reviews: 
Comments: - The authors present a foundation model for audio generation and editing which encompasses 11 tasks spanning multiple modalities of audio such as speech, music, and general sounds. 
- The paper proposes a different approach to modeling audio tokenized into discrete codes using audio codecs. The proposed architecture is a single multi-scale transformer which involves a global transformer which operates on a summarized form of each audio frame, and a local transformer which performs autoregressive generation of codes within an audio frame. The design is proposed as a way to alleviate training transformers on very long sequences of flattened audio codes (SPEAR-TTS), or having train an autoregressive transformer on one level of codes and another non-autoregressive transformer on the remaining level of audio codes (VALL-E).
- The training process involves 7 different tasks with various task indicator tokens being used. The remaining tasks are incorporated in a fine-tuning phase post training of the base model.
- The authors perform evaluation using subjective and objective metrics and compare against various other models which may be specialized for each task. The results indicate that UniAudio is competitive against all the baselines.
Strengths: - As the authors show in the first table, this system is the first to tackle so many tasks in audio generation using a single backbone with only additional fine-tuning.
- The proposed model architecture is different from those used in other audio generation models and shows some gains. The comparison is not necessarily fair because of differences in training data, but it does give the community a new option to consider while building audio generation models.
- Results for speech data and denoising are decent.
Weaknesses: - The paper is a very dense read with some details relegated to the appendix while some details difficult to glean from the text. For example, the details about the audio tokenizer is moved to the appendix and no specifics are mentioned in the main text. Also, the text and the figure in the model architecture section are a little difficult to understand. I think I got it after reading through a few times, but adding some annotations to the figure and its caption will greatly improve readability.
- While the results for speech are decent, I found some issues w.r.t words being skipped. Also, the results on text to music/audio are pretty poor. The authors have not discussed any of the limitations in terms of quality of the generated audio despite the model being competitive with current state-of-the-art. 
- The paper does not offer too much in terms of insights. Not to take away from the effort it takes to setup such a large scale experiment, but my main takeaways from the paper are as follows:
  1. Existing audio codecs are not suitable for wide range of tasks, so one needs to train them on more diverse data.
  2. As long as we collect 100k+ hours of audio data we can achieve such performance using the multi-scale transformer. 

  The multi-scale transformer architecture is definitely interesting to the community but the architecture contribution is not well ablated, and in my opinion, that is the most important part of the paper.
- Minor correction: In Table 3, subjective and objective metrics column headers should be interchanged.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 3 good