3“temporal” refers to the physical time of the audio sample, not the time t ∈ [0, 1] of the flow in CNF.
4A Mel-spectrogram can be converted back to a waveform using a vocoder.

5

To embed the flow step, a sinusoidal positional encoding is applied to map t ∈ [0, 1] to xt ∈ RD.
The sequence Xt ∈ R(N +1)×D, which serves as the input to the Transformer model, is derived
by concatenating Xc with the vector xt along the time dimension. Given the Transformer output
vt(w, xmis, z; θ) ∈ RN×F , which is the sub-sequence corresponding to Xc, the loss is computed as:
(5)
with w = (1 − (1 − σmin)t)x0 + tx. This function computes the loss on all frames, including those
that are not masked and would not be required during inference. To divert the model’s focus to
masked frames, we present a masked version of Laudio-CFM:

Laudio-CFM(θ) = Et,m,q(x,z),p0(x0)||(x − (1 − σmin)x0) − vt(w, xctx, z; θ)||2,

Laudio-CFM-m(θ) = Et,m,q(x,z),p0(x0)||m ⊙ ((x − (1 − σmin)x0) − vt(w, xctx, z; θ))||2,

(6)
Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale