def build_wavenet(batch_size=1, sample_length=64000):
config = Config()
x = tf.placeholder(tf.float32, shape=[batch_size, sample_length])
graph = config.build({"wav": x}, is_training=False)
graph.update({"X": x})
return graph
def load_style_nsynth(initial):
"""Load the NSynth autoencoder network for stylizing."""
config = Config()
with tf.device("/gpu:0"):
initial = initial.reshape([1, -1, 1]) # [Batch_size, length, channel]
x = tf.Variable(initial)
graph = config.build({"wav": x}, is_training=False)
graph.update({"X": x})
return graph
def load_nsynth(batch_size=1, sample_length=40000):
# Load the NSynth autoencoder network.
config = Config()
print("Inside load_nsynth function")
with tf.device("/device:GPU:0"):
print("Loading nsynth")
x = tf.placeholder(tf.float32, shape=[batch_size, sample_length])
graph = config.build({"wav": x}, is_training=False)
graph.update({"X": x})
return graph
def load_nsynth(batch_size=1, sample_length=64000):
"""Load the NSynth autoencoder network.
Args:
batch_size: Batch size number of observations to process. [1]
sample_length: Number of samples in the input audio. [64000]
Returns:
graph: The network as a dict with input placeholder in {"X"}
"""
config = Config()
with tf.device("/gpu:0"):
x = tf.placeholder(tf.float32, shape=[batch_size, sample_length])
graph = config.build({"wav": x}, is_training=False)
graph.update({"X": x})
return graph
def load_nsynth(batch_size=1, sample_length=64000):
"""Load the NSynth autoencoder network.
Args:
batch_size: Batch size number of observations to process. [1]
sample_length: Number of samples in the input audio. [64000]
Returns:
graph: The network as a dict with input placeholder in {"X"}
"""
config = Config()
with tf.device("/gpu:0"):
x = tf.placeholder(tf.float32, shape=[batch_size, sample_length])
graph = config.build({"wav": x}, is_training=False)
graph.update({"X": x})
return graph