def testSquash(self):
# Melody in C, transposed to C, and squashed to 1 octave.
events = [12 * 5, NO_EVENT, 12 * 5 + 2, NOTE_OFF, 12 * 6 + 4, NO_EVENT]
melody = melodies_lib.Melody()
melody.from_event_list(events)
melody.squash(min_note=12 * 5, max_note=12 * 6, transpose_to_key=0)
expected = [
12 * 5, NO_EVENT, 12 * 5 + 2, NOTE_OFF, 12 * 5 + 4, NO_EVENT
]
self.assertEqual(expected, list(melody))
# Melody in D, transposed to C, and squashed to 1 octave.
events = [12 * 5 + 2, 12 * 5 + 4, 12 * 6 + 7, 12 * 6 + 6, 12 * 5 + 1]
melody = melodies_lib.Melody()
melody.from_event_list(events)
melody.squash(min_note=12 * 5, max_note=12 * 6, transpose_to_key=0)
expected = [12 * 5, 12 * 5 + 2, 12 * 5 + 5, 12 * 5 + 4, 12 * 5 + 11]
self.assertEqual(expected, list(melody))
# Melody in D, transposed to E, and squashed to 1 octave.
events = [12 * 5 + 2, 12 * 5 + 4, 12 * 6 + 7, 12 * 6 + 6, 12 * 4 + 11]
melody = melodies_lib.Melody()
melody.from_event_list(events)
melody.squash(min_note=12 * 5, max_note=12 * 6, transpose_to_key=4)
expected = [12 * 5 + 4, 12 * 5 + 6, 12 * 5 + 9, 12 * 5 + 8, 12 * 5 + 1]
self.assertEqual(expected, list(melody))
def testSquashCenterOctaves(self):
# Move up an octave.
events = [
12 * 4, NO_EVENT, 12 * 4 + 2, NOTE_OFF, 12 * 4 + 4, NO_EVENT,
12 * 4 + 5, 12 * 5 + 2, 12 * 4 - 1, NOTE_OFF
]
melody = melodies_lib.Melody()
melody.from_event_list(events)
melody.squash(min_note=12 * 4, max_note=12 * 7, transpose_to_key=0)
expected = [
12 * 5, NO_EVENT, 12 * 5 + 2, NOTE_OFF, 12 * 5 + 4, NO_EVENT,
12 * 5 + 5, 12 * 6 + 2, 12 * 5 - 1, NOTE_OFF
]
self.assertEqual(expected, list(melody))
# Move down an octave.
events = [
12 * 6, NO_EVENT, 12 * 6 + 2, NOTE_OFF, 12 * 6 + 4, NO_EVENT,
12 * 6 + 5, 12 * 7 + 2, 12 * 6 - 1, NOTE_OFF
]
melody = melodies_lib.Melody()
melody.from_event_list(events)
melody.squash(min_note=12 * 4, max_note=12 * 7, transpose_to_key=0)
expected = [
12 * 5, NO_EVENT, 12 * 5 + 2, NOTE_OFF, 12 * 5 + 4, NO_EVENT,
12 * 5 + 5, 12 * 6 + 2, 12 * 5 - 1, NOTE_OFF
]
self.assertEqual(expected, list(melody))
def testGetMajorKey(self):
# D Major.
events = [
NO_EVENT, 12 * 2 + 2, 12 * 3 + 4, 12 * 5 + 1, 12 * 6 + 6,
12 * 4 + 11, 12 * 3 + 9, 12 * 5 + 7, NOTE_OFF
]
melody = melodies_lib.Melody()
melody.from_event_list(events)
self.assertEqual(2, melody.get_major_key())
# C# Major with accidentals.
events = [
NO_EVENT, 12 * 2 + 1, 12 * 4 + 8, 12 * 5 + 5, 12 * 6 + 6,
12 * 3 + 3, 12 * 2 + 11, 12 * 3 + 10, 12 * 5, 12 * 2 + 8,
12 * 4 + 1, 12 * 3 + 5, 12 * 5 + 9, 12 * 4 + 3, NOTE_OFF
]
melody = melodies_lib.Melody()
melody.from_event_list(events)
self.assertEqual(1, melody.get_major_key())
