(self,
               model,
               details,
               steps_per_second=note_seq.DEFAULT_STEPS_PER_SECOND,
               num_velocity_bins=0,
               control_signals=None,
               optional_conditioning=False,
               max_note_duration=MAX_NOTE_DURATION_SECONDS,
               fill_generate_section=True,
               checkpoint=None,
               bundle=None,
               note_performance=False)
Creates a PerformanceRnnSequenceGenerator.

    Args:
      model: Instance of PerformanceRnnModel.
      details: A generator_pb2.GeneratorDetails for this generator.
      steps_per_second: Number of quantized steps per second.
      num_velocity_bins: Number of quantized velocity bins. If 0, don't use
          velocity.
      control_signals: A list of PerformanceControlSignal objects.
      optional_conditioning: If True, conditioning can be disabled dynamically.
      max_note_duration: The maximum note duration in seconds to allow during
          generation. This model often forgets to release notes; specifying a
          maximum duration can force it to do so.
      fill_generate_section: If True, the model will generate RNN steps until
          the entire generate section has been filled. If False, the model will
          estimate the number of RNN steps needed and then generate that many
          events, even if the generate section isn't completely filled.
      checkpoint: Where to search for the most recent model checkpoint. Mutually
          exclusive with `bundle`.
      bundle: A GeneratorBundle object that includes both the model checkpoint
          and metagraph. Mutually exclusive with `checkpoint`.
      note_performance: If true, a NotePerformance object will be used
          for the primer.
Creates a PerformanceRnnSequenceGenerator.
Creates a PerformanceRnnSequenceGenerator .
def __init__(self,
               model,
               details,
               steps_per_second=note_seq.DEFAULT_STEPS_PER_SECOND,
               num_velocity_bins=0,
               control_signals=None,
               optional_conditioning=False,
               max_note_duration=MAX_NOTE_DURATION_SECONDS,
               fill_generate_section=True,
               checkpoint=None,
               bundle=None,
               note_performance=False):
    """Creates a PerformanceRnnSequenceGenerator.

    Args:
      model: Instance of PerformanceRnnModel.
      details: A generator_pb2.GeneratorDetails for this generator.
      steps_per_second: Number of quantized steps per second.
      num_velocity_bins: Number of quantized velocity bins. If 0, don't use
          velocity.
      control_signals: A list of PerformanceControlSignal objects.
      optional_conditioning: If True, conditioning can be disabled dynamically.
      max_note_duration: The maximum note duration in seconds to allow during
          generation. This model often forgets to release notes; specifying a
          maximum duration can force it to do so.
      fill_generate_section: If True, the model will generate RNN steps until
          the entire generate section has been filled. If False, the model will
          estimate the number of RNN steps needed and then generate that many
          events, even if the generate section isn't completely filled.
      checkpoint: Where to search for the most recent model checkpoint. Mutually
          exclusive with `bundle`.
      bundle: A GeneratorBundle object that includes both the model checkpoint
          and metagraph. Mutually exclusive with `checkpoint`.
      note_performance: If true, a NotePerformance object will be used
          for the primer.
    """
    super(PerformanceRnnSequenceGenerator, self).__init__(
        model, details, checkpoint, bundle)
    self.steps_per_second = steps_per_second
    self.num_velocity_bins = num_velocity_bins
    self.control_signals = control_signals
    self.optional_conditioning = optional_conditioning
    self.max_note_duration = max_note_duration
    self.fill_generate_section = fill_generate_section
    self._note_performance = note_performance
def __init__ ( self , model , details , steps_per_second = note_seq . DEFAULT_STEPS_PER_SECOND , num_velocity_bins = 0 , control_signals = None , optional_conditioning = False , max_note_duration = MAX_NOTE_DURATION_SECONDS , fill_generate_section = True , checkpoint = None , bundle = None , note_performance = False ) : super ( PerformanceRnnSequenceGenerator , self ) . __init__ ( model , details , checkpoint , bundle ) self . steps_per_second = steps_per_second self . num_velocity_bins = num_velocity_bins self . control_signals = control_signals self . optional_conditioning = optional_conditioning self . max_note_duration = max_note_duration self . fill_generate_section = fill_generate_section self . _note_performance = note_performance