def train(self):
self.model.train()
dataset = SpeechDataset(N_CLASS, SLICE_LENGTH, FRAME_LENGTH,
FRAME_STRIDE, TEST_SIZE, self.device)
dataset.init_dataset(test_mode=False)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
optimizer = torch.optim.Adam(self.model.parameters(), lr=LEARNING_RATE)
for epoch in range(MAX_EPOCHS):
for i, data in enumerate(data_loader):
x, y, cond = data
pred_y = self.model(x, cond)
loss = F.cross_entropy(pred_y, y)
optimizer.zero_grad()
loss.backward()
clip_grad_norm_(self.model.parameters(), MAX_NORM)
optimizer.step()
if i % PRINT_FREQ == 0:
self.logger.info(
'epoch: %d, step:%d, tot_step:%d, loss: %f' %
(epoch, i, self.tot_steps, loss.item()))
if i % VALID_FREQ == 0:
self.validate()
self.model.eval()
self.tot_steps += 1
if self.tot_steps % 100 == 0:
self.save_model()
def validate(self):
self.model.eval()
dataset = SpeechDataset(N_CLASS, SLICE_LENGTH, FRAME_LENGTH,
FRAME_STRIDE, TEST_SIZE, self.device)
dataset.init_dataset(test_mode=True)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False)
res = []
for i, data in enumerate(data_loader):
if i == MAX_VALID:
break
x, y, cond = data
pred_y = self.model(x, cond)
loss = F.cross_entropy(pred_y.squeeze(), y.squeeze())
res.append(loss.item())
self.logger.info('valid loss: ' + str(sum(res) / len(res)))
def generate(self):
self.model.eval()
dataset = SpeechDataset(N_CLASS, SLICE_LENGTH, FRAME_LENGTH,
FRAME_STRIDE, TEST_SIZE, self.device)
dataset.init_dataset(test_mode=True)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False)
for i, data in enumerate(data_loader):
if i == MAX_GENERATE:
break
_, _, cond = data
res = self.model.generate(cond, MAX_GENERATE_LENGTH)
res = dequantize_signal(res, N_CLASS)
for j in range(res.shape[0]):
librosa.output.write_wav(
'./samples/sample%d.wav' % (self.sample_count), res[j],
SAMPLE_RATE)
self.sample_count += 1