keonlee9420/vaenar-tts

PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

vaenar-tts's Issues

inference results

Hi! Thank you for the port. Have you been able to get results on inference stage? I successfully train model, validation losses are decreasing, but at inference there's garbage.
I started logging log-probabilities of posterior and prior networks and see that they're also going down throughout training. Logpgobs in around -70000 for both networks which is very very small number, say zero in probability space.
Also if remove clipping kl divergence torch.max(kl_divergence, torch.tensor(0., device=device)) something bad happens and kl goes negative, which is not possible in math point of view, but can be if our values are not valid distributions. Then I set n_samples to 4 and reduce batch_size to 8 but still get negative values. Pytorch's implementation of KLDivloss with log_targets=True always give 0 loss values....
so.. have you had any success?

Any samples?

@keonlee9420 Hi, are there any samples to show?

For model/prior.py _initial_sample, why the prob is calculated as from N(0,1)?

Hello, thanks for sharing the pytorch-based code!
However, I have some question about the _initial_sample func in model/prior.py .
epsilon is sampled from N(0, t) (t is the temperature), how its logprob is calculated? For norm distribution,

After log (the mean is 0)
.
Can you explain why use \sigma as 1 instead of t here?