def split_on_downbeats(sequence,
bars_per_segment,
downbeats=None,
skip_bars=0,
min_notes_per_segment=0,
include_span=False,
tolerance=1e-6):
"""Split a note sequence on bar boundaries.
Args:
sequence: A `NoteSequence`.
bars_per_segment: The number of bars (downbeats) per segment.
downbeats: Downbeat times in seconds. If not specified, they will be determined using
`pretty_midi`.
skip_bars: The index of the bar to start at.
min_notes_per_segment: The minimum required number of notes per segment. Segments
containing fewer notes will be skipped.
include_span: If `True`, the output will be tuples of the form
`(start_bar_idx, end_bar_idx, segment)`.
tolerance: The maximum time in seconds a note can precede a downbeat to be included in the
following bar.
Yields:
`NoteSequence`s, or tuples of the form `(start_bar_idx, end_bar_idx, segment)`.
"""
if downbeats is None:
downbeats = midi_io.sequence_proto_to_pretty_midi(
sequence).get_downbeats()
downbeats = [d - tolerance for d in 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 testSplitNoteSequenceSkipSplitsInsideNotes(self):
# Tests splitting a NoteSequence at regular hop size, skipping splits that
# would have occurred inside a 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(
sequence, 0,
[(12, 100, 0.01, 3.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50),
(55, 120, 4.0, 4.01), (52, 99, 4.75, 5.0)])
testing_lib.add_chords_to_sequence(
sequence, [('C', 0.0), ('G7', 3.0), ('F', 4.5)])
expected_subsequence_1 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(
expected_subsequence_1, 0,
[(12, 100, 0.01, 3.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50)])
testing_lib.add_chords_to_sequence(
expected_subsequence_1, [('C', 0.0), ('G7', 3.0)])
expected_subsequence_1.total_time = 3.50
expected_subsequence_1.subsequence_info.end_time_offset = 1.5
expected_subsequence_2 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(
expected_subsequence_2, 0,
[(55, 120, 0.0, 0.01), (52, 99, 0.75, 1.0)])
testing_lib.add_chords_to_sequence(
expected_subsequence_2, [('G7', 0.0), ('F', 0.5)])
expected_subsequence_2.total_time = 1.0
expected_subsequence_2.subsequence_info.start_time_offset = 4.0
subsequences = sequences_lib.split_note_sequence(
sequence, hop_size_seconds=2.0, skip_splits_inside_notes=True)
self.assertEquals(2, len(subsequences))
self.assertProtoEquals(expected_subsequence_1, subsequences[0])
self.assertProtoEquals(expected_subsequence_2, subsequences[1])
def testSplitNoteSequenceSkipSplitsInsideNotes(self):
# Tests splitting a NoteSequence at regular hop size, skipping splits that
# would have occurred inside a 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(
sequence, 0,
[(12, 100, 0.01, 3.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50),
(55, 120, 4.0, 4.01), (52, 99, 4.75, 5.0)])
testing_lib.add_chords_to_sequence(
sequence, [('C', 0.0), ('G7', 3.0), ('F', 4.5)])
expected_subsequence_1 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(
expected_subsequence_1, 0,
[(12, 100, 0.01, 3.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50)])
testing_lib.add_chords_to_sequence(
expected_subsequence_1, [('C', 0.0), ('G7', 3.0)])
expected_subsequence_1.total_time = 3.50
expected_subsequence_1.subsequence_info.end_time_offset = 1.5
expected_subsequence_2 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(
expected_subsequence_2, 0,
[(55, 120, 0.0, 0.01), (52, 99, 0.75, 1.0)])
testing_lib.add_chords_to_sequence(
expected_subsequence_2, [('G7', 0.0), ('F', 0.5)])
expected_subsequence_2.total_time = 1.0
expected_subsequence_2.subsequence_info.start_time_offset = 4.0
subsequences = sequences_lib.split_note_sequence(
sequence, hop_size_seconds=2.0, skip_splits_inside_notes=True)
self.assertEquals(2, len(subsequences))
self.assertProtoEquals(expected_subsequence_1, subsequences[0])
self.assertProtoEquals(expected_subsequence_2, subsequences[1])
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 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 _to_tensors(self, note_sequence):
# Performance sequences require sustain to be correctly interpreted.
note_sequence = sequences_lib.apply_sustain_control_changes(
note_sequence)
if self._chord_encoding and not any(
ta.annotation_type == CHORD_SYMBOL
for ta in note_sequence.text_annotations):
try:
