def render_token_sequence(token_sequence, use_program=True, use_drums=True):
note_sequence = token_sequence_to_note_sequence(token_sequence,
use_program=use_program,
use_drums=use_drums)
synth = note_seq.midi_synth.fluidsynth
note_seq.plot_sequence(note_sequence)
note_seq.play_sequence(note_sequence, synth)
decode_length = 1024
# Generate sample events.
sample_ids = next(unconditional_samples)['outputs']
# Decode to NoteSequence.
midi_filename = decode(
sample_ids,
encoder=unconditional_encoders['targets'])
unconditional_ns = note_seq.midi_file_to_note_sequence(midi_filename)
# Play and plot.
note_seq.play_sequence(
unconditional_ns,
synth=note_seq.fluidsynth, sample_rate=SAMPLE_RATE, sf2_path=SF2_PATH)
note_seq.plot_sequence(unconditional_ns)
#@title Download Performance as MIDI
#@markdown Download generated performance as MIDI (optional).
note_seq.sequence_proto_to_midi_file(
unconditional_ns, '/tmp/unconditional.mid')
files.download('/tmp/unconditional.mid')
#@title Choose Priming Sequence
#@markdown Here you can choose a priming sequence to be continued
#@markdown by the model. We have provided a few, or you can
#@markdown upload your own MIDI file.
#@markdown
#@markdown Set `max_primer_seconds` below to trim the primer to a
#@markdown fixed number of seconds (this will have no effect if
#@title Load Model
#@markdown The `ismir2021` model transcribes piano only, with note velocities.
#@markdown The `mt3` model transcribes multiple simultaneous instruments,
#@markdown but without velocities.
MODEL = "mt3" #@param["ismir2021", "mt3"]
checkpoint_path = f'/content/checkpoints/{MODEL}/'
inference_model = InferenceModel(checkpoint_path, MODEL)
#@title Upload Audio
audio = upload_audio(sample_rate=SAMPLE_RATE)
note_seq.notebook_utils.colab_play(audio, sample_rate=SAMPLE_RATE)
#@title Transcribe Audio
#@markdown This may take a few minutes depending on the length of the WAV file
#@markdown you uploaded.
est_ns = inference_model(audio)
note_seq.play_sequence(est_ns, synth=note_seq.fluidsynth,
sample_rate=SAMPLE_RATE, sf2_path=SF2_PATH)
note_seq.plot_sequence(est_ns)
#@title Download MIDI Transcription
note_seq.sequence_proto_to_midi_file(est_ns, '/tmp/transcribed.mid')
files.download('/tmp/transcribed.mid')
twinkle_twinkle.notes.add(pitch=69, start_time=2.0, end_time=2.5, velocity=80) twinkle_twinkle.notes.add(pitch=69, start_time=2.5, end_time=3.0, velocity=80) twinkle_twinkle.notes.add(pitch=67, start_time=3.0, end_time=4.0, velocity=80) twinkle_twinkle.notes.add(pitch=65, start_time=4.0, end_time=4.5, velocity=80) twinkle_twinkle.notes.add(pitch=65, start_time=4.5, end_time=5.0, velocity=80) twinkle_twinkle.notes.add(pitch=64, start_time=5.0, end_time=5.5, velocity=80) twinkle_twinkle.notes.add(pitch=64, start_time=5.5, end_time=6.0, velocity=80) twinkle_twinkle.notes.add(pitch=62, start_time=6.0, end_time=6.5, velocity=80) twinkle_twinkle.notes.add(pitch=62, start_time=6.5, end_time=7.0, velocity=80) twinkle_twinkle.notes.add(pitch=60, start_time=7.0, end_time=8.0, velocity=80) twinkle_twinkle.total_time = 8 twinkle_twinkle.tempos.add(qpm=60) # This is a colab utility method that visualizes a NoteSequence. note_seq.plot_sequence(twinkle_twinkle) # This is a colab utility method that plays a NoteSequence. # note_seq.play_sequence(twinkle_twinkle,synth=note_seq.fluidsynth) # Here's another NoteSequence! teapot = music_pb2.NoteSequence() teapot.notes.add(pitch=69, start_time=0, end_time=0.5, velocity=80) teapot.notes.add(pitch=71, start_time=0.5, end_time=1, velocity=80) teapot.notes.add(pitch=73, start_time=1, end_time=1.5, velocity=80) teapot.notes.add(pitch=74, start_time=1.5, end_time=2, velocity=80) teapot.notes.add(pitch=76, start_time=2, end_time=2.5, velocity=80) teapot.notes.add(pitch=81, start_time=3, end_time=4, velocity=80) teapot.notes.add(pitch=78, start_time=4, end_time=5, velocity=80) teapot.notes.add(pitch=81, start_time=5, end_time=6, velocity=80) teapot.notes.add(pitch=76, start_time=6, end_time=8, velocity=80)
def create_test_sequences():
"""
Author: Akshit and Jenny
Last Modified: 01/21/21
Version: 1.0
Creating test music sequences
"""
twinkle_twinkle = music_pb2.NoteSequence()
