]
# Multiple trims
jam_trim = jam.trim(0, 10).trim(8, 10)
ann_trim = ann_copy.trim(0, 10).trim(8, 10)
for ann in jam_trim.annotations:
assert ann.data == ann_trim.data
assert jam_trim.sandbox.trim == ([{
'start_time': 0,
'end_time': 10
}, {
'start_time': 8,
'end_time': 10
}])
# Make sure file metadata copied over correctly
assert jam_trim.file_metadata == jam.file_metadata
def parse_smc(input_dir, output_dir):
'''Convert smc to jams'''
# Get a list of the wavs, tags, and txts
wav_files = find_with_extension(os.path.join(input_dir, 'SMC_MIREX_Audio'),
'wav',
depth=1)
ann_files = find_with_extension(os.path.join(
input_dir, 'SMC_MIREX_Annotations_05_08_2014'),
'txt',
depth=1)
tag_files = find_with_extension(os.path.join(input_dir, 'SMC_MIREX_Tags'),
'tag',
depth=1)
# Make sure everything lines up
assert len(wav_files) == len(ann_files)
assert len(wav_files) == len(tag_files)
for wav, ann, tag in zip(wav_files, ann_files, tag_files):
# Get the file metadata
metadata = smc_file_metadata(wav)
# Get the annotation
beat_annotation = smc_annotation(ann)
# Get the tags
tag_annotation = smc_tags(tag, metadata.duration)
jam = jams.JAMS(file_metadata=metadata)
jam.annotations.append(beat_annotation)
jam.annotations.append(tag_annotation)
# Add content path to the top-level sandbox
jam.sandbox.content_path = os.path.basename(wav)
# Save the jam
save_jam(output_dir, jam)
def test_task_chord_absent():
jam = jams.JAMS(file_metadata=dict(duration=4.0))
T = crema.task.ChordTransformer()
output = T.transform(jam)
# Mask should be false since we have no matching namespace
eq_(output['mask_chord'], False)
# Check the shape
assert np.allclose(output['output_pitches'].shape, [4 * N_REPEAT, 12])
assert np.allclose(output['output_root'].shape, [4 * N_REPEAT, 13])
assert np.allclose(output['output_bass'].shape, [4 * N_REPEAT, 13])
# Make sure it's empty
assert not np.any(output['output_pitches'])
assert not np.any(output['output_root'][:, :12])
assert not np.any(output['output_bass'][:, :12])
assert np.all(output['output_root'][:, 12])
assert np.all(output['output_bass'][:, 12])
def __test(dimension, name):
var_name = 'output_{:s}'.format(name)
mask_name = 'mask_{:s}'.format(name)
jam = jams.JAMS(file_metadata=dict(duration=4.0))
T = crema.task.VectorTransformer(namespace='vector',
dimension=dimension,
name=name)
output = T.transform(jam)
# Mask should be false since we have no matching namespace
eq_(output[mask_name], False)
# Check the shape
eq_(output[var_name].ndim, 1)
eq_(output[var_name].shape[0], dimension)
# Make sure it's empty
assert not np.any(output[var_name])
def test_task_key_tag_absent(SR, HOP_LENGTH, SPARSE):
jam = jams.JAMS(file_metadata=dict(duration=4.0))
trans = pumpp.task.KeyTagTransformer(name='key',
sr=SR,
hop_length=HOP_LENGTH,
sparse=SPARSE)
output = trans.transform(jam)
# Valid range is 0 since we have no matching namespace
assert not np.any(output['key/_valid'])
# Make sure it's all no-key
Y_pred = trans.encoder.inverse_transform(output['key/tag'][0])
assert all([_ == 'N' for _ in Y_pred])
# Check the shape
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_dlabel_present(SR, HOP_LENGTH):
labels = ['alpha', 'beta', 'psycho', 'aqua', 'disco']
jam = jams.JAMS(file_metadata=dict(duration=4.0))
ann = jams.Annotation(namespace='tag_open')
ann.append(time=0, duration=1.0, value='alpha')
ann.append(time=0, duration=1.0, value='beta')
ann.append(time=1, duration=1.0, value='some nonsense')
ann.append(time=3, duration=1.0, value='disco')
jam.annotations.append(ann)
trans = pumpp.task.DynamicLabelTransformer(name='madeup',
namespace='tag_open',
labels=labels)
output = trans.transform(jam)
