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Questo tutorial mostra come generare note musicali utilizzando un semplice RNN. Addestrerai un modello utilizzando una raccolta di file MIDI per pianoforte dal dataset MAESTRO . Data una sequenza di note, il tuo modello imparerà a prevedere la nota successiva nella sequenza. È possibile generare sequenze di note più lunghe chiamando ripetutamente il modello.
Questo tutorial contiene il codice completo per analizzare e creare file MIDI. Puoi saperne di più sul funzionamento degli RNN visitando Generazione di testo con un RNN .
Impostare
Questo tutorial utilizza la libreria pretty_midi per creare e analizzare file MIDI e pyfluidsynth per generare la riproduzione audio in Colab.
sudo apt install -y fluidsynth
The following packages were automatically installed and are no longer required: linux-gcp-5.4-headers-5.4.0-1040 linux-gcp-5.4-headers-5.4.0-1043 linux-gcp-5.4-headers-5.4.0-1044 linux-gcp-5.4-headers-5.4.0-1049 linux-headers-5.4.0-1049-gcp linux-image-5.4.0-1049-gcp linux-modules-5.4.0-1049-gcp linux-modules-extra-5.4.0-1049-gcp Use 'sudo apt autoremove' to remove them. The following additional packages will be installed: fluid-soundfont-gm libasyncns0 libdouble-conversion1 libevdev2 libflac8 libfluidsynth1 libgudev-1.0-0 libinput-bin libinput10 libjack-jackd2-0 libmtdev1 libogg0 libpulse0 libqt5core5a libqt5dbus5 libqt5gui5 libqt5network5 libqt5svg5 libqt5widgets5 libqt5x11extras5 libsamplerate0 libsndfile1 libvorbis0a libvorbisenc2 libwacom-bin libwacom-common libwacom2 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-shape0 libxcb-util1 libxcb-xinerama0 libxcb-xkb1 libxkbcommon-x11-0 qsynth qt5-gtk-platformtheme qttranslations5-l10n Suggested packages: fluid-soundfont-gs timidity jackd2 pulseaudio qt5-image-formats-plugins qtwayland5 jackd The following NEW packages will be installed: fluid-soundfont-gm fluidsynth libasyncns0 libdouble-conversion1 libevdev2 libflac8 libfluidsynth1 libgudev-1.0-0 libinput-bin libinput10 libjack-jackd2-0 libmtdev1 libogg0 libpulse0 libqt5core5a libqt5dbus5 libqt5gui5 libqt5network5 libqt5svg5 libqt5widgets5 libqt5x11extras5 libsamplerate0 libsndfile1 libvorbis0a libvorbisenc2 libwacom-bin libwacom-common libwacom2 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-shape0 libxcb-util1 libxcb-xinerama0 libxcb-xkb1 libxkbcommon-x11-0 qsynth qt5-gtk-platformtheme qttranslations5-l10n 0 upgraded, 41 newly installed, 0 to remove and 120 not upgraded. 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pip install --upgrade pyfluidsynth
pip install pretty_midi
import collections
import datetime
import fluidsynth
import glob
import numpy as np
import pathlib
import pandas as pd
import pretty_midi
import seaborn as sns
import tensorflow as tf
from IPython import display
from matplotlib import pyplot as plt
from typing import Dict, List, Optional, Sequence, Tuple
seed = 42
tf.random.set_seed(seed)
np.random.seed(seed)
# Sampling rate for audio playback
_SAMPLING_RATE = 16000
Scarica il dataset di Maestro
data_dir = pathlib.Path('data/maestro-v2.0.0')
if not data_dir.exists():
tf.keras.utils.get_file(
'maestro-v2.0.0-midi.zip',
origin='https://storage.googleapis.com/magentadata/datasets/maestro/v2.0.0/maestro-v2.0.0-midi.zip',
extract=True,
cache_dir='.', cache_subdir='data',
)
Downloading data from https://storage.googleapis.com/magentadata/datasets/maestro/v2.0.0/maestro-v2.0.0-midi.zip 59244544/59243107 [==============================] - 3s 0us/step 59252736/59243107 [==============================] - 3s 0us/step
Il set di dati contiene circa 1.200 file MIDI.
filenames = glob.glob(str(data_dir/'**/*.mid*'))
print('Number of files:', len(filenames))
Number of files: 1282
Elabora un file MIDI
Per prima cosa, usa pretty_midi per analizzare un singolo file MIDI e controllare il formato delle note. Se desideri scaricare il file MIDI di seguito per riprodurlo sul tuo computer, puoi farlo in colab scrivendo files.download(sample_file) .
sample_file = filenames[1]
print(sample_file)
data/maestro-v2.0.0/2013/ORIG-MIDI_02_7_6_13_Group__MID--AUDIO_08_R1_2013_wav--3.midi
Genera un oggetto PrettyMIDI per il file MIDI di esempio.
pm = pretty_midi.PrettyMIDI(sample_file)
Riproduci il file di esempio. Il caricamento del widget di riproduzione potrebbe richiedere diversi secondi.
def display_audio(pm: pretty_midi.PrettyMIDI, seconds=30):
waveform = pm.fluidsynth(fs=_SAMPLING_RATE)
# Take a sample of the generated waveform to mitigate kernel resets
waveform_short = waveform[:seconds*_SAMPLING_RATE]
return display.Audio(waveform_short, rate=_SAMPLING_RATE)
display_audio(pm)
Fai qualche ispezione sul file MIDI. Che tipo di strumenti vengono utilizzati?
print('Number of instruments:', len(pm.instruments))
instrument = pm.instruments[0]
instrument_name = pretty_midi.program_to_instrument_name(instrument.program)
print('Instrument name:', instrument_name)
Number of instruments: 1 Instrument name: Acoustic Grand Piano
Estrai note
for i, note in enumerate(instrument.notes[:10]):
note_name = pretty_midi.note_number_to_name(note.pitch)
duration = note.end - note.start
print(f'{i}: pitch={note.pitch}, note_name={note_name},'
f' duration={duration:.4f}')
0: pitch=56, note_name=G#3, duration=0.0352 1: pitch=44, note_name=G#2, duration=0.0417 2: pitch=68, note_name=G#4, duration=0.0651 3: pitch=80, note_name=G#5, duration=0.1693 4: pitch=78, note_name=F#5, duration=0.1523 5: pitch=76, note_name=E5, duration=0.1120 6: pitch=75, note_name=D#5, duration=0.0612 7: pitch=49, note_name=C#3, duration=0.0378 8: pitch=85, note_name=C#6, duration=0.0352 9: pitch=37, note_name=C#2, duration=0.0417
Utilizzerai tre variabili per rappresentare una nota durante l'allenamento del modello: pitch , step e duration . L'altezza è la qualità percettiva del suono come numero di nota MIDI. Il step è il tempo trascorso dalla nota precedente o dall'inizio della traccia. La duration è la durata in secondi della riproduzione della nota ed è la differenza tra l'ora di fine e di inizio della nota.
Estrarre le note dal file MIDI di esempio.
def midi_to_notes(midi_file: str) -> pd.DataFrame:
pm = pretty_midi.PrettyMIDI(midi_file)
instrument = pm.instruments[0]
notes = collections.defaultdict(list)
# Sort the notes by start time
sorted_notes = sorted(instrument.notes, key=lambda note: note.start)
prev_start = sorted_notes[0].start
for note in sorted_notes:
start = note.start
end = note.end
notes['pitch'].append(note.pitch)
notes['start'].append(start)
notes['end'].append(end)
notes['step'].append(start - prev_start)
notes['duration'].append(end - start)
prev_start = start
return pd.DataFrame({name: np.array(value) for name, value in notes.items()})
raw_notes = midi_to_notes(sample_file)
raw_notes.head()
Potrebbe essere più facile interpretare i nomi delle note piuttosto che le altezze, quindi puoi usare la funzione seguente per convertire dai valori numerici di altezza ai nomi delle note. Il nome della nota mostra il tipo di nota, l'alterazione e il numero di ottava (es. C#4).
get_note_names = np.vectorize(pretty_midi.note_number_to_name)
sample_note_names = get_note_names(raw_notes['pitch'])
sample_note_names[:10]
array(['G#3', 'G#5', 'G#4', 'G#2', 'F#5', 'E5', 'D#5', 'C#3', 'C#6',
'C#5'], dtype='<U3')
Per visualizzare il brano musicale, tracciare l'altezza della nota, l'inizio e la fine per tutta la lunghezza della traccia (es. piano roll). Inizia con le prime 100 note
def plot_piano_roll(notes: pd.DataFrame, count: Optional[int] = None):
if count:
title = f'First {count} notes'
else:
title = f'Whole track'
count = len(notes['pitch'])
plt.figure(figsize=(20, 4))
plot_pitch = np.stack([notes['pitch'], notes['pitch']], axis=0)
plot_start_stop = np.stack([notes['start'], notes['end']], axis=0)
plt.plot(
plot_start_stop[:, :count], plot_pitch[:, :count], color="b", marker=".")
