WaveFlow: A Compact Flow-based Model for Raw Audio
In this work, we present WaveFlow, a small-footprint generative flow for raw audio, which is trained with maximum likelihood without complicated density distillation and auxiliary losses as used in Parallel WaveNet. It provides a unified view of flow-based 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 only 5.91M parameters and can generate 22.05kHz speech 15.39 times faster than real-time on a GPU without customized inference kernels.
This submission belongs to the field of text-to-speech synthesis. In particular it looks at a novel way of formulating a normalising flow using 2D rather than conventional 1D representation. Such reformulation enables to provide interpretations to several existing approaches as well as formulate a new one with quite interesting properties. This submission would benefit from a discussion of limitations of your approach. I believe there is a great deal of interest in the use of normalising flows in the text-to-speech area. I believe this submission could be a good contribution to the area. The test log-likelihoods look comparable to existing approaches with significantly worse inference times. The mean opinion scores (MOS) seem to approach one of the standard baselines with significantly worse inference times though at the expense of increasing the number of model parameters from 6M to 86M parameters whilst gaining only 0.2 in MOS. The submission would have benefited from discussion about model complexity/expressivity and it*s impact on MOS for WaveFlow, WaveNet and other approaches. The largest issues with this submission are: 1) lack of proper technical description of your model in sections 1 and 2 making reading sections 1,2,3,etc in order awkward. It seems the order should be 3,4,(5),1,2,(5). 2) complete omission of conditioning on text to be synthesised; anyone not familiar deeply with speech synthesis will wonder where does the text come in 3) explicit statement of complexity for the operations involved using proper big-O notation; helps to avoid confusion about what do you mean by *parallel* (autoregressive WaveNet followed by parallel computation != parallel computation)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
WaveFlow: A Compact Flow-based Model for Raw Audio
In this work, we present WaveFlow, a small-footprint generative flow for raw audio, which is trained with maximum likelihood without complicated density distillation and auxiliary losses as used in Parallel WaveNet. It provides a unified view of flow-based 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 only 5.91M parameters and can generate 22.05kHz speech 15.39 times faster than real-time on a GPU without customized inference kernels.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This submission belongs to the field of text-to-speech synthesis. In particular it looks at a novel way of formulating a normalising flow using 2D rather than conventional 1D representation. Such reformulation enables to provide interpretations to several existing approaches as well as formulate a new one with quite interesting properties. This submission would benefit from a discussion of limitations of your approach. I believe there is a great deal of interest in the use of normalising flows in the text-to-speech area. I believe this submission could be a good contribution to the area. The test log-likelihoods look comparable to existing approaches with significantly worse inference times. The mean opinion scores (MOS) seem to approach one of the standard baselines with significantly worse inference times though at the expense of increasing the number of model parameters from 6M to 86M parameters whilst gaining only 0.2 in MOS. The submission would have benefited from discussion about model complexity/expressivity and it*s impact on MOS for WaveFlow, WaveNet and other approaches. The largest issues with this submission are: 1) lack of proper technical description of your model in sections 1 and 2 making reading sections 1,2,3,etc in order awkward. It seems the order should be 3,4,(5),1,2,(5). 2) complete omission of conditioning on text to be synthesised; anyone not familiar deeply with speech synthesis will wonder where does the text come in 3) explicit statement of complexity for the operations involved using proper big-O notation; helps to avoid confusion about what do you mean by *parallel* (autoregressive WaveNet followed by parallel computation != parallel computation)
assistant
This submission belongs to the field of text-to-speech synthesis. In particular it looks at a novel way of formulating a normalising flow using 2D rather than conventional 1D representation. Such reformulation enables to provide interpretations to several existing approaches as well as formulate a new one with quite interesting properties. This submission would benefit from a discussion of limitations of your approach. I believe there is a great deal of interest in the use of normalising flows in the text-to-speech area. I believe this submission could be a good contribution to the area. The test log-likelihoods look comparable to existing approaches with significantly worse inference times. The mean opinion scores (MOS) seem to approach one of the standard baselines with significantly worse inference times though at the expense of increasing the number of model parameters from 6M to 86M parameters whilst gaining only 0.2 in MOS. The submission would have benefited from discussion about model complexity/expressivity and it*s impact on MOS for WaveFlow, WaveNet and other approaches. The largest issues with this submission are: 1) lack of proper technical description of your model in sections 1 and 2 making reading sections 1,2,3,etc in order awkward. It seems the order should be 3,4,(5),1,2,(5). 2) complete omission of conditioning on text to be synthesised; anyone not familiar deeply with speech synthesis will wonder where does the text come in 3) explicit statement of complexity for the operations involved using proper big-O notation; helps to avoid confusion about what do you mean by *parallel* (autoregressive WaveNet followed by parallel computation != parallel computation)