# Mask should be true
assert np.all(
output['madeup/_valid'] == [0, 4 * trans.sr // trans.hop_length])
y = output['madeup/tags']
# Check the shape
assert y.shape == (1, 4 * (SR // HOP_LENGTH), len(labels))
# Decode the labels
predictions = trans.encoder.inverse_transform(y[0, ::(SR // HOP_LENGTH)])
true_labels = [['alpha', 'beta'], [], [], ['disco']]
for truth, pred in zip(true_labels, predictions):
assert set(truth) == set(pred)
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_slabel_present():
labels = ['alpha', 'beta', 'psycho', 'aqua', 'disco']
jam = jams.JAMS(file_metadata=dict(duration=4.0))
ann = jams.Annotation(namespace='tag_open')
ann.append(time=0, duration=1.0, value='alpha')
ann.append(time=0, duration=1.0, value='beta')
ann.append(time=1, duration=1.0, value='some nonsense')
ann.append(time=3, duration=1.0, value='disco')
jam.annotations.append(ann)
trans = pumpp.task.StaticLabelTransformer(name='madeup',
namespace='tag_open',
labels=labels)
output = trans.transform(jam)
# Mask should be true
assert np.all(
output['madeup/_valid'] == [0, 4 * trans.sr // trans.hop_length])
# Check the shape
assert output['madeup/tags'].ndim == 2
assert output['madeup/tags'].shape[1] == len(labels)
# Decode the labels
y_pred = output['madeup/tags'][0]
predictions = trans.encoder.inverse_transform(y_pred.reshape((1, -1)))[0]
true_labels = ['alpha', 'beta', 'disco']
assert set(true_labels) == set(predictions)
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_beat_absent(SR, HOP_LENGTH):
# Construct a jam
jam = jams.JAMS(file_metadata=dict(duration=4.0))
# One second = one frame
trans = pumpp.task.BeatTransformer(name='beat')
output = trans.transform(jam)
# Make sure we have the mask
assert not np.any(output['beat/_valid'])
assert not output['beat/mask_downbeat']
# Check the shape: 4 seconds at 2 samples per second
assert output['beat/beat'].shape == (1, 4 * (SR // HOP_LENGTH), 1)
assert output['beat/downbeat'].shape == (1, 4 * (SR // HOP_LENGTH), 1)
assert not np.any(output['beat/beat'])
assert not np.any(output['beat/downbeat'])
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_slabel_absent():
labels = ['alpha', 'beta', 'psycho', 'aqua', 'disco']
jam = jams.JAMS(file_metadata=dict(duration=4.0))
trans = pumpp.task.StaticLabelTransformer(name='madeup',
namespace='tag_open',
labels=labels)
output = trans.transform(jam)
# Mask should be false since we have no matching namespace
assert not np.any(output['madeup/_valid'])
# Check the shape
assert output['madeup/tags'].ndim == 2
assert output['madeup/tags'].shape[1] == len(labels)
# Make sure it's empty
assert not np.any(output['madeup/tags'])
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def test_task_structure_absent(SR, HOP_LENGTH):
jam = jams.JAMS(file_metadata=dict(duration=4.0))
trans = pumpp.task.StructureTransformer(name='struct',
sr=SR,
hop_length=HOP_LENGTH)
output = trans.transform(jam)
# Mask should be true
assert not np.any(output['struct/_valid'])
y = output['struct/agree']
# Check the shape
assert y.shape == (1, 4 * (SR // HOP_LENGTH), 4 * (SR // HOP_LENGTH))
# With a null structure annotation, all frames are similar
assert np.all(y)
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def process_track(input_dir, output_dir, metadata, tags, compress):
# Construct track metadata
duration = get_track_duration(
os.path.join(input_dir, 'mp3',
os.path.extsep.join([metadata['track'], 'mp3'])))
artist, _ = metadata['track'].split('-', 1)
artist = ARTIST_MAP.get(artist, artist)
title = metadata['track'].replace('{:s}-'.format(artist), '')
artist = artist.replace('_', ' ')
title = title.replace('_', ' ')
file_meta = jams.FileMetadata(title=title,
