def music_vae_interpolate(sequence1, sequence2, model_id, model_config, num):
music_vae = TrainedModel(configs.CONFIG_MAP[model_config],
batch_size=4,
checkpoint_dir_or_path=model_id + '.tar')
note_sequences = music_vae.interpolate(sequence1,
sequence2,
num_steps=num,
length=32)
# Concatenate them into one long sequence, with the start and
# end sequences at each end.
return mm.sequences_lib.concatenate_sequences(note_sequences)
for ns in generated_sequences:
# print(ns)
#mm.plot_sequence(ns)
#mm.play_sequence(ns, synth=mm.fluidsynth)
# We're going to interpolate between the Twinkle Twinkle Little Star
# NoteSequence we defined in the first section, and one of the generated
# sequences from the previous VAE example
# How many sequences, including the start and end ones, to generate.
num_steps = 8
# This gives us a list of sequences.
note_sequences = music_vae.interpolate(twinkle_twinkle,
teapot,
num_steps=num_steps,
length=32)
# Concatenate them into one long sequence, with the start and
# end sequences at each end.
interp_seq = mm.sequences_lib.concatenate_sequences(note_sequences)
#mm.play_sequence(interp_seq, synth=mm.fluidsynth)
#mm.plot_sequence(interp_seq)
mm.sequence_proto_to_midi_file(interp_seq, 'interp_seq.mid')
files.download('interp_seq.mid')
# pylint: enable=g-import-not-at-top
# Allow pretty_midi to read MIDI files with absurdly high tick rates.
# Useful for reading the MAPS dataset.
babyshark = mm.midi_file_to_note_sequence('./mid/babyshark.mid')
babyshark = mm.extract_subsequence(babyshark, 0, 8)
babyshark.ticks_per_quarter = 0
babyshark.time_signatures.pop()
babyshark.key_signatures.pop()
babyshark.tempos.pop()
babyshark.tempos.add(qpm=60)
for note in babyshark.notes:
if note.pitch < 60:
note.pitch = 60
note.instrument = 0
note.is_drum = False
# This gives us a list of sequences.
note_sequences = music_vae.interpolate(twinkle_twinkle,
babyshark,
num_steps=num_steps,
length=8)
# Concatenate them into one long sequence, with the start and
# end sequences at each end.
interp_seq = mm.sequences_lib.concatenate_sequences(note_sequences)
mm.play_sequence(interp_seq, synth=mm.fluidsynth)
mm.plot_sequence(interp_seq)
mm.sequence_proto_to_midi_file(interp_seq, 'twinkle_shark.mid')
class MusicVae:
"""
Author: Tanish and Akshit
Last Modified: 02/02/21
Version: 1.2
Class to wrap the music vae trained from magenta
"""
def __init__(self):
"""
Loads and initializes the vae
"""
print("Initializing Music VAE...")
self.music_vae = TrainedModel(
configs.CONFIG_MAP['cat-mel_2bar_big'],
batch_size=4,
checkpoint_dir_or_path='/content/mel_2bar_big.ckpt')
print('🎉 Done!')
def generate(self, n=2, length=80, temperature=1.0):
"""
Generates a random music sequence
Args:
n: number of samples to generate
type: int
length: length of each sample
type: int
temperature: emphirical magnitude of randomness in generated sequences
type: float
Returns:
List[NotesSequence] of generated music
"""
generated_sequences = music_vae.sample(n=2, length=80, temperature=1.0)
for ns in generated_sequences:
note_seq.play_sequence(ns, synth=note_seq.fluidsynth)
return generated_sequences
def interpolate(self, sequence_one, sequence_two, num_steps=8):
"""
Continues a music sequence
Args:
sequence_one: first sequence
type: NoteSequence object
sequence_two: second sequence
type: NoteSequence object
num_steps: number of sequences to interpolate through
type: int
Returns:
NotesSequence object of interpolated music
"""
# This gives us a list of sequences.
note_sequences = self.music_vae.interpolate(twinkle_twinkle,
teapot,
num_steps=num_steps,
length=32)
# Concatenate them into one long sequence, with the start and
# end sequences at each end.
interp_seq = note_seq.sequences_lib.concatenate_sequences(
note_sequences)
note_seq.play_sequence(interp_seq, synth=note_seq.fluidsynth)
return interp_seq
