def create_example(filename):
"""Processes an audio file into an Example proto."""
wav_data = tf.gfile.Open(filename).read()
example_list = list(
split_audio_and_label_data.process_record(
wav_data=wav_data,
ns=music_pb2.NoteSequence(),
example_id=filename,
min_length=0,
max_length=-1,
allow_empty_notesequence=True))
assert len(example_list) == 1
return example_list[0].SerializeToString()
def testStartCapture_Iterate_Period_Overrun(self):
start_time = 1.0
captor = self.midi_hub.start_capture(120,
start_time,
stop_signal=midi_hub.MidiSignal(
type='control_change',
control=1))
for msg in self.capture_messages[:-1]:
threading.Timer(0.1 * msg.time, self.port.callback,
args=[msg]).start()
period = 0.26
captured_seqs = []
wall_start_time = time.time()
for captured_seq in captor.iterate(period=period):
time.sleep(0.5)
captured_seqs.append(captured_seq)
self.assertEqual(2, len(captured_seqs))
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
end_time = captured_seqs[0].total_time
self.assertAlmostEqual(wall_start_time + period, end_time, delta=0.005)
expected_seq.total_time = end_time
testing_lib.add_track_to_sequence(expected_seq, 0,
[Note(1, 64, 2, end_time)])
self.assertProtoEquals(captured_seqs[0], expected_seq)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 6
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5),
Note(2, 64, 3, 4),
Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seqs[1], expected_seq)
def to_sequence(self,
velocity=100,
instrument=0,
sequence_start_time=0.0,
bpm=120.0):
"""Converts the Melody to Sequence proto.
Args:
velocity: Midi velocity to give each note. Between 1 and 127 (inclusive).
instrument: Midi instrument to give each note.
sequence_start_time: A time in seconds (float) that the first note in the
sequence will land on.
bpm: Beats per minute (float).
Returns:
A NoteSequence proto encoding the given melody.
"""
seconds_per_step = 60.0 / bpm * BEATS_PER_BAR / self.steps_per_bar
sequence = music_pb2.NoteSequence()
sequence.tempos.add().bpm = bpm
sequence.ticks_per_beat = STANDARD_PPQ
current_sequence_note = None
for step, note in enumerate(self):
if MIN_MIDI_PITCH <= note <= MAX_MIDI_PITCH:
# End any sustained notes.
if current_sequence_note is not None:
current_sequence_note.end_time = (step * seconds_per_step +
sequence_start_time)
# Add a note.
current_sequence_note = sequence.notes.add()
current_sequence_note.start_time = (step * seconds_per_step +
sequence_start_time)
# Give the note an end time now just to be sure it gets closed.
current_sequence_note.end_time = (
(step + 1) * seconds_per_step + sequence_start_time)
current_sequence_note.pitch = note
current_sequence_note.velocity = velocity
current_sequence_note.instrument = instrument
elif note == NOTE_OFF:
# End any sustained notes.
if current_sequence_note is not None:
current_sequence_note.end_time = (step * seconds_per_step +
sequence_start_time)
current_sequence_note = None
return sequence
def encode(self, s):
"""Transform a MusicXML filename into a list of score event index tuples.
Args:
s: Path to the MusicXML file.
Returns:
ids: List of score event index tuples.
"""
if s:
ns = magenta.music.musicxml_file_to_sequence_proto(s)
else:
ns = music_pb2.NoteSequence()
return self.encode_note_sequence(ns)
def encode(self, s):
"""Transform a MIDI filename into a list of performance event indices.
Args:
s: Path to the MIDI file.
Returns:
ids: List of performance event indices.
