def load(self, checkpoint_path, hparams, model_name='WaveNet'):
log('Constructing model: {}'.format(model_name))
self._hparams = hparams
local_cond, global_cond = self._check_conditions()
self.local_conditions = tf.placeholder(
tf.float32,
shape=[1, None, hparams.num_mels],
name='local_condition_features') if local_cond else None
self.global_conditions = tf.placeholder(
tf.int32, shape=(),
name='global_condition_features') if global_cond else None
self.synthesis_length = tf.placeholder(
tf.int32, shape=(),
name='synthesis_length') if not local_cond else None
with tf.variable_scope('model') as scope:
self.model = create_model(model_name, hparams)
self.model.initialize(y=None,
c=self.local_conditions,
g=self.global_conditions,
input_lengths=None,
synthesis_length=self.synthesis_length)
self._hparams = hparams
sh_saver = create_shadow_saver(self.model)
log('Loading checkpoint: {}'.format(checkpoint_path))
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
load_averaged_model(self.session, sh_saver, checkpoint_path)
def load(self, checkpoint_path, hparams, model_name='WaveNet'):
print('Constructing model: {}'.format(model_name))
log('Constructing model: {}'.format(model_name))
self._hparams = hparams
local_cond, global_cond = self._check_conditions()
self.local_conditions = tf.placeholder(
tf.float32,
shape=(None, None, hparams.num_mels),
name='local_condition_features') if local_cond else None
self.global_conditions = tf.placeholder(
tf.int32, shape=(None, 1),
name='global_condition_features') if global_cond else None
self.synthesis_length = tf.placeholder(
tf.int32, shape=(),
name='synthesis_length') if not local_cond else None
self.targets = tf.placeholder(
tf.float32, shape=(1, None, 1), name='audio_targets'
) if hparams.wavenet_synth_debug else None #Debug only with 1 wav
self.input_lengths = tf.placeholder(
tf.int32, shape=(1, ),
name='input_lengths') if hparams.wavenet_synth_debug else None
self.synth_debug = hparams.wavenet_synth_debug
with tf.variable_scope('WaveNet_model') as scope:
self.model = create_model(model_name, hparams)
self.model.initialize(y=None,
c=self.local_conditions,
g=self.global_conditions,
input_lengths=self.input_lengths,
synthesis_length=self.synthesis_length,
test_inputs=self.targets)
self._hparams = hparams
sh_saver = create_shadow_saver(self.model)
print('Loading checkpoint: {}'.format(checkpoint_path))
log('Loading checkpoint: {}'.format(checkpoint_path))
#Memory allocation on the GPU as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
self.session = tf.Session(config=config)
self.session.run(tf.global_variables_initializer())
load_averaged_model(self.session, sh_saver, checkpoint_path)