plt.xlabel('Time [s]')
plt.ylabel('Pitch')
_ = plt.title(title)
plot_piano_roll(raw_notes, count=100)
Traccia le note per l'intera traccia.
plot_piano_roll(raw_notes)
Controlla la distribuzione di ogni variabile nota.
def plot_distributions(notes: pd.DataFrame, drop_percentile=2.5):
plt.figure(figsize=[15, 5])
plt.subplot(1, 3, 1)
sns.histplot(notes, x="pitch", bins=20)
plt.subplot(1, 3, 2)
max_step = np.percentile(notes['step'], 100 - drop_percentile)
sns.histplot(notes, x="step", bins=np.linspace(0, max_step, 21))
plt.subplot(1, 3, 3)
max_duration = np.percentile(notes['duration'], 100 - drop_percentile)
sns.histplot(notes, x="duration", bins=np.linspace(0, max_duration, 21))
plot_distributions(raw_notes)
Crea un file MIDI
È possibile generare il proprio file MIDI da un elenco di note utilizzando la funzione seguente.
def notes_to_midi(
notes: pd.DataFrame,
out_file: str,
instrument_name: str,
velocity: int = 100, # note loudness
) -> pretty_midi.PrettyMIDI:
pm = pretty_midi.PrettyMIDI()
instrument = pretty_midi.Instrument(
program=pretty_midi.instrument_name_to_program(
instrument_name))
prev_start = 0
for i, note in notes.iterrows():
start = float(prev_start + note['step'])
end = float(start + note['duration'])
note = pretty_midi.Note(
velocity=velocity,
pitch=int(note['pitch']),
start=start,
end=end,
)
instrument.notes.append(note)
prev_start = start
pm.instruments.append(instrument)
pm.write(out_file)
return pm
example_file = 'example.midi'
example_pm = notes_to_midi(
raw_notes, out_file=example_file, instrument_name=instrument_name)
Riproduci il file MIDI generato e verifica se c'è qualche differenza.
display_audio(example_pm)
Come prima, puoi scrivere files.download(example_file) per scaricare e riprodurre questo file.
Crea il set di dati di addestramento
Crea il set di dati di allenamento estraendo le note dai file MIDI. Puoi iniziare utilizzando un numero limitato di file e sperimentarne altri in seguito. Questo potrebbe richiedere un paio di minuti.
num_files = 5
all_notes = []
for f in filenames[:num_files]:
notes = midi_to_notes(f)
all_notes.append(notes)
all_notes = pd.concat(all_notes)
n_notes = len(all_notes)
print('Number of notes parsed:', n_notes)
Number of notes parsed: 23163
Quindi, crea un tf.data.Dataset dalle note analizzate.
key_order = ['pitch', 'step', 'duration']
train_notes = np.stack([all_notes[key] for key in key_order], axis=1)
notes_ds = tf.data.Dataset.from_tensor_slices(train_notes)
notes_ds.element_spec
TensorSpec(shape=(3,), dtype=tf.float64, name=None)
Addestrerai il modello su lotti di sequenze di note. Ciascun esempio consisterà in una sequenza di note come funzioni di input e nella nota successiva come etichetta. In questo modo, il modello verrà addestrato a prevedere la nota successiva in una sequenza. Puoi trovare un diagramma che spiega questo processo (e maggiori dettagli) in Classificazione del testo con un RNN .
