def load_model(weights_fpath, verbose=True):
global _model, _device
if verbose:
print("Building Wave-RNN")
_model = WaveRNN(rnn_dims=hp.voc_rnn_dims,
fc_dims=hp.voc_fc_dims,
bits=hp.bits,
pad=hp.voc_pad,
upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels,
compute_dims=hp.voc_compute_dims,
res_out_dims=hp.voc_res_out_dims,
res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length,
sample_rate=hp.sample_rate,
mode=hp.voc_mode)
if torch.cuda.is_available():
_model = _model.cuda()
_device = torch.device('cuda')
else:
_device = torch.device('cpu')
if verbose:
print("Loading model weights at %s" % weights_fpath)
checkpoint = torch.load(weights_fpath, _device)
_model.load_state_dict(checkpoint['model_state'])
_model.eval()
def train(run_id: str, syn_dir: Path, voc_dir: Path, models_dir: Path,
ground_truth: bool, save_every: int, backup_every: int,
force_restart: bool):
# Check to make sure the hop length is correctly factorised
assert np.cumprod(hp.voc_upsample_factors)[-1] == hp.hop_length
# Instantiate the model
print("Initializing the model...")
model = WaveRNN(rnn_dims=hp.voc_rnn_dims,
fc_dims=hp.voc_fc_dims,
bits=hp.bits,
pad=hp.voc_pad,
upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels,
compute_dims=hp.voc_compute_dims,
res_out_dims=hp.voc_res_out_dims,
res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length,
sample_rate=hp.sample_rate,
mode=hp.voc_mode)
if torch.cuda.is_available():
model = model.cuda()
device = torch.device('cuda')
else:
device = torch.device('cpu')
# Initialize the optimizer
optimizer = optim.Adam(model.parameters())
for p in optimizer.param_groups:
p["lr"] = hp.voc_lr
loss_func = F.cross_entropy if model.mode == "RAW" else discretized_mix_logistic_loss
# Load the weights
model_dir = models_dir.joinpath(run_id)
model_dir.mkdir(exist_ok=True)
weights_fpath = model_dir.joinpath(run_id + ".pt")
if force_restart or not weights_fpath.exists():
print("\nStarting the training of WaveRNN from scratch\n")
model.save(weights_fpath, optimizer)
else:
print("\nLoading weights at %s" % weights_fpath)
model.load(weights_fpath, optimizer)
print("WaveRNN weights loaded from step %d" % model.step)
# Initialize the dataset
metadata_fpath = syn_dir.joinpath("train.txt") if ground_truth else \
voc_dir.joinpath("synthesized.txt")
mel_dir = syn_dir.joinpath("mels") if ground_truth else voc_dir.joinpath(
"mels_gta")
wav_dir = syn_dir.joinpath("audio")
dataset = VocoderDataset(metadata_fpath, mel_dir, wav_dir)
test_loader = DataLoader(dataset,
batch_size=1,
shuffle=True,
pin_memory=True)
# Begin the training
simple_table([('Batch size', hp.voc_batch_size), ('LR', hp.voc_lr),
('Sequence Len', hp.voc_seq_len)])
for epoch in range(1, 350):
data_loader = DataLoader(
dataset,
collate_fn=collate_vocoder,
batch_size=hp.voc_batch_size,
num_workers=2 if platform.system() != "Windows" else 0,
shuffle=True,
pin_memory=True)
start = time.time()
running_loss = 0.
for i, (x, y, m) in enumerate(data_loader, 1):
if torch.cuda.is_available():
x, m, y = x.cuda(), m.cuda(), y.cuda()
# Forward pass
y_hat = model(x, m)
if model.mode == 'RAW':
y_hat = y_hat.transpose(1, 2).unsqueeze(-1)
elif model.mode == 'MOL':
y = y.float()
y = y.unsqueeze(-1)
# Backward pass
loss = loss_func(y_hat, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
speed = i / (time.time() - start)
avg_loss = running_loss / i
step = model.get_step()
k = step // 1000
if backup_every != 0 and step % backup_every == 0:
model.checkpoint(model_dir, optimizer)
if save_every != 0 and step % save_every == 0:
model.save(weights_fpath, optimizer)
msg = f"| Epoch: {epoch} ({i}/{len(data_loader)}) | " \
f"Loss: {avg_loss:.4f} | {speed:.1f} " \
f"steps/s | Step: {k}k | "
stream(msg)
gen_testset(model, test_loader, hp.voc_gen_at_checkpoint,
hp.voc_gen_batched, hp.voc_target, hp.voc_overlap,
model_dir)
print("")