def test_taco():
B, T_out, D_out = 2, 400, 80
r = 5
T_encoder = T_out // r
texts = ["Thank you very much.", "Hello"]
seqs = [
np.array(text_to_sequence(t, ["english_cleaners"]), dtype=np.int)
for t in texts
]
input_lengths = np.array([len(s) for s in seqs])
max_len = np.max(input_lengths)
seqs = np.array([_pad(s, max_len) for s in seqs])
x = torch.LongTensor(seqs)
y = torch.rand(B, T_out, D_out)
x = Variable(x)
y = Variable(y)
model = Tacotron(n_vocab=len(symbols), r=r)
print("Encoder input shape: ", x.size())
print("Decoder input shape: ", y.size())
a, b, c = model(x, y, input_lengths=input_lengths)
print("Mel shape:", a.size())
print("Linear shape:", b.size())
print("Attention shape:", c.size())
assert c.size() == (B, T_encoder, max_len)
# Test greddy decoding
a, b, c = model(x, input_lengths=input_lengths)
# Dataset and Dataloader setup
dataset = PyTorchDataset(X, Mel, Y)
data_loader = data_utils.DataLoader(dataset,
batch_size=hparams.batch_size,
num_workers=hparams.num_workers,
shuffle=True,
collate_fn=collate_fn,
pin_memory=hparams.pin_memory)
# Model
model = Tacotron(
n_vocab=len(symbols),
embedding_dim=256,
mel_dim=hparams.num_mels,
linear_dim=hparams.num_freq,
r=hparams.outputs_per_step,
padding_idx=hparams.padding_idx,
use_memory_mask=hparams.use_memory_mask,
)
optimizer = optim.Adam(model.parameters(),
lr=hparams.initial_learning_rate,
betas=(hparams.adam_beta1, hparams.adam_beta2),
weight_decay=hparams.weight_decay)
# Load checkpoint
if checkpoint_path:
print("Load checkpoint from: {}".format(checkpoint_path))
checkpoint = torch.load(checkpoint_path)
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
D -= hp.mask_nth_mgc_for_adv_loss
if hp.discriminator_linguistic_condition:
D = D + X_data_min.shape[-1]
hp.discriminator_params["in_dim"] = D
dataset = PyTorchDataset(X, Mel, Y)
data_loader = data_utils.DataLoader(
dataset, batch_size=hparams.batch_size,
num_workers=hparams.num_workers, shuffle=True,
collate_fn=collate_fn, pin_memory=hparams.pin_memory)
# Models
model_g = Tacotron(n_vocab = len(symbols),
embedding_dim = 256,
mel_dim = hparams.num_mels,
linear_dim = hparams.num_freq,
r = hparams.outputs_per_step,
padding_idx = hparams.padding_idx,
use_memory_mask = hparams.use_memory_mask,
)
model_d = getattr(gantts_models, hp.discriminator)(**hp.discriminator_params)
print("Generator:", model_g)
print("Discriminator:", model_d)
if use_cuda:
model_g, model_d = model_g.cuda(), model_d.cuda()
# Optimizers
optimizer_g = optim.Adam( model.parameters(),
lr = hparams.initial_learning_rate, betas = ( hparams.adam_beta1, hparams.adam_beta2 ),
weight_decay = hparams.weight_decay )
optimizer_d = getattr(optim, hp.optimizer_d)(model_d.parameters(),