def testFromQuantizedNoteSequenceWithQuantizedVelocity(self):
testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(60, 100, 0.0, 4.0),
(64, 100, 0.0, 3.0),
(67, 127, 1.0, 2.0)])
quantized_sequence = sequences_lib.quantize_note_sequence_absolute(
self.note_sequence, steps_per_second=100)
performance = list(
performance_lib.Performance(quantized_sequence,
num_velocity_bins=16))
pe = performance_lib.PerformanceEvent
expected_performance = [
pe(pe.VELOCITY, 13),
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.VELOCITY, 16),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 67),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 60),
]
self.assertEqual(expected_performance, performance)
def testSetLengthAddSteps(self):
performance = performance_lib.Performance(steps_per_second=100)
performance.set_length(50)
self.assertEqual(50, performance.num_steps)
pe = performance_lib.PerformanceEvent
perf_events = [pe(pe.TIME_SHIFT, 50)]
self.assertEqual(perf_events, list(performance))
performance.set_length(150)
self.assertEqual(150, performance.num_steps)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.TIME_SHIFT, 100),
pe(pe.TIME_SHIFT, 50),
]
self.assertEqual(perf_events, list(performance))
performance.set_length(200)
self.assertEqual(200, performance.num_steps)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.TIME_SHIFT, 100),
pe(pe.TIME_SHIFT, 100),
]
self.assertEqual(perf_events, list(performance))
def testToSequenceWithRepeatedNotes(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_ON, 60),
pe(pe.TIME_SHIFT, 100),
]
for event in perf_events:
performance.append(event)
performance_ns = performance.to_sequence()
testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(60, 100, 0.0, 2.0),
(64, 100, 0.0, 2.0),
(60, 100, 1.0, 2.0)])
# Make comparison easier by sorting.
performance_ns.notes.sort(key=lambda n: (n.start_time, n.pitch))
self.note_sequence.notes.sort(key=lambda n: (n.start_time, n.pitch))
self.assertEqual(self.note_sequence, performance_ns)
def testPerformanceNoteDensitySequence(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 50),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_OFF, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 67),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 64)
]
for event in perf_events:
performance.append(event)
expected_density_sequence = [
4.0, 4.0, 4.0, 4.0, 2.0, 2.0, 2.0, 4.0, 4.0, 4.0, 0.0]
density_sequence = performance_lib.performance_note_density_sequence(
performance, window_size_seconds=1.0)
self.assertEqual(expected_density_sequence, density_sequence)
def testToSequence(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(60, 100, 0.0, 4.0), (64, 100, 0.0, 3.0), (67, 100, 1.0, 2.0)])
quantized_sequence = sequences_lib.quantize_note_sequence_absolute(
self.note_sequence, steps_per_second=100)
performance = performance_lib.Performance(quantized_sequence)
performance_ns = performance.to_sequence()
# Make comparison easier by sorting.
performance_ns.notes.sort(key=lambda n: (n.start_time, n.pitch))
self.note_sequence.notes.sort(key=lambda n: (n.start_time, n.pitch))
self.assertEqual(self.note_sequence, performance_ns)
def testNumSteps(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_OFF, 64),
]
for event in perf_events:
performance.append(event)
self.assertEqual(100, performance.num_steps)
def testPerformancePitchHistogramSequence(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 50),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_OFF, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 67),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 64)
]
for event in perf_events:
performance.append(event)
expected_histogram_sequence = [
[0.25, 0, 0, 0, 0.375, 0, 0, 0.375, 0, 0, 0, 0],
[0.25, 0, 0, 0, 0.375, 0, 0, 0.375, 0, 0, 0, 0],
[0.25, 0, 0, 0, 0.375, 0, 0, 0.375, 0, 0, 0, 0],
[0.25, 0, 0, 0, 0.375, 0, 0, 0.375, 0, 0, 0, 0],
[0.0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0, 0],
[0.0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0, 0],
[0.0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0, 0],