artist=artist,
duration=duration)
# Get the tag annotation
amd = jams.AnnotationMetadata(curator=jams.Curator(**__curator__),
corpus=__corpus__)
ann = jams.Annotation('tag_cal500', annotation_metadata=amd)
for value, confidence in tags.iteritems():
ann.append(time=0,
duration=duration,
value=value,
confidence=confidence)
jam = jams.JAMS(file_metadata=file_meta)
jam.annotations.append(ann)
jam.sandbox.content_path = metadata['track']
save_jam(output_dir, jam, metadata.name, compress)
def test_task_beatpos_tail(SR, HOP_LENGTH, SPARSE):
# This test checks for implicit end-of-bar encodings
jam = jams.JAMS(file_metadata=dict(duration=10.0))
ann = jams.Annotation(namespace='beat')
Y_true = [0, 2, 3, 1, 2, 3, 1, 2, 3]
Y_true_out = [
'X', '03/02', '03/03', '03/01', '03/02', '03/03', '03/01', '03/02',
'03/03'
]
for i, y in enumerate(Y_true):
ann.append(time=i, duration=0, value=y)
jam.annotations.append(ann)
trans = pumpp.task.BeatPositionTransformer(name='beat',
max_divisions=4,
sr=SR,
hop_length=HOP_LENGTH,
sparse=SPARSE)
output = trans.transform(jam)
assert np.all(
output['beat/_valid'] == [0, 10 * trans.sr // trans.hop_length])
Y_pred = trans.encoder.inverse_transform(output['beat/position'][0])
# This trimming is here because duration is inferred from the track,
# not the ytrue_out
Y_expected = np.repeat(Y_true_out, (SR // HOP_LENGTH),
axis=0).astype(Y_pred.dtype)
for i, (y1, y2) in enumerate(zip(Y_pred, Y_expected)):
assert y1 == y2
def test_task_simple_chord_present(SR, HOP_LENGTH):
# Construct a jam
jam = jams.JAMS(file_metadata=dict(duration=5.0))
ann = jams.Annotation(namespace='chord')
ann.append(time=0, duration=1.0, value='C:maj')
ann.append(time=1, duration=1.0, value='C:maj/3')
ann.append(time=3, duration=1.0, value='D:maj')
ann.append(time=4, duration=1.0, value='N')
jam.annotations.append(ann)
# One second = one frame
trans = pumpp.task.SimpleChordTransformer(name='chord_s')
output = trans.transform(jam)
# Make sure we have the mask
assert np.all(
output['chord_s/_valid'] == [0, 5 * trans.sr // trans.hop_length])
# Ideal vectors:
# pcp = Cmaj, Cmaj, N, Dmaj, N
pcp_true = np.asarray([[1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
assert np.all(output['chord_s/pitch'] == np.repeat(
pcp_true, (SR // HOP_LENGTH), axis=0))
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def pred_to_jam(beats, pred):
time = 0.0
label = ud.labels[pred[0]]
observations = []
n = len(pred)
for i in range(0,n):
if ud.labels[pred[i]] == label: continue
else:
new_time = beats[i]
new_label = ud.labels[pred[i]]
observations.append(jams.Observation(time, new_time-time, label, None))
time = new_time
label = new_label
observations.append(jams.Observation(time, beats[-1]-time, ud.labels[pred[-1]], None))
annotation = jams.Annotation("segment_open"
, observations
, jd.get_annotation_metadata()
, jd.get_sandbox())
jam = jams.JAMS([annotation], jams.FileMetadata(duration=beats[-1]))
return jam
def test_task_beat_nometer():
# Construct a jam
jam = jams.JAMS(file_metadata=dict(duration=4.0))
ann = jams.Annotation(namespace='beat')
ann.append(time=0, duration=0.0)
ann.append(time=1, duration=0.0)
ann.append(time=2, duration=0.0)
ann.append(time=3, duration=0.0)
jam.annotations.append(ann)
# One second = one frame
T = crema.task.BeatTransformer()
output = T.transform(jam)
# Make sure we have the mask
eq_(output['mask_beat'], True)
eq_(output['mask_downbeat'], False)
# Check the shape: 4 seconds at 2 samples per second
assert np.allclose(output['output_beat'].shape, [4 * N_REPEAT, 1])
assert np.allclose(output['output_downbeat'].shape, [4 * N_REPEAT, 1])
# Ideal vectors:
# a beat every second (two samples)
# no downbeats
beat_true = np.asarray([1, 1, 1, 1])
