from scipy.io import wavfile
import os
import sys
from kdllib import load_checkpoint, theano_one_hot, concatenate
from kdllib import fetch_fruitspeech_spectrogram, list_iterator
from kdllib import np_zeros, GRU, GRUFork, dense_to_one_hot
from kdllib import make_weights, make_biases, relu, run_loop
from kdllib import as_shared, adam, gradient_clipping
from kdllib import get_values_from_function, set_shared_variables_in_function
from kdllib import soundsc, categorical_crossentropy
from kdllib import sample_binomial, sigmoid
if __name__ == "__main__":
import argparse
speech = fetch_fruitspeech_spectrogram()
X = speech["data"]
y = speech["target"]
vocabulary = speech["vocabulary"]
vocabulary_size = speech["vocabulary_size"]
reconstruct = speech["reconstruct"]
fs = speech["sample_rate"]
X = np.array([x.astype(theano.config.floatX) for x in X])
y = np.array([yy.astype(theano.config.floatX) for yy in y])
minibatch_size = 1
n_epochs = 200 # Used way at the bottom in the training loop!
checkpoint_every_n = 10
cut_len = 41 # Used way at the bottom in the training loop!
random_state = np.random.RandomState(1999)
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from scipy.io import wavfile
import os
import sys
from kdllib import make_conv_weights, conv2d_transpose, unpool, softmax, make_biases
from kdllib import load_checkpoint, dense_to_one_hot, categorical_crossentropy
from kdllib import fetch_fruitspeech_spectrogram, list_iterator, np_zeros, GRU, GRUFork
from kdllib import make_weights, as_shared, adam, gradient_clipping, theano_one_hot
from kdllib import get_values_from_function, set_shared_variables_in_function
from kdllib import save_checkpoint, save_weights, relu, tanh, soundsc
from kdllib import run_loop
if __name__ == "__main__":
import argparse
speech = fetch_fruitspeech_spectrogram()
X = speech["data"]
y = speech["target"]
vocabulary = speech["vocabulary"]
vocabulary_size = speech["vocabulary_size"]
reconstruct = speech["reconstruct"]
fs = speech["sample_rate"]
X = np.array([x.astype(theano.config.floatX) for x in X])
y = np.array([yy.astype(theano.config.floatX) for yy in y])
minibatch_size = 20
n_epochs = 20000 # Used way at the bottom in the training loop!
checkpoint_every_n = 500
# Was 300 for handwriting
cut_len = 31 # Used way at the bottom in the training loop!
random_state = np.random.RandomState(1999)