def __init__(self, wrnn_dims, fc_dims, cond_channels, global_cond_channels):
super().__init__()
conv_channels = 128
rnn_channels = 512
self.warmup_steps = 64
self.conv0 = Conv4(1, conv_channels, global_cond_channels)
self.conv1 = Conv4(conv_channels, conv_channels, global_cond_channels)
self.conv2 = Conv4(conv_channels, conv_channels, global_cond_channels)
self.rnn0 = RNN4(conv_channels + cond_channels, rnn_channels, self.warmup_steps, global_cond_channels)
self.rnn1 = RNN4(conv_channels + rnn_channels, rnn_channels, self.warmup_steps, global_cond_channels)
self.rnn2 = RNN4(conv_channels + rnn_channels, rnn_channels, self.warmup_steps, global_cond_channels)
self.wavernn = WaveRNN(wrnn_dims, fc_dims, rnn_channels + global_cond_channels, 0)
self.delay_c0 = 9
self.delay_c1 = self.delay_c0 + 9 * 4
self.delay_c2 = self.delay_c1 + 9 * 16
self.delay_r0 = self.delay_c2 + self.warmup_steps * 64
self.delay_r1 = self.delay_r0 + self.warmup_steps * 16
self.delay_r2 = self.delay_r1 + self.warmup_steps * 4
self.delay_wr = self.delay_r2 + self.warmup_steps
cond_delay = self.delay_wr - self.delay_c2
if cond_delay % 64 != 0:
raise RuntimeError(f'Overtone: bad cond delay: {cond_delay}')
self.cond_pad = cond_delay // 64
class Model(nn.Module):
def __init__(self,
quantization_channels=256,
gru_channels=896,
fc_channels=896,
lc_channels=80,
upsample_factor=(5, 5, 8),
use_gru_in_upsample=True):
super().__init__()
self.upsample = ConvInUpsampleNetwork(upsample_scales=upsample_factor,
upsample_activation="none",
upsample_activation_params={},
mode="nearest",
cin_channels=lc_channels,
use_gru=use_gru_in_upsample)
self.wavernn = WaveRNN(quantization_channels, gru_channels,
fc_channels, lc_channels)
def forward(self, inputs, conditions):
conditions = self.upsample(conditions.transpose(1, 2))
return self.wavernn(inputs, conditions[:, 1:, :])
def after_update(self):
self.wavernn.after_update()
def generate(self, conditions):
self.eval()
with torch.no_grad():
conditions = self.upsample(conditions.transpose(1, 2))
output = self.wavernn.generate(conditions)
self.train()
return output
def __init__(self,
rnn_dims,
fc_dims,
pad,
upsample_factors,
feat_dims,
DEVICE="cuda"):
super().__init__()
self.n_classes = 256
self.upsample = UpsampleNetwork(feat_dims,
upsample_factors,
DEVICE=DEVICE)
self.wavernn = WaveRNN(rnn_dims, fc_dims, feat_dims, 0, DEVICE=DEVICE)
self.num_params()
self.DEVICE = DEVICE
def __init__(self,
quantization_channels=256,
gru_channels=896,
fc_channels=896,
lc_channels=80,
upsample_factor=(5, 5, 8),
use_gru_in_upsample=True):
super().__init__()
self.upsample = ConvInUpsampleNetwork(upsample_scales=upsample_factor,
upsample_activation="none",
upsample_activation_params={},
mode="nearest",
cin_channels=lc_channels,
use_gru=use_gru_in_upsample)
self.wavernn = WaveRNN(quantization_channels, gru_channels,
fc_channels, lc_channels)
class Model(nn.Module):
def __init__(self,
quantization_channels=256,
gru_channels=896,
fc_channels=896,
lc_channels=80,
lc_out_channles=80,
upsample_factor=(5, 5, 8),
use_lstm=True,
lstm_layer=2,
upsample_method='duplicate'):
super().__init__()
self.frame_net = FrameRateNet(lc_channels, lc_out_channles)
self.upsample = UpsampleNet(input_size=lc_out_channles,
output_size=lc_out_channles,
upsample_factor=upsample_factor,
use_lstm=use_lstm,
lstm_layer=lstm_layer,
upsample_method=upsample_method)
self.wavernn = WaveRNN(quantization_channels, gru_channels,
fc_channels, lc_channels)
self.num_params()
def forward(self, inputs, conditions):
conditions = self.frame_net(conditions.transpose(1, 2))
conditions = self.upsample(conditions.transpose(1, 2))
return self.wavernn(inputs, conditions[:, 1:, :])
def after_update(self):
self.wavernn.after_update()
def generate(self, conditions):
self.eval()
with torch.no_grad():
conditions = self.frame_net(conditions.transpose(1, 2))
conditions = self.upsample(conditions.transpose(1, 2))
output = self.wavernn.generate(conditions)
self.train()
return output
def num_params(self):
parameters = filter(lambda p: p.requires_grad, self.parameters())
parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
print('Trainable Parameters: %.3f million' % parameters)
def __init__(self,
quantization_channels=256,
gru_channels=896,
fc_channels=896,
lc_channels=80,
lc_out_channles=80,
upsample_factor=(5, 5, 8),
use_lstm=True,
lstm_layer=2,
upsample_method='duplicate'):
super().__init__()
self.frame_net = FrameRateNet(lc_channels, lc_out_channles)
self.upsample = UpsampleNet(input_size=lc_out_channles,
output_size=lc_out_channles,
upsample_factor=upsample_factor,
use_lstm=use_lstm,
lstm_layer=lstm_layer,
upsample_method=upsample_method)
self.wavernn = WaveRNN(quantization_channels, gru_channels,
fc_channels, lc_channels)
self.num_params()
class Model(nn.Module):
def __init__(self, rnn_dims, fc_dims, pad, upsample_factors, feat_dims):
super().__init__()
self.n_classes = 256
self.upsample = UpsampleNetwork(feat_dims, upsample_factors)
self.wavernn = WaveRNN(rnn_dims, fc_dims, feat_dims, 0)
self.num_params()
def forward(self, x, mels):
#logger.log(f'x: {x.size()} mels: {mels.size()}')
cond = self.upsample(mels)
#logger.log(f'cond: {cond.size()}')
return self.wavernn(x, cond.transpose(1, 2), None, None, None)
def after_update(self):
self.wavernn.after_update()
def preview_upsampling(self, mels):
return self.upsample(mels)
def forward_generate(self,
mels,
deterministic=False,
use_half=False,
verbose=False):
n = mels.size(0)
if use_half:
mels = mels.half()
self.eval()
with torch.no_grad():
cond = self.upsample(mels)
output = self.wavernn.generate(cond.transpose(1, 2),
None,
None,
None,
use_half=use_half,
verbose=verbose)
self.train()
return output
def num_params(self):
parameters = filter(lambda p: p.requires_grad, self.parameters())
parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
logger.log('Trainable Parameters: %.3f million' % parameters)
def load_state_dict(self, dict):
return super().load_state_dict(upgrade_state_dict(dict))
def do_train(self,
paths,
dataset,
optimiser,
epochs,
batch_size,
step,
lr=1e-4,
valid_index=[],
use_half=False):
if use_half:
import apex
optimiser = apex.fp16_utils.FP16_Optimizer(optimiser,
dynamic_loss_scale=True)
for p in optimiser.param_groups:
p['lr'] = lr
criterion = nn.NLLLoss().cuda()
k = 0
saved_k = 0
print(win_length, hop_length, win_length / hop_length)
for e in range(epochs):
# trn_loader = DataLoader(dataset, collate_fn=lambda batch: env.collate(0, int( win_length/hop_length), 0, batch), batch_size=batch_size,
# num_workers=2, shuffle=True, pin_memory=True)
trn_loader = DataLoader(
dataset,
collate_fn=lambda batch: env.collate(0, 16, 0, batch),
batch_size=batch_size,
num_workers=2,
shuffle=True,
pin_memory=True)
start = time.time()
running_loss_c = 0.
running_loss_f = 0.