È possibile utilizzare la pratica funzione finestra con size seq_length per creare le caratteristiche e le etichette in questo formato.
def create_sequences(
dataset: tf.data.Dataset,
seq_length: int,
vocab_size = 128,
) -> tf.data.Dataset:
"""Returns TF Dataset of sequence and label examples."""
seq_length = seq_length+1
# Take 1 extra for the labels
windows = dataset.window(seq_length, shift=1, stride=1,
drop_remainder=True)
# `flat_map` flattens the" dataset of datasets" into a dataset of tensors
flatten = lambda x: x.batch(seq_length, drop_remainder=True)
sequences = windows.flat_map(flatten)
# Normalize note pitch
def scale_pitch(x):
x = x/[vocab_size,1.0,1.0]
return x
# Split the labels
def split_labels(sequences):
inputs = sequences[:-1]
labels_dense = sequences[-1]
labels = {key:labels_dense[i] for i,key in enumerate(key_order)}
return scale_pitch(inputs), labels
return sequences.map(split_labels, num_parallel_calls=tf.data.AUTOTUNE)
Impostare la lunghezza della sequenza per ogni esempio. Sperimenta con lunghezze diverse (ad es. 50, 100, 150) per vedere quale funziona meglio per i dati, oppure usa l' ottimizzazione degli iperparametri . La dimensione del vocabolario ( vocab_size ) è impostata su 128 che rappresenta tutte le altezze supportate da pretty_midi .
seq_length = 25
vocab_size = 128
seq_ds = create_sequences(notes_ds, seq_length, vocab_size)
seq_ds.element_spec
(TensorSpec(shape=(25, 3), dtype=tf.float64, name=None),
{'pitch': TensorSpec(shape=(), dtype=tf.float64, name=None),
'step': TensorSpec(shape=(), dtype=tf.float64, name=None),
'duration': TensorSpec(shape=(), dtype=tf.float64, name=None)})
La forma del set di dati è (100,1) , il che significa che il modello prenderà 100 note come input e imparerà a prevedere la nota seguente come output.
for seq, target in seq_ds.take(1):
print('sequence shape:', seq.shape)
print('sequence elements (first 10):', seq[0: 10])
print()
print('target:', target)
sequence shape: (25, 3)
sequence elements (first 10): tf.Tensor(
[[0.578125 0. 0.1484375 ]
[0.390625 0.00130208 0.0390625 ]
[0.3828125 0.03255208 0.07421875]
[0.390625 0.08203125 0.14713542]
[0.5625 0.14973958 0.07421875]
[0.546875 0.09375 0.07421875]
[0.5390625 0.12239583 0.04947917]
[0.296875 0.01692708 0.31119792]
[0.5234375 0.09895833 0.04036458]
[0.5078125 0.12369792 0.06380208]], shape=(10, 3), dtype=float64)
target: {'pitch': <tf.Tensor: shape=(), dtype=float64, numpy=67.0>, 'step': <tf.Tensor: shape=(), dtype=float64, numpy=0.1171875>, 'duration': <tf.Tensor: shape=(), dtype=float64, numpy=0.04947916666666652>}
Unisci gli esempi in batch e configura il set di dati per le prestazioni.
batch_size = 64
buffer_size = n_notes - seq_length # the number of items in the dataset
train_ds = (seq_ds
.shuffle(buffer_size)
.batch(batch_size, drop_remainder=True)
.cache()
.prefetch(tf.data.experimental.AUTOTUNE))
train_ds.element_spec
(TensorSpec(shape=(64, 25, 3), dtype=tf.float64, name=None),
{'pitch': TensorSpec(shape=(64,), dtype=tf.float64, name=None),
'step': TensorSpec(shape=(64,), dtype=tf.float64, name=None),
'duration': TensorSpec(shape=(64,), dtype=tf.float64, name=None)})
Crea e addestra il modello
Il modello avrà tre uscite, una per ogni variabile nota. Per pitch e duration , utilizzerai una funzione di perdita personalizzata basata sull'errore quadratico medio che incoraggia il modello a produrre valori non negativi.
def mse_with_positive_pressure(y_true: tf.Tensor, y_pred: tf.Tensor):
mse = (y_true - y_pred) ** 2
positive_pressure = 10 * tf.maximum(-y_pred, 0.0)
return tf.reduce_mean(mse + positive_pressure)
input_shape = (seq_length, 3)
learning_rate = 0.005
inputs = tf.keras.Input(input_shape)
x = tf.keras.layers.LSTM(128)(inputs)
outputs = {
'pitch': tf.keras.layers.Dense(128, name='pitch')(x),
'step': tf.keras.layers.Dense(1, name='step')(x),
'duration': tf.keras.layers.Dense(1, name='duration')(x),
}
model = tf.keras.Model(inputs, outputs)
loss = {
'pitch': tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
'step': mse_with_positive_pressure,
'duration': mse_with_positive_pressure,
}
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
model.compile(loss=loss, optimizer=optimizer)
model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 25, 3)] 0 []
lstm (LSTM) (None, 128) 67584 ['input_1[0][0]']
duration (Dense) (None, 1) 129 ['lstm[0][0]']
pitch (Dense) (None, 128) 16512 ['lstm[0][0]']
step (Dense) (None, 1) 129 ['lstm[0][0]']
==================================================================================================
Total params: 84,354
Trainable params: 84,354
Non-trainable params: 0
__________________________________________________________________________________________________
Testando la funzione model.evaluate , puoi vedere che la perdita di pitch è significativamente maggiore delle perdite di step e duration . Si noti che la loss è la perdita totale calcolata sommando tutte le altre perdite ed è attualmente dominata dalla perdita di pitch .
losses = model.evaluate(train_ds, return_dict=True)
losses
361/361 [==============================] - 6s 4ms/step - loss: 5.0011 - duration_loss: 0.1213 - pitch_loss: 4.8476 - step_loss: 0.0322
{'loss': 5.001128196716309,
'duration_loss': 0.12134315073490143,
'pitch_loss': 4.847629547119141,
'step_loss': 0.03215572610497475}
Un modo per bilanciare questo è usare l'argomento loss_weights per compilare:
model.compile(
loss=loss,
loss_weights={
'pitch': 0.05,
'step': 1.0,
'duration':1.0,
},
optimizer=optimizer,
)
La loss diventa quindi la somma ponderata delle singole perdite.
model.evaluate(train_ds, return_dict=True)
361/361 [==============================] - 2s 4ms/step - loss: 0.3959 - duration_loss: 0.1213 - pitch_loss: 4.8476 - step_loss: 0.0322
{'loss': 0.39588069915771484,
'duration_loss': 0.12134315073490143,
'pitch_loss': 4.847629547119141,
'step_loss': 0.03215572610497475}
Allena il modello.
callbacks = [
tf.keras.callbacks.ModelCheckpoint(
filepath='./training_checkpoints/ckpt_{epoch}',
save_weights_only=True),
tf.keras.callbacks.EarlyStopping(
monitor='loss',
patience=5,
verbose=1,
restore_best_weights=True),
]
%%time
epochs = 50
history = model.fit(
train_ds,
epochs=epochs,
callbacks=callbacks,
)
Epoch 1/50 361/361 [==============================] - 4s 5ms/step - loss: 0.3075 - duration_loss: 0.0732 - pitch_loss: 4.0974 - step_loss: 0.0294 Epoch 2/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2950 - duration_loss: 0.0696 - pitch_loss: 3.9526 - step_loss: 0.0278 Epoch 3/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2927 - duration_loss: 0.0682 - pitch_loss: 3.9372 - step_loss: 0.0276 Epoch 4/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2918 - duration_loss: 0.0681 - pitch_loss: 3.9232 - step_loss: 0.0275 Epoch 5/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2874 - duration_loss: 0.0657 - pitch_loss: 3.9079 - step_loss: 0.0264 Epoch 6/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2842 - duration_loss: 0.0653 - pitch_loss: 3.8509 - step_loss: 0.0263 Epoch 7/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2820 - duration_loss: 0.0650 - pitch_loss: 3.8090 - step_loss: 0.0265 Epoch 8/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2806 - duration_loss: 0.0654 - pitch_loss: 3.7903 - step_loss: 0.0257 Epoch 9/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2806 - duration_loss: 0.0651 - pitch_loss: 3.7888 - step_loss: 0.0261 Epoch 10/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2778 - duration_loss: 0.0637 - pitch_loss: 3.7690 - step_loss: 0.0256 Epoch 11/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2762 - duration_loss: 0.0624 - pitch_loss: 3.7704 - step_loss: 0.0253 Epoch 12/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2746 - duration_loss: 0.0616 - pitch_loss: 3.7644 - step_loss: 0.0248 Epoch 13/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2728 - duration_loss: 0.0604 - pitch_loss: 3.7591 - step_loss: 0.0244 Epoch 14/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2710 - duration_loss: 0.0584 - pitch_loss: 3.7573 - step_loss: 0.0247 Epoch 15/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2694 - duration_loss: 0.0574 - pitch_loss: 3.7610 - step_loss: 0.0239 Epoch 16/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2686 - duration_loss: 0.0569 - pitch_loss: 3.7529 - step_loss: 0.0240 Epoch 17/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2695 - duration_loss: 0.0577 - pitch_loss: 3.7486 - step_loss: 0.0243 Epoch 18/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2663 - duration_loss: 0.0560 - pitch_loss: 3.7473 - step_loss: 0.0229 Epoch 19/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2642 - duration_loss: 0.0543 - pitch_loss: 3.7366 - step_loss: 0.0231 Epoch 20/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2691 - duration_loss: 0.0587 - pitch_loss: 3.7421 - step_loss: 0.0233 Epoch 21/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2636 - duration_loss: 0.0547 - pitch_loss: 3.7314 - step_loss: 0.0223 Epoch 22/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2613 - duration_loss: 0.0533 - pitch_loss: 3.7313 - step_loss: 0.0215 Epoch 23/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2595 - duration_loss: 0.0516 - pitch_loss: 3.7219 - step_loss: 0.0218 Epoch 24/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2548 - duration_loss: 0.0493 - pitch_loss: 3.7148 - step_loss: 0.0198 Epoch 25/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2539 - duration_loss: 0.0483 - pitch_loss: 3.7150 - step_loss: 0.0199 Epoch 26/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2526 - duration_loss: 0.0474 - pitch_loss: 3.7138 - step_loss: 0.0196 Epoch 27/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2502 - duration_loss: 0.0460 - pitch_loss: 3.7036 - step_loss: 0.0190 Epoch 28/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2467 - duration_loss: 0.0442 - pitch_loss: 3.6970 - step_loss: 0.0177 Epoch 29/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2458 - duration_loss: 0.0438 - pitch_loss: 3.6938 - step_loss: 0.0172 Epoch 30/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2434 - duration_loss: 0.0418 - pitch_loss: 3.6836 - step_loss: 0.0174 Epoch 31/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2404 - duration_loss: 0.0403 - pitch_loss: 3.6703 - step_loss: 0.0166 Epoch 32/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2421 - duration_loss: 0.0412 - pitch_loss: 3.6833 - step_loss: 0.0168 Epoch 33/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2391 - duration_loss: 0.0399 - pitch_loss: 3.6585 - step_loss: 0.0163 Epoch 34/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2376 - duration_loss: 0.0390 - pitch_loss: 3.6467 - step_loss: 0.0163 Epoch 35/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2403 - duration_loss: 0.0417 - pitch_loss: 3.6448 - step_loss: 0.0164 Epoch 36/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2394 - duration_loss: 0.0417 - pitch_loss: 3.6218 - step_loss: 0.0166 Epoch 37/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2337 - duration_loss: 0.0369 - pitch_loss: 3.6155 - step_loss: 0.0161 Epoch 38/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2320 - duration_loss: 0.0357 - pitch_loss: 3.6080 - step_loss: 0.0158 Epoch 39/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2291 - duration_loss: 0.0353 - pitch_loss: 3.5896 - step_loss: 0.0143 Epoch 40/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2285 - duration_loss: 0.0352 - pitch_loss: 3.5784 - step_loss: 0.0144 Epoch 41/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2276 - duration_loss: 0.0338 - pitch_loss: 3.5928 - step_loss: 0.0142 Epoch 42/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2233 - duration_loss: 0.0316 - pitch_loss: 3.5582 - step_loss: 0.0137 Epoch 43/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2211 - duration_loss: 0.0304 - pitch_loss: 3.5453 - step_loss: 0.0134 Epoch 44/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2206 - duration_loss: 0.0307 - pitch_loss: 3.5396 - step_loss: 0.0129 Epoch 45/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2223 - duration_loss: 0.0322 - pitch_loss: 3.5352 - step_loss: 0.0133 Epoch 46/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2213 - duration_loss: 0.0312 - pitch_loss: 3.5323 - step_loss: 0.0135 Epoch 47/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2240 - duration_loss: 0.0329 - pitch_loss: 3.5405 - step_loss: 0.0142 Epoch 48/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2217 - duration_loss: 0.0322 - pitch_loss: 3.5160 - step_loss: 0.0137 Epoch 49/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2167 - duration_loss: 0.0296 - pitch_loss: 3.4894 - step_loss: 0.0126 Epoch 50/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2142 - duration_loss: 0.0278 - pitch_loss: 3.4757 - step_loss: 0.0126 CPU times: user 2min 16s, sys: 23.9 s, total: 2min 40s Wall time: 1min 41s
plt.plot(history.epoch, history.history['loss'], label='total loss')
plt.show()
Genera note
Per utilizzare il modello per generare note, dovrai prima fornire una sequenza iniziale di note. La funzione seguente genera una nota da una sequenza di note.
