def _split_on_bars(self, note_seq):
bars_length_sec = self._calc_bar_length_sec(
note_seq) * self._split_in_bar_chunks
return sequences_lib.split_note_sequence(
note_seq, bars_length_sec
) # only works on non-quantized sequences (for some reason...)
def testSplitter(self):
note_sequence = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(note_sequence, 0,
[(12, 100, 0.01, 10.0),
(11, 55, 0.22, 0.50),
(40, 45, 2.50, 3.50),
(55, 120, 4.0, 4.01),
(52, 99, 4.75, 5.0)])
expected_sequences = sequences_lib.split_note_sequence(
note_sequence, 1.0)
unit = note_sequence_pipelines.Splitter(1.0)
self._unit_transform_test(unit, note_sequence, expected_sequences)
def split_on_downbeats(sequence,
bars_per_segment,
downbeats=None,
skip_bars=0,
min_notes_per_segment=0,
include_span=False):
if downbeats is None:
downbeats = midi_io.sequence_proto_to_pretty_midi(
sequence).get_downbeats()
downbeats = [d for d in downbeats if d < sequence.total_time]
try:
iter(bars_per_segment)
except TypeError:
bars_per_segment = [bars_per_segment]
for bps in bars_per_segment:
first_split = skip_bars or bps # Do not split at time 0
split_times = list(downbeats[first_split::bps])
segments = sequences_lib.split_note_sequence(
sequence, hop_size_seconds=split_times)
if skip_bars:
# The first segment will contain the bars we want to skip
segments.pop(0)
for i, segment in enumerate(segments):
start = skip_bars + i * bps
end = start + bps
if len(segment.notes) < min_notes_per_segment:
print(
f'Skipping segment {start}-{end} with {len(segment.notes)} notes',
file=sys.stderr)
continue
if include_span:
yield start, end, segment
else:
yield segment
def process(self, kv):
# Seed random number generator based on key so that hop times are
# deterministic.
key, ns_str = kv
m = hashlib.md5(key.encode('utf-8'))
random.seed(int(m.hexdigest(), 16))
# Deserialize NoteSequence proto.
ns = note_seq.NoteSequence.FromString(ns_str)
# Apply sustain pedal.
ns = sequences_lib.apply_sustain_control_changes(ns)
# Remove control changes as there are potentially a lot of them and they are
# no longer needed.
del ns.control_changes[:]
if (self._min_hop_size_seconds
and ns.total_time < self._min_hop_size_seconds):
Metrics.counter('extract_examples', 'sequence_too_short').inc()
return
sequences = []
for _ in range(self._num_replications):
if self._max_hop_size_seconds:
if self._max_hop_size_seconds == self._min_hop_size_seconds:
# Split using fixed hop size.
sequences += sequences_lib.split_note_sequence(
ns, self._max_hop_size_seconds)
else:
# Sample random hop positions such that each segment size is within
# the specified range.
hop_times = [0.0]
while hop_times[
-1] <= ns.total_time - self._min_hop_size_seconds:
if hop_times[
-1] + self._max_hop_size_seconds < ns.total_time:
# It's important that we get a valid hop size here, since the
# remainder of the sequence is too long.
max_offset = min(
self._max_hop_size_seconds, ns.total_time -
self._min_hop_size_seconds - hop_times[-1])
else:
# It's okay if the next hop time is invalid (in which case we'll
# just stop).
max_offset = self._max_hop_size_seconds
offset = random.uniform(self._min_hop_size_seconds,
max_offset)
hop_times.append(hop_times[-1] + offset)
# Split at the chosen hop times (ignoring zero and the final invalid
# time).
sequences += sequences_lib.split_note_sequence(
ns, hop_times[1:-1])
else:
sequences += [ns]
for performance_sequence in sequences:
if self._encode_score_fns:
# We need to extract a score.
if not self._absolute_timing:
# Beats are required to extract a score with metric timing.
beats = [
ta for ta in performance_sequence.text_annotations
if ta.annotation_type == BEAT
and ta.time <= performance_sequence.total_time
]
if len(beats) < 2:
Metrics.counter('extract_examples',
'not_enough_beats').inc()
continue
# Ensure the sequence starts and ends on a beat.
performance_sequence = sequences_lib.extract_subsequence(
performance_sequence,
start_time=min(beat.time for beat in beats),
end_time=max(beat.time for beat in beats))
# Infer beat-aligned chords (only for relative timing).
try:
chord_inference.infer_chords_for_sequence(
performance_sequence,
chord_change_prob=0.25,
chord_note_concentration=50.0,
add_key_signatures=True)
except chord_inference.ChordInferenceError:
Metrics.counter('extract_examples',
'chord_inference_failed').inc()
continue
# Infer melody regardless of relative/absolute timing.
try:
melody_instrument = melody_inference.infer_melody_for_sequence(
performance_sequence,
melody_interval_scale=2.0,
rest_prob=0.1,
instantaneous_non_max_pitch_prob=1e-15,
instantaneous_non_empty_rest_prob=0.0,
instantaneous_missing_pitch_prob=1e-15)
except melody_inference.MelodyInferenceError:
Metrics.counter('extract_examples',
'melody_inference_failed').inc()
continue
if not self._absolute_timing:
# Now rectify detected beats to occur at fixed tempo.
# TODO(iansimon): also include the alignment
score_sequence, unused_alignment = sequences_lib.rectify_beats(
performance_sequence, beats_per_minute=SCORE_BPM)
else:
# Score uses same timing as performance.
score_sequence = copy.deepcopy(performance_sequence)
# Remove melody notes from performance.
performance_notes = []
for note in performance_sequence.notes:
if note.instrument != melody_instrument:
performance_notes.append(note)
del performance_sequence.notes[:]
performance_sequence.notes.extend(performance_notes)
# Remove non-melody notes from score.
score_notes = []
for note in score_sequence.notes:
if note.instrument == melody_instrument:
score_notes.append(note)
del score_sequence.notes[:]
score_sequence.notes.extend(score_notes)
# Remove key signatures and beat/chord annotations from performance.
del performance_sequence.key_signatures[:]
del performance_sequence.text_annotations[:]
Metrics.counter('extract_examples', 'extracted_score').inc()
for augment_fn in self._augment_fns:
# Augment and encode the performance.
try:
augmented_performance_sequence = augment_fn(
performance_sequence)
except DataAugmentationError:
Metrics.counter('extract_examples',
'augment_performance_failed').inc()
continue
example_dict = {
'targets':
self._encode_performance_fn(augmented_performance_sequence)
}
if not example_dict['targets']:
Metrics.counter('extract_examples',
'skipped_empty_targets').inc()
continue
if (self._random_crop_length and len(example_dict['targets']) >
self._random_crop_length):
# Take a random crop of the encoded performance.
max_offset = len(
example_dict['targets']) - self._random_crop_length
offset = random.randrange(max_offset + 1)
example_dict['targets'] = example_dict['targets'][
offset:offset + self._random_crop_length]
if self._encode_score_fns:
# Augment the extracted score.
try:
augmented_score_sequence = augment_fn(score_sequence)
except DataAugmentationError:
Metrics.counter('extract_examples',
'augment_score_failed').inc()
continue
# Apply all score encoding functions.
skip = False
for name, encode_score_fn in self._encode_score_fns.items(
):
example_dict[name] = encode_score_fn(
augmented_score_sequence)
if not example_dict[name]:
Metrics.counter('extract_examples',
'skipped_empty_%s' % name).inc()
skip = True
break
if skip:
continue
Metrics.counter('extract_examples', 'encoded_example').inc()
Metrics.distribution(
'extract_examples',
'performance_length_in_seconds').update(
int(augmented_performance_sequence.total_time))
yield generator_utils.to_example(example_dict)
def transform(self, note_sequence):
return sequences_lib.split_note_sequence(note_sequence,
self._hop_size_seconds)
def transform(self, input_object):
note_sequence = input_object
return sequences_lib.split_note_sequence(
note_sequence, self._hop_size_seconds)