chapter_04_example_03.py

"""
This example shows how to sample a trio (drums, melody, bass) sequence
using MusicVAE and various configurations.

VERSION: Magenta 1.1.7
"""

import os
from typing import List

import tensorflow as tf
from magenta.models.music_vae import TrainedModel, configs
from magenta.music import DEFAULT_STEPS_PER_BAR
from magenta.protobuf.music_pb2 import NoteSequence
from six.moves import urllib
from visual_midi import Coloring

from note_sequence_utils import save_midi, save_plot


def download_checkpoint(model_name: str,
                        checkpoint_name: str,
                        target_dir: str):
  """
  Downloads a Magenta checkpoint to target directory.

  Target directory target_dir will be created if it does not already exist.

      :param model_name: magenta model name to download
      :param checkpoint_name: magenta checkpoint name to download
      :param target_dir: local directory in which to write the checkpoint
  """
  tf.gfile.MakeDirs(target_dir)
  checkpoint_target = os.path.join(target_dir, checkpoint_name)
  if not os.path.exists(checkpoint_target):
    response = urllib.request.urlopen(
      f"https://storage.googleapis.com/magentadata/models/"
      f"{model_name}/checkpoints/{checkpoint_name}")
    data = response.read()
    local_file = open(checkpoint_target, 'wb')
    local_file.write(data)
    local_file.close()


def get_model(name: str):
  """
  Returns the model instance from its name.

      :param name: the model name
  """
  checkpoint = name + ".tar"
  download_checkpoint("music_vae", checkpoint, "checkpoints")
  return TrainedModel(
    # Removes the .lohl in some training checkpoint which shares the same config
    configs.CONFIG_MAP[name.split(".")[0] if "." in name else name],
    # The batch size changes the number of sequences to be processed together
    batch_size=8,
    checkpoint_dir_or_path=os.path.join("checkpoints", checkpoint))


def sample(model_name: str,
           num_steps_per_sample: int) -> List[NoteSequence]:
  """
  Samples 2 sequences using the given model.
  """
  model = get_model(model_name)

  # Uses the model to sample 2 sequences,
  # with the number of steps and default temperature
  sample_sequences = model.sample(n=2, length=num_steps_per_sample)

  # Saves the midi and the plot in the sample folder
  save_midi(sample_sequences, "sample", model_name)
  save_plot(sample_sequences, "sample", model_name,
            plot_max_length_bar=16, coloring=Coloring.INSTRUMENT)

  return sample_sequences


def app(unused_argv):
  # Number of bar per sample, also giving the size of the interpolation splits
  num_bar_per_sample = 16

  # Number of steps per sample and interpolation splits
  num_steps_per_sample = num_bar_per_sample * DEFAULT_STEPS_PER_BAR

  # Samples 2 new sequences
  generated_sample_sequences = sample("hierdec-trio_16bar",
                                      num_steps_per_sample)

  print(f"Generated sample sequence total time: "
        f"{generated_sample_sequences[0].total_time}")

  return 0


if __name__ == "__main__":
  tf.app.run(app)