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এই টিউটোরিয়ালটি আপনাকে দেখায় কিভাবে একটি সাধারণ RNN ব্যবহার করে মিউজিক্যাল নোট তৈরি করতে হয়। আপনি MAESTRO ডেটাসেট থেকে পিয়ানো MIDI ফাইলের সংগ্রহ ব্যবহার করে একটি মডেলকে প্রশিক্ষণ দেবেন। নোটের একটি ক্রম দেওয়া হলে, আপনার মডেলটি অনুক্রমের পরবর্তী নোটের পূর্বাভাস দিতে শিখবে। আপনি মডেলটিকে বারবার কল করে নোটের দীর্ঘ ক্রম তৈরি করতে পারেন।
এই টিউটোরিয়ালটিতে MIDI ফাইল পার্স এবং তৈরি করার সম্পূর্ণ কোড রয়েছে। আপনি RNN-এর সাথে টেক্সট জেনারেশনে গিয়ে RNN কিভাবে কাজ করে সে সম্পর্কে আরও জানতে পারেন।
সেটআপ
এই টিউটোরিয়ালটি MIDI ফাইল তৈরি এবং পার্স করতে pretty_midi লাইব্রেরি ব্যবহার করে এবং pyfluidsynth এ অডিও প্লেব্যাক তৈরি করার জন্য pyfluidsynth ব্যবহার করে।
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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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
Maestro ডেটাসেট ডাউনলোড করুন
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
ডেটাসেটে প্রায় 1,200টি MIDI ফাইল রয়েছে।
filenames = glob.glob(str(data_dir/'**/*.mid*'))
print('Number of files:', len(filenames))
Number of files: 1282
একটি MIDI ফাইল প্রক্রিয়া করুন
প্রথমে, একটি একক MIDI ফাইল পার্স করতে pretty_midi ব্যবহার করুন এবং নোটের বিন্যাস পরিদর্শন করুন। আপনি যদি আপনার কম্পিউটারে চালানোর জন্য নিচের MIDI ফাইলটি ডাউনলোড করতে চান, তাহলে আপনি files.download(sample_file) লিখে কোলাবে তা করতে পারেন।
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
নমুনা MIDI ফাইলের জন্য একটি PrettyMIDI অবজেক্ট তৈরি করুন।
pm = pretty_midi.PrettyMIDI(sample_file)
নমুনা ফাইল চালান. প্লেব্যাক উইজেটটি লোড হতে কয়েক সেকেন্ড সময় লাগতে পারে।
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)
MIDI ফাইলে কিছু পরিদর্শন করুন। কি ধরনের যন্ত্র ব্যবহার করা হয়?
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
নোট বের করুন
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
মডেল প্রশিক্ষণের সময় আপনি একটি নোট উপস্থাপন করতে তিনটি ভেরিয়েবল ব্যবহার করবেন: pitch , step এবং duration । পিচ হল একটি MIDI নোট নম্বর হিসাবে শব্দের উপলব্ধিগত গুণমান। step হল আগের নোট বা ট্র্যাকের শুরু থেকে অতিবাহিত সময়। duration হল নোটটি সেকেন্ডে কতক্ষণ চলবে এবং নোটের শেষ এবং নোট শুরুর সময়ের মধ্যে পার্থক্য।
নমুনা MIDI ফাইল থেকে নোট বের করুন.
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()
পিচগুলির পরিবর্তে নোটের নামগুলি ব্যাখ্যা করা সহজ হতে পারে, তাই আপনি সাংখ্যিক পিচ মানগুলি থেকে নোটের নামগুলিতে রূপান্তর করতে নীচের ফাংশনটি ব্যবহার করতে পারেন৷ নোটের নাম নোটের ধরন, দুর্ঘটনাজনিত এবং অষ্টক নম্বর দেখায় (যেমন 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')
মিউজিক্যাল পিসটি কল্পনা করতে, নোট পিচ প্লট করুন, ট্র্যাকের দৈর্ঘ্য জুড়ে শুরু করুন এবং শেষ করুন (যেমন পিয়ানো রোল)। প্রথম 100টি নোট দিয়ে শুরু করুন
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)
পুরো ট্র্যাকের জন্য নোটগুলি প্লট করুন।
plot_piano_roll(raw_notes)
প্রতিটি নোট ভেরিয়েবলের বিতরণ পরীক্ষা করুন।
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)
একটি MIDI ফাইল তৈরি করুন
আপনি নীচের ফাংশনটি ব্যবহার করে নোটগুলির একটি তালিকা থেকে আপনার নিজের MIDI ফাইল তৈরি করতে পারেন।
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)
জেনারেট করা MIDI ফাইলটি চালান এবং দেখুন কোন পার্থক্য আছে কিনা।
display_audio(example_pm)
আগের মতো, আপনি এই ফাইলটি ডাউনলোড এবং প্লে করতে files.download(example_file) লিখতে পারেন।
প্রশিক্ষণ ডেটাসেট তৈরি করুন
MIDI ফাইল থেকে নোট বের করে প্রশিক্ষণ ডেটাসেট তৈরি করুন। আপনি অল্প সংখ্যক ফাইল ব্যবহার করে শুরু করতে পারেন এবং পরে আরও কিছু নিয়ে পরীক্ষা করতে পারেন। এতে কয়েক মিনিট সময় লাগতে পারে।
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
এরপর, পার্স করা নোট থেকে একটি tf.data.Dataset তৈরি করুন।
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)
আপনি নোটের অনুক্রমের ব্যাচে মডেলটিকে প্রশিক্ষণ দেবেন। প্রতিটি উদাহরণে ইনপুট বৈশিষ্ট্য হিসাবে নোটের একটি ক্রম এবং লেবেল হিসাবে পরবর্তী নোট থাকবে। এইভাবে, মডেলটিকে একটি ক্রমানুসারে পরবর্তী নোটের পূর্বাভাস দেওয়ার জন্য প্রশিক্ষণ দেওয়া হবে। আপনি একটি RNN এর সাথে পাঠ্য শ্রেণীবিভাগে এই প্রক্রিয়াটি (এবং আরও বিশদ বিবরণ) ব্যাখ্যা করে একটি চিত্র খুঁজে পেতে পারেন।
আপনি এই বিন্যাসে বৈশিষ্ট্য এবং লেবেল তৈরি করতে সাইজ seq_length সহ সহজ উইন্ডো ফাংশন ব্যবহার করতে পারেন।
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)
প্রতিটি উদাহরণের জন্য ক্রম দৈর্ঘ্য সেট করুন। বিভিন্ন দৈর্ঘ্যের সাথে পরীক্ষা করুন (যেমন 50, 100, 150) ডেটার জন্য কোনটি সবচেয়ে ভালো কাজ করে তা দেখতে, অথবা হাইপারপ্যারামিটার টিউনিং ব্যবহার করুন। শব্দভান্ডারের আকার ( vocab_size ) 128 এ সেট করা হয়েছে যা pretty_midi দ্বারা সমর্থিত সমস্ত পিচের প্রতিনিধিত্ব করে।
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)})
ডেটাসেটের আকৃতি হল (100,1) , যার অর্থ হল মডেলটি 100টি নোট ইনপুট হিসাবে নেবে এবং আউটপুট হিসাবে নিম্নলিখিত নোটটি ভবিষ্যদ্বাণী করতে শিখবে৷
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>}
উদাহরণগুলি ব্যাচ করুন এবং কর্মক্ষমতার জন্য ডেটাসেট কনফিগার করুন।
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)})
মডেল তৈরি এবং প্রশিক্ষণ
মডেলটিতে তিনটি আউটপুট থাকবে, প্রতিটি নোট ভেরিয়েবলের জন্য একটি। pitch এবং duration জন্য, আপনি গড় বর্গক্ষেত্র ত্রুটির উপর ভিত্তি করে একটি কাস্টম ক্ষতি ফাংশন ব্যবহার করবেন যা মডেলটিকে অ-নেতিবাচক মান আউটপুট করতে উত্সাহিত করে।
