espnet2.enh.diffusion.sdes.OUVESDE

source

class espnet2.enh.diffusion.sdes.OUVESDE(theta=1.5, sigma_min=0.05, sigma_max=0.5, N=1000, **ignored_kwargs)

Bases: SDE

Construct an Ornstein-Uhlenbeck Variance Exploding SDE.

Note that the “steady-state mean” y is not provided at construction, but must rather be given as an argument to the methods which require it (e.g., sde or marginal_prob).

dx = -theta (y-x) dt + sigma(t) dw

with

sigma(t) = sigma_min (sigma_max/sigma_min)^t * sqrt(2 log(sigma_max/sigma_min))

• Parameters:
  • theta – stiffness parameter.
  • sigma_min – smallest sigma.
  • sigma_max – largest sigma.
  • N – number of discretization steps

property T

End time of the SDE.

copy()

marginal_prob(x0, t, y)

Parameters to determine the marginal distribution of

the SDE, $p_t(x|args)$.

prior_logp(z)

Compute log-density of the prior distribution.

Useful for computing the log-likelihood via probability flow ODE.

• Parameters:z – latent code
• Returns: log probability density

prior_sampling(shape, y)

Generate one sample from the prior distribution,

$p_T(x|args)$ with shape shape.

sde(x, t, y)