def wavegen(model,
length=None,
c=None,
g=None,
initial_value=None,
fast=False,
tqdm=tqdm):
"""Generate waveform samples by WaveNet.
Args:
model (nn.Module) : WaveNet decoder
length (int): Time steps to generate. If conditinlal features are given,
then this is determined by the feature size.
c (numpy.ndarray): Conditional features, of shape T x C
g (scaler): Speaker ID
initial_value (int) : initial_value for the WaveNet decoder.
fast (Bool): Whether to remove weight normalization or not.
tqdm (lambda): tqdm
Returns:
numpy.ndarray : Generated waveform samples
"""
from train import sanity_check
sanity_check(model, c, g)
c = _to_numpy(c)
g = _to_numpy(g)
model.eval()
if fast:
model.make_generation_fast_()
if c is None:
assert length is not None
else:
# (Tc, D)
if c.ndim != 2:
raise RuntimeError(
"Expected 2-dim shape (T, {}) for the conditional feature, but {} was actually given."
.format(hparams.cin_channels, c.shape))
assert c.ndim == 2
Tc = c.shape[0]
upsample_factor = audio.get_hop_size()
# Overwrite length according to feature size
length = Tc * upsample_factor
# (Tc, D) -> (Tc', D)
# Repeat features before feeding it to the network
if not hparams.upsample_conditional_features:
c = np.repeat(c, upsample_factor, axis=0)
# B x C x T
c = torch.FloatTensor(c.T).unsqueeze(0)
if initial_value is None:
if is_mulaw_quantize(hparams.input_type):
initial_value = P.mulaw_quantize(0, hparams.quantize_channels)
else:
initial_value = 0.0
if is_mulaw_quantize(hparams.input_type):
assert initial_value >= 0 and initial_value < hparams.quantize_channels
initial_input = np_utils.to_categorical(
initial_value,
num_classes=hparams.quantize_channels).astype(np.float32)
initial_input = torch.from_numpy(initial_input).view(
1, 1, hparams.quantize_channels)
else:
initial_input = torch.zeros(1, 1, 1).fill_(initial_value)
g = None if g is None else torch.LongTensor([g])
# Transform data to GPU
initial_input = initial_input.to(device)
g = None if g is None else g.to(device)
c = None if c is None else c.to(device)
with torch.no_grad():
y_hat = model.incremental_forward(initial_input,
c=c,
g=g,
T=length,
tqdm=tqdm,
softmax=True,
quantize=True,
log_scale_min=hparams.log_scale_min)
if is_mulaw_quantize(hparams.input_type):
y_hat = y_hat.max(1)[1].view(-1).long().cpu().data.numpy()
y_hat = P.inv_mulaw_quantize(y_hat, hparams.quantize_channels)
elif is_mulaw(hparams.input_type):
y_hat = P.inv_mulaw(
y_hat.view(-1).cpu().data.numpy(), hparams.quantize_channels)
else:
y_hat = y_hat.view(-1).cpu().data.numpy()
return y_hat
def wavegen(model, length=None, c=None, g=None, initial_value=None,
fast=False, tqdm=tqdm):
"""Generate waveform samples by WaveNet.
Args:
model (nn.Module) : WaveNet decoder
length (int): Time steps to generate. If conditinlal features are given,
then this is determined by the feature size.
c (numpy.ndarray): Conditional features, of shape T x C
g (scaler): Speaker ID
initial_value (int) : initial_value for the WaveNet decoder.
fast (Bool): Whether to remove weight normalization or not.
tqdm (lambda): tqdm
Returns:
numpy.ndarray : Generated waveform samples
"""
from train import sanity_check
sanity_check(model, c, g)
c = _to_numpy(c)
g = _to_numpy(g)
if use_cuda:
model = model.cuda()
model.eval()
if fast:
model.make_generation_fast_()
if c is None:
assert length is not None
else:
# (Tc, D)
assert c.ndim == 2
Tc = c.shape[0]
upsample_factor = audio.get_hop_size()
# Overwrite length according to feature size
length = Tc * upsample_factor
# (Tc, D) -> (Tc', D)
# Repeat features before feeding it to the network
if not hparams.upsample_conditional_features:
c = np.repeat(c, upsample_factor, axis=0)
# B x C x T
c = Variable(torch.FloatTensor(c.T).unsqueeze(0))
if initial_value is None:
initial_value = P.mulaw_quantize(0) # dummy silence
assert initial_value >= 0 and initial_value < 256
initial_input = np_utils.to_categorical(
initial_value, num_classes=256).astype(np.float32)
initial_input = Variable(torch.from_numpy(initial_input)).view(1, 1, 256)
g = None if g is None else Variable(torch.LongTensor([g]))
if use_cuda:
initial_input = initial_input.cuda()
g = None if g is None else g.cuda()
c = None if c is None else c.cuda()
y_hat = model.incremental_forward(
initial_input, c=c, g=g, T=length, tqdm=tqdm, softmax=True, quantize=True)
y_hat = y_hat.max(1)[1].view(-1).long().cpu().data.numpy()
y_hat = P.inv_mulaw_quantize(y_hat)
return y_hat