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
__________________________________________________________________________________________________
model.evaluate ফাংশন পরীক্ষা করে, আপনি দেখতে পারেন যে pitch ক্ষতি step এবং duration ক্ষতির চেয়ে উল্লেখযোগ্যভাবে বেশি। মনে রাখবেন যে loss হল অন্যান্য সমস্ত ক্ষতির যোগফলের দ্বারা গণনা করা মোট ক্ষতি এবং বর্তমানে pitch ক্ষতি দ্বারা প্রভাবিত হয়।
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}
এক উপায়ে ভারসাম্য বজায় রাখা হল কম্পাইল করার জন্য loss_weights আর্গুমেন্ট ব্যবহার করা:
model.compile(
loss=loss,
loss_weights={
'pitch': 0.05,
'step': 1.0,
'duration':1.0,
},
optimizer=optimizer,
)
loss তখন ব্যক্তিগত ক্ষতির ওজনযুক্ত সমষ্টিতে পরিণত হয়।
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}
মডেলকে প্রশিক্ষণ দিন।
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()
নোট তৈরি করুন
নোট তৈরি করতে মডেলটি ব্যবহার করতে, আপনাকে প্রথমে নোটগুলির একটি শুরুর ক্রম প্রদান করতে হবে। নিচের ফাংশনটি নোটের একটি ক্রম থেকে একটি নোট তৈরি করে।
নোট পিচের জন্য, এটি মডেল দ্বারা উত্পাদিত নোটের সফ্টম্যাক্স বিতরণ থেকে একটি নমুনা আঁকে এবং সর্বোচ্চ সম্ভাব্যতা সহ নোটটি কেবল বাছাই করে না। সর্বদা সর্বোচ্চ সম্ভাবনার সাথে নোটটি বাছাই করা নোটের পুনরাবৃত্তিমূলক ক্রম তৈরি করে।
উত্পন্ন নোটের এলোমেলোতা নিয়ন্ত্রণ করতে temperature পরামিতি ব্যবহার করা যেতে পারে। আপনি একটি RNN এর মাধ্যমে টেক্সট জেনারেশনে তাপমাত্রা সম্পর্কে আরও বিশদ জানতে পারেন।
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)
এখন কিছু নোট তৈরি করুন। আপনি next_notes তাপমাত্রা এবং শুরুর ক্রম নিয়ে খেলতে পারেন এবং দেখুন কী হয়৷
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)
আপনি নীচের দুটি লাইন যোগ করে অডিও ফাইল ডাউনলোড করতে পারেন:
from google.colab import files
files.download(out_file)
উত্পন্ন নোট কল্পনা করুন.
plot_piano_roll(generated_notes)
pitch , step এবং duration বিতরণ পরীক্ষা করুন।
plot_distributions(generated_notes)
উপরের প্লটে, আপনি নোট ভেরিয়েবলের বন্টনের পরিবর্তন লক্ষ্য করবেন। যেহেতু মডেলের আউটপুট এবং ইনপুটগুলির মধ্যে একটি ফিডব্যাক লুপ রয়েছে, তাই মডেলটি ক্ষতি কমাতে আউটপুটগুলির অনুরূপ ক্রম তৈরি করে। এটি step এবং duration জন্য বিশেষভাবে প্রাসঙ্গিক, যা MSE ক্ষতি ব্যবহার করেছে। pitch জন্য, আপনি predict_next_note এ temperature বাড়িয়ে এলোমেলোতা বাড়াতে পারেন।
পরবর্তী পদক্ষেপ
এই টিউটোরিয়ালটি MIDI ফাইলের ডেটাসেট থেকে নোটের ক্রম তৈরি করতে RNN ব্যবহার করার মেকানিক্স প্রদর্শন করেছে। আরও জানতে, আপনি একটি RNN টিউটোরিয়াল সহ ঘনিষ্ঠভাবে সম্পর্কিত পাঠ্য প্রজন্ম দেখতে পারেন, যাতে অতিরিক্ত চিত্র এবং ব্যাখ্যা রয়েছে।
সঙ্গীত প্রজন্মের জন্য RNN ব্যবহার করার একটি বিকল্প হল GAN ব্যবহার করা। অডিও তৈরি করার পরিবর্তে, একটি GAN-ভিত্তিক পদ্ধতি সমান্তরালভাবে একটি সম্পূর্ণ ক্রম তৈরি করতে পারে। ম্যাজেন্টা দল GANSynth-এর সাথে এই পদ্ধতির উপর চিত্তাকর্ষক কাজ করেছে। এছাড়াও আপনি Magenta প্রকল্প ওয়েবসাইটে অনেক বিস্ময়কর সঙ্গীত এবং শিল্প প্রকল্প এবং ওপেন-সোর্স কোড খুঁজে পেতে পারেন।