"""
if s:
ns = magenta.music.midi_file_to_sequence_proto(s)
else:
ns = music_pb2.NoteSequence()
return self.encode_note_sequence(ns)
def setUp(self):
sequence = music_pb2.NoteSequence()
sequence.tempos.add(qpm=60)
testing_lib.add_track_to_sequence(sequence, 0, [(32, 100, 2, 4),
(33, 100, 6, 11),
(34, 100, 11, 13),
(35, 100, 17, 18)])
testing_lib.add_track_to_sequence(sequence,
1, [(57, 80, 4, 4.1),
(58, 80, 12, 12.1)],
is_drum=True)
testing_lib.add_chords_to_sequence(sequence, [('N.C.', 0), ('C', 8),
('Am', 16)])
self.sequence = sequence
def testEmptySequenceToPrettyMidi_DropEventsAfterLastNote(self):
source_sequence = music_pb2.NoteSequence()
# Translate without dropping.
translated_midi = midi_io.sequence_proto_to_pretty_midi(
source_sequence)
self.assertEqual(1, len(translated_midi.instruments))
self.assertEqual(0, len(translated_midi.instruments[0].notes))
# Translate dropping anything after 30 seconds.
translated_midi = midi_io.sequence_proto_to_pretty_midi(
source_sequence, drop_events_n_seconds_after_last_note=30)
self.assertEqual(1, len(translated_midi.instruments))
self.assertEqual(0, len(translated_midi.instruments[0].notes))
def concatenate_sequences(sequences, sequence_durations=None):
"""Concatenate a series of NoteSequences together.
Individual sequences will be shifted using shift_sequence_times and then
merged together using the protobuf MergeFrom method. This means that any
global values (e.g., ticks_per_quarter) will be overwritten by each sequence
and only the final value will be used. After this, redundant data will be
removed with remove_redundant_data.
Args:
sequences: A list of sequences to concatenate.
sequence_durations: An optional list of sequence durations to use. If not
specified, the total_time value will be used. Specifying durations is
useful if the sequences to be concatenated are effectively longer than
their total_time (e.g., a sequence that ends with a rest).
Returns:
A new sequence that is the result of concatenating *sequences.
Raises:
ValueError: If the length of sequences and sequence_durations do not match
or if a specified duration is less than the total_time of the sequence.
"""
if sequence_durations and len(sequences) != len(sequence_durations):
raise ValueError(
'sequences and sequence_durations must be the same length.')
current_total_time = 0
cat_seq = music_pb2.NoteSequence()
for i in range(len(sequences)):
sequence = sequences[i]
if sequence_durations and sequence_durations[i] < sequence.total_time:
raise ValueError(
'Specified sequence duration ({}) must not be less than the '
'total_time of the sequence ({})'.format(
sequence_durations[i], sequence.total_time))
if current_total_time > 0:
cat_seq.MergeFrom(
shift_sequence_times(sequence, current_total_time))
else:
cat_seq.MergeFrom(sequence)
if sequence_durations:
current_total_time += sequence_durations[i]
else:
current_total_time = cat_seq.total_time
# Delete subsequence_info because we've joined several subsequences.
cat_seq.ClearField('subsequence_info')
return remove_redundant_data(cat_seq)
def testToNoteSequence(self):
converter = data.OneHotMelodyConverter(
steps_per_quarter=1, slice_bars=4, max_tensors_per_notesequence=1)
_, output_tensors = converter.to_tensors(
filter_instrument(self.sequence, 0))
sequences = converter.to_notesequences(output_tensors)
self.assertEqual(1, len(sequences))
expected_sequence = music_pb2.NoteSequence(ticks_per_quarter=220)
expected_sequence.tempos.add(qpm=120)
testing_lib.add_track_to_sequence(
expected_sequence, 0,
[(32, 80, 1.0, 2.0), (33, 80, 3.0, 5.5), (34, 80, 5.5, 6.5)])
self.assertProtoEquals(expected_sequence, sequences[0])
def create_example(filename):
"""Processes an audio file into an Example proto."""
