sudo apt install musescore timidity lilypond
pip install music21 matplotlib scipy tensorflow
from music21 import *
us = environment.UserSettings()
us.getSettingsPath()
'/home/tsu-nera/.music21rc'
#us["musicxmlPath"] = "/usr/bin/gedit"
us["musicxmlPath"] = "/usr/bin/musescore"
us["midiPath"] = "/usr/bin/timidity"
us["showFormat"] = "lilypond"
us["writeFormat"] = "lilypond"
us["musescoreDirectPNGPath"] = "/usr/bin/musescore"
!mkdir composer
import glob
REP="@\n"
def trim_metadata(output_path, glob_path):
comp_txt = open(output_path,"w")
ll = glob.glob(glob_path)
for song in ll:
lines = open(song,"r").readlines()
out = []
found_first = False
for l in lines:
if l.startswith("="):
## new measure, replace the measure with the @ sign, not part of humdrum
out.append(REP)
found_first = True
continue
if not found_first:
## keep going until we find the end of the header and metadata
continue
if l.startswith("!"):
## ignore comments
continue
out.append(l)
comp_txt.writelines(out)
comp_txt.close()
%mkdir kernscore
%mkdir kernscore/bach
from urllib.request import urlopen
for i in range(1,15+1):
filename = "inven{0:02d}.krn".format(i)
file = urlopen("http://kern.humdrum.org/cgi-bin/ksdata?l=osu/classical/bach/inventions&file=%s&f=kern"%filename)
with open("kernscore/bach/"+filename,'wb') as output:
output.write(file.read())
output_path = "composer/bach.txt"
glob_path = "kernscore/bach/*.krn"
trim_metadata(output_path, glob_path)
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
filename = 'composer/bach.txt'
with open(filename, 'r') as f:
text=f.read()
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
encoded = np.array([vocab_to_int[c] for c in text], dtype=np.int32)
text[:50]
'@\n4.r\t16dL\n.\t16e\n.\t16f\n.\t16g\n.\t16a\n.\t16b-J\n@\n4.r\t1'
encoded[:100]
array([11, 4, 8, 31, 15, 35, 7, 3, 27, 6, 4, 31, 35, 7, 3, 12, 4,
31, 35, 7, 3, 39, 4, 31, 35, 7, 3, 24, 4, 31, 35, 7, 3, 2,
4, 31, 35, 7, 3, 9, 13, 1, 4, 11, 4, 8, 31, 15, 35, 7, 3,
22, 32, 6, 4, 31, 35, 7, 3, 9, 13, 4, 31, 35, 7, 3, 2, 4,
31, 35, 7, 3, 24, 4, 31, 35, 7, 3, 39, 4, 31, 35, 7, 3, 12,
1, 4, 11, 4, 7, 3, 41, 6, 35, 21, 39, 6, 4, 7, 3], dtype=int32)
vocab_size = len(vocab)
vocab_size
43
def get_batches(arr, n_seqs, n_steps):
'''Create a generator that returns batches of size
n_seqs x n_steps from arr.
Arguments
---------
arr: Array you want to make batches from
n_seqs: Batch size, the number of sequences per batch
n_steps: Number of sequence steps per batch
'''
# Get the batch size and number of batches we can make
batch_size = n_seqs * n_steps
n_batches = len(arr)//batch_size
# Keep only enough characters to make full batches
arr = arr[:n_batches * batch_size]
# Reshape into n_seqs rows
arr = arr.reshape((n_seqs, -1))
for n in range(0, arr.shape[1], n_steps):
# The features
x = arr[:, n:n+n_steps]
# The targets, shifted by one
y = np.zeros_like(x)
y[:, :-1], y[:, -1] = x[:, 1:], x[:, 0]
yield x, y
batches = get_batches(encoded, 10, 50)
x, y = next(batches)
print('x\n', x[:10, :10])
print('\ny\n', y[:10, :10])
x [[11 4 8 31 15 35 7 3 27 6] [35 7 3 24 24 42 5 4 7 3] [ 7 3 22 22 32 4 31 35 7 3] [ 9 9 4 7 3 38 35 31 4 11] [ 3 24 4 7 3 33 35 7 3 39] [ 2 2 4 7 3 27 35 31 4 7] [16 18 41 32 35 7 3 39 32 1] [35 31 4 7 3 38 35 8 9 13] [27 1 4 11 4 7 3 33 6 35] [ 7 3 24 4 11 4 7 3 0 35]] y [[ 4 8 31 15 35 7 3 27 6 4] [ 7 3 24 24 42 5 4 7 3 36] [ 3 22 22 32 4 31 35 7 3 2] [ 9 4 7 3 38 35 31 4 11 4] [24 4 7 3 33 35 7 3 39 32] [ 2 4 7 3 27 35 31 4 7 3] [18 41 32 35 7 3 39 32 1 4] [31 4 7 3 38 35 8 9 13 19] [ 1 4 11 4 7 3 33 6 35 21] [ 3 24 4 11 4 7 3 0 35 21]]
def build_inputs(batch_size, num_steps):
''' Define placeholders for inputs, targets, and dropout
Arguments
---------
batch_size: Batch size, number of sequences per batch
num_steps: Number of sequence steps in a batch
'''
# Declare placeholders we'll feed into the graph
inputs = tf.placeholder(tf.int32, [batch_size, num_steps], name='inputs')
targets = tf.placeholder(tf.int32, [batch_size, num_steps], name='targets')
# Keep probability placeholder for drop out layers
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
return inputs, targets, keep_prob
def build_lstm(lstm_size, num_layers, batch_size, keep_prob):
''' Build LSTM cell.
Arguments
---------
keep_prob: Scalar tensor (tf.placeholder) for the dropout keep probability
lstm_size: Size of the hidden layers in the LSTM cells
num_layers: Number of LSTM layers
batch_size: Batch size
'''
### Build the LSTM Cell
# Use a basic LSTM cell
lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
# Add dropout to the cell
drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
# Stack up multiple LSTM layers, for deep learning
cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)
initial_state = cell.zero_state(batch_size, tf.float32)
return cell, initial_state
def build_output(lstm_output, in_size, out_size):
''' Build a softmax layer, return the softmax output and logits.
Arguments
---------
x: Input tensor
in_size: Size of the input tensor, for example, size of the LSTM cells
out_size: Size of this softmax layer
'''
# Reshape output so it's a bunch of rows, one row for each step for each sequence.
# That is, the shape should be batch_size*num_steps rows by lstm_size columns
seq_output = tf.concat(lstm_output, axis=1)
x = tf.reshape(seq_output, [-1, in_size])
# Connect the RNN outputs to a softmax layer
with tf.variable_scope('softmax'):
softmax_w = tf.Variable(tf.truncated_normal((in_size, out_size), stddev=0.1))
softmax_b = tf.Variable(tf.zeros(out_size))
# Since output is a bunch of rows of RNN cell outputs, logits will be a bunch
# of rows of logit outputs, one for each step and sequence
logits = tf.matmul(x, softmax_w) + softmax_b
# Use softmax to get the probabilities for predicted characters
out = tf.nn.softmax(logits, name='predictions')
return out, logits
def build_loss(logits, targets, lstm_size, num_classes):
''' Calculate the loss from the logits and the targets.
Arguments
---------
logits: Logits from final fully connected layer
targets: Targets for supervised learning
lstm_size: Number of LSTM hidden units
num_classes: Number of classes in targets
'''
# One-hot encode targets and reshape to match logits, one row per batch_size per step
y_one_hot = tf.one_hot(targets, num_classes)
y_reshaped = tf.reshape(y_one_hot, logits.get_shape())
# Softmax cross entropy loss
loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped)
loss = tf.reduce_mean(loss)
return loss
def build_optimizer(loss, learning_rate, grad_clip):
''' Build optmizer for training, using gradient clipping.
Arguments:
loss: Network loss
learning_rate: Learning rate for optimizer
'''
# Optimizer for training, using gradient clipping to control exploding gradients
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), grad_clip)
train_op = tf.train.AdamOptimizer(learning_rate)
optimizer = train_op.apply_gradients(zip(grads, tvars))
return optimizer
class CharRNN:
def __init__(self, num_classes, batch_size=64, num_steps=50,
lstm_size=128, num_layers=2, learning_rate=0.001,
grad_clip=5, sampling=False):
# When we're using this network for sampling later, we'll be passing in
# one character at a time, so providing an option for that
if sampling == True:
batch_size, num_steps = 1, 1
else:
batch_size, num_steps = batch_size, num_steps
tf.reset_default_graph()
# Build the input placeholder tensors
self.inputs, self.targets, self.keep_prob = build_inputs(batch_size, num_steps)
# Build the LSTM cell
cell, self.initial_state = build_lstm(lstm_size, num_layers, batch_size, self.keep_prob)
### Run the data through the RNN layers
# First, one-hot encode the input tokens
x_one_hot = tf.one_hot(self.inputs, num_classes)
# Run each sequence step through the RNN and collect the outputs
outputs, state = tf.nn.dynamic_rnn(cell, x_one_hot, initial_state=self.initial_state)
self.final_state = state
# Get softmax predictions and logits
self.prediction, self.logits = build_output(outputs, lstm_size, num_classes)
# Loss and optimizer (with gradient clipping)
self.loss = build_loss(self.logits, self.targets, lstm_size, num_classes)
self.optimizer = build_optimizer(self.loss, learning_rate, grad_clip)
batch_size = 10
num_steps = 10
lstm_size = 512
num_layers = 2
learning_rate = 0.001
keep_prob = 0.5
epochs = 20
# Save every N iterations
save_every_n = 200
model = CharRNN(len(vocab), batch_size=batch_size, num_steps=num_steps,
lstm_size=lstm_size, num_layers=num_layers,
learning_rate=learning_rate)
saver = tf.train.Saver(max_to_keep=100)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Use the line below to load a checkpoint and resume training
#saver.restore(sess, 'checkpoints/______.ckpt')
counter = 0
for e in range(epochs):
# Train network
new_state = sess.run(model.initial_state)
loss = 0
for x, y in get_batches(encoded, batch_size, num_steps):
counter += 1
start = time.time()
feed = {model.inputs: x,
model.targets: y,
model.keep_prob: keep_prob,
model.initial_state: new_state}
batch_loss, new_state, _ = sess.run([model.loss,
model.final_state,
model.optimizer],
feed_dict=feed)
end = time.time()
print('Epoch: {}/{}... '.format(e+1, epochs),
'Training Step: {}... '.format(counter),
'Training loss: {:.4f}... '.format(batch_loss),
'{:.4f} sec/batch'.format((end-start)))
if (counter % save_every_n == 0):
saver.save(sess, "checkpoints/i{}_l{}.ckpt".format(counter, lstm_size))
saver.save(sess, "checkpoints/i{}_l{}.ckpt".format(counter, lstm_size))
Epoch: 1/20... Training Step: 1... Training loss: 3.7620... 0.1642 sec/batch Epoch: 1/20... Training Step: 2... Training loss: 3.7256... 0.1233 sec/batch Epoch: 1/20... Training Step: 3... Training loss: 3.6567... 0.1201 sec/batch Epoch: 1/20... Training Step: 4... Training loss: 3.4860... 0.1188 sec/batch Epoch: 1/20... Training Step: 5... Training loss: 3.2803... 0.1250 sec/batch Epoch: 1/20... Training Step: 6... Training loss: 3.6022... 0.1220 sec/batch Epoch: 1/20... Training Step: 7... Training loss: 4.0865... 0.1183 sec/batch Epoch: 1/20... Training Step: 8... Training loss: 3.1127... 0.1227 sec/batch Epoch: 1/20... Training Step: 9... Training loss: 3.3907... 0.1215 sec/batch Epoch: 1/20... Training Step: 10... Training loss: 3.4856... 0.1189 sec/batch Epoch: 1/20... Training Step: 11... Training loss: 3.4883... 0.1214 sec/batch Epoch: 1/20... Training Step: 12... Training loss: 3.3462... 0.1254 sec/batch Epoch: 1/20... Training Step: 13... Training loss: 3.3203... 0.1224 sec/batch Epoch: 1/20... Training Step: 14... Training loss: 3.1857... 0.1243 sec/batch Epoch: 1/20... Training Step: 15... Training loss: 3.1654... 0.1260 sec/batch Epoch: 1/20... Training Step: 16... Training loss: 3.3089... 0.1259 sec/batch Epoch: 1/20... Training Step: 17... Training loss: 3.0935... 0.1218 sec/batch Epoch: 1/20... Training Step: 18... Training loss: 3.0318... 0.1229 sec/batch Epoch: 1/20... Training Step: 19... Training loss: 3.1196... 0.1216 sec/batch Epoch: 1/20... Training Step: 20... Training loss: 2.9132... 0.1198 sec/batch Epoch: 1/20... Training Step: 21... Training loss: 3.1271... 0.1283 sec/batch Epoch: 1/20... Training Step: 22... Training loss: 3.1013... 0.1219 sec/batch Epoch: 1/20... Training Step: 23... Training loss: 3.1496... 0.1247 sec/batch Epoch: 1/20... Training Step: 24... Training loss: 3.0565... 0.1232 sec/batch Epoch: 1/20... Training Step: 25... Training loss: 2.9929... 0.1239 sec/batch Epoch: 1/20... 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