def test_train_step():
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8, )).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
mel_lengths[-1] = 120
stop_targets = torch.zeros(8, 120, 1).float().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input_dummy.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron(
num_chars=32,
num_speakers=5,
gst=True,
postnet_output_dim=c.audio['num_freq'],
decoder_output_dim=c.audio['num_mels'],
r=c.r,
memory_size=c.memory_size
).to(device) #FIXME: missing num_speakers parameter to Tacotron ctor
model.train()
print(model)
print(" > Num parameters for Tacotron GST model:%s" %
(count_parameters(model)))
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for _ in range(10):
mel_out, linear_out, align, stop_tokens = model.forward(
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
loss = loss + criterion(linear_out, linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def test_train_step(self):
input = torch.randint(0, 24, (8, 128)).long().to(device)
mel_spec = torch.rand(8, 30, c.num_mels).to(device)
linear_spec = torch.rand(8, 30, c.num_freq).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
criterion = L1LossMasked().to(device)
model = Tacotron(c.embedding_size, c.num_freq, c.num_mels,
c.r).to(device)
model.train()
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for i in range(5):
mel_out, linear_out, align = model.forward(input, mel_spec)
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
loss = 0.5 * loss + 0.5 * criterion(linear_out, linear_spec,
mel_lengths)
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
if count not in [139, 59]:
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def test_train_step(self):
input = torch.randint(0, 24, (8, 128)).long().to(device)
mel_spec = torch.rand(8, 30, c.num_mels).to(device)
linear_spec = torch.rand(8, 30, c.num_freq).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
stop_targets = torch.zeros(8, 30, 1).float().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float()
criterion = L1LossMasked().to(device)
criterion_st = nn.BCELoss().to(device)
model = Tacotron(c.embedding_size, c.num_freq, c.num_mels,
c.r).to(device)
model.train()
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for i in range(5):
mel_out, linear_out, align, stop_tokens = model.forward(
input, mel_spec)
assert stop_tokens.data.max() <= 1.0
assert stop_tokens.data.min() >= 0.0
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
loss = loss + criterion(linear_out, linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
if count not in [145, 59]:
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1