wav_data = tf.gfile.Open(filename, 'rb').read()
example_list = list(
audio_label_data_utils.process_record(
wav_data=wav_data,
ns=music_pb2.NoteSequence(),
# decode to handle filenames with extended characters.
example_id=six.ensure_text(filename, 'utf-8'),
min_length=0,
max_length=-1,
allow_empty_notesequence=True))
assert len(example_list) == 1
return example_list[0].SerializeToString()
def testPerformanceRnnPipeline(self):
note_sequence = music_pb2.NoteSequence()
magenta.music.testing_lib.add_track_to_sequence(
note_sequence, 0, [(36, 100, 0.00, 2.0), (40, 55, 2.1, 5.0),
(44, 80, 3.6, 5.0), (41, 45, 5.1, 8.0),
(64, 100, 6.6, 10.0), (55, 120, 8.1, 11.0),
(39, 110, 9.6, 9.7), (53, 99, 11.1, 14.1),
(51, 40, 12.6, 13.0), (55, 100, 14.1, 15.0),
(54, 90, 15.6, 17.0), (60, 100, 17.1, 18.0)])
pipeline_inst = performance_rnn_pipeline.get_pipeline(
min_events=32, max_events=512, eval_ratio=0, config=self.config)
result = pipeline_inst.transform(note_sequence)
self.assertTrue(len(result['training_performances']))
def testAddChordsToSequence(self):
note_sequence = music_pb2.NoteSequence(ticks_per_quarter=220)
note_sequence.tempos.add(qpm=60.0)
testing_lib.add_chords_to_sequence(note_sequence,
[('N.C.', 0), ('C', 2), ('G7', 6)])
note_sequence.total_time = 8.0
expected_sequence = copy.deepcopy(note_sequence)
del note_sequence.text_annotations[:]
chords = [NO_CHORD, 'C', 'C', 'G7']
chord_times = [0.0, 2.0, 4.0, 6.0]
chords_lib.add_chords_to_sequence(note_sequence, chords, chord_times)
self.assertEqual(expected_sequence, note_sequence)
def run_with_flags(generator):
FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir)
primer_midi = None
if FLAGS.primer_midi:
primer_midi = os.path.expanduser(FLAGS.primer_midi)
if not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MakeDirs(FLAGS.output_dir)
primer_sequence = None
qpm = FLAGS.qpm if FLAGS.qpm else magenta.music.DEFAULT_QUARTERS_PER_MINUTE
if FLAGS.primer_melody:
primer_melody = magenta.music.Melody(ast.literal_eval(FLAGS.primer_melody))
primer_sequence = primer_melody.to_sequence(qpm=qpm)
elif primer_midi:
primer_sequence = magenta.music.midi_file_to_sequence_proto(primer_midi)
if primer_sequence.tempos and primer_sequence.tempos[0].qpm:
qpm = primer_sequence.tempos[0].qpm
else:
tf.logging.warning('No priming sequence specified.')
primer_melody = magenta.music.Melody([60])
primer_sequence = primer_melody.to_sequence(qpm=qpm)
seconds_per_step = 60.0 / qpm / generator.steps_per_quarter
total_seconds = FLAGS.num_steps * seconds_per_step
generator_options = generator_pb2.GeneratorOptions()
if primer_sequence:
input_sequence = primer_sequence
last_end_time = (max(n.end_time for n in primer_sequence.notes)if primer_sequence.notes else 0)
generate_section = generator_options.generate_sections.add(start_time=last_end_time + seconds_per_step,end_time=total_seconds)
if generate_section.start_time >= generate_section.end_time:
tf.logging.fatal('requested: Priming sequence length: %s, Generation length requested: %s',generate_section.start_time, total_seconds)
return
else:
input_sequence = music_pb2.NoteSequence()
input_sequence.tempos.add().qpm = qpm
generate_section = generator_options.generate_sections.add(start_time=0,end_time=total_seconds)
generator_options.args['temperature'].float_value = FLAGS.temperature
generator_options.args['beam_size'].int_value = FLAGS.beam_size
generator_options.args['branch_factor'].int_value = FLAGS.branch_factor
generator_options.args['steps_per_iteration'].int_value = FLAGS.steps_per_iteration
tf.logging.debug('input_sequence: %s', input_sequence)
tf.logging.debug('generator_options: %s', generator_options)
date_and_time = time.strftime('%Y-%m-%d_%H%M%S')
digits = len(str(FLAGS.num_outputs))
for i in range(FLAGS.num_outputs):
generated_sequence = generator.generate(input_sequence, generator_options)
midi_filename = '%s_%s.mid' % (date_and_time, str(i + 1).zfill(digits))
midi_path = os.path.join(FLAGS.output_dir, midi_filename)
magenta.music.sequence_proto_to_midi_file(generated_sequence, midi_path)
tf.logging.info('Wrote %d MIDI files to %s',FLAGS.num_outputs, FLAGS.output_dir)
def stretch_note_sequence(note_sequence, stretch_factor):
"""Apply a constant temporal stretch to a NoteSequence proto.