# One note in C Major.
events = [NO_EVENT, 12 * 2 + 11, NOTE_OFF]
melody = melodies_lib.Melody()
melody.from_event_list(events)
self.assertEqual(0, melody.get_major_key())
def testBasicOneHotEncoderTruncateNoteOff(self):
steps_per_beat = 4
transpose_to_key = 0
min_note = 48
max_note = 84
num_classes = max_note - min_note + 2
melody = melodies_lib.Melody()
melody.from_event_list([
NO_EVENT, 60, 62, 64, NO_EVENT, NOTE_OFF, 65, 67, NOTE_OFF, 69, 71,
72, NO_EVENT, NOTE_OFF, 74, 76, NOTE_OFF
])
transformed_melody = [
NO_EVENT, 12, 14, 16, NO_EVENT, NOTE_OFF, 17, 19, NOTE_OFF, 21, 23,
24, NO_EVENT, NOTE_OFF, 26, 28
]
expected_inputs = [
one_hot(note + 2, num_classes) for note in transformed_melody
]
expected_labels = ([note + 2 for note in transformed_melody[1:]] +
[NOTE_OFF + 2])
expected_sequence_example = make_sequence_example(
expected_inputs, expected_labels)
sequence_example, _ = encoders.basic_one_hot_encoder(
melody, steps_per_beat, min_note, max_note, transpose_to_key)
self.assertEqual(expected_sequence_example, sequence_example)
def testFromNotesPolyphonic(self):
sequence = music_pb2.NoteSequence()
add_track(sequence, 0, [(12, 100, 1.0, 4.0), (19, 100, 0.95, 3.0)])
melody = melodies_lib.Melody(steps_per_bar=16)
with self.assertRaises(melodies_lib.PolyphonicMelodyException):
melody.from_notes(sequence.notes, bpm=60.0)
self.assertFalse(list(melody))
def testFromNotesPolyphonicWithIgnorePolyphonicNotes(self): sequence = music_pb2.NoteSequence() add_track(sequence, 0, [(12, 100, 0, 2.0), (19, 100, 0, 3.0)]) melody = melodies_lib.Melody(steps_per_bar=16) melody.from_notes(sequence.notes, bpm=60.0, ignore_polyphonic_notes=True) expected = [12] + [-2] * 7 + [-1] self.assertEqual(expected, list(melody))
def classes_to_melody(model_output, reconstruction_data, min_note=48):
"""Convert list of model outputs to Melody object.
This method decodes sequence_to_melodies.basic_one_hot_encoder.
Each model output is the index of the softmax class that is chosen.
Args:
model_output: List of integers. Each int is the chosen softmax class
from the model output.
reconstruction_data: basic_one_hot_encoder specific information
needed to reconstruct the input Melody.
min_note: Minimum pitch in model will be mapped to this MIDI pitch.
Returns:
A melodies_lib.Melody.
"""
transpose_amount = reconstruction_data
output = melodies_lib.Melody()
output.from_event_list(
[e - melodies_lib.NUM_SPECIAL_EVENTS
if e < melodies_lib.NUM_SPECIAL_EVENTS
else e + min_note - transpose_amount
for e in model_output])
return output
def testFromNotesTrimEmptyMeasures(self):
sequence = music_pb2.NoteSequence()
add_track(sequence, 0, [(12, 100, 9.5, 9.75), (11, 100, 10.0, 10.25)])
melody = melodies_lib.Melody(steps_per_bar=16)
melody.from_notes(sequence.notes, bpm=60.0)
expected = [NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT, 12,
NOTE_OFF, 11, NOTE_OFF]
self.assertEqual(expected, list(melody))
def testGetNoteHistogram(self):
events = [NO_EVENT, NOTE_OFF, 12 * 2 + 1, 12 * 3, 12 * 5 + 11, 12 * 6 + 3,
12 * 4 + 11]
melody = melodies_lib.Melody()
melody.from_event_list(events)
expected = [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2]
self.assertEqual(expected, list(melody.get_note_histogram()))
events = [0, 1, NO_EVENT, NOTE_OFF, 12 * 2 + 1, 12 * 3, 12 * 6 + 3,
12 * 5 + 11, NO_EVENT, 12 * 4 + 11, 12 * 7 + 1]
melody = melodies_lib.Melody()
melody.from_event_list(events)
expected = [2, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2]
self.assertEqual(expected, list(melody.get_note_histogram()))
melody = melodies_lib.Melody()
expected = [0] * 12
self.assertEqual(expected, list(melody.get_note_histogram()))
def testSquashMaxNote(self):
events = [12 * 5, 12 * 5 + 2, 12 * 5 + 4, 12 * 5 + 5, 12 * 5 + 11, 12 * 6,
12 * 6 + 1]
melody = melodies_lib.Melody()
melody.from_event_list(events)
melody.squash(min_note=12 * 5, max_note=12 * 6, transpose_to_key=0)
expected = [12 * 5, 12 * 5 + 2, 12 * 5 + 4, 12 * 5 + 5, 12 * 5 + 11, 12 * 5,
12 * 5 + 1]
self.assertEqual(expected, list(melody))
def testFromNotesTimeOverlap(self):
sequence = music_pb2.NoteSequence()
add_track(sequence, 0, [(12, 100, 1.0, 5.0), (11, 100, 3.25, 3.75),
(13, 100, 2.0, 4.0)])
melody = melodies_lib.Melody(steps_per_bar=16)
melody.from_notes(sequence.notes, bpm=60.0)
expected = [NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT, 12, NO_EVENT, NO_EVENT,
NO_EVENT, 13, NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT, 11,
NO_EVENT, NOTE_OFF]
self.assertEqual(expected, list(melody))
def testFromNotes(self):
sequence = music_pb2.NoteSequence()
add_track(
sequence, 0,
[(12, 100, 0.01, 10.0), (11, 100, 0.22, 0.50), (40, 100, 2.50, 3.50),
(55, 100, 4.0, 4.01), (52, 100, 4.75, 5.0)])
melody = melodies_lib.Melody(steps_per_bar=16)
melody.from_notes(sequence.notes, bpm=60.0)
expected = [12, 11, NOTE_OFF, NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT,
NO_EVENT, NO_EVENT, NO_EVENT, 40, NO_EVENT, NO_EVENT, NO_EVENT,
NOTE_OFF, NO_EVENT, 55, NOTE_OFF, NO_EVENT, 52, NOTE_OFF]
self.assertEqual(expected, list(melody))
def testBasicOneHotEncoderOctaveSquash(self):
steps_per_beat = 4
transpose_to_key = 0
min_note = 48
max_note = 84
num_classes = max_note - min_note + 2
melody = melodies_lib.Melody()
melody.from_event_list(
[NO_EVENT, 84, 86, 52, NO_EVENT, NOTE_OFF, 65, 67, NOTE_OFF, 69, 71, 72,
NO_EVENT, NOTE_OFF, 38, 40, NOTE_OFF])
transformed_melody = [NO_EVENT, 24, 26, 4, NO_EVENT, NOTE_OFF, 17, 19,
NOTE_OFF, 21, 23, 24, NO_EVENT, NOTE_OFF, 2, 4]
expected_inputs = [one_hot(note + 2, num_classes)
for note in transformed_melody]
expected_labels = ([note + 2 for note in transformed_melody[1:]] +
[NOTE_OFF + 2])
expected_sequence_example = make_sequence_example(expected_inputs,
expected_labels)
sequence_example, _ = sequence_to_melodies.basic_one_hot_encoder(
melody, steps_per_beat, min_note, max_note, transpose_to_key)
self.assertEqual(expected_sequence_example, sequence_example)
def testSquashAllNotesOff(self):
events = [NO_EVENT, NOTE_OFF, NO_EVENT, NO_EVENT]
melody = melodies_lib.Melody()
melody.from_event_list(events)
melody.squash(min_note=12 * 4, max_note=12 * 7, transpose_to_key=0)
self.assertEqual(events, list(melody))
def sampler_loop(graph, checkpoint_dir, primer, num_gen_steps):
"""Generate many melodies simulatneously given a primer.
Generate melodies by sampling from model output and feeding it back into
the model as input at every step.
Args:
graph: A tf.Graph instance containing the graph to sample from.
checkpoint_dir: Directory to look for most recent model checkpoint in.
primer: A Melody object.
num_gen_steps: How many time steps to generate.
Returns:
List of generated melodies, each as a Melody object.
"""
logits = graph.get_collection('logits')[0]
softmax = graph.get_collection('softmax')[0]
initial_state = graph.get_collection('initial_state')[0]
final_state = graph.get_collection('final_state')[0]
melody_sequence = graph.get_collection('melody_sequence')[0]
lengths = graph.get_collection('lengths')[0]
with graph.as_default():
saver = tf.train.Saver()
session = tf.Session(graph=graph)
logging.info('Checkpoint dir: %s', checkpoint_dir)
checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
saver.restore(session, checkpoint_file)
batch_size = logits.get_shape()[0].value
# Convert primer Melody to model inputs.
sequence_example, transpose_amount = encoders.basic_one_hot_encoder(primer)
primer_input = [
list(i.float_list.value)
for i in sequence_example.feature_lists.feature_list['inputs'].feature
]
# Run model over primer sequence.
primer_input_batch = np.tile([primer_input], (batch_size, 1, 1))
state, _ = session.run(
[final_state, logits],
feed_dict={
initial_state: np.zeros(initial_state.get_shape().as_list()),
melody_sequence: primer_input_batch,
lengths: np.full(batch_size, len(primer), dtype=int)
})
# Sample from model repeatedly to generate melodies.
generated_sequences = [list() for i in range(batch_size)]
last_outputs = [melody_sequence] * batch_size
singleton_lengths = np.full(batch_size, 1, dtype=int)
for i in range(num_gen_steps):
input_batch = np.transpose(
[make_onehot(last_outputs, basic_rnn_ops.NUM_CLASSES)], (1, 0, 2))
state, batch_logits, batch_softmax = session.run(
[final_state, logits, softmax],
feed_dict={
initial_state: state,
melody_sequence: input_batch,
lengths: singleton_lengths
})
last_outputs = [
np.random.choice(basic_rnn_ops.NUM_CLASSES, p=p_dist.flatten())
for p_dist in batch_softmax
]
for generated_seq, next_output in zip(generated_sequences,
last_outputs):
generated_seq.append(next_output)
def decoder(event_list):
return [
e - melodies_lib.NUM_SPECIAL_EVENTS if
e < melodies_lib.NUM_SPECIAL_EVENTS else e + 48 - transpose_amount
for e in event_list
]
primer_event_list = list(primer)
generated_melodies = []
for seq in generated_sequences:
melody = melodies_lib.Melody(steps_per_bar=primer.steps_per_bar)
melody.from_event_list(primer_event_list + decoder(seq))
generated_melodies.append(melody)
return generated_melodies
