def gen(folder, actF, fft, inputSongs):
config = nn_config.get_neural_net_configuration()
nn = "TOYPAJ-NPWeights50-"
model_filename = folder + nn + actF
actF = actF + "moid" if actF == "sig" else actF
sample_frequency = config['sampling_frequency']
inputFile = folder + "TOYPAYJ-Processed"
output_filename = folder + actF + 'generated_'
#Load up the training data
print ('Loading training data')
#X_train is a tensor of size (num_train_examples, num_timesteps, num_frequency_dims)
#y_train is a tensor of size (num_train_examples, num_timesteps, num_frequency_dims)
#X_mean is a matrix of size (num_frequency_dims,) containing the mean for each frequency dimension
#X_var is a matrix of size (num_frequency_dims,) containing the variance for each frequency dimension
X_train = np.load(inputFile + '_x.npy')
print( X_train.shape)
print( type(X_train))
y_train = np.load(inputFile + '_y.npy')
X_mean = np.load(inputFile + '_mean.npy')
X_var = np.load(inputFile + '_var.npy')
print ('Finished loading training data')
#Figure out how many frequencies we have in the data
freq_space_dims = X_train.shape[1:]
hidden_dims = config['hidden_dimension_size']
#Creates a lstm network
model = network_utils.create_lstm_network(num_frequency_dimensions=freq_space_dims, num_hidden_dimensions=hidden_dims, actF = actF)
#You could also substitute this with a RNN or GRU
#model = network_utils.create_gru_network()
print( model_filename)
#Load existing weights if available
if os.path.isfile(model_filename):
model.load_weights(model_filename)
else:
print('Model filename ' + model_filename + ' could not be found!')
print ('Starting generation!')
#Here's the interesting part
#We need to create some seed sequence for the algorithm to start with
#Currently, we just grab an existing seed sequence from our training data and use that
#However, this will generally produce verbatum copies of the original songs
#In a sense, choosing good seed sequences = how you get interesting compositions
#There are many, many ways we can pick these seed sequences such as taking linear combinations of certain songs
#We could even provide a uniformly random sequence, but that is highly unlikely to produce good results
seed_len = 1
block_size = X_train.shape[2] / 2 if fft else X_train.shape[2]
for song in inputSongs:
name = song[song.rfind('/') + 1:]
print( name)
"""
seed_seq = seed_generator.generate_from_file(
filename=song,
seed_length = 1,
block_size = block_size,
seq_len = 40,
std = X_var,
mean = X_mean,
fft = fft,
offsetSec = 53)
"""
seed_seq = seed_generator.generate_copy_seed_sequence(1, X_train)
print(seed_seq.shape)
max_seq_len = 6; #Defines how long the final song is. Total song length in samples = max_seq_len * example_len
output = []
for i in xrange(seed_seq.shape[1]):
output.append(seed_seq[0][i].copy())
save_generated_example(folder + "input_3" + name, output, sample_frequency=sample_frequency, useTimeDomain=not fft)
output = sequence_generator.generate_from_seed(model=model, seed=seed_seq, sequence_length=max_seq_len,
data_variance=X_var, data_mean=X_mean)
print( len(output))
print ('Finished generation!')
#Save the generated sequence to a WAV file
save_generated_example(output_filename + "3" + name, output, sample_frequency=sample_frequency, useTimeDomain=not fft)
from __future__ import absolute_import
from __future__ import print_function
import argparse
import os
import warnings
from matplotlib import pyplot as plt
import numpy as np
import nn_utils.network_utils as network_utils
import config.nn_config as nn_config
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
warnings.filterwarnings("ignore", category=FutureWarning)
CONFIG = nn_config.get_neural_net_configuration()
CUR_ITER = CONFIG['current_iteration']
FREQ_SPACE_DIMS = CONFIG['num_frequency_dimensions']
HIDDEN_DIMS = CONFIG['hidden_dimension_size']
INPUTFILE = CONFIG['model_file']
MODEL_BASENAME = CONFIG['model_basename']
MODEL_FILENAME = MODEL_BASENAME + str(CUR_ITER)
NUM_RECURR = CONFIG['num_recurrent_units']
parser = argparse.ArgumentParser(description='Train model')
parser.add_argument('-n', '--n_iter', type=int, default=10,
help='number of iterations')
parser.add_argument('-e', '--n_epochs', type=int, default=5,
help='epochs per iterations')
parser.add_argument('-b', '--n_batch', type=int, default=5,
help='batchsize per iterations')
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import os
import nn_utils.network_utils as network_utils
import config.nn_config as nn_config
from keras.utils.np_utils import accuracy
config = nn_config.get_neural_net_configuration()
inputFile = config['model_file']
cur_iter = 0
model_basename = config['model_basename']
model_filename = model_basename + str(cur_iter)
#Load up the training data
print('Loading training data')
#X_train is a tensor of size (num_train_examples, num_timesteps, num_frequency_dims)
#y_train is a tensor of size (num_train_examples, num_timesteps, num_frequency_dims)
X_train = np.load(inputFile + '_x.npy')
#y_train = np.load(inputFile + '_y.npy')
y_train = np.ones(X_train[0])
'''
X_test = np.load(inputFile + '_x.npy')
y_test = np.load(inputFile + '_y.npy')
'''
print('Finished loading training data')
#Figure out how many frequencies we have in the data
freq_space_dims = X_train.shape[2]
hidden_dims = config['hidden_dimension_size']
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import os
import nn_utils.network_utils as network_utils
import gen_utils.seed_generator as seed_generator
import gen_utils.sequence_generator as sequence_generator
from data_utils.parse_files import *
import config.nn_config as nn_config
config = nn_config.get_neural_net_configuration()
sample_frequency = config['sampling_frequency']
inputFile = config['model_file']
model_basename = config['model_basename']
cur_iter = 1000
model_filename = model_basename + str(cur_iter)
output_filename = './generated_song.wav'
# Load up the training data
print ('Loading training data')
# X_train is a tensor of size (num_train_examples, num_timesteps, num_frequency_dims)
# y_train is a tensor of size (num_train_examples, num_timesteps, num_frequency_dims)
# X_mean is a matrix of size (num_frequency_dims,) containing the mean for each frequency dimension
# X_var is a matrix of size (num_frequency_dims,) containing the variance for each frequency dimension
X_train = np.load(inputFile + '_x.npy')
y_train = np.load(inputFile + '_y.npy')
X_mean = np.load(inputFile + '_mean.npy')
X_var = np.load(inputFile + '_var.npy')
print ('Finished loading training data')