Args:
note_sequence: The NoteSequence to stretch.
stretch_factor: How much to stretch the NoteSequence. Values greater than
one increase the length of the NoteSequence (making it "slower"). Values
less than one decrease the length of the NoteSequence (making it
"faster").
Returns:
A stretched copy of the original NoteSequence.
Raises:
QuantizationStatusException: If the `note_sequence` is quantized. Only
unquantized NoteSequences can be stretched.
"""
if is_quantized_sequence(note_sequence):
raise QuantizationStatusException(
'Can only stretch unquantized NoteSequence.')
stretched_sequence = music_pb2.NoteSequence()
stretched_sequence.CopyFrom(note_sequence)
if stretch_factor == 1.0:
return stretched_sequence
# Stretch all notes.
for note in stretched_sequence.notes:
note.start_time *= stretch_factor
note.end_time *= stretch_factor
stretched_sequence.total_time *= stretch_factor
# Stretch all other event times.
events = itertools.chain(stretched_sequence.time_signatures,
stretched_sequence.key_signatures,
stretched_sequence.tempos,
stretched_sequence.pitch_bends,
stretched_sequence.control_changes,
stretched_sequence.text_annotations)
for event in events:
event.time *= stretch_factor
# Stretch tempos.
for tempo in stretched_sequence.tempos:
tempo.qpm /= stretch_factor
return stretched_sequence
def __init__(self, tune_lines):
self._ns = music_pb2.NoteSequence()
# Standard ABC fields.
self._ns.source_info.source_type = (
music_pb2.NoteSequence.SourceInfo.SCORE_BASED)
self._ns.source_info.encoding_type = (
music_pb2.NoteSequence.SourceInfo.ABC)
self._ns.source_info.parser = (
music_pb2.NoteSequence.SourceInfo.MAGENTA_ABC)
self._ns.ticks_per_quarter = constants.STANDARD_PPQ
self._current_time = 0
self._accidentals = ABCTune._sig_to_accidentals(0)
self._bar_accidentals = {}
self._current_unit_note_length = None
self._current_expected_repeats = None
# Default dynamic should be !mf! as per:
# http://abcnotation.com/wiki/abc:standard:v2.1#decorations
self._current_velocity = ABCTune.DECORATION_TO_VELOCITY['!mf!']
self._in_header = True
self._header_tempo_unit = None
self._header_tempo_rate = None
for line in tune_lines:
line = re.sub('%.*$', '', line) # Strip comments.
line = line.strip() # Strip whitespace.
if not line:
continue
# If the lines begins with a letter and a colon, it's an information
# field. Extract it.
info_field_match = ABCTune.INFORMATION_FIELD_PATTERN.match(line)
if info_field_match:
self._parse_information_field(info_field_match.group(1),
info_field_match.group(2))
else:
if self._in_header:
self._set_values_from_header()
self._in_header = False
self._parse_music_code(line)
if self._in_header:
self._set_values_from_header()
self._finalize()
if self._ns.notes:
self._ns.total_time = self._ns.notes[-1].end_time
def testCaptureSequence_StopSignal(self):
start_time = 1.0
threading.Timer(0.1, self.send_capture_messages).start()
captured_seq = self.midi_hub.capture_sequence(
120, start_time,
stop_signal=midi_hub.MidiSignal(type='control_change', control=1))
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = 6.0
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, 6)])
self.assertProtoEquals(captured_seq, expected_seq)
def testNonEmptySequenceWithNoNotesToPrettyMidi_DropEventsAfterLastNote(self):
source_sequence = music_pb2.NoteSequence()
source_sequence.tempos.add(time=0, qpm=120)
source_sequence.tempos.add(time=10, qpm=160)
source_sequence.tempos.add(time=40, qpm=240)