Per l'altezza delle note, estrae un campione dalla distribuzione softmax delle note prodotte dal modello e non seleziona semplicemente la nota con la probabilità più alta. Scegliere sempre la nota con la probabilità più alta comporterebbe la generazione di sequenze ripetitive di note.
Il parametro della temperature può essere utilizzato per controllare la casualità delle note generate. Puoi trovare maggiori dettagli sulla temperatura in Generazione di testo con un RNN .
def predict_next_note(
notes: np.ndarray,
keras_model: tf.keras.Model,
temperature: float = 1.0) -> int:
"""Generates a note IDs using a trained sequence model."""
assert temperature > 0
# Add batch dimension
inputs = tf.expand_dims(notes, 0)
predictions = model.predict(inputs)
pitch_logits = predictions['pitch']
step = predictions['step']
duration = predictions['duration']
pitch_logits /= temperature
pitch = tf.random.categorical(pitch_logits, num_samples=1)
pitch = tf.squeeze(pitch, axis=-1)
duration = tf.squeeze(duration, axis=-1)
step = tf.squeeze(step, axis=-1)
# `step` and `duration` values should be non-negative
step = tf.maximum(0, step)
duration = tf.maximum(0, duration)
return int(pitch), float(step), float(duration)
Ora genera alcune note. Puoi giocare con la temperatura e la sequenza di partenza in next_notes e vedere cosa succede.
temperature = 2.0
num_predictions = 120
sample_notes = np.stack([raw_notes[key] for key in key_order], axis=1)
# The initial sequence of notes; pitch is normalized similar to training
# sequences
input_notes = (
sample_notes[:seq_length] / np.array([vocab_size, 1, 1]))
generated_notes = []
prev_start = 0
for _ in range(num_predictions):
pitch, step, duration = predict_next_note(input_notes, model, temperature)
start = prev_start + step
end = start + duration
input_note = (pitch, step, duration)
generated_notes.append((*input_note, start, end))
input_notes = np.delete(input_notes, 0, axis=0)
input_notes = np.append(input_notes, np.expand_dims(input_note, 0), axis=0)
prev_start = start
generated_notes = pd.DataFrame(
generated_notes, columns=(*key_order, 'start', 'end'))
generated_notes.head(10)
out_file = 'output.mid'
out_pm = notes_to_midi(
generated_notes, out_file=out_file, instrument_name=instrument_name)
display_audio(out_pm)
Puoi anche scaricare il file audio aggiungendo le due righe seguenti:
from google.colab import files
files.download(out_file)
Visualizza le note generate.
plot_piano_roll(generated_notes)
Controllare le distribuzioni di pitch , step e duration .
plot_distributions(generated_notes)
Nei grafici sopra, noterai il cambiamento nella distribuzione delle variabili nota. Poiché esiste un ciclo di feedback tra gli output e gli input del modello, il modello tende a generare sequenze di output simili per ridurre la perdita. Ciò è particolarmente rilevante per il step e la duration , che ha utilizzato la perdita di MSE. Per l' pitch , puoi aumentare la casualità aumentando la temperature in predict_next_note .
Prossimi passi
Questo tutorial ha dimostrato i meccanismi di utilizzo di un RNN per generare sequenze di note da un set di dati di file MIDI. Per saperne di più, puoi visitare la strettamente correlata Generazione di testo con un tutorial RNN , che contiene diagrammi e spiegazioni aggiuntivi.
Un'alternativa all'utilizzo degli RNN per la generazione di musica è l'utilizzo dei GAN. Invece di generare audio, un approccio basato su GAN può generare un'intera sequenza in parallelo. Il team Magenta ha svolto un lavoro impressionante su questo approccio con GANSynth . Puoi anche trovare molti meravigliosi progetti musicali e artistici e codice open source sul sito Web del progetto Magenta .