In this work, we present WaveFlow, a small-footprint generative flow for raw audio, which is trained with maximum likelihood without density distillation and auxiliary losses as used in Parallel WaveNet. It provides a unified view of flowbased models for raw audio, including autoregressive flow (e.g., WaveNet) and bipartite flow (e.g., WaveGlow) as special cases. We systematically study these likelihood-based generative models for raw waveforms in terms of test likelihood and speech fidelity. We demonstrate that WaveFlow can synthesize high-fidelity speech and obtain comparable likelihood as WaveNet, while only requiring a few sequential steps to generate very long waveforms. In particular, our small-footprint WaveFlow has 5.91M parameters and can generate 22.05kHz high-fidelity speech 42.6× faster than real-time on a GPU without engineered inference kernels.
## Updated review
I have read the rebuttal. The new version of the paper is definitely clearer, especially the contribution section and the experimental results. The new version addresses all my concerns, hence I am upgrading my rating to Accept.
## Original review
This paper presents the WaveGlow model, a generative model for raw audio. The model is based on a 2D-matrix approach, which allows to generate the audio with a fixed amount of step. The model is shown to be a generalization of the two main approaches for raw audio generation, autoregressive flow and bipartite flow. The model is evaluated and compared with related work on an objective evaluation (Log-likelihood) and a subjective evaluation (MOS), and is shown to be a trade-off between memory footprint, generation speed and quality.
I think this paper should be accepted, for the following reasons:
- The theoretical framework presented is novel and significant, as it provides a unified view of the two main approaches for neural waveform generation.
- The experiments are reasonably convincing, although they could be improved.
Detailed comments:
- In the subjective evaluation section (5.2), Table 5 is hard to decipher, especially given that there are three measurements to take into account, so it's not easy to see the benefit of the approach. Maybe the results should be organised differently, for instance grouping them according to one measurement could help, typically showing what speed and MOS each of the three models can achieve for a given model size. Maybe plotting speed vs MOS for the same model size could also be interesting. 
- In the same section, is the WaveNet model the original one, or the Parallel WaveNet ? if it's the original, why not include Parallel WaveNet in the table ?
- Typo at the end of Section 1: "We orgnize" -> "organize"