# Translate without dropping.
translated_midi = midi_io.sequence_proto_to_pretty_midi(
source_sequence)
self.CheckPrettyMidiAndSequence(translated_midi, source_sequence)
# Translate dropping anything after 30 seconds.
translated_midi = midi_io.sequence_proto_to_pretty_midi(
source_sequence, drop_events_n_seconds_after_last_note=30)
del source_sequence.tempos[-1]
self.CheckPrettyMidiAndSequence(translated_midi, source_sequence)
def testInferChordsForSequenceWithBeats(self):
sequence = music_pb2.NoteSequence()
testing_lib.add_track_to_sequence(
sequence, 0,
[(60, 100, 0.0, 1.1), (64, 100, 0.0, 1.1), (67, 100, 0.0, 1.1), # C
(62, 100, 1.1, 1.9), (65, 100, 1.1, 1.9), (69, 100, 1.1, 1.9), # Dm
(60, 100, 1.9, 3.0), (65, 100, 1.9, 3.0), (69, 100, 1.9, 3.0), # F
(59, 100, 3.0, 4.5), (62, 100, 3.0, 4.5), (67, 100, 3.0, 4.5)]) # G
testing_lib.add_beats_to_sequence(sequence, [0.0, 1.1, 1.9, 1.9, 3.0])
chord_inference.infer_chords_for_sequence(sequence)
expected_chords = [('C', 0.0), ('Dm', 1.1), ('F', 1.9), ('G', 3.0)]
chords = [(ta.text, ta.time) for ta in sequence.text_annotations
if ta.annotation_type == CHORD_SYMBOL]
self.assertEqual(expected_chords, chords)
def __init__(self, qpm, start_time=0, stop_time=None, stop_signal=None):
# A lock for synchronization.
self._lock = threading.RLock()
self._receive_queue = Queue.Queue()
self._captured_sequence = music_pb2.NoteSequence()
self._captured_sequence.tempos.add(qpm=qpm)
self._start_time = start_time
self._stop_time = stop_time
self._stop_regex = re.compile(str(stop_signal))
# A set of active MidiSignals being used by iterators.
self._iter_signals = []
# An event that is set when `stop` has been called.
self._stop_signal = threading.Event()
# Active callback threads keyed by unique thread name.
self._callbacks = {}
super(MidiCaptor, self).__init__()
def testSimpleSequenceToPrettyMidi_DefaultTicksAndTempo(self):
source_midi = pretty_midi.PrettyMIDI(self.midi_simple_filename)
stripped_sequence_proto = midi_io.midi_to_sequence_proto(source_midi)
del stripped_sequence_proto.tempos[:]
stripped_sequence_proto.ClearField('ticks_per_quarter')
expected_sequence_proto = music_pb2.NoteSequence()
expected_sequence_proto.CopyFrom(stripped_sequence_proto)
expected_sequence_proto.tempos.add(
qpm=constants.DEFAULT_QUARTERS_PER_MINUTE)
expected_sequence_proto.ticks_per_quarter = constants.STANDARD_PPQ
translated_midi = midi_io.sequence_proto_to_pretty_midi(
stripped_sequence_proto)
self.CheckPrettyMidiAndSequence(translated_midi, expected_sequence_proto)
def testNoteSequenceRecordWriterAndIterator(self):
sequences = []
for i in xrange(4):
sequence = music_pb2.NoteSequence()
sequence.id = str(i)
sequence.notes.add().pitch = i
sequences.append(sequence)
with tempfile.NamedTemporaryFile(prefix='NoteSequenceIoTest') as temp_file:
with note_sequence_io.NoteSequenceRecordWriter(temp_file.name) as writer:
for sequence in sequences:
writer.write(sequence)
for i, sequence in enumerate(
note_sequence_io.note_sequence_record_iterator(temp_file.name)):
self.assertEquals(sequence, sequences[i])
def run_conversion(encoder,
sequences_file,
train_output,
eval_output='',
eval_ratio=0.0):
"""Loop that converts NoteSequence protos to SequenceExample protos.
