День сообщества ML - 9 ноября! Присоединяйтесь к нам для обновления от TensorFlow, JAX, и многое другое Подробнее

Создавайте музыку с помощью RNN

Посмотреть на TensorFlow.org Запускаем в Google Colab Посмотреть исходный код на GitHub Скачать блокнот

В этом руководстве показано, как создавать музыкальные ноты с помощью простой RNN. Вы обучите модель , используя коллекцию фортепианных MIDI - файлы из набора данных MAESTRO . Учитывая последовательность заметок, ваша модель научится предсказывать следующую заметку в последовательности. Вы можете создавать более длинные последовательности заметок, повторно вызывая модель.

Это руководство содержит полный код для синтаксического анализа и создания файлов MIDI. Вы можете узнать больше о том , как RNNs работать, посещая поколение текста с RNN .

Настраивать

Этот учебник использует pretty_midi библиотеки для создания и разбора MIDI - файлов и pyfluidsynth для генерации воспроизведения звука в 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
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 115 not upgraded.
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Get:35 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 libfluidsynth1 amd64 1.1.9-1 [137 kB]
Get:36 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 fluidsynth amd64 1.1.9-1 [20.7 kB]
Get:37 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 libqt5x11extras5 amd64 5.9.5-0ubuntu1 [8596 B]
Get:38 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libwacom-bin amd64 0.29-1 [4712 B]
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Get:41 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 qttranslations5-l10n all 5.9.5-0ubuntu1 [1485 kB]
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7[0;24r8[1A[J
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

Скачать набор данных 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 [==============================] - 1s 0us/step
59252736/59243107 [==============================] - 1s 0us/step

Набор данных содержит около 1200 файлов MIDI.

filenames = glob.glob(str(data_dir/'**/*.mid*'))
print('Number of files:', len(filenames))
Number of files: 1282

Обработка файла MIDI

Во- первых, использование pretty_midi разобрать один MIDI файл и проверьте формат заметок. Если вы хотите , чтобы загрузить файл MIDI ниже , чтобы играть на вашем компьютере, вы можете сделать это в colab написав files.download(sample_file) .

sample_file = filenames[1]
print(sample_file)
data/maestro-v2.0.0/2015/MIDI-Unprocessed_R1_D1-1-8_mid--AUDIO-from_mp3_02_R1_2015_wav--6.midi

Сформировать PrettyMIDI объект для образца MIDI - файла.

pm = pretty_midi.PrettyMIDI(sample_file)

Воспроизведите образец файла. Для загрузки виджета воспроизведения может потребоваться несколько секунд.

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)

Проведите некоторую проверку MIDI-файла. Какие инструменты используются?

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

Извлечь заметки

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=63, note_name=D#4, duration=0.0760
1: pitch=75, note_name=D#5, duration=0.0687
2: pitch=75, note_name=D#5, duration=0.0615
3: pitch=63, note_name=D#4, duration=0.0688
4: pitch=75, note_name=D#5, duration=0.0448
5: pitch=63, note_name=D#4, duration=0.0573
6: pitch=87, note_name=D#6, duration=0.0302
7: pitch=99, note_name=D#7, duration=0.0260
8: pitch=87, note_name=D#6, duration=0.0271
9: pitch=99, note_name=D#7, duration=0.0260

Вы будете использовать три переменные для представления к сведению при подготовке модели: pitch , step и duration . Высота звука - это качество восприятия звука как номер ноты MIDI. step является время , прошедшее от предыдущей ноты или начала дорожки. duration , как долго нота будет играть в секундах , а разница между концом ноты и ноты времени начала.

Извлеките ноты из образца MIDI-файла.

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()

Возможно, будет проще интерпретировать названия нот, а не высоту звука, поэтому вы можете использовать приведенную ниже функцию для преобразования числовых значений высоты звука в названия нот. Название ноты показывает тип ноты, случайность и номер октавы (например, 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(['D#4', 'D#5', 'D#4', 'D#5', 'D#4', 'D#5', 'D#6', 'D#7', 'D#6',
       'D#7'], dtype='<U3')

Чтобы визуализировать музыкальную пьесу, нарисуйте высоту звука, начало и конец ноты по длине дорожки (например, рояль). Начните с первых 100 нот

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)

PNG

Постройте ноты для всего трека.

plot_piano_roll(raw_notes)

PNG

Проверьте распределение каждой переменной ноты.

