Inference

3.4
To sample from the the learned audio distribution p1(x | z, xctx), a noise x0 is first sampled from p0,
and then an ODE solver is used to evaluate ϕ1(x0) given dϕt(x0)/dt = vt(ϕt(x0), xctx, z; θ) and
the initial condition ϕ0(x0) = x0.
Intuitively, the ODE solver computes ϕ1(x0) by evaluating vt at multiple t to approximate the
integration from t = 0 to t = 1 given the initial condition ϕ0(x0) = x0. A larger number of function
evaluations (NFEs) often leads to a more accurate solution of ϕ1(x0) at the cost of longer run time.
There are also solvers that can adjust the number of evaluations adaptively. This provides great
flexibility for users to decide the trade-off between speed and accuracy. Moreover, we find that
empirically Voicebox can already generate very high quality speech with less than 10 NFEs, making
it significantly faster compared to auto-regressive models.

3.5 Classifier-Free Guidance
Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale