एमएल समुदाय दिवस 9 नवंबर है! TensorFlow, JAX से नई जानकारी के लिए हमसे जुड़ें, और अधिक जानें

आरएनएन के साथ संगीत उत्पन्न करें

TensorFlow.org पर देखें Google Colab में चलाएं GitHub पर स्रोत देखें नोटबुक डाउनलोड करें

यह ट्यूटोरियल आपको दिखाता है कि एक साधारण आरएनएन का उपयोग करके संगीत नोट्स कैसे उत्पन्न करें। आप एक मॉडल से पियानो मिडी फ़ाइलों का एक संग्रह का उपयोग कर प्रशिक्षित करेंगे MAESTRO डाटासेट । नोट्स के अनुक्रम को देखते हुए, आपका मॉडल अनुक्रम में अगले नोट की भविष्यवाणी करना सीख जाएगा। आप मॉडल को बार-बार कॉल करके नोटों के लंबे क्रम उत्पन्न कर सकते हैं।

इस ट्यूटोरियल में MIDI फ़ाइलों को पार्स करने और बनाने के लिए पूरा कोड है। आप के बारे में कैसे RNNs पर जाकर और जान सकते हैं एक RNN साथ पाठ पीढ़ी

सेट अप

इस ट्यूटोरियल का उपयोग करता pretty_midi बना सकते हैं और मिडी फ़ाइलों, और पार्स करने के लिए पुस्तकालय pyfluidsynth Colab में ऑडियो प्लेबैक पैदा करने के लिए।

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:36 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 fluidsynth amd64 1.1.9-1 [20.7 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

मेस्ट्रो डेटासेट डाउनलोड करें

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

डेटासेट में लगभग 1,200 MIDI फ़ाइलें हैं।

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

MIDI फ़ाइल संसाधित करें

सबसे पहले, उपयोग pretty_midi एक भी मिडी फ़ाइल को पार्स और नोटों की प्रारूप का निरीक्षण करने के। आप अपने कंप्यूटर पर खेलने के लिए नीचे मिडी फ़ाइल डाउनलोड करने के लिए चाहते हैं, तो आप लिख कर colab में ऐसा कर सकते 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

एक उत्पन्न PrettyMIDI नमूना मिडी फ़ाइल के लिए वस्तु।

pm = pretty_midi.PrettyMIDI(sample_file)

नमूना फ़ाइल चलाएं। प्लेबैक विजेट को लोड होने में कई सेकंड लग सकते हैं।

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 फ़ाइल पर कुछ निरीक्षण करें। किस प्रकार के उपकरणों का उपयोग किया जाता है?

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

नोट निकालें

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

: आप जब मॉडल को प्रशिक्षण एक नोट प्रतिनिधित्व करने के लिए तीन चर का उपयोग करेगा pitch , step और duration । पिच मिडी नोट नंबर के रूप में ध्वनि की अवधारणात्मक गुणवत्ता है। step समय पिछले नोट से गुजरे या ट्रैक के शुरू है। duration कितनी देर टिप्पणी सेकंड में खेल हो जाएगा और टिप्पणी अंत और टिप्पणी शुरू समय के बीच अंतर है।

नमूना MIDI फ़ाइल से नोट्स निकालें।

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()

पिचों के बजाय नोट नामों की व्याख्या करना आसान हो सकता है, इसलिए आप नीचे दिए गए फ़ंक्शन का उपयोग संख्यात्मक पिच मानों से नोट नामों में बदलने के लिए कर सकते हैं। नोट का नाम नोट के प्रकार, आकस्मिक और सप्तक संख्या (जैसे C#4) को दर्शाता है।

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')

म्यूजिकल पीस की कल्पना करने के लिए, नोट पिच को प्लॉट करें, ट्रैक की लंबाई (यानी पियानो रोल) पर शुरू और खत्म करें। पहले 100 नोटों से शुरू करें

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)

पीएनजी

पूरे ट्रैक के लिए नोट्स प्लॉट करें।

plot_piano_roll(raw_notes)

पीएनजी

प्रत्येक नोट चर के वितरण की जाँच करें।

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)

पीएनजी

एक MIDI फ़ाइल बनाएँ

आप नीचे दिए गए फ़ंक्शन का उपयोग करके नोट्स की सूची से अपनी स्वयं की MIDI फ़ाइल उत्पन्न कर सकते हैं।

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)

उत्पन्न MIDI फ़ाइल चलाएँ और देखें कि क्या कोई अंतर है।

display_audio(example_pm)

पहले की तरह, आप लिख सकते हैं files.download(example_file) डाउनलोड करने के लिए और इस फ़ाइल खेलते हैं।

प्रशिक्षण डेटासेट बनाएं

MIDI फ़ाइलों से नोट्स निकालकर प्रशिक्षण डेटासेट बनाएँ। आप कम संख्या में फ़ाइलों का उपयोग करके शुरू कर सकते हैं, और बाद में अधिक के साथ प्रयोग कर सकते हैं। इसमें कुछ मिनट लग सकते हैं।

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

इसके बाद, एक बनाने tf.data.Dataset पार्स नोटों से।

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)

आप नोटों के अनुक्रमों के बैचों पर मॉडल को प्रशिक्षित करेंगे। प्रत्येक उदाहरण में इनपुट सुविधाओं के रूप में नोट्स का एक क्रम होगा, और अगला नोट लेबल के रूप में होगा। इस तरह, मॉडल को अनुक्रम में अगले नोट की भविष्यवाणी करने के लिए प्रशिक्षित किया जाएगा। आप एक चित्र में इस प्रक्रिया को (और अधिक जानकारी) समझा पा सकते हैं एक RNN साथ पाठ वर्गीकरण

आप काम का उपयोग कर सकते खिड़की के आकार के साथ समारोह seq_length इस प्रारूप में सुविधाओं और लेबल बनाने के लिए।

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)

प्रत्येक उदाहरण के लिए अनुक्रम लंबाई निर्धारित करें। अलग-अलग लंबाई के साथ प्रयोग (जैसे 50, 100, 150), जो एक डेटा के लिए सबसे अच्छा काम करता है, या उपयोग को देखने के लिए hyperparameter ट्यूनिंग । शब्दावली (के आकार vocab_size ) 128 सभी के द्वारा समर्थित पिचों का प्रतिनिधित्व करने के लिए सेट है 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)})

डाटासेट के आकार है (100,1) जिसका अर्थ है कि मॉडल इनपुट के रूप में 100 नोट ले जाएगा, और आउटपुट के रूप में निम्नलिखित टिप्पणी की भविष्यवाणी करने के जानने के लिए,।

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>}

उदाहरणों को बैचें, और प्रदर्शन के लिए डेटासेट को कॉन्फ़िगर करें।

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)})

मॉडल बनाएं और प्रशिक्षित करें

मॉडल में तीन आउटपुट होंगे, प्रत्येक नोट वेरिएबल के लिए एक। के लिए pitch और duration , आपको लगता है कि उत्पादन गैर नकारात्मक मूल्यों के मॉडल को प्रोत्साहित करती है मतलब वर्ग त्रुटि के आधार पर एक कस्टम नुकसान समारोह का प्रयोग करेंगे।

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
__________________________________________________________________________________________________

परीक्षण model.evaluate समारोह, आप देख सकते हैं कि pitch नुकसान की तुलना में काफी अधिक है step और duration नुकसान। ध्यान दें कि loss कुल नुकसान अन्य सभी नुकसान संक्षेप द्वारा गणना है और वर्तमान में प्रभुत्व है pitch नुकसान।

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}

एक तरह से संतुलन इस का उपयोग करने के लिए है loss_weights संकलन करने के लिए तर्क:

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

loss तो व्यक्तिगत नुकसान की भारित योग बन जाता है।

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}

मॉडल को प्रशिक्षित करें।

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()

पीएनजी

नोट्स उत्पन्न करें

नोट्स बनाने के लिए मॉडल का उपयोग करने के लिए, आपको पहले नोट्स का एक प्रारंभिक क्रम प्रदान करना होगा। नीचे दिया गया फ़ंक्शन नोट्स के अनुक्रम से एक नोट उत्पन्न करता है।

नोट पिच के लिए, यह मॉडल द्वारा उत्पादित नोटों के सॉफ्टमैक्स वितरण से एक नमूना खींचता है, और केवल उच्चतम संभावना वाले नोट को नहीं चुनता है। हमेशा उच्चतम संभावना वाले नोट को चुनने से नोटों के दोहराव वाले क्रम उत्पन्न होंगे।

temperature पैरामीटर उत्पन्न नोटों की अनियमितता को नियंत्रित किया जा सकता है। आप में तापमान के बारे में अधिक जानकारी प्राप्त कर सकते एक RNN साथ पाठ पीढ़ी

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)

अब कुछ नोट्स बनाएं। आप तापमान और में शुरू अनुक्रम के साथ चारों ओर खेल सकते हैं next_notes और देखो क्या होता।

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)

आप नीचे दी गई दो पंक्तियों को जोड़कर ऑडियो फ़ाइल भी डाउनलोड कर सकते हैं:

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

उत्पन्न नोटों की कल्पना करें।

plot_piano_roll(generated_notes)

पीएनजी

का वितरण चेक pitch , step और duration

plot_distributions(generated_notes)

पीएनजी

उपरोक्त भूखंडों में, आप नोट चर के वितरण में परिवर्तन देखेंगे। चूंकि मॉडल के आउटपुट और इनपुट के बीच फीडबैक लूप होता है, इसलिए मॉडल नुकसान को कम करने के लिए आउटपुट के समान अनुक्रम उत्पन्न करता है। इस के लिए विशेष रूप से प्रासंगिक है step और duration है, जो का उपयोग करता है एमएसई नुकसान है। के लिए pitch , आप में वृद्धि से अनियमितता को बढ़ा सकते हैं temperature में predict_next_note

अगला कदम

इस ट्यूटोरियल ने MIDI फ़ाइलों के डेटासेट से नोट्स के अनुक्रम उत्पन्न करने के लिए RNN का उपयोग करने के यांत्रिकी का प्रदर्शन किया। अधिक जानकारी के लिए आप बारीकी से संबंधित यात्रा कर सकते हैं एक RNN साथ पाठ पीढ़ी ट्यूटोरियल है, जो अतिरिक्त चित्र और स्पष्टीकरण शामिल हैं।

संगीत निर्माण के लिए RNN का उपयोग करने का एक विकल्प GAN का उपयोग कर रहा है। ऑडियो उत्पन्न करने के बजाय, एक GAN-आधारित दृष्टिकोण समानांतर में एक संपूर्ण अनुक्रम उत्पन्न कर सकता है। मैजेंटा टीम के साथ इस दृष्टिकोण पर प्रभावशाली काम किया है GANSynth । तुम भी पर कई अद्भुत संगीत और कला परियोजनाओं और मुक्त स्रोत कोड प्राप्त कर सकते हैं मैजेंटा परियोजना वेबसाइट