def _generate(self, input_sequence, generator_options):
if len(generator_options.input_sections) > 1:
raise sequence_generator.SequenceGeneratorError(
'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 sequence_generator.SequenceGeneratorError(
'This model supports only 1 generate_sections message, but got %s'
% len(generator_options.generate_sections))
if input_sequence and input_sequence.tempos:
qpm = input_sequence.tempos[0].qpm
else:
qpm = note_seq.DEFAULT_QUARTERS_PER_MINUTE
steps_per_second = note_seq.steps_per_quarter_to_steps_per_second(
self.steps_per_quarter, qpm)
generate_section = generator_options.generate_sections[0]
if generator_options.input_sections:
input_section = generator_options.input_sections[0]
primer_sequence = note_seq.trim_note_sequence(
input_sequence, input_section.start_time,
input_section.end_time)
input_start_step = note_seq.quantize_to_step(
input_section.start_time,
steps_per_second,
quantize_cutoff=0.0)
else:
primer_sequence = input_sequence
input_start_step = 0
if primer_sequence.notes:
last_end_time = max(n.end_time for n in primer_sequence.notes)
else:
last_end_time = 0
if last_end_time > generate_section.start_time:
raise sequence_generator.SequenceGeneratorError(
'Got GenerateSection request for section that is before 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_sequence = note_seq.quantize_note_sequence(
primer_sequence, self.steps_per_quarter)
# Setting gap_bars to infinite ensures that the entire input will be used.
extracted_drum_tracks, _ = drum_pipelines.extract_drum_tracks(
quantized_sequence,
search_start_step=input_start_step,
min_bars=0,
gap_bars=float('inf'),
ignore_is_drum=True)
assert len(extracted_drum_tracks) <= 1
start_step = note_seq.quantize_to_step(generate_section.start_time,
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.
end_step = note_seq.quantize_to_step(generate_section.end_time,
steps_per_second,
quantize_cutoff=1.0)
if extracted_drum_tracks and extracted_drum_tracks[0]:
drums = extracted_drum_tracks[0]
else:
# If no drum track could be extracted, create an empty drum track that
# starts 1 step before the request start_step. This will result in 1 step
# of silence when the drum track is extended below.
steps_per_bar = int(
note_seq.steps_per_bar_in_quantized_sequence(
quantized_sequence))
drums = note_seq.DrumTrack(
[],
start_step=max(0, start_step - 1),
steps_per_bar=steps_per_bar,
steps_per_quarter=self.steps_per_quarter)
# Ensure that the drum track extends up to the step we want to start
# generating.
drums.set_length(start_step - drums.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)
generated_drums = self._model.generate_drum_track(
end_step - drums.start_step, drums, **args)
generated_sequence = generated_drums.to_sequence(qpm=qpm)
assert (generated_sequence.total_time -
generate_section.end_time) <= 1e-5
return generated_sequence
def run_with_flags(generator):
"""Generates drum tracks and saves them as MIDI files.
Uses the options specified by the flags defined in this module.
Args:
generator: The DrumsRnnSequenceGenerator to use for generation.
"""
if not FLAGS.output_dir:
tf.logging.fatal('--output_dir required')
return
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 note_seq.DEFAULT_QUARTERS_PER_MINUTE
if FLAGS.primer_drums:
primer_drums = note_seq.DrumTrack([
frozenset(pitches)
for pitches in ast.literal_eval(FLAGS.primer_drums)
])
primer_sequence = primer_drums.to_sequence(qpm=qpm)
elif primer_midi:
primer_sequence = note_seq.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. Defaulting to a single bass drum hit.'
)
primer_drums = note_seq.DrumTrack([frozenset([36])])
primer_sequence = primer_drums.to_sequence(qpm=qpm)
# Derive the total number of seconds to generate based on the QPM of the
# priming sequence and the num_steps flag.
seconds_per_step = 60.0 / qpm / generator.steps_per_quarter
total_seconds = FLAGS.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()
if primer_sequence:
input_sequence = primer_sequence
# Set the start time to begin on the next step after the last note ends.
if primer_sequence.notes:
last_end_time = max(n.end_time for n in primer_sequence.notes)
else:
last_end_time = 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(
'Priming sequence is longer than the total number of steps '
'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)
# Make the generate request num_outputs times and save the output as midi
# files.
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)
note_seq.sequence_proto_to_midi_file(generated_sequence, midi_path)
tf.logging.info('Wrote %d MIDI files to %s', FLAGS.num_outputs,
FLAGS.output_dir)