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מדריך זה מראה לך כיצד ליצור תווים מוזיקליים באמצעות RNN פשוט. אתה תאמן מודל באמצעות אוסף של קבצי MIDI של פסנתר ממערך הנתונים של MAESTRO . בהינתן רצף של הערות, המודל שלך ילמד לחזות את התו הבא ברצף. אתה יכול ליצור רצפים ארוכים יותר של הערות על ידי קריאה חוזרת למודל.
מדריך זה מכיל קוד מלא לניתוח ויצירת קובצי MIDI. תוכל ללמוד עוד על אופן פעולת RNN על ידי ביקור ביצירת טקסט עם RNN .
להכין
מדריך זה משתמש בספריית pretty_midi כדי ליצור ולנתח קבצי MIDI, ו- pyfluidsynth ליצירת השמעת אודיו ב-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. Need to get 132 MB of archives. After this operation, 198 MB of additional disk space will be used. 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1.0.28-4ubuntu0.18.04.2 [170 kB] Get:34 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpulse0 amd64 1:11.1-1ubuntu7.11 [266 kB] 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] Get:39 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 qsynth amd64 0.5.0-2 [191 kB] Get:40 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 qt5-gtk-platformtheme amd64 5.9.5+dfsg-0ubuntu2.6 [117 kB] Get:41 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 qttranslations5-l10n all 5.9.5-0ubuntu1 [1485 kB] Fetched 132 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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
הורד את מערך הנתונים של 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
מערך הנתונים מכיל כ-1,200 קבצי MIDI.
filenames = glob.glob(str(data_dir/'**/*.mid*'))
print('Number of files:', len(filenames))
Number of files: 1282
עבד קובץ MIDI
ראשית, השתמש ב- pretty_midi כדי לנתח קובץ MIDI בודד ולבדוק את הפורמט של התווים. אם תרצה להוריד את קובץ ה-MIDI שלהלן כדי לנגן במחשב שלך, תוכל לעשות זאת ב-colab על ידי כתיבת 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
צור אובייקט PrettyMIDI עבור קובץ ה-MIDI לדוגמה.
pm = pretty_midi.PrettyMIDI(sample_file)
הפעל את הקובץ לדוגמה. הטעינה של ווידג'ט ההפעלה עשויה להימשך מספר שניות.
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)
תעשה קצת בדיקה על קובץ ה-MIDI. באילו סוגי מכשירים משתמשים?
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
חלץ הערות
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
תשתמש בשלושה משתנים כדי לייצג הערה בעת אימון המודל: pitch , step duration . הגובה הוא האיכות התפיסתית של הצליל כמספר תו MIDI. step הוא הזמן שחלף מהתו הקודם או מההתחלה של הרצועה. duration הוא כמה זמן התו יתנגן בשניות והוא ההבדל בין זמני סיום התו וזמני ההתחלה של התו.
חלץ את התווים מקובץ ה-MIDI לדוגמה.
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()
יתכן שיהיה קל יותר לפרש את שמות התווים ולא את גובה הצלילים, כך שתוכל להשתמש בפונקציה שלהלן כדי להמיר מערכי הצליל המספריים לשמות תווים. שם התו מציג את סוג התו, המספר המקרי והאוקטבה (למשל 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')
כדי לדמיין את היצירה המוזיקלית, התווה את גובה התו, התחל וסיום לאורך הרצועה (כלומר גלגול פסנתר). התחל עם 100 התווים הראשונים
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)
צייר את ההערות עבור המסלול כולו.
plot_piano_roll(raw_notes)
בדוק את ההתפלגות של כל משתנה תו.
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)
צור קובץ MIDI
אתה יכול ליצור קובץ MIDI משלך מתוך רשימה של הערות באמצעות הפונקציה שלהלן.
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)
הפעל את קובץ ה-MIDI שנוצר וראה אם יש הבדל כלשהו.
display_audio(example_pm)
כמו קודם, אתה יכול לכתוב files.download(example_file) כדי להוריד ולהפעיל את הקובץ הזה.
צור את מערך ההדרכה
צור את מערך האימון על ידי חילוץ הערות מקובצי ה-MIDI. אתה יכול להתחיל על ידי שימוש במספר קטן של קבצים, ולהתנסות מאוחר יותר עם עוד. זה עשוי לקחת כמה דקות.
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
לאחר מכן, צור מערך נתונים tf.data .מההערות המנתחות.