# Quantize just for the purpose of chord inference.
# TODO(iansimon): Allow chord inference in unquantized sequences.
quantized_sequence = mm.quantize_note_sequence(
note_sequence, self._steps_per_quarter)
if (mm.steps_per_bar_in_quantized_sequence(quantized_sequence)
!= self._steps_per_bar):
return data.ConverterTensors()
# Infer chords in quantized sequence.
mm.infer_chords_for_sequence(quantized_sequence)
except (mm.BadTimeSignatureException,
mm.NonIntegerStepsPerBarException,
mm.NegativeTimeException, mm.ChordInferenceException):
return data.ConverterTensors()
# Copy inferred chords back to original sequence.
for qta in quantized_sequence.text_annotations:
if qta.annotation_type == CHORD_SYMBOL:
ta = note_sequence.text_annotations.add()
ta.annotation_type = CHORD_SYMBOL
ta.time = qta.time
ta.text = qta.text
quarters_per_minute = (note_sequence.tempos[0].qpm
if note_sequence.tempos else
mm.DEFAULT_QUARTERS_PER_MINUTE)
quarters_per_bar = self._steps_per_bar / self._steps_per_quarter
hop_size_quarters = quarters_per_bar * self._hop_size_bars
hop_size_seconds = 60.0 * hop_size_quarters / quarters_per_minute
# Split note sequence by bar hop size (in seconds).
subsequences = sequences_lib.split_note_sequence(
note_sequence, hop_size_seconds)
if self._first_subsequence_only and len(subsequences) > 1:
return data.ConverterTensors()
sequence_tensors = []
sequence_chord_tensors = []
for subsequence in subsequences:
# Quantize this subsequence.
try:
quantized_subsequence = mm.quantize_note_sequence(
subsequence, self._steps_per_quarter)
if (mm.steps_per_bar_in_quantized_sequence(
quantized_subsequence) != self._steps_per_bar):
return data.ConverterTensors()
except (mm.BadTimeSignatureException,
mm.NonIntegerStepsPerBarException,
mm.NegativeTimeException):
return data.ConverterTensors()
# Convert the quantized subsequence to tensors.
tensors, chord_tensors = self._quantized_subsequence_to_tensors(
quantized_subsequence)
if tensors:
sequence_tensors.append(tensors)
if self._chord_encoding:
sequence_chord_tensors.append(chord_tensors)
return data.ConverterTensors(inputs=sequence_tensors,
outputs=sequence_tensors,
controls=sequence_chord_tensors)
def transform(self, note_sequence):
return sequences_lib.split_note_sequence(
note_sequence, self._hop_size_seconds)
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)
random.seed(int(m.hexdigest(), 16))
# Deserialize NoteSequence proto.
ns = music_pb2.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 ==
music_pb2.NoteSequence.TextAnnotation.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._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 testSplitNoteSequence(self):