# Add the notes to the sequence.
twinkle_twinkle.notes.add(pitch=60,
start_time=0.0,
end_time=0.5,
velocity=80)
twinkle_twinkle.notes.add(pitch=60,
start_time=0.5,
end_time=1.0,
velocity=80)
twinkle_twinkle.notes.add(pitch=67,
start_time=1.0,
end_time=1.5,
velocity=80)
twinkle_twinkle.notes.add(pitch=67,
start_time=1.5,
end_time=2.0,
velocity=80)
twinkle_twinkle.notes.add(pitch=69,
start_time=2.0,
end_time=2.5,
velocity=80)
twinkle_twinkle.notes.add(pitch=69,
start_time=2.5,
end_time=3.0,
velocity=80)
twinkle_twinkle.notes.add(pitch=67,
start_time=3.0,
end_time=4.0,
velocity=80)
twinkle_twinkle.notes.add(pitch=65,
start_time=4.0,
end_time=4.5,
velocity=80)
twinkle_twinkle.notes.add(pitch=65,
start_time=4.5,
end_time=5.0,
velocity=80)
twinkle_twinkle.notes.add(pitch=64,
start_time=5.0,
end_time=5.5,
velocity=80)
twinkle_twinkle.notes.add(pitch=64,
start_time=5.5,
end_time=6.0,
velocity=80)
twinkle_twinkle.notes.add(pitch=62,
start_time=6.0,
end_time=6.5,
velocity=80)
twinkle_twinkle.notes.add(pitch=62,
start_time=6.5,
end_time=7.0,
velocity=80)
twinkle_twinkle.notes.add(pitch=60,
start_time=7.0,
end_time=8.0,
velocity=80)
twinkle_twinkle.total_time = 8
twinkle_twinkle.tempos.add(qpm=60)
note_seq.plot_sequence(twinkle_twinkle)
note_seq.play_sequence(twinkle_twinkle, synth=note_seq.fluidsynth)
teapot = music_pb2.NoteSequence()
teapot.notes.add(pitch=69, start_time=0, end_time=0.5, velocity=80)
teapot.notes.add(pitch=71, start_time=0.5, end_time=1, velocity=80)
teapot.notes.add(pitch=73, start_time=1, end_time=1.5, velocity=80)
teapot.notes.add(pitch=74, start_time=1.5, end_time=2, velocity=80)
teapot.notes.add(pitch=76, start_time=2, end_time=2.5, velocity=80)
teapot.notes.add(pitch=81, start_time=3, end_time=4, velocity=80)
teapot.notes.add(pitch=78, start_time=4, end_time=5, velocity=80)
teapot.notes.add(pitch=81, start_time=5, end_time=6, velocity=80)
teapot.notes.add(pitch=76, start_time=6, end_time=8, velocity=80)
teapot.total_time = 8
teapot.tempos.add(qpm=60)
note_seq.plot_sequence(teapot)
note_seq.play_sequence(teapot, synth=note_seq.synthesize)
return twinkle_twinkle, teapot
def generate_midi(midi_input):
# Create input generator.
def input_generator():
global inputs
while True:
yield {
'inputs': np.array([[inputs]], dtype=np.int32),
'targets': np.zeros([1, 0], dtype=np.int32),
'decode_length': np.array(decode_length, dtype=np.int32)
}
# Start the Estimator, loading from the specified checkpoint.
input_fn = decoding.make_input_fn_from_generator(input_generator())
melody_conditioned_samples = estimator.predict(
input_fn, checkpoint_path=ckpt_path)
# "Burn" one.
_ = next(melody_conditioned_samples)
#@title Choose Melody
#@markdown Here you can choose a melody to be accompanied by the
#@markdown model. We have provided a few, or you can upload a
#@markdown MIDI file; if your MIDI file is polyphonic, the notes
#@markdown with highest pitch will be used as the melody.
# Tokens to insert between melody events.
# @title Generate from Scratch
# @markdown Generate a piano performance from scratch.
# @markdown
# @markdown This can take a minute or so depending on the length
# @markdown of the performance the model ends up generating.
# @markdown Because we use a
# @markdown [representation](http://g.co/magenta/performance-rnn)
# @markdown where each event corresponds to a variable amount of
# @markdown time, the actual number of seconds generated may vary.
event_padding = 2 * [note_seq.MELODY_NO_EVENT]
events = [event + 12 if event != note_seq.MELODY_NO_EVENT else event
for e in midi_input
for event in [e] + event_padding]
inputs = melody_conditioned_encoders['inputs'].encode(
' '.join(str(e) for e in events))
melody_ns = note_seq.Melody(events).to_sequence(qpm=150)
targets = []
decode_length = 4096
# decode_length = np.random.randint(len(inputs)*3,len(inputs)*5)
# print(((decode_length) - len(inputs))/len(inputs))
sample_ids = next(melody_conditioned_samples)['outputs']
# Decode to NoteSequence.
midi_filename = decode(
sample_ids,
encoder=melody_conditioned_encoders['targets'])
accompaniment_ns = note_seq.midi_file_to_note_sequence(midi_filename)
# Use one of the provided melodies.
events = [event + 12 if event != note_seq.MELODY_NO_EVENT else event
for e in midi_input
for event in [e] + event_padding]
inputs = melody_conditioned_encoders['inputs'].encode(
' '.join(str(e) for e in events))
melody_ns = note_seq.Melody(events).to_sequence(qpm=150)
# Play and plot the melody.
note_seq.play_sequence(
melody_ns,
synth=note_seq.fluidsynth, sample_rate=SAMPLE_RATE, sf2_path=SF2_PATH)
note_seq.plot_sequence(melody_ns)