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)
אתה תאמן את המודל על קבוצות של רצפים של הערות. כל דוגמה תהיה מורכבת מרצף של הערות כתכונות הקלט, והערה הבאה בתור התווית. בדרך זו, המודל יוכשר לחזות את התו הבא ברצף. אתה יכול למצוא תרשים המסביר את התהליך הזה (ופרטים נוספים) בסיווג טקסט עם RNN .
אתה יכול להשתמש בפונקציית החלון השימושית עם size seq_length כדי ליצור את התכונות והתוויות בפורמט זה.
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)
הגדר את אורך הרצף עבור כל דוגמה. נסה עם אורכים שונים (למשל 50, 100, 150) כדי לראות איזה מהם עובד הכי טוב עבור הנתונים, או השתמש בכוונון היפרפרמטר . גודל אוצר המילים ( vocab_size ) מוגדר ל- 128 המייצג את כל המגרשים הנתמכים על ידי 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)})
צורת מערך הנתונים היא (100,1) , כלומר המודל ייקח 100 הערות כקלט, וילמד לחזות את ההערה הבאה כפלט.
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>}
צרף את הדוגמאות והגדר את מערך הנתונים לביצועים.
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)})
צור והכשיר את המודל
לדגם יהיו שלוש יציאות, אחת לכל משתנה תו. עבור pitch duration , תשתמש בפונקציית אובדן מותאמת אישית המבוססת על שגיאה בריבוע ממוצעת המעודדת את המודל להפיק ערכים לא שליליים.
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
__________________________________________________________________________________________________
בדיקת הפונקציה model.evaluate , אתה יכול לראות שהפסד pitch גדול משמעותית מהפסדי step duration . שימו לב loss הוא ההפסד הכולל המחושב על ידי סיכום כל שאר ההפסדים ונשלט כרגע על ידי הפסד 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}
דרך אחת לאזן את זה היא להשתמש בארגומנט loss_weights כדי להדר:
model.compile(
loss=loss,
loss_weights={
'pitch': 0.05,
'step': 1.0,
'duration':1.0,
},
optimizer=optimizer,
)
לאחר מכן, loss הופך לסכום המשוקלל של ההפסדים האישיים.
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}
אימון הדגם.
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()
צור הערות
כדי להשתמש במודל כדי ליצור הערות, תחילה יהיה עליך לספק רצף התחלתי של הערות. הפונקציה שלהלן יוצרת הערה אחת מרצף של הערות.
עבור גובה הצליל, הוא שואב מדגם מהתפלגות softmax של תווים המיוצרים על ידי המודל, ולא פשוט בוחר את התו עם ההסתברות הגבוהה ביותר. בחירת הפתק עם ההסתברות הגבוהה ביותר תמיד תוביל לרצפים חוזרים ונשנים של הערות שנוצרו.
ניתן להשתמש בפרמטר temperature כדי לשלוט באקראיות של הערות שנוצרו. תוכל למצוא פרטים נוספים על טמפרטורה ביצירת טקסט עם 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)
כעת צור כמה הערות. אתה יכול לשחק עם הטמפרטורה ורצף ההתחלה ב- next_notes ולראות מה קורה.
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)
אתה יכול גם להוריד את קובץ האודיו על ידי הוספת שתי השורות למטה:
from google.colab import files
files.download(out_file)
דמיינו את ההערות שנוצרו.
plot_piano_roll(generated_notes)
בדוק את התפלגות pitch , step duration .
plot_distributions(generated_notes)
בעלילות לעיל, תוכלו להבחין בשינוי התפלגות של משתני התווים. מכיוון שקיימת לולאת משוב בין הפלטים והכניסות של המודל, המודל נוטה ליצור רצפים דומים של פלטים כדי להפחית את ההפסד. זה רלוונטי במיוחד step duration , אשר יש לו אובדן MSE משתמש. עבור pitch , אתה יכול להגדיל את האקראיות על ידי הגדלת temperature ב- predict_next_note .
הצעדים הבאים
מדריך זה הדגים את המכניקה של שימוש ב-RNN ליצירת רצפים של הערות ממערך נתונים של קבצי MIDI. למידע נוסף, אתה יכול לבקר בדור הטקסט הקשור באופן הדוק עם מדריך RNN , המכיל דיאגרמות והסברים נוספים.
חלופה לשימוש ב-RNN ליצירת מוזיקה היא שימוש ב-GAN. במקום לייצר אודיו, גישה מבוססת GAN יכולה ליצור רצף שלם במקביל. צוות מג'נטה עשה עבודה מרשימה על גישה זו עם GANSynth . אתה יכול גם למצוא הרבה פרויקטי מוזיקה ואמנות נפלאים וקוד קוד פתוח באתר פרויקט מג'נטה .