Args:
encoder: String name of encoder function from encoders.py to use.
sequences_file: String path pointing to TFRecord file of NoteSequence
protos.
train_output: String path to TFRecord file that training samples will be
saved to.
eval_output: If set, string path to TFRecord file that evaluation samples
will be saved to. Omit this argument to not produce an eval set.
eval_ratio: Fraction of input that will be saved to eval set. A random
partition is chosen, so the actual train/eval ratio will vary.
"""
encoder_func = getattr(encoders, encoder)
reader = tf.python_io.tf_record_iterator(sequences_file)
train_writer = tf.python_io.TFRecordWriter(train_output)
eval_writer = (tf.python_io.TFRecordWriter(eval_output)
if eval_output else None)
input_count = 0
train_output_count = 0
eval_output_count = 0
for buf in reader:
sequence_data = music_pb2.NoteSequence()
sequence_data.ParseFromString(buf)
extracted_melodies = melodies_lib.extract_melodies(sequence_data)
for melody in extracted_melodies:
sequence_example, _ = encoder_func(melody)
serialized = sequence_example.SerializeToString()
if eval_writer and random.random() < eval_ratio:
eval_writer.write(serialized)
eval_output_count += 1
else:
train_writer.write(serialized)
train_output_count += 1
input_count += 1
logging.info('Found %d sequences', input_count)
logging.info('Extracted %d melodies for training', train_output_count)
if eval_writer:
logging.info('Extracted %d melodies for evaluation', eval_output_count)
def performance_rnn(record_file):
start_time = 0.0
timestep = 0.5
end_time = 0.5
melody_sequence = music_pb2.NoteSequence()
with open(record_file, 'r') as record:
for line in record:
melody_sequence.notes.add(pitch=pitch_dict[line.strip()], \
start_time=start_time, end_time=end_time, velocity=80)
start_time += timestep
end_time += timestep
melody_sequence.total_time = end_time
melody_sequence.tempos.add(qpm=60)
input_sequence = melody_sequence
num_steps = 8192 # change this for shorter or longer sequences
temperature = 1.0 # the higher the temperature the more random the sequence.
bundle = mm.sequence_generator_bundle.read_bundle_file(
'/home/ubuntu/team15/bundle/performance_with_dynamics.mag')
generator_map = performance_sequence_generator.get_generator_map()
generator = generator_map['performance_with_dynamics'](checkpoint=None,
bundle=bundle)
generator.initialize()
# Derive the total number of seconds to generate.
seconds_per_step = 1.0 / generator.steps_per_second
generate_end_time = num_steps * seconds_per_step
# Specify start/stop time for generation based on starting generation at the
# end of the priming sequence and continuing until the sequence is num_steps
# long.
generator_options = generator_pb2.GeneratorOptions()
# Set the start time to begin when the last note ends.
generate_section = generator_options.generate_sections.add(
start_time=input_sequence.total_time, end_time=generate_end_time)
generator_options.args[
'temperature'].float_value = 1.0 # Higher is more random; 1.0 is default.
sequence = generator.generate(input_sequence, generator_options)
new_file_name = record_file.split('/')[-1].split(
'.')[0] + '_performance_rnn.mid'
mm.sequence_proto_to_midi_file(
sequence, '/home/ubuntu/team15/midi/performance_rnn/' + new_file_name)
return new_file_name
def setUp(self):
sequence = music_pb2.NoteSequence()
sequence.tempos.add(qpm=60)
testing_lib.add_track_to_sequence(sequence, 0, [(32, 100, 2, 4),
(33, 1, 6, 11),
(34, 1, 11, 13),
(35, 1, 17, 19)])
testing_lib.add_track_to_sequence(sequence, 1, [(35, 127, 2, 4),
(36, 50, 6, 8),
(71, 100, 33, 37),
(73, 100, 34, 37),
(33, 1, 50, 55),
(34, 1, 55, 56)])