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)

PNG

Создать MIDI-файл

Вы можете сгенерировать свой собственный MIDI-файл из списка заметок, используя функцию ниже.

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)

Воспроизведите сгенерированный файл MIDI и посмотрите, есть ли разница.

display_audio(example_pm)

Как и прежде, вы можете написать files.download(example_file) для загрузки и воспроизведения этого файла.

Создайте набор обучающих данных

Создайте набор тренировочных данных, извлекая примечания из файлов MIDI. Вы можете начать с небольшого количества файлов, а позже поэкспериментировать с другими. Это может занять пару минут.

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: 13606

Затем создайте tf.data.Dataset из разобранных нот.

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)

Вы будете обучать модель на пачках последовательностей заметок. Каждый пример будет состоять из последовательности заметок в качестве входных характеристик и следующей заметки в качестве метки. Таким образом, модель будет обучена предсказывать следующую ноту в последовательности. Вы можете найти схему , объясняющую в этот процесс (и более подробно) Текст классификации с RNN .

Вы можете использовать удобную оконную функцию с размером seq_length , чтобы создать возможности и ярлыки в этом формате.

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)

Установите длину последовательности для каждого примера. Поэкспериментируйте с различными длинами (например , 50, 100, 150) , чтобы увидеть , который работает лучше всего для данных, или использовать настройки гиперпараметра . Размер словаря ( vocab_size ) установлен на 128 , представляющие все смолы , поддерживаемые 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)})

Форма набора данных (100,1) , а это означает , что модель будет принимать 100 нот в качестве входных данных, и научиться предсказывать следующее примечание как выход.

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(
[[0.5625     0.         0.034375  ]
 [0.5234375  0.52083333 0.04270833]
 [0.5        0.53854167 0.0375    ]
 [0.375      0.25       0.04166667]
 [0.28125    0.27395833 0.05520833]
 [0.5390625  0.24375    0.03958333]
 [0.484375   0.26458333 0.03333333]
 [0.375      0.24583333 0.04270833]
 [0.28125    0.26354167 0.04270833]
 [0.5234375  0.25       0.04270833]], shape=(10, 3), dtype=float64)

target: {'pitch': <tf.Tensor: shape=(), dtype=float64, numpy=36.0>, 'step': <tf.Tensor: shape=(), dtype=float64, numpy=0.25>, 'duration': <tf.Tensor: shape=(), dtype=float64, numpy=0.078125>}

Сгруппируйте примеры и настройте набор данных для повышения производительности.

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)})

Создайте и обучите модель

Модель будет иметь три выхода, по одному для каждой переменной ноты. Для pitch и duration , вы будете использовать пользовательскую функцию потерь , основанную на средней квадратичной ошибке, поощряющую модель для значений неотрицательных выходных.

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
__________________________________________________________________________________________________

Тестирование model.evaluate функции, вы можете увидеть , что pitch потери значительно больше , чем step и duration потерь. Обратите внимание , что loss является полная потеря рассчитывается путем суммирования всех других потерь и в настоящее время доминируют pitch потери.

losses = model.evaluate(train_ds, return_dict=True)
losses
212/212 [==============================] - 6s 3ms/step - loss: 5.0686 - duration_loss: 0.1985 - pitch_loss: 4.8481 - step_loss: 0.0220
{'loss': 5.068634510040283,
 'duration_loss': 0.19852526485919952,
 'pitch_loss': 4.8480706214904785,
 'step_loss': 0.022038981318473816}

Один из способов баланса это использовать loss_weights аргумент для компиляции:

model.compile(
    loss=loss,
    loss_weights={
        'pitch': 0.05,
        'step': 1.0,
        'duration':1.0,
    },
    optimizer=optimizer,
)

loss становятся взвешенной суммой отдельных потерь.

model.evaluate(train_ds, return_dict=True)
212/212 [==============================] - 1s 3ms/step - loss: 0.4630 - duration_loss: 0.1985 - pitch_loss: 4.8481 - step_loss: 0.0220
{'loss': 0.46296781301498413,
 'duration_loss': 0.19852526485919952,
 'pitch_loss': 4.8480706214904785,
 'step_loss': 0.022038981318473816}

Обучите модель.

