reviews: 
Comments: The paper presents a foundation model for audio generation capable of handling a number of different tasks, all of which output audio (including speech, environmental sounds, and music) conditioned on input from multiple modalities including text, audio, and MIDI.  

The paper also introduces a two-level Transformer architecture where the top-level attention operates across audio *frames* (where each frame consists of multiple discrete tokens) and the bottom-level attention operates on the tokens within a frame.
Strengths: I am very much in favor of the goal of the paper: to introduce a multitask foundation model for audio generation.  I am also totally on board with the proposed architecture; decomposing the quadratic attention into frame-level and token-level is an excellent idea for handling the longer sequence lengths that are necessary for freeform audio generation.
Weaknesses: Two main areas of weakness:

1) Most of the evaluation tasks do not seem ideal for demonstrating the effectiveness of an architecture designed to enable long sequence lengths; other than text-to-music, the tasks are all quite local, with no real dependencies longer than a few seconds.  Notably, text-to-music is the task on which the model in the paper performs worst compared to baseline.  And the experimental evaluation of the architecture vs others consumes only a small section of the paper and is fairly inconclusive.

Essentially, the architecture seems overkill for most of these tasks, and for the task for which one might expect it to help most (text-to-music), possibly the smaller training data size prevents the model from taking advantage of its capability.

2) The paper does not convincingly demonstrate that training a single model on speech-, music-, and sound-generating tasks exhibits synergy across domains.  While for most of the evaluation tasks the multitask model outperforms the single task model, a) the difference in performance is small and b) it's not clear that the model really benefits from training on all of speech, music, and sound compared to a separate speech model, music model, and sound model as this experiment was not performed.  It's also not clear how much of the benefit depends on specifics of the datasets and tasks used here vs a more general principle; e.g. the overall amount of speech data here is much larger than the other domains and so a music task might benefit from training jointly with speech more than would be the case if the amount of music data were larger.

Overall, I'm just not satisfied that the main hypothesis of the paper -- training a model on all audio generation domains at once is better than training separate models for each domain -- is sufficiently backed up with experimental evidence.  The fact that the joint model outperforms state of the art on about half the tasks (and *not* the music tasks) corroborates my dissatisfaction here.

One other possible reason for training on multiple domains could be: only a small quantity of training data exists for certain domains.  However, for the case of foundation models this is certainly not true; it is usually *labels* that are in short supply, and raw audio -- speech, music, and sound -- exists in enormous quantity.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 3 good