save_path = '/data/lisatmp/chungjun/nips2015/timit/pkl/'
batch_size = 64
frame_size = 200
main_lstm_dim = 2000
p_x_dim = 450
x2s_dim = 450
k = 20
target_size = frame_size * k
lr = 1e-3
debug = 0
model = Model()
train_data = TIMIT(name='train',
path=data_path,
frame_size=frame_size,
shuffle=0,
use_n_gram=1)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = TIMIT(name='valid',
path=data_path,
frame_size=frame_size,
shuffle=0,
use_n_gram=1,
X_mean=X_mean,
X_std=X_std)
init_W = InitCell('rand')
save_path = '/home/junyoung/repos/sk/cle/models/nips2015/timit/sample/'
exp_path = '/home/junyoung/repos/sk/cle/models/nips2015/timit/pkl/'
frame_size = 200
label_size = 200
# How many samples to generate
batch_size = 1
num_sample = 10
debug = 1
exp_name = 'm7_cond_v2'
save_name = 'm7_cond_v2_sample_'
train_data = TIMIT(name='train',
path=data_path,
frame_size=frame_size,
shuffle=0,
use_n_gram=1)
X_mean = train_data.X_mean
X_std = train_data.X_std
test_data = TIMIT(name='test',
path=data_path,
frame_size=frame_size,
shuffle=0,
use_n_gram=1,
X_mean=X_mean,
X_std=X_std)
exp = unpickle(exp_path + exp_name + '_best.pkl')
