def kl_loss(self, ff_dict, num_samples=100):
teacher = self.teacher
quant_chann = self.quant_chann
use_mu_law = self.use_mu_law
mel = ff_dict['mel']
x = ff_dict['x']
mean = ff_dict['mean_tot']
scale = ff_dict['scale_tot']
log_scale = ff_dict['log_scale_tot']
batch_size, length = x.get_shape().as_list()
rl = self._logistic_0_1(batch_size * num_samples, length)
mean = utils.tf_repeat(mean, [num_samples, 1])
scale = utils.tf_repeat(scale, [num_samples, 1])
# (x_i|x_<i), x given x_previous
x_xp = rl * scale + mean
# clip x and x_xp to real audio range [-1.0, 1.0)
# if use_mu_law = True,
# take iaf output as mu_law encoded real audio signal.
x_scaled = self._clip_quant_scale(x, quant_chann, use_mu_law)
x_xp_scaled = self._clip_quant_scale(x_xp, quant_chann, use_mu_law)
wn_ff_dict = teacher.feed_forward({'wav': x_scaled,
'mel': mel})
te_mol = wn_ff_dict['out_params']
te_mol = utils.tf_repeat(te_mol, [num_samples, 1, 1])
# teacher always use log_scale, so use_log_scale of
# loss_func.mol_log_probs is set to default value True.
log_te_probs = loss_func.mol_log_probs(
te_mol, x_xp_scaled, quant_chann)
# H_Ps_Pt for batch * length
H_Ps_Pt_bl = -tf.reduce_mean(
tf.reshape(log_te_probs, [batch_size, num_samples, length]),
axis=1)
H_Ps = tf.reduce_mean(log_scale) + 2
H_Ps_Pt = tf.reduce_mean(H_Ps_Pt_bl)
kl_loss = H_Ps_Pt - H_Ps
return {'kl_loss': kl_loss,
'H_Ps': H_Ps,
'H_Ps_Pt': H_Ps_Pt}
def kl_loss(self, ff_dict, num_samples=100):
teacher = self.teacher
quant_chann = self.quant_chann
mel = ff_dict['mel']
x = ff_dict['x']
mean = ff_dict['mean_tot']
scale = ff_dict['scale_tot']
log_scale = ff_dict['log_scale_tot']
batch_size, length = x.get_shape().as_list()
rl = self._logistic_0_1(batch_size * num_samples, length)
mean = utils.tf_repeat(mean, [num_samples, 1])
scale = utils.tf_repeat(scale, [num_samples, 1])
# (x_i|x_<i), x given x_previous from student
x_xp = rl * scale + mean
x_scaled = PWNHelper.clip_or_not_fn(self, x)
x_xp_scaled = PWNHelper.clip_or_not_fn(self, x_xp)
wn_ff_dict = teacher.feed_forward({'wav_scaled': x_scaled,
'mel': mel})
te_mol = wn_ff_dict['out_params']
te_mol = utils.tf_repeat(te_mol, [num_samples, 1, 1])
# teacher always use log_scale, so use_log_scale of
# loss_func.mol_log_probs is set to default value True.
log_te_probs = loss_func.mol_log_probs(
te_mol, x_xp_scaled, quant_chann)
# H_Ps_Pt for batch * length
H_Ps_Pt_bl = -tf.reduce_mean(
tf.reshape(log_te_probs, [batch_size, num_samples, length]),
axis=1)
H_Ps = tf.reduce_mean(log_scale) + 2
H_Ps_Pt = tf.reduce_mean(H_Ps_Pt_bl)
kl_loss = H_Ps_Pt - H_Ps
return {'kl_loss': kl_loss,
'H_Ps': H_Ps,
'H_Ps_Pt': H_Ps_Pt}