[0.0, 0, 0, 0, 1.0, 0, 0, 0.0, 0, 0, 0, 0],
[0.0, 0, 0, 0, 1.0, 0, 0, 0.0, 0, 0, 0, 0],
[0.0, 0, 0, 0, 1.0, 0, 0, 0.0, 0, 0, 0, 0],
[1.0 / 12.0] * 12
]
histogram_sequence = performance_lib.performance_pitch_histogram_sequence(
performance, window_size_seconds=1.0, prior_count=0)
self.assertEqual(expected_histogram_sequence, histogram_sequence)
def testSetLengthRemoveSteps(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 64),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 67),
]
for event in perf_events:
performance.append(event)
performance.set_length(200)
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 64),
pe(pe.NOTE_ON, 67),
]
self.assertEqual(perf_events, list(performance))
performance.set_length(50)
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.TIME_SHIFT, 50),
]
self.assertEqual(perf_events, list(performance))
def _generate(self, input_sequence, generator_options):
if len(generator_options.input_sections) > 1:
raise mm.SequenceGeneratorException(
'This model supports at most one input_sections message, but got %s' %
len(generator_options.input_sections))
if len(generator_options.generate_sections) != 1:
raise mm.SequenceGeneratorException(
'This model supports only 1 generate_sections message, but got %s' %
len(generator_options.generate_sections))
generate_section = generator_options.generate_sections[0]
if generator_options.input_sections:
input_section = generator_options.input_sections[0]
primer_sequence = mm.trim_note_sequence(
input_sequence, input_section.start_time, input_section.end_time)
input_start_step = mm.quantize_to_step(
input_section.start_time, self.steps_per_second, quantize_cutoff=0.0)
else:
primer_sequence = input_sequence
input_start_step = 0
last_end_time = (max(n.end_time for n in primer_sequence.notes)
if primer_sequence.notes else 0)
if last_end_time > generate_section.start_time:
raise mm.SequenceGeneratorException(
'Got GenerateSection request for section that is before or equal to '
'the end of the NoteSequence. This model can only extend sequences. '
'Requested start time: %s, Final note end time: %s' %
(generate_section.start_time, last_end_time))
# Quantize the priming sequence.
quantized_primer_sequence = mm.quantize_note_sequence_absolute(
primer_sequence, self.steps_per_second)
extracted_perfs, _ = performance_lib.extract_performances(
quantized_primer_sequence, start_step=input_start_step,
num_velocity_bins=self.num_velocity_bins)
assert len(extracted_perfs) <= 1
generate_start_step = mm.quantize_to_step(
generate_section.start_time, self.steps_per_second, quantize_cutoff=0.0)
# Note that when quantizing end_step, we set quantize_cutoff to 1.0 so it
# always rounds down. This avoids generating a sequence that ends at 5.0
# seconds when the requested end time is 4.99.
generate_end_step = mm.quantize_to_step(
generate_section.end_time, self.steps_per_second, quantize_cutoff=1.0)
if extracted_perfs and extracted_perfs[0]:
performance = extracted_perfs[0]
else:
# If no track could be extracted, create an empty track that starts at the
# requested generate_start_step.
performance = performance_lib.Performance(
steps_per_second=(
quantized_primer_sequence.quantization_info.steps_per_second),
start_step=generate_start_step,
num_velocity_bins=self.num_velocity_bins)
# Ensure that the track extends up to the step we want to start generating.
performance.set_length(generate_start_step - performance.start_step)
# Extract generation arguments from generator options.
arg_types = {
'temperature': lambda arg: arg.float_value,
'beam_size': lambda arg: arg.int_value,
'branch_factor': lambda arg: arg.int_value,
'steps_per_iteration': lambda arg: arg.int_value
}
args = dict((name, value_fn(generator_options.args[name]))
for name, value_fn in arg_types.items()
if name in generator_options.args)
total_steps = performance.num_steps + (
generate_end_step - generate_start_step)
if not performance:
# Primer is empty; let's just start with silence.
performance.set_length(min(performance_lib.MAX_SHIFT_STEPS, total_steps))
while performance.num_steps < total_steps:
# Assume there's around 10 notes per second and 4 RNN steps per note.
# Can't know for sure until generation is finished because the number of
# notes per quantized step is variable.
steps_to_gen = total_steps - performance.num_steps
rnn_steps_to_gen = 40 * int(math.ceil(
float(steps_to_gen) / performance_lib.DEFAULT_STEPS_PER_SECOND))
tf.logging.info(
'Need to generate %d more steps for this sequence, will try asking '
'for %d RNN steps' % (steps_to_gen, rnn_steps_to_gen))
performance = self._model.generate_performance(
len(performance) + rnn_steps_to_gen, performance, **args)
if not self.fill_generate_section:
# In the interest of speed just go through this loop once, which may not
# entirely fill the generate section.
break
performance.set_length(total_steps)
generated_sequence = performance.to_sequence(
max_note_duration=self.max_note_duration)
assert (generated_sequence.total_time - generate_section.end_time) <= 1e-5
return generated_sequence
def _generate(self, input_sequence, generator_options):
if len(generator_options.input_sections) > 1:
raise mm.SequenceGeneratorException(
'This model supports at most one input_sections message, but got %s'
% len(generator_options.input_sections))
if len(generator_options.generate_sections) != 1:
raise mm.SequenceGeneratorException(
'This model supports only 1 generate_sections message, but got %s'
% len(generator_options.generate_sections))
generate_section = generator_options.generate_sections[0]
if generator_options.input_sections:
input_section = generator_options.input_sections[0]
primer_sequence = mm.trim_note_sequence(input_sequence,
input_section.start_time,
input_section.end_time)
input_start_step = mm.quantize_to_step(input_section.start_time,
self.steps_per_second,
quantize_cutoff=0.0)
else:
primer_sequence = input_sequence
input_start_step = 0
last_end_time = (max(
n.end_time
for n in primer_sequence.notes) if primer_sequence.notes else 0)
if last_end_time > generate_section.start_time:
raise mm.SequenceGeneratorException(
'Got GenerateSection request for section that is before or equal to '
'the end of the NoteSequence. This model can only extend sequences. '
'Requested start time: %s, Final note end time: %s' %
(generate_section.start_time, last_end_time))
# Quantize the priming sequence.
quantized_primer_sequence = mm.quantize_note_sequence_absolute(
primer_sequence, self.steps_per_second)
extracted_perfs, _ = performance_lib.extract_performances(
quantized_primer_sequence,
start_step=input_start_step,
num_velocity_bins=self.num_velocity_bins)
assert len(extracted_perfs) <= 1
generate_start_step = mm.quantize_to_step(generate_section.start_time,
self.steps_per_second,
quantize_cutoff=0.0)
# Note that when quantizing end_step, we set quantize_cutoff to 1.0 so it
# always rounds down. This avoids generating a sequence that ends at 5.0
# seconds when the requested end time is 4.99.
generate_end_step = mm.quantize_to_step(generate_section.end_time,
self.steps_per_second,
quantize_cutoff=1.0)
if extracted_perfs and extracted_perfs[0]:
performance = extracted_perfs[0]
else:
# If no track could be extracted, create an empty track that starts at the
# requested generate_start_step.
performance = performance_lib.Performance(
steps_per_second=(quantized_primer_sequence.quantization_info.
steps_per_second),
start_step=generate_start_step,
num_velocity_bins=self.num_velocity_bins)
# Ensure that the track extends up to the step we want to start generating.
performance.set_length(generate_start_step - performance.start_step)
# Extract generation arguments from generator options.
arg_types = {
'note_density': lambda arg: ast.literal_eval(arg.string_value),
'pitch_histogram': lambda arg: ast.literal_eval(arg.string_value),
'disable_conditioning':
lambda arg: ast.literal_eval(arg.string_value),
'temperature': lambda arg: arg.float_value,
'beam_size': lambda arg: arg.int_value,
'branch_factor': lambda arg: arg.int_value,
'steps_per_iteration': lambda arg: arg.int_value
}
args = dict((name, value_fn(generator_options.args[name]))
for name, value_fn in arg_types.items()
if name in generator_options.args)
# Make sure note density is present when conditioning on it and not present
# otherwise.
if not self.note_density_conditioning and 'note_density' in args:
tf.logging.warning(
'Not conditioning on note density, ignoring requested density.'
)
del args['note_density']
if self.note_density_conditioning and 'note_density' not in args:
tf.logging.warning(
'Conditioning on note density but none requested, using default.'
)
args['note_density'] = [DEFAULT_NOTE_DENSITY]
# Make sure pitch class histogram is present when conditioning on it and not
# present otherwise.
if not self.pitch_histogram_conditioning and 'pitch_histogram' in args:
tf.logging.warning(
'Not conditioning on pitch histogram, ignoring requested histogram.'
)
del args['pitch_histogram']
if self.pitch_histogram_conditioning and 'pitch_histogram' not in args:
tf.logging.warning(
'Conditioning on pitch histogram but none requested, using default.'
)
args['pitch_histogram'] = [DEFAULT_PITCH_HISTOGRAM]
# Make sure disable conditioning flag is present when conditioning is
# optional and not present otherwise.
if not self.optional_conditioning and 'disable_conditioning' in args:
tf.logging.warning(
'No optional conditioning, ignoring disable conditioning flag.'
)
del args['disable_conditioning']
if self.optional_conditioning and 'disable_conditioning' not in args:
args['disable_conditioning'] = [False]
# If a single note density, pitch class histogram, or disable flag is
# present, convert to list to simplify further processing.
if (self.note_density_conditioning
and not isinstance(args['note_density'], list)):
args['note_density'] = [args['note_density']]
if (self.pitch_histogram_conditioning
and not isinstance(args['pitch_histogram'][0], list)):
args['pitch_histogram'] = [args['pitch_histogram']]
if (self.optional_conditioning
and not isinstance(args['disable_conditioning'], list)):
args['disable_conditioning'] = [args['disable_conditioning']]
# Make sure each pitch class histogram sums to one.
if self.pitch_histogram_conditioning:
for i in range(len(args['pitch_histogram'])):
total = sum(args['pitch_histogram'][i])
if total > 0:
args['pitch_histogram'][i] = [
float(count) / total
for count in args['pitch_histogram'][i]
]
else:
tf.logging.warning(
'Pitch histogram is empty, using default.')
args['pitch_histogram'][i] = DEFAULT_PITCH_HISTOGRAM
total_steps = performance.num_steps + (generate_end_step -
generate_start_step)
# Set up functions that map generation step to note density, pitch
# histogram, and disable conditioning flag.
mean_note_density = DEFAULT_NOTE_DENSITY
if self.note_density_conditioning:
args['note_density_fn'] = partial(
_step_to_note_density,
num_steps=total_steps,
note_densities=args['note_density'])
mean_note_density = sum(args['note_density']) / len(
args['note_density'])
del args['note_density']
if self.pitch_histogram_conditioning:
args['pitch_histogram_fn'] = partial(
_step_to_pitch_histogram,
num_steps=total_steps,
pitch_histograms=args['pitch_histogram'])
del args['pitch_histogram']
if self.optional_conditioning:
args['disable_conditioning_fn'] = partial(
_step_to_disable_conditioning,
num_steps=total_steps,
disable_conditioning_flags=args['disable_conditioning'])
del args['disable_conditioning']
if not performance:
# Primer is empty; let's just start with silence.
performance.set_length(
min(performance_lib.MAX_SHIFT_STEPS, total_steps))
while performance.num_steps < total_steps:
# Assume the average specified (or default) note density and 4 RNN steps
# per note. Can't know for sure until generation is finished because the
# number of notes per quantized step is variable.
note_density = max(1.0, mean_note_density)
steps_to_gen = total_steps - performance.num_steps
rnn_steps_to_gen = int(
math.ceil(4.0 * note_density * steps_to_gen /
self.steps_per_second))
tf.logging.info(
'Need to generate %d more steps for this sequence, will try asking '
'for %d RNN steps' % (steps_to_gen, rnn_steps_to_gen))
performance = self._model.generate_performance(
len(performance) + rnn_steps_to_gen, performance, **args)
if not self.fill_generate_section:
# In the interest of speed just go through this loop once, which may not
# entirely fill the generate section.
break
performance.set_length(total_steps)
generated_sequence = performance.to_sequence(
max_note_duration=self.max_note_duration)
assert (generated_sequence.total_time -
generate_section.end_time) <= 1e-5
return generated_sequence