# Tests splitting a NoteSequence at regular hop size, truncating notes.
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(sequence, 0, [(12, 100, 0.01, 8.0),
(11, 55, 0.22, 0.50),
(40, 45, 2.50, 3.50),
(55, 120, 4.0, 4.01),
(52, 99, 4.75, 5.0)])
testing_lib.add_chords_to_sequence(sequence, [('C', 1.0), ('G7', 2.0),
('F', 4.0)])
expected_subsequence_1 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(expected_subsequence_1, 0,
[(12, 100, 0.01, 3.0),
(11, 55, 0.22, 0.50),
(40, 45, 2.50, 3.0)])
testing_lib.add_chords_to_sequence(expected_subsequence_1,
[('C', 1.0), ('G7', 2.0)])
expected_subsequence_1.total_time = 3.0
expected_subsequence_1.subsequence_info.end_time_offset = 5.0
expected_subsequence_2 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(expected_subsequence_2, 0,
[(55, 120, 1.0, 1.01),
(52, 99, 1.75, 2.0)])
testing_lib.add_chords_to_sequence(expected_subsequence_2,
[('G7', 0.0), ('F', 1.0)])
expected_subsequence_2.total_time = 2.0
expected_subsequence_2.subsequence_info.start_time_offset = 3.0
expected_subsequence_2.subsequence_info.end_time_offset = 3.0
expected_subsequence_3 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_chords_to_sequence(expected_subsequence_3,
[('F', 0.0)])
expected_subsequence_3.total_time = 0.0
expected_subsequence_3.subsequence_info.start_time_offset = 6.0
expected_subsequence_3.subsequence_info.end_time_offset = 2.0
subsequences = sequences_lib.split_note_sequence(sequence,
hop_size_seconds=3.0)
self.assertEquals(3, len(subsequences))
self.assertProtoEquals(expected_subsequence_1, subsequences[0])
self.assertProtoEquals(expected_subsequence_2, subsequences[1])
self.assertProtoEquals(expected_subsequence_3, subsequences[2])
def _to_tensors(self, note_sequence):
# Performance sequences require sustain to be correctly interpreted.
note_sequence = sequences_lib.apply_sustain_control_changes(note_sequence)
if self._chord_encoding and not any(
ta.annotation_type == CHORD_SYMBOL
for ta in note_sequence.text_annotations):
try:
# Quantize just for the purpose of chord inference.
# TODO(iansimon): Allow chord inference in unquantized sequences.
quantized_sequence = mm.quantize_note_sequence(
note_sequence, self._steps_per_quarter)
if (mm.steps_per_bar_in_quantized_sequence(quantized_sequence) !=
self._steps_per_bar):
return data.ConverterTensors()
# Infer chords in quantized sequence.
mm.infer_chords_for_sequence(quantized_sequence)
except (mm.BadTimeSignatureError, mm.NonIntegerStepsPerBarError,
mm.NegativeTimeError, mm.ChordInferenceError):
return data.ConverterTensors()
# Copy inferred chords back to original sequence.
for qta in quantized_sequence.text_annotations:
if qta.annotation_type == CHORD_SYMBOL:
ta = note_sequence.text_annotations.add()
ta.annotation_type = CHORD_SYMBOL
ta.time = qta.time
ta.text = qta.text
if note_sequence.tempos:
quarters_per_minute = note_sequence.tempos[0].qpm
else:
quarters_per_minute = mm.DEFAULT_QUARTERS_PER_MINUTE
quarters_per_bar = self._steps_per_bar / self._steps_per_quarter
hop_size_quarters = quarters_per_bar * self._hop_size_bars
hop_size_seconds = 60.0 * hop_size_quarters / quarters_per_minute
# Split note sequence by bar hop size (in seconds).
subsequences = sequences_lib.split_note_sequence(
note_sequence, hop_size_seconds)
if self._first_subsequence_only and len(subsequences) > 1:
return data.ConverterTensors()
sequence_tensors = []
sequence_chord_tensors = []
for subsequence in subsequences:
# Quantize this subsequence.
try:
quantized_subsequence = mm.quantize_note_sequence(
subsequence, self._steps_per_quarter)
if (mm.steps_per_bar_in_quantized_sequence(quantized_subsequence) !=
self._steps_per_bar):
return data.ConverterTensors()
except (mm.BadTimeSignatureError, mm.NonIntegerStepsPerBarError,
mm.NegativeTimeError):
return data.ConverterTensors()
# Convert the quantized subsequence to tensors.
tensors, chord_tensors = self._quantized_subsequence_to_tensors(
quantized_subsequence)
if tensors:
sequence_tensors.append(tensors)
if self._chord_encoding:
sequence_chord_tensors.append(chord_tensors)
return data.ConverterTensors(
inputs=sequence_tensors, outputs=sequence_tensors,
controls=sequence_chord_tensors)
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)
random.seed(int(m.hexdigest(), 16))
# Deserialize NoteSequence proto.
ns = music_pb2.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 ==
music_pb2.NoteSequence.TextAnnotation.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._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 process_midi(self, f):
def augment_note_sequence(ns, stretch_factor, transpose_amount):
"""Augment a NoteSequence by time stretch and pitch transposition."""