self.sequence = sequence
# Subtract min pitch (21).
expected_unsliced_events = [
(NO_EVENT, NO_EVENT, 11, NO_EVENT, NOTE_OFF, NO_EVENT, 12,
NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT, 13, NO_EVENT, NOTE_OFF,
NO_EVENT, NO_EVENT),
(NO_EVENT, 14, NO_EVENT, NOTE_OFF),
(NO_EVENT, NO_EVENT, 14, NO_EVENT, NOTE_OFF, NO_EVENT, 15,
NO_EVENT),
(NO_EVENT, 50, 52, NO_EVENT, NO_EVENT, NOTE_OFF, NO_EVENT,
NO_EVENT),
(NO_EVENT, NO_EVENT, 12, NO_EVENT, NO_EVENT, NO_EVENT, NO_EVENT,
13),
]
self.expected_unsliced_labels = [
np.array(es) + 2 for es in expected_unsliced_events
]
expected_sliced_events = [(NO_EVENT, NO_EVENT, 11, NO_EVENT, NOTE_OFF,
NO_EVENT, 12, NO_EVENT),
(NO_EVENT, NO_EVENT, 12, NO_EVENT, NO_EVENT,
NO_EVENT, NO_EVENT, 13),
(NO_EVENT, NO_EVENT, NO_EVENT, 13, NO_EVENT,
NOTE_OFF, NO_EVENT, NO_EVENT),
(NO_EVENT, NO_EVENT, 14, NO_EVENT, NOTE_OFF,
NO_EVENT, 15, NO_EVENT),
(NO_EVENT, 50, 52, NO_EVENT, NO_EVENT,
NOTE_OFF, NO_EVENT, NO_EVENT)]
self.expected_sliced_labels = [
np.array(es) + 2 for es in expected_sliced_events
]
self.converter_class = data.OneHotMelodyConverter
def testCaptureSequence_StopTime(self):
start_time = 1.0
stop_time = time.time() + 1.0
self.capture_messages[-1].time += time.time()
threading.Timer(0.1, self.send_capture_messages).start()
captured_seq = self.midi_hub.capture_sequence(
120, start_time, stop_time=stop_time)
expected_seq = music_pb2.NoteSequence()
expected_seq.tempos.add(qpm=120)
expected_seq.total_time = stop_time
testing_lib.add_track_to_sequence(
expected_seq, 0,
[Note(1, 64, 2, 5), Note(2, 64, 3, 4), Note(3, 64, 4, stop_time)])
self.assertProtoEquals(captured_seq, expected_seq)
def write_to_file(file):
fp = open("pitch_mond2.txt", "w+")
ft = open("tempo_mond2.txt", "w+")
fp.write('pitch,start_time,end_time,time_diff,note_len\n')
# fp = open("velocity_mond2.txt","w+")
# fp.write('pitch,start_time,end_time,velocity\n')
sequence = music_pb2.NoteSequence()
for i, sequence in enumerate(
note_sequence_io.note_sequence_record_iterator(file)):
for tempo in sequence.tempos:
ft.write('time:{}\n'.format(tempo.time))
ft.write('qpm:{}\n'.format(tempo.qpm))
for note in sequence.notes:
fp.write('{},{},{},{},{}/{}\n'.format(
note.pitch, note.start_time, note.end_time,
note.end_time - note.start_time, note.numerator,
note.denominator))
def testToNoteSequence(self):
converter = data.DrumsConverter(steps_per_quarter=1,
slice_bars=2,
max_tensors_per_notesequence=1)
tensors = converter.to_tensors(filter_instrument(self.sequence, 1))
sequences = converter.to_notesequences(tensors.outputs)
self.assertEqual(1, len(sequences))
expected_sequence = music_pb2.NoteSequence(ticks_per_quarter=220)
expected_sequence.tempos.add(qpm=120)
testing_lib.add_track_to_sequence(expected_sequence,
9, [(38, 80, 0.5, 1.0),
(48, 80, 2.0, 2.5),
(49, 80, 2.0, 2.5),
(51, 80, 3.5, 4.0)],
is_drum=True)
self.assertProtoEquals(expected_sequence, sequences[0])
def testSequenceToPianoroll(self):
sequence = music_pb2.NoteSequence(total_time=1.21)
testing_lib.add_track_to_sequence(sequence, 0, [(1, 100, 0.11, 1.01),
(2, 55, 0.22, 0.50),
(3, 100, 0.3, 0.8),
(2, 45, 1.0, 1.21)])
expected_pianoroll = [[0, 0], [1, 0], [1, 1], [1, 1], [1, 1], [1, 0],
[1, 0], [1, 0], [1, 0], [1, 0], [1, 1], [0, 1],
[0, 1]]
output, _, _, _ = data.sequence_to_pianoroll(sequence,
frames_per_second=10,
min_pitch=1,
max_pitch=2)
np.testing.assert_allclose(expected_pianoroll, output)
def inference(filename):
# 오디오 파일(.wav) 읽기
wav_file = open(filename, mode='rb')
wav_data = wav_file.read()
wav_file.close()
print('User uploaded file "{name}" with length {length} bytes'.format(name=filename, length=len(wav_data)))
# 청크로 분할 후 protobufs 포맷으로 데이터 생성
to_process = []
example_list = list(
audio_label_data_utils.process_record(wav_data=wav_data, ns=music_pb2.NoteSequence(),
example_id=filename, min_length=0, max_length=-1, allow_empty_notesequence=True))
# Serialize
to_process.append(example_list[0].SerializeToString())
# 세션 실행
sess.run(iterator.initializer, {examples: to_process})
# 예측
prediction_list = list(estimator.predict(input_fn, yield_single_examples=False))
assert len(prediction_list) == 1
# 예측 결과 데이터 가져오기
frame_predictions = prediction_list[0]['frame_predictions'][0]
onset_predictions = prediction_list[0]['onset_predictions'][0]
velocity_values = prediction_list[0]['velocity_values'][0]
# 예측 결과 데이터를 이용해서 미디 시퀀스 생성
sequence_prediction = sequences_lib.pianoroll_to_note_sequence(
frame_predictions,
frames_per_second=data.hparams_frames_per_second(hparams),
min_duration_ms=0,
min_midi_pitch=constants.MIN_MIDI_PITCH,
onset_predictions=onset_predictions,
velocity_values=velocity_values)
basename = os.path.split(os.path.splitext(filename)[0])[1] + '.mid'
output_filename = os.path.join(env.MIDI_DIRECTORY, basename)
# 미디 시퀀스를 파일로 내보내기
midi_filename = (output_filename)
midi_io.sequence_proto_to_midi_file(sequence_prediction, midi_filename)
return basename
def testInferChordsForSequence(self):
sequence = music_pb2.NoteSequence()
testing_lib.add_track_to_sequence(
sequence, 0,
[(60, 100, 0.0, 1.0), (64, 100, 0.0, 1.0), (67, 100, 0.0, 1.0), # C
(62, 100, 1.0, 2.0), (65, 100, 1.0, 2.0), (69, 100, 1.0, 2.0), # Dm
(60, 100, 2.0, 3.0), (65, 100, 2.0, 3.0), (69, 100, 2.0, 3.0), # F
(59, 100, 3.0, 4.0), (62, 100, 3.0, 4.0), (67, 100, 3.0, 4.0)]) # G
quantized_sequence = sequences_lib.quantize_note_sequence(
sequence, steps_per_quarter=4)
chord_inference.infer_chords_for_sequence(
quantized_sequence, chords_per_bar=2)
expected_chords = [('C', 0.0), ('Dm', 1.0), ('F', 2.0), ('G', 3.0)]
chords = [(ta.text, ta.time) for ta in quantized_sequence.text_annotations]
self.assertEqual(expected_chords, chords)