iters = len(trn_loader)
for i, (mels, coarse, fine, coarse_f,
fine_f) in enumerate(trn_loader):
mels, coarse, fine, coarse_f, fine_f = mels.cuda(
), coarse.cuda(), fine.cuda(), coarse_f.cuda(), fine_f.cuda()
coarse, fine, coarse_f, fine_f = [
t[:, hop_length:1 - hop_length]
for t in [coarse, fine, coarse_f, fine_f]
]
if use_half:
mels = mels.half()
coarse_f = coarse_f.half()
fine_f = fine_f.half()
x = torch.cat([
coarse_f[:, :-1].unsqueeze(-1),
fine_f[:, :-1].unsqueeze(-1), coarse_f[:, 1:].unsqueeze(-1)
],
dim=2)
p_c, p_f, _h_n = self(x, mels)
loss_c = criterion(p_c.transpose(1, 2).float(), coarse[:, 1:])
loss_f = criterion(p_f.transpose(1, 2).float(), fine[:, 1:])
loss = loss_c + loss_f
optimiser.zero_grad()
if use_half:
optimiser.backward(loss)
else:
loss.backward()
optimiser.step()
running_loss_c += loss_c.item()
running_loss_f += loss_f.item()
self.after_update()
speed = (i + 1) / (time.time() - start)
avg_loss_c = running_loss_c / (i + 1)
avg_loss_f = running_loss_f / (i + 1)
step += 1
k = step // 1000
logger.status(
f'Epoch: {e+1}/{epochs} -- Batch: {i+1}/{iters} -- Loss: c={avg_loss_c:#.4} f={avg_loss_f:#.4} -- Speed: {speed:#.4} steps/sec -- Step: {k}k '
)
os.makedirs(paths.checkpoint_dir, exist_ok=True)
torch.save(self.state_dict(), paths.model_path())
np.save(paths.step_path(), step)
logger.log_current_status()
logger.log(
f' <saved>; w[0][0] = {self.wavernn.gru.weight_ih_l0[0][0]}')
if k > saved_k + 50:
torch.save(self.state_dict(), paths.model_hist_path(step))
saved_k = k
self.do_generate(paths,
step,
dataset.path,
valid_index,
use_half=use_half)
def do_generate(self,
paths,
step,
data_path,
test_index,
deterministic=False,
use_half=False,
verbose=False):
k = step // 1000
test_mels = [np.load(f'{data_path}/mel/{id}.npy') for id in test_index]
maxlen = max([x.shape[1] for x in test_mels])
aligned = [
torch.cat([
torch.FloatTensor(x),
torch.zeros(80, maxlen - x.shape[1] + 1)
],
dim=1) for x in test_mels
]
print(torch.stack(aligned).size())
out = self.forward_generate(torch.stack(aligned).cuda(),
deterministic,
use_half=use_half,
verbose=verbose)
os.makedirs(paths.gen_path(), exist_ok=True)
for i, id in enumerate(test_index):
gt = np.load(f'{data_path}/quant/{id}.npy')
gt = (gt.astype(np.float32) + 0.5) / (2**15 - 0.5)
librosa.output.write_wav(
f'{paths.gen_path()}/{k}k_steps_{i}_target.wav',
gt,
sr=sample_rate)
audio = out[i][:len(gt)].cpu().numpy()
librosa.output.write_wav(
f'{paths.gen_path()}/{k}k_steps_{i}_generated.wav',
audio,
sr=sample_rate)
class Overtone(nn.Module):
def __init__(self, wrnn_dims, fc_dims, cond_channels, global_cond_channels):
super().__init__()
conv_channels = 128
rnn_channels = 512
self.warmup_steps = 64
self.conv0 = Conv4(1, conv_channels, global_cond_channels)
self.conv1 = Conv4(conv_channels, conv_channels, global_cond_channels)
self.conv2 = Conv4(conv_channels, conv_channels, global_cond_channels)
self.rnn0 = RNN4(conv_channels + cond_channels, rnn_channels, self.warmup_steps, global_cond_channels)
self.rnn1 = RNN4(conv_channels + rnn_channels, rnn_channels, self.warmup_steps, global_cond_channels)
self.rnn2 = RNN4(conv_channels + rnn_channels, rnn_channels, self.warmup_steps, global_cond_channels)
self.wavernn = WaveRNN(wrnn_dims, fc_dims, rnn_channels + global_cond_channels, 0)