augmented_ns = sequences_lib.stretch_note_sequence(ns,
stretch_factor,
in_place=False)
try:
_, num_deleted_notes = sequences_lib.transpose_note_sequence(
augmented_ns,
transpose_amount,
min_allowed_pitch=MIN_PITCH,
max_allowed_pitch=MAX_PITCH,
in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError(
'Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError(
'Transposition caused out-of-range pitch(es).')
return augmented_ns
self._min_hop_size_seconds = 0.0
self._max_hop_size_seconds = 0.0
self._num_replications = 1
self._encode_performance_fn = self.performance_encoder(
).encode_note_sequence
self._encode_score_fns = dict(
(name, encoder.encode_note_sequence)
for name, encoder in self.score_encoders())
augment_params = itertools.product(self.stretch_factors,
self.transpose_amounts)
augment_fns = [
functools.partial(augment_note_sequence,
stretch_factor=s,
transpose_amount=t) for s, t in augment_params
]
self._augment_fns = augment_fns
self._absolute_timing = self.absolute_timing
self._random_crop_length = self.random_crop_length_in_datagen
if self._random_crop_length is not None:
self._augment_fns = self._augment_fns
rets = []
ns = magenta.music.midi_file_to_sequence_proto(f)
# 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):
print("sequence_too_short")
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 ==
music_pb2.NoteSequence.TextAnnotation.BEAT)
and ta.time <= performance_sequence.total_time
]
if len(beats) < 2:
print('not_enough_beats')
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:
print("chord_inference_failed")
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:
print('melody_inference_failed')
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[:]
for augment_fn in self._augment_fns:
# Augment and encode the performance.
try:
augmented_performance_sequence = augment_fn(
performance_sequence)
except DataAugmentationError as e:
print("augment_performance_failed", e)
continue
example_dict = {
'targets':
self._encode_performance_fn(augmented_performance_sequence)
}
if not example_dict['targets']:
print('skipped_empty_targets')
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:
print('augment_score_failed')
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]:
print('skipped_empty_%s' % name)
skip = True
break
if skip:
continue
rets.append(example_dict)
return rets
def testSplitNoteSequenceWithHopSize(self):
# Tests splitting a NoteSequence at regular hop size, truncating notes.
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(
sequence, 0,
[(12, 100, 0.01, 8.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50),
(55, 120, 4.0, 4.01), (52, 99, 4.75, 5.0)])
testing_lib.add_chords_to_sequence(
sequence, [('C', 1.0), ('G7', 2.0), ('F', 4.0)])
expected_subsequence_1 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(
expected_subsequence_1, 0,
[(12, 100, 0.01, 3.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.0)])
testing_lib.add_chords_to_sequence(
expected_subsequence_1, [('C', 1.0), ('G7', 2.0)])
expected_subsequence_1.total_time = 3.0
expected_subsequence_1.subsequence_info.end_time_offset = 5.0
expected_subsequence_2 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_track_to_sequence(
expected_subsequence_2, 0,
[(55, 120, 1.0, 1.01), (52, 99, 1.75, 2.0)])
testing_lib.add_chords_to_sequence(
expected_subsequence_2, [('G7', 0.0), ('F', 1.0)])
expected_subsequence_2.total_time = 2.0
expected_subsequence_2.subsequence_info.start_time_offset = 3.0
expected_subsequence_2.subsequence_info.end_time_offset = 3.0
expected_subsequence_3 = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
testing_lib.add_chords_to_sequence(
expected_subsequence_3, [('F', 0.0)])
expected_subsequence_3.total_time = 0.0
expected_subsequence_3.subsequence_info.start_time_offset = 6.0
expected_subsequence_3.subsequence_info.end_time_offset = 2.0
subsequences = sequences_lib.split_note_sequence(
sequence, hop_size_seconds=3.0)
self.assertEquals(3, len(subsequences))
self.assertProtoEquals(expected_subsequence_1, subsequences[0])
self.assertProtoEquals(expected_subsequence_2, subsequences[1])
self.assertProtoEquals(expected_subsequence_3, subsequences[2])
