class WaveNetWrapper(nn.Module):
"""A wrapper around r9y9's WaveNet implementation to integrate it seamlessly into the framework."""
IDENTIFIER = "r9y9WaveNet"
def __init__(self, dim_in, dim_out, hparams):
super().__init__()
self.len_in_out_multiplier = hparams.len_in_out_multiplier
# Use the wavenet_vocoder builder to create the model.
self.model = WaveNet(out_channels=hparams.out_channels,
layers=hparams.layers,
stacks=hparams.stacks,
residual_channels=hparams.residual_channels,
gate_channels=hparams.gate_channels,
skip_out_channels=hparams.skip_out_channels,
kernel_size=hparams.kernel_size,
dropout=hparams.dropout,
weight_normalization=hparams.weight_normalization,
cin_channels=hparams.cin_channels,
gin_channels=hparams.gin_channels,
n_speakers=hparams.n_speakers,
upsample_conditional_features=hparams.upsample_conditional_features,
upsample_scales=hparams.upsample_scales,
freq_axis_kernel_size=hparams.freq_axis_kernel_size,
scalar_input=is_scalar_input(hparams.input_type),
use_speaker_embedding=hparams.use_speaker_embedding,
)
def forward(self, inputs, hidden, seq_lengths_inputs, max_length_inputs, target=None, seq_lengths_target=None):
if target is not None: # During training and testing with teacher forcing.
output = self.model(target, c=inputs, g=None, softmax=False)
# output = self.model(target, c=inputs[:, :, :target.shape[2]], g=None, softmax=False)
# Output shape is B x C x T. Don't permute here because CrossEntropyLoss requires the same shape.
else: # During inference.
with torch.no_grad():
self.model.make_generation_fast_()
assert(len(seq_lengths_inputs) == 1), "Batch synthesis is not supported yet."
num_frames_to_gen = seq_lengths_inputs[0] * self.len_in_out_multiplier
output = self.model.incremental_forward(c=inputs, T=num_frames_to_gen, softmax=True, quantize=True)
# Output shape is B x C x T.
return output, None
def set_gpu_flag(self, use_gpu):
self.use_gpu = use_gpu
def init_hidden(self, batch_size=1):
return None
def parameters(self):
return self.model.parameters()
class WaveNetWrapper(nn.Module):
"""A wrapper around r9y9's WaveNet implementation to integrate it seamlessly into the framework."""
IDENTIFIER = "r9y9WaveNet"
class Config:
INPUT_TYPE_MULAW = "mulaw-quantize"
INPUT_TYPE_RAW = "raw"
def __init__(
self,
cin_channels=80,
dropout=0.05,
freq_axis_kernel_size=3,
gate_channels=512,
gin_channels=-1,
hinge_regularizer=True, # Only used in MoL prediction (INPUT_TYPE_RAW).
kernel_size=3,
layers=24,
log_scale_min=float(np.log(1e-14)), # Only used in INPUT_TYPE_RAW.
n_speakers=1,
out_channels=256, # Use num_mixtures * 3 (pi, mean, log_scale) for INPUT_TYPE_RAW.
residual_channels=512,
scalar_input=is_scalar_input(INPUT_TYPE_MULAW),
skip_out_channels=256,
stacks=4,
upsample_conditional_features=False,
upsample_scales=[5, 4, 2],
use_speaker_embedding=False,
weight_normalization=True,
legacy=False):
self.cin_channels = cin_channels
self.dropout = dropout
self.freq_axis_kernel_size = freq_axis_kernel_size
self.gate_channels = gate_channels
self.gin_channels = gin_channels
self.hinge_regularizer = hinge_regularizer
self.kernel_size = kernel_size
self.layers = layers
self.log_scale_min = log_scale_min
self.n_speakers = n_speakers
self.out_channels = out_channels
self.residual_channels = residual_channels
self.scalar_input = scalar_input
self.skip_out_channels = skip_out_channels
self.stacks = stacks
self.upsample_conditional_features = upsample_conditional_features
self.upsample_scales = upsample_scales
self.use_speaker_embedding = use_speaker_embedding
self.weight_normalization = weight_normalization
self.legacy = legacy
def create_model(self):
return WaveNetWrapper(self)
def __init__(self, config):
super().__init__()
# self.len_in_out_multiplier = hparams.len_in_out_multiplier
# Use the wavenet_vocoder builder to create the model.
self.model = WaveNet(
out_channels=config.out_channels,
layers=config.layers,
stacks=config.stacks,
residual_channels=config.residual_channels,
gate_channels=config.gate_channels,
skip_out_channels=config.skip_out_channels,
kernel_size=config.kernel_size,
dropout=config.dropout,
weight_normalization=config.weight_normalization,
cin_channels=config.cin_channels,
gin_channels=config.gin_channels,
n_speakers=config.n_speakers,
upsample_conditional_features=config.upsample_conditional_features,
upsample_scales=config.upsample_scales,
freq_axis_kernel_size=config.freq_axis_kernel_size,
scalar_input=config.scalar_input,
use_speaker_embedding=config.use_speaker_embedding,
legacy=config.legacy
)
self.has_weight_norm = True
# self.__deepcopy__ = MethodType(__deepcopy__, self)
def forward(self, input_, target, seq_lengths, *_):
if target is not None: # During training and testing with teacher forcing.
assert self.has_weight_norm, "Model has been used for generation " \
"and weight norm was removed, cannot continue training. Remove"\
" the make_generation_fast_() call to continue training after" \
" generation."
output = self.model(target, c=input_, g=None, softmax=False)
# output = self.model(target, c=inputs[:, :, :target.shape[2]], g=None, softmax=False)
# Output shape is B x C x T. Don't permute here because CrossEntropyLoss requires the same shape.
else: # During inference.
with torch.no_grad():
self.model.make_generation_fast_() # After calling this the training cannot be continued.
self.has_weight_norm = False
assert(len(seq_lengths) == 1), "Batch synth is not supported."
num_frames_to_gen = seq_lengths[0] * self.len_in_out_multiplier
output = self.model.incremental_forward(
c=input_, T=num_frames_to_gen, softmax=True, quantize=True)
# output = self.model.incremental_forward(
# c=inputs[:, :, :1000], T=torch.tensor(1000), softmax=True, quantize=True)
# Output shape is B x C x T.
return output, None
def set_gpu_flag(self, use_gpu):
self.use_gpu = use_gpu
def init_hidden(self, batch_size=1):
return None
def parameters(self):
return self.model.parameters()