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
212/212 [==============================] - 2s 4ms/step - loss: 0.3816 - duration_loss: 0.1560 - pitch_loss: 4.1258 - step_loss: 0.0193
Epoch 2/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3563 - duration_loss: 0.1439 - pitch_loss: 3.8772 - step_loss: 0.0185
Epoch 3/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3529 - duration_loss: 0.1421 - pitch_loss: 3.8537 - step_loss: 0.0181
Epoch 4/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3478 - duration_loss: 0.1398 - pitch_loss: 3.7948 - step_loss: 0.0183
Epoch 5/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3480 - duration_loss: 0.1426 - pitch_loss: 3.7489 - step_loss: 0.0180
Epoch 6/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3459 - duration_loss: 0.1414 - pitch_loss: 3.7280 - step_loss: 0.0180
Epoch 7/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3389 - duration_loss: 0.1379 - pitch_loss: 3.6642 - step_loss: 0.0178
Epoch 8/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3352 - duration_loss: 0.1352 - pitch_loss: 3.6510 - step_loss: 0.0175
Epoch 9/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3337 - duration_loss: 0.1347 - pitch_loss: 3.6349 - step_loss: 0.0172
Epoch 10/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3319 - duration_loss: 0.1333 - pitch_loss: 3.6339 - step_loss: 0.0169
Epoch 11/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3280 - duration_loss: 0.1300 - pitch_loss: 3.6183 - step_loss: 0.0171
Epoch 12/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3239 - duration_loss: 0.1267 - pitch_loss: 3.6040 - step_loss: 0.0170
Epoch 13/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3217 - duration_loss: 0.1250 - pitch_loss: 3.5992 - step_loss: 0.0168
Epoch 14/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3161 - duration_loss: 0.1200 - pitch_loss: 3.5855 - step_loss: 0.0168
Epoch 15/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3143 - duration_loss: 0.1192 - pitch_loss: 3.5692 - step_loss: 0.0166
Epoch 16/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3117 - duration_loss: 0.1164 - pitch_loss: 3.5670 - step_loss: 0.0169
Epoch 17/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3080 - duration_loss: 0.1134 - pitch_loss: 3.5653 - step_loss: 0.0164
Epoch 18/50
212/212 [==============================] - 1s 4ms/step - loss: 0.3084 - duration_loss: 0.1123 - pitch_loss: 3.5965 - step_loss: 0.0163
Epoch 19/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2988 - duration_loss: 0.1055 - pitch_loss: 3.5410 - step_loss: 0.0162
Epoch 20/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2980 - duration_loss: 0.1028 - pitch_loss: 3.5758 - step_loss: 0.0164
Epoch 21/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2915 - duration_loss: 0.0993 - pitch_loss: 3.5235 - step_loss: 0.0160
Epoch 22/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2975 - duration_loss: 0.1024 - pitch_loss: 3.5846 - step_loss: 0.0159
Epoch 23/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2831 - duration_loss: 0.0911 - pitch_loss: 3.5186 - step_loss: 0.0161
Epoch 24/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2787 - duration_loss: 0.0883 - pitch_loss: 3.5001 - step_loss: 0.0154
Epoch 25/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2792 - duration_loss: 0.0889 - pitch_loss: 3.4912 - step_loss: 0.0158
Epoch 26/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2795 - duration_loss: 0.0897 - pitch_loss: 3.4919 - step_loss: 0.0153
Epoch 27/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2705 - duration_loss: 0.0810 - pitch_loss: 3.4829 - step_loss: 0.0153
Epoch 28/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2707 - duration_loss: 0.0819 - pitch_loss: 3.4695 - step_loss: 0.0154
Epoch 29/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2696 - duration_loss: 0.0804 - pitch_loss: 3.4733 - step_loss: 0.0156
Epoch 30/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2681 - duration_loss: 0.0804 - pitch_loss: 3.4568 - step_loss: 0.0149
Epoch 31/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2569 - duration_loss: 0.0700 - pitch_loss: 3.4456 - step_loss: 0.0146
Epoch 32/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2516 - duration_loss: 0.0647 - pitch_loss: 3.4387 - step_loss: 0.0149
Epoch 33/50
212/212 [==============================] - 1s 5ms/step - loss: 0.2556 - duration_loss: 0.0674 - pitch_loss: 3.4626 - step_loss: 0.0150
Epoch 34/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2418 - duration_loss: 0.0559 - pitch_loss: 3.4249 - step_loss: 0.0146
Epoch 35/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2352 - duration_loss: 0.0506 - pitch_loss: 3.4185 - step_loss: 0.0136
Epoch 36/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2559 - duration_loss: 0.0633 - pitch_loss: 3.5603 - step_loss: 0.0145
Epoch 37/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2411 - duration_loss: 0.0557 - pitch_loss: 3.4405 - step_loss: 0.0134
Epoch 38/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2358 - duration_loss: 0.0521 - pitch_loss: 3.4078 - step_loss: 0.0132
Epoch 39/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2312 - duration_loss: 0.0477 - pitch_loss: 3.3963 - step_loss: 0.0137
Epoch 40/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2319 - duration_loss: 0.0486 - pitch_loss: 3.3890 - step_loss: 0.0139
Epoch 41/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2338 - duration_loss: 0.0512 - pitch_loss: 3.3824 - step_loss: 0.0135
Epoch 42/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2383 - duration_loss: 0.0549 - pitch_loss: 3.4013 - step_loss: 0.0133
Epoch 43/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2454 - duration_loss: 0.0615 - pitch_loss: 3.3930 - step_loss: 0.0142
Epoch 44/50
212/212 [==============================] - 1s 4ms/step - loss: 0.2313 - duration_loss: 0.0498 - pitch_loss: 3.3607 - step_loss: 0.0134
Restoring model weights from the end of the best epoch.
Epoch 00044: early stopping
CPU times: user 56.9 s, sys: 12.1 s, total: 1min 8s
Wall time: 42.2 s
plt.plot(history.epoch, history.history['loss'], label='total loss')
plt.show()