downbeat_true = np.asarray([0, 0, 0, 0])
assert np.allclose(output['output_beat'][::N_REPEAT], beat_true)
assert np.allclose(output['output_downbeat'][::N_REPEAT], downbeat_true)
def test_task_beat_present():
# Construct a jam
jam = jams.JAMS(file_metadata=dict(duration=4.0))
ann = jams.Annotation(namespace='beat')
ann.append(time=0, duration=0.0, value=1)
ann.append(time=1, duration=0.0, value=2)
ann.append(time=2, duration=0.0, value=3)
ann.append(time=3, duration=0.0, value=1)
jam.annotations.append(ann)
T = crema.task.BeatTransformer()
output = T.transform(jam)
# Make sure we have the masks
eq_(output['mask_beat'], True)
eq_(output['mask_downbeat'], True)
# The first channel measures beats
# The second channel measures downbeats
assert np.allclose(output['output_beat'].shape, [4 * N_REPEAT, 1])
assert np.allclose(output['output_downbeat'].shape, [4 * N_REPEAT, 1])
# Ideal vectors:
# a beat every second (two samples)
# a downbeat every three seconds (6 samples)
beat_true = np.asarray([[1, 1, 1, 1]]).T
downbeat_true = np.asarray([[1, 0, 0, 1]]).T
assert np.allclose(output['output_beat'][::N_REPEAT], beat_true)
assert np.allclose(output['output_downbeat'][::N_REPEAT], downbeat_true)
def test_transform_noprefix():
labels = ['foo', 'bar', 'baz']
jam = jams.JAMS(file_metadata=dict(duration=4.0))
trans = pumpp.task.StaticLabelTransformer(name=None,
namespace='tag_open',
labels=labels)
output = trans.transform(jam)
# Mask should be false since we have no matching namespace
assert not np.any(output['_valid'])
# Check the shape
assert output['tags'].ndim == 2
assert output['tags'].shape[1] == len(labels)
# Make sure it's empty
assert not np.any(output['tags'])
for key in trans.fields:
assert shape_match(output[key].shape[1:], trans.fields[key].shape)
assert type_match(output[key].dtype, trans.fields[key].dtype)
def parse_patterns(csv_file, kern_file, patterns, out_file):
"""Parses the set of patterns and saves the results into the output file.
Parameters
----------
csv_file : str
Path to the main csv file from which the pattern is extracted.
kern_file : str
Path to the main kern file from which to extract the metadata.
patterns: list of list of strings (files)
Set of all the patterns with the occurrences of a given piece.
out_file: string (path)
Path to the output file to save the set of patterns in the MIREX
format.
"""
# Create JAMS and add some metada
jam = jams.JAMS()
curator = jams.Curator(name="Tom Collins", email="*****@*****.**")
fill_file_metadata(jam, kern_file, csv_file)
ann_meta = jams.AnnotationMetadata(curator=curator,
version="August2013",
corpus="JKU Development Dataset")
# Create actual annotation
annot = jams.Annotation(namespace="pattern_jku",
annotation_metadata=ann_meta)
# Get bpm and first and last onsets
bpm = get_bpm(kern_file)
first_onset, last_onset = get_first_last_onset(csv_file)
pattern_n = 1
for pattern in patterns:
occ_n = 1
for occ_file in pattern:
start, end = find_in_csv(csv_file, occ_file)
with open(csv_file, "r") as f:
file_reader = list(csv.reader(f))
for i in range(start, end):
value = {
"midi_pitch": float(file_reader[i][1]),
"morph_pitch": float(file_reader[i][2]),
"staff": int(float(
file_reader[i]
[4])), # Hack to convert 0.000000000 into an int
"pattern_id": pattern_n,
"occurrence_id": occ_n
}
# Transform onset to time
time = onset_to_seconds(float(file_reader[i][0]),
first_onset, bpm)
dur = onset_to_seconds(float(file_reader[i][3]), 0, bpm)
annot.data.add_observation(time=time,
duration=dur,
value=value)
occ_n += 1
pattern_n += 1
# Annotation to the jams
jam.annotations.append(annot)
# Save file
jam.save(out_file)
