def __prepare_model(self, params):
"""Prepare G2P model for training."""
self.params = params
self.session = tf.Session()
# Prepare model.
print("Creating model with parameters:")
print(params)
if self.mode == 'g2p':
self.model = seq2seq_model.Seq2SeqModel(len(self.gr_vocab),
len(self.ph_vocab), self._BUCKETS,
self.params.size,
self.params.num_layers,
self.params.max_gradient_norm,
self.params.batch_size,
self.params.learning_rate,
self.params.lr_decay_factor,
forward_only=False,
optimizer=self.params.optimizer)
else:
self.model = seq2seq_model.Seq2SeqModel(len(self.ph_vocab),
len(self.gr_vocab), self._BUCKETS,
self.params.size,
self.params.num_layers,
self.params.max_gradient_norm,
self.params.batch_size,
self.params.learning_rate,
self.params.lr_decay_factor,
forward_only=False,
optimizer=self.params.optimizer)
self.model.saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)
def load_decode_model(self):
"""Load G2P model and initialize or load parameters in session."""
if not os.path.exists(os.path.join(self.model_dir, 'checkpoint')):
raise RuntimeError("Model not found in %s" % self.model_dir)
self.batch_size = 1 # We decode one word at a time.
#Load model parameters.
num_layers, size = data_utils.load_params(self.model_dir)
# Load vocabularies
print("Loading vocabularies from %s" % self.model_dir)
self.gr_vocab = data_utils.load_vocabulary(os.path.join(self.model_dir,
"vocab.grapheme"))
self.ph_vocab = data_utils.load_vocabulary(os.path.join(self.model_dir,
"vocab.phoneme"))
self.rev_ph_vocab =\
data_utils.load_vocabulary(os.path.join(self.model_dir, "vocab.phoneme"),
reverse=True)
self.session = tf.Session()
# Restore model.
print("Creating %d layers of %d units." % (num_layers, size))
self.model = seq2seq_model.Seq2SeqModel(len(self.gr_vocab),
len(self.ph_vocab), self._BUCKETS,
size, num_layers, 0,
self.batch_size, 0, 0,
forward_only=True)
self.model.saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)
# Check for saved models and restore them.
print("Reading model parameters from %s" % self.model_dir)
self.model.saver.restore(self.session, os.path.join(self.model_dir,
"model"))