PNG

Создавать заметки

Чтобы использовать модель для создания заметок, вам сначала нужно указать начальную последовательность заметок. Функция ниже генерирует одну ноту из последовательности нот.

Для высоты звука он извлекает выборку из распределения softmax нот, созданных моделью, а не просто выбирает ноту с наибольшей вероятностью. Выбор ноты с наибольшей вероятностью приведет к созданию повторяющихся последовательностей нот.

temperature параметр может быть использован для управления хаотичностью нот , генерируемых. Вы можете найти более подробную информацию о температуре в генерации текста с 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)

Теперь создайте заметки. Вы можете играть с температурой и последовательность запуска в next_notes и посмотреть , что происходит.

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)

Вы также можете загрузить аудиофайл, добавив две строки ниже:

from google.colab import files
files.download(out_file)

Визуализируйте созданные заметки.

plot_piano_roll(generated_notes)

PNG

Проверьте распределение pitch , step и duration .

plot_distributions(generated_notes)

PNG

На приведенных выше графиках вы заметите изменение в распределении переменных нот. Поскольку существует петля обратной связи между выходами и входами модели, модель имеет тенденцию генерировать аналогичные последовательности выходов, чтобы уменьшить потери. Это особенно актуально для step и duration , которая имеет потерю использования MSE. Для pitch , вы можете увеличить хаотичность за счет увеличения temperature в predict_next_note .

Следующие шаги

В этом руководстве продемонстрирована механика использования RNN для создания последовательностей нот из набора данных файлов MIDI. Чтобы узнать больше, вы можете посетить тесно связанное поколение текста с РНН учебник, который содержит дополнительные схемы и пояснение.

Альтернативой использованию RNN для создания музыки является использование GAN. Вместо генерации звука подход на основе GAN может генерировать всю последовательность параллельно. Команда Magenta сделала впечатляющую работу на таком подходе , с GANSynth . Вы также можете найти много замечательных музыкальных и художественных проектов и с открытым исходным кодом на сайте проекта Magenta .