self.delay_c0 = 9
self.delay_c1 = self.delay_c0 + 9 * 4
self.delay_c2 = self.delay_c1 + 9 * 16
self.delay_r0 = self.delay_c2 + self.warmup_steps * 64
self.delay_r1 = self.delay_r0 + self.warmup_steps * 16
self.delay_r2 = self.delay_r1 + self.warmup_steps * 4
self.delay_wr = self.delay_r2 + self.warmup_steps
cond_delay = self.delay_wr - self.delay_c2
if cond_delay % 64 != 0:
raise RuntimeError(f'Overtone: bad cond delay: {cond_delay}')
self.cond_pad = cond_delay // 64
def forward(self, x, cond, global_cond):
n = x.size(0)
x_coarse = x[:, :, :1]
c0 = self.conv0(x_coarse, global_cond)
c1 = self.conv1(c0, global_cond)
c2 = self.conv2(c1, global_cond)
r0 = self.rnn0(torch.cat(filter_none([c2, cond]), dim=2), global_cond)[0]
r1 = self.rnn1(torch.cat([c1[:, (self.delay_r0 - self.delay_c1) // 16:], r0], dim=2), global_cond)[0]
r2 = self.rnn2(torch.cat([c0[:, (self.delay_r1 - self.delay_c0) // 4:], r1], dim=2), global_cond)[0]
if global_cond is not None:
global_cond = global_cond.unsqueeze(1).expand(-1, r2.size(1), -1)
cond_w = torch.cat(filter_none([r2, global_cond]), dim=2)
p_c, p_f, _ = self.wavernn(x[:, self.delay_r2:], cond_w, None, None, None)
return p_c[:, self.warmup_steps:], p_f[:, self.warmup_steps:]
def generate(self, cond, global_cond, n=None, seq_len=None, verbose=False, use_half=False):
start = time.time()
if n is None:
n = cond.size(0)
if seq_len is None:
seq_len = (cond.size(1) - self.cond_pad) * 64
if use_half:
std_tensor = torch.tensor([]).cuda().half()
else:
std_tensor = torch.tensor([]).cuda()
# Warmup
c0 = self.conv0(std_tensor.new_zeros(n, 10, 1), global_cond).repeat(1, 10, 1)
c1 = self.conv1(c0, global_cond).repeat(1, 10, 1)
c2 = self.conv2(c1, global_cond)
if cond is None:
pad_cond = None
else:
pad_cond = cond[:, :self.cond_pad]
#logger.log(f'pad_cond: {pad_cond.size()}')
r0, h0 = self.rnn0(torch.cat(filter_none([c2.repeat(1, 85, 1), pad_cond]), dim=2), global_cond)
r1, h1 = self.rnn1(torch.cat([c1.repeat(1, 9, 1)[:, :84], r0], dim=2), global_cond)
r2, h2 = self.rnn2(torch.cat([c0.repeat(1, 8, 1), r1], dim=2), global_cond)
if global_cond is not None:
global_cond_1 = global_cond.unsqueeze(1).expand(-1, r2.size(1), -1)
else:
global_cond_1 = None
h3 = self.wavernn(std_tensor.new_zeros(n, 64, 3), torch.cat(filter_none([r2, global_cond_1]), dim=2))[2]
# Create cells
cell0 = self.rnn0.to_cell()
cell1 = self.rnn1.to_cell()
cell2 = self.rnn2.to_cell()
wcell = self.wavernn.to_cell()
# Main loop!
coarse = std_tensor.new_zeros(n, 10, 1)
c_val = std_tensor.new_zeros(n)
f_val = std_tensor.new_zeros(n)
zero = std_tensor.new_zeros(n)
output = []
for t in range(seq_len):
#logger.log(f't = {t}')
t0 = t % 4
ct0 = (-t) % 4
if t0 == 0:
t1 = (t // 4) % 4
ct1 = ((-t) // 4) % 4
#logger.log(f'written to c0[{-ct1-1}]')
c0[:, -ct1-1].copy_(self.conv0(coarse, global_cond).squeeze(1))
coarse[:, :-4].copy_(coarse[:, 4:])
if t1 == 0:
t2 = (t // 16) % 4
ct2 = ((-t) // 16) % 4
#logger.log('read c0')
#logger.log(f'written to c1[{-ct2-1}]')
c1[:, -ct2-1].copy_(self.conv1(c0, global_cond).squeeze(1))
c0[:, :-4].copy_(c0[:, 4:])
if t2 == 0:
#logger.log('read c1')
#logger.log('written to c2')
c2 = self.conv2(c1, global_cond).squeeze(1)
c1[:, :-4].copy_(c1[:, 4:])
#logger.log('read c2')
#logger.log('written to r0')
if cond is None:
inp0 = c2
else:
inp0 = torch.cat([c2, cond[:, t // 64 + self.cond_pad]], dim=1)
r0, h0 = cell0(inp0, global_cond, h0)
#logger.log(f'read r0[{t2}]')
#logger.log(f'written to r1')
#logger.log(f'c1: {c1.size()} r0: {r0.size()}')
r1, h1 = cell1(torch.cat([c1[:, -ct2-1], r0[:, t2]], dim=1), global_cond, h1)
#logger.log(f'read r1[{t1}]')
#logger.log(f'written to r2')
#logger.log(f'c0: {c0.size()} r1: {r1.size()}')
r2, h2 = cell2(torch.cat([c0[:, -ct1-1], r1[:, t1]], dim=1), global_cond, h2)
#logger.log(f'read r2[{t0}]')
wcond = torch.cat(filter_none([r2[:, t0], global_cond]), dim=1)
x = torch.stack([c_val, f_val, zero], dim=1)
o_c = wcell.forward_c(x, wcond, None, None, h3)
c_cat = utils.nn.sample_softmax(o_c).float()
c_val_new = (c_cat / 127.5 - 1.0).to(std_tensor)
x = torch.stack([c_val, f_val, c_val_new], dim=1)
o_f, h3 = wcell.forward_f(x, wcond, None, None, h3)
f_cat = utils.nn.sample_softmax(o_f).float()
f_val = (f_cat / 127.5 - 1.0).to(std_tensor)
c_val = c_val_new
sample = (c_cat * 256 + f_cat) / 32767.5 - 1.0
coarse[:, 6+t0].copy_(c_val.unsqueeze(1))
if verbose and t % 10000 < 100:
logger.log(f'c={c_cat[0]} f={f_cat[0]} sample={sample[0]}')
output.append(sample)
if t % 100 == 0 :
speed = int((t + 1) / (time.time() - start))
logger.status(f'{t+1}/{seq_len} -- Speed: {speed} samples/sec')
return torch.stack(output, dim=1)
def after_update(self):
self.wavernn.after_update()
def pad(self):
return self.delay_wr
class Overtone(nn.Module):
def __init__(self, wrnn_dims, fc_dims, cond_channels, global_cond_channels):
super().__init__()
conv_channels = 128
rnn_channels = 512
self.warmup_steps = 64
self.conv0 = Conv4(1, conv_channels, global_cond_channels)
self.conv1 = Conv4(conv_channels, conv_channels, global_cond_channels)
self.conv2 = Conv4(conv_channels, conv_channels, global_cond_channels)
self.rnn0 = RNN4(conv_channels + cond_channels, rnn_channels, self.warmup_steps, global_cond_channels)
self.rnn1 = RNN4(conv_channels + rnn_channels, rnn_channels, self.warmup_steps, global_cond_channels)
self.rnn2 = RNN4(conv_channels + rnn_channels, rnn_channels, self.warmup_steps, global_cond_channels)
self.wavernn = WaveRNN(wrnn_dims, fc_dims, rnn_channels + global_cond_channels, 0)
self.delay_c0 = 9
self.delay_c1 = self.delay_c0 + 9 * 4
self.delay_c2 = self.delay_c1 + 9 * 16
self.delay_r0 = self.delay_c2 + self.warmup_steps * 64
self.delay_r1 = self.delay_r0 + self.warmup_steps * 16
self.delay_r2 = self.delay_r1 + self.warmup_steps * 4
self.delay_wr = self.delay_r2 + self.warmup_steps
cond_delay = self.delay_wr - self.delay_c2
if cond_delay % 64 != 0:
raise RuntimeError(f'Overtone: bad cond delay: {cond_delay}')
self.cond_pad = cond_delay // 64
def forward(self, x, cond, global_cond):
"""
Arguments:
global_cond -- speaker one-hot embedding
"""
n = x.size(0)
x_coarse = x[:, :, :1]
c0 = self.conv0(x_coarse, global_cond)
c1 = self.conv1(c0, global_cond)
c2 = self.conv2(c1, global_cond)
r0 = self.rnn0(torch.cat(filter_none([c2, cond]), dim=2), global_cond)[0]
r1 = self.rnn1(torch.cat([c1[:, (self.delay_r0 - self.delay_c1) // 16:], r0], dim=2), global_cond)[0]
r2 = self.rnn2(torch.cat([c0[:, (self.delay_r1 - self.delay_c0) // 4:], r1], dim=2), global_cond)[0]
if global_cond is not None:
global_cond = global_cond.unsqueeze(1).expand(-1, r2.size(1), -1)
cond_w = torch.cat(filter_none([r2, global_cond]), dim=2)
p_c, p_f, _ = self.wavernn(x[:, self.delay_r2:], cond_w, None, None, None)
return p_c[:, self.warmup_steps:], p_f[:, self.warmup_steps:]
def generate(self, cond, global_cond, n=None, seq_len=None, verbose=False, use_half=False):
"""
usecase #1: called from vqvae model during test generation
"""
start = time.time()
if n is None:
n = cond.size(0)
if seq_len is None:
seq_len = (cond.size(1) - self.cond_pad) * 64
if use_half:
std_tensor = torch.tensor([]).cuda().half()
else:
std_tensor = torch.tensor([]).cuda()
# Warmup
c0 = self.conv0(std_tensor.new_zeros(n, 10, 1), global_cond).repeat(1, 10, 1)
c1 = self.conv1(c0, global_cond).repeat(1, 10, 1)
c2 = self.conv2(c1, global_cond)
if cond is None:
pad_cond = None
else:
pad_cond = cond[:, :self.cond_pad]
r0, h0 = self.rnn0(torch.cat(filter_none([c2.repeat(1, 85, 1), pad_cond]), dim=2), global_cond)
r1, h1 = self.rnn1(torch.cat([c1.repeat(1, 9, 1)[:, :84], r0], dim=2), global_cond)
r2, h2 = self.rnn2(torch.cat([c0.repeat(1, 8, 1), r1], dim=2), global_cond)
if global_cond is not None:
global_cond_1 = global_cond.unsqueeze(1).expand(-1, r2.size(1), -1)
else:
global_cond_1 = None
h3 = self.wavernn(std_tensor.new_zeros(n, 64, 3), torch.cat(filter_none([r2, global_cond_1]), dim=2))[2]
# Create cells
cell0 = self.rnn0.to_cell()
cell1 = self.rnn1.to_cell()
cell2 = self.rnn2.to_cell()
wcell = self.wavernn.to_cell()
# Main loop!
coarse = std_tensor.new_zeros(n, 10, 1)
c_val = std_tensor.new_zeros(n)
f_val = std_tensor.new_zeros(n)
zero = std_tensor.new_zeros(n)
output = []
for t in range(seq_len):
t0 = t % 4
ct0 = (-t) % 4
if t0 == 0:
t1 = (t // 4) % 4
ct1 = ((-t) // 4) % 4
# Conv stride4
c0[:, -ct1-1].copy_(self.conv0(coarse, global_cond).squeeze(1))
coarse[:, :-4].copy_(coarse[:, 4:])
if t1 == 0:
t2 = (t // 16) % 4
ct2 = ((-t) // 16) % 4
# Conv stride4
c1[:, -ct2-1].copy_(self.conv1(c0, global_cond).squeeze(1))
c0[:, :-4].copy_(c0[:, 4:])
if t2 == 0:
# Conv stride4
c2 = self.conv2(c1, global_cond).squeeze(1)
c1[:, :-4].copy_(c1[:, 4:])
if cond is None:
inp0 = c2
else:
# Time-slice of speech-conditioning??
inp0 = torch.cat([c2, cond[:, t // 64 + self.cond_pad]], dim=1)
# RNN0 manually looped here (see autoregressive h0)
r0, h0 = cell0(inp0, global_cond, h0)
# RNN1 manually looped here (see autoregressive h1)
r1, h1 = cell1(torch.cat([c1[:, -ct2-1], r0[:, t2]], dim=1), global_cond, h1)
# RNN2 manually looped here (see autoregressive h2)
r2, h2 = cell2(torch.cat([c0[:, -ct1-1], r1[:, t1]], dim=1), global_cond, h2)
# conditioning for WaveRNN
wcond = torch.cat(filter_none([r2[:, t0], global_cond]), dim=1)
# WaveRNN w/ dual-softmax
x = torch.stack([c_val, f_val, zero], dim=1)
o_c = wcell.forward_c(x, wcond, None, None, h3)
c_cat = utils.nn.sample_softmax(o_c).float()
c_val_new = (c_cat / 127.5 - 1.0).to(std_tensor)
x = torch.stack([c_val, f_val, c_val_new], dim=1)
o_f, h3 = wcell.forward_f(x, wcond, None, None, h3)
f_cat = utils.nn.sample_softmax(o_f).float()
f_val = (f_cat / 127.5 - 1.0).to(std_tensor)
c_val = c_val_new
sample = (c_cat * 256 + f_cat) / 32767.5 - 1.0
coarse[:, 6+t0].copy_(c_val.unsqueeze(1))
output.append(sample)
return torch.stack(output, dim=1)
def after_update(self):
self.wavernn.after_update()
def pad(self):
return self.delay_wr