def extract_performances(
quantized_sequence, start_step=0, min_events_discard=None,
max_events_truncate=None, max_steps_truncate=None, num_velocity_bins=0,
split_instruments=False, note_performance=False):
"""Extracts one or more performances from the given quantized NoteSequence.
Args:
quantized_sequence: A quantized NoteSequence.
start_step: Start extracting a sequence at this time step.
min_events_discard: Minimum length of tracks in events. Shorter tracks are
discarded.
max_events_truncate: Maximum length of tracks in events. Longer tracks are
truncated.
max_steps_truncate: Maximum length of tracks in quantized time steps. Longer
tracks are truncated.
num_velocity_bins: Number of velocity bins to use. If 0, velocity events
will not be included at all.
split_instruments: If True, will extract a performance for each instrument.
Otherwise, will extract a single performance.
note_performance: If True, will create a NotePerformance object. If
False, will create either a MetricPerformance or Performance based on
how the sequence was quantized.
Returns:
performances: A python list of Performance or MetricPerformance (if
`quantized_sequence` is quantized relative to meter) instances.
stats: A dictionary mapping string names to `statistics.Statistic` objects.
"""
sequences_lib.assert_is_quantized_sequence(quantized_sequence)
stats = dict([(stat_name, statistics.Counter(stat_name)) for stat_name in
['performances_discarded_too_short',
'performances_truncated', 'performances_truncated_timewise',
'performances_discarded_more_than_1_program',
'performance_discarded_too_many_time_shift_steps',
'performance_discarded_too_many_duration_steps']])
if sequences_lib.is_absolute_quantized_sequence(quantized_sequence):
steps_per_second = quantized_sequence.quantization_info.steps_per_second
# Create a histogram measuring lengths in seconds.
stats['performance_lengths_in_seconds'] = statistics.Histogram(
'performance_lengths_in_seconds',
[5, 10, 20, 30, 40, 60, 120])
else:
steps_per_bar = sequences_lib.steps_per_bar_in_quantized_sequence(
quantized_sequence)
# Create a histogram measuring lengths in bars.
stats['performance_lengths_in_bars'] = statistics.Histogram(
'performance_lengths_in_bars',
[1, 10, 20, 30, 40, 50, 100, 200, 500])
if split_instruments:
instruments = set(note.instrument for note in quantized_sequence.notes)
else:
instruments = set([None])
# Allow only 1 program.
programs = set()
for note in quantized_sequence.notes:
programs.add(note.program)
if len(programs) > 1:
stats['performances_discarded_more_than_1_program'].increment()
return [], stats.values()
performances = []
for instrument in instruments:
# Translate the quantized sequence into a Performance.
if note_performance:
try:
performance = NotePerformance(
quantized_sequence, start_step=start_step,
num_velocity_bins=num_velocity_bins, instrument=instrument)
except NotePerformanceTooManyTimeShiftSteps:
stats['performance_discarded_too_many_time_shift_steps'].increment()
continue
except NotePerformanceTooManyDurationSteps:
stats['performance_discarded_too_many_duration_steps'].increment()
continue
elif sequences_lib.is_absolute_quantized_sequence(quantized_sequence):
performance = Performance(quantized_sequence, start_step=start_step,
num_velocity_bins=num_velocity_bins,
instrument=instrument)
else:
performance = MetricPerformance(quantized_sequence, start_step=start_step,
num_velocity_bins=num_velocity_bins,
instrument=instrument)
if (max_steps_truncate is not None and
performance.num_steps > max_steps_truncate):
performance.set_length(max_steps_truncate)
stats['performances_truncated_timewise'].increment()
if (max_events_truncate is not None and
len(performance) > max_events_truncate):
performance.truncate(max_events_truncate)
stats['performances_truncated'].increment()
if min_events_discard is not None and len(performance) < min_events_discard:
stats['performances_discarded_too_short'].increment()
else:
performances.append(performance)
if sequences_lib.is_absolute_quantized_sequence(quantized_sequence):
stats['performance_lengths_in_seconds'].increment(
performance.num_steps // steps_per_second)
else:
stats['performance_lengths_in_bars'].increment(
performance.num_steps // steps_per_bar)
return performances, stats.values()
def extract_performances(quantized_sequence,
start_step=0,
min_events_discard=None,
max_events_truncate=None,
num_velocity_bins=0):
"""Extracts a performance from the given quantized NoteSequence.
Currently, this extracts only one performance from a given track.
Args:
quantized_sequence: A quantized NoteSequence.
start_step: Start extracting a sequence at this time step.
min_events_discard: Minimum length of tracks in events. Shorter tracks are
discarded.
max_events_truncate: Maximum length of tracks in events. Longer tracks are
truncated.
num_velocity_bins: Number of velocity bins to use. If 0, velocity events
will not be included at all.
Returns:
performances: A python list of Performance instances.
stats: A dictionary mapping string names to `statistics.Statistic` objects.
"""
sequences_lib.assert_is_quantized_sequence(quantized_sequence)
stats = dict([(stat_name, statistics.Counter(stat_name)) for stat_name in [
'performances_discarded_too_short', 'performances_truncated',
'performances_discarded_more_than_1_program'
]])
if sequences_lib.is_absolute_quantized_sequence(quantized_sequence):
steps_per_second = quantized_sequence.quantization_info.steps_per_second
# Create a histogram measuring lengths in seconds.
stats['performance_lengths_in_seconds'] = statistics.Histogram(
'performance_lengths_in_seconds', [5, 10, 20, 30, 40, 60, 120])
else:
steps_per_bar = sequences_lib.steps_per_bar_in_quantized_sequence(
quantized_sequence)
# Create a histogram measuring lengths in bars.
stats['performance_lengths_in_bars'] = statistics.Histogram(
'performance_lengths_in_bars',
[1, 10, 20, 30, 40, 50, 100, 200, 500])
# Allow only 1 program.
programs = set()
for note in quantized_sequence.notes:
programs.add(note.program)
if len(programs) > 1:
stats['performances_discarded_more_than_1_program'].increment()
return [], stats.values()
performances = []
# Translate the quantized sequence into a Performance.
if sequences_lib.is_absolute_quantized_sequence(quantized_sequence):
performance = Performance(quantized_sequence,
start_step=start_step,
num_velocity_bins=num_velocity_bins)
else:
performance = MetricPerformance(quantized_sequence,
start_step=start_step,
num_velocity_bins=num_velocity_bins)
if (max_events_truncate is not None
and len(performance) > max_events_truncate):
performance.truncate(max_events_truncate)
stats['performances_truncated'].increment()
if min_events_discard is not None and len(
performance) < min_events_discard:
stats['performances_discarded_too_short'].increment()
else:
performances.append(performance)
if sequences_lib.is_absolute_quantized_sequence(quantized_sequence):
stats['performance_lengths_in_seconds'].increment(
performance.num_steps // steps_per_second)
else:
stats['performance_lengths_in_bars'].increment(
performance.num_steps // steps_per_bar)
return performances, stats.values()