Taming Data and Transformers for Audio Generation
Audio;Diffusion Models;T2A
Yes
No ethics review needed.
Please see weakness section.
- The illustrations in the paper are clear and it is well written.
- No evident flaws.
- AutoReCap-XL will be great asset for the audio research community if open-sourced.
This paper proposes an audio captioning method called AutoCap, an audio generation model called GenAu and an audio dataset called AutoReCap-XL.
- The authors shared the audio samples from Stable-Audio 1.0[7] but the comparison is missing in results table.
- Typo in table 5, last row "Quality" column: "-".
- The performance improvement when compared to increase in number of parameters in GenAu is marginal.
- Recently, Large Audio Language models [1,2,3,4] are being employed for audio captioning task, but the authors don't compare AutoCap to these baselines which in my opinion should be an important comparison.
- Inconsistent use of word "Q-Former" and "Qformer".
- Why do authors not show generation results of GenAu without Recap dataset or baseline + Recap in normal settings? It should be an important ablation to show where the performance gain is coming from.
- The CLAP text conditioner is confusing, the text encoder employed by CLAP [5,6] is BERT. What do authors mean when they say that they have used CLAP text encoder, do they use the text encoder weights from CLAP's checkpoint?
- There should be clear distinction between CLAP and EnCLAP. Authors mention CLAP but cite EnCLAP.
- The authors propose to use video caption and title as metadata, how sensitive is the output to the accuracy and relevance of metadata? Have the authors performed any ablation to show the robustness of this method?
- It would be interesting to analyse the distribution of sounds across various classes of non-ambient sounds in AutoReCap-XL. Have the authors explored any distribution patterns, and is there a noticeable bias toward any particular class as there is a lot of filtering while preparing the dataset?
- In section A.2 authors mention "Given that AutoReCap was trained for 10-second audio", I think there is a typo and it should be "AutoCap" instead of "AutoReCap" as the later one is the dataset.
- I want to understand what is the novelty of GenAu when compared to other models? In my opinion all other baselines and other audio generative models are **almost** capable of generating more or less similar audios with lesser parameters.

References

[1] GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities

[2] Listen, Think, and Understand

[3] Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities

[4] Pengi: An Audio Language Model for Audio Tasks

[5] ENCLAP: COMBINING NEURAL AUDIO CODEC AND AUDIO-TEXT JOINT EMBEDDING FOR AUTOMATED AUDIO CAPTIONING

[6] CLAP : LEARNING AUDIO CONCEPTS FROM NATURAL LANGUAGE SUPERVISION

[7] Stable Audio Open
Yes
No ethics review needed.
* Didn't train baselines on the new dataset to show the proposed architecture is actually superior. I would have liked to see the baselines trained on the 57M example dataset. This would clearly show if the better performance of the proposed method is due to architecture or just the scaling of the dataset.
* Is there a way to verify the quality of the dataset in terms of captioning? assuming the community adopts the use of this new data how are you to ensure the data is of high quality? maybe some human evaluation would be in place.
* Please move the limitations section into the main body of the paper.
* The paper presents a simple pipeline to label audio data in order to generate larger data than ever before.
* The paper shows the new dataset improves the quality of trained models.
* Train a SOTA model using the new dataset.
* The authors say they will release the dataset which could be a good contribution to the community.
Introduce two new models. First, we propose AutoCap, a high-quality and efficient automatic audio captioning model. By using a compact audio representation and leveraging audio metadata, AutoCap substantially enhances caption quality Second, the work proposes GenAu, a scalable transformer-based audio generation architecture that is scaled up to 1.25B parameters. Using AutoCap to generate caption clips from existing audio datasets.
* In scenarios where metadata lacks detail, audio captioning may struggle to disambiguate sounds accurately. The model also tends to falter in
capturing the temporal relationships between sounds and differentiating foreground from background
noises. 
* Fine-tuned on AudioCaps, which contains a limited vocabulary of 4,892 unique words. The limited vocabulary of the paired texts, even though extensive, hampers the model’s ability to accurately generate audio for long and detailed prompts.

* The proposed dataset, AutoReCap-XL, is substantial in size but features a constrained vocabulary of
only 4,461 unique words. Furthermore, despite its potential as a significant contribution, this dataset has not
yet been extensively analyzed for caption accuracy or performance in downstream tasks.
Yes
No ethics review needed.
My questions are broadly mentioned in the Weaknesses section. I am mostly looking for the "Why?" answers to the choices made that are related to my listed Weaknesses.
- The paper is well written and well presented. The figures are good and the writing and everything is crisp. It is a nice to read paper.
- The method shows good improvements. Open-sourcing the artifacts in future would help the audio community.
- Th intuitions are good. The fact that good captions can improve audio generation is a good finding and well conveyed. Although I feel some parts are over-claimed which I mention next.
- I don't see many technical flaws with the paper.
The paper proposes a new method to improve audio (sound) generation. The authors first propose AutoCap, a novel method to generate audio captions using auto-regressive models. Next, the authors propose a novel audio generation model which is trained on their generated dataset. The proposed method shows improvement on benchmark datasets.
I have several issues with the paper. I will first point out the technical weaknesses:
- Fig. 1 says CLAP Encoder has one token. CLAP uses HTSAT as the audio encoder  which also has intermediate representations. This only means that the authors used the CLS token (or some pooled representations) which is not specified. The authors should also clearly mention "CLAP audio encoder". 
- The caption says "We then compact this representation into 4x fewer tokens using a Q-Former (Li et al., 2023a) module.". The figure shows only HTSAT encoder representation is passed to QFormer. The authors should rewrite the caption.
- The claim "4x fewer tokens" just because QFormer was employed in one part is not justified. The authors are also using BART embeddings, etc. Do you mean "4x fewer tokens" compared to your own baseline which does not use a QFormer?
- QFormer has been used for audio earlier [3]. Additionally, