def __init__(self,
size='large',
f0_bins=128,
spectral_fn=lambda x: spectral_ops.compute_mag(x, size=1024),
name='resnet_f0_encoder'):
super().__init__(name=name)
self.f0_bins = f0_bins
self.spectral_fn = spectral_fn
# Layers.
self.resnet = nn.ResNet(size=size)
self.dense_out = tfkl.Dense(f0_bins)
def __init__(self,
output_splits=(('frequencies', 100 * 64), ('amplitudes', 100),
('noise_magnitudes', 60)),
spectral_fn=spectral_ops.compute_logmel,
size='tiny',
name='resnet_sinusoidal_encoder'):
super().__init__(name=name)
self.output_splits = output_splits
self.spectral_fn = spectral_fn
# Layers.
self.resnet = nn.ResNet(size=size)
self.dense_outs = [tfkl.Dense(v[1]) for v in output_splits]
def __init__(self,
output_splits=(('frequencies', 100 * 64),
('amplitudes', 100),
('noise_magnitudes', 60)),
spectral_fn=spectral_ops.compute_logmel,
size='tiny',
**kwargs):
super().__init__(output_keys=[key for key, dim in output_splits], **kwargs)
self.output_splits = output_splits
self.spectral_fn = spectral_fn
# Layers.
self.resnet = nn.ResNet(size=size)
self.dense_outs = [tfkl.Dense(v[1]) for v in output_splits]
def __init__(self,
input_keys=('z', ),
output_splits=(('frequencies', 100 * 64), ('amplitudes', 100),
('noise_magnitudes', 60)),
size='small',
**kwargs):
super().__init__(input_keys=input_keys,
output_keys=[key for key, dim in output_splits],
**kwargs)
self.output_splits = output_splits
# Layers.
self.resnet = nn.ResNet(size=size)
self.dense_outs = [tfkl.Dense(v[1]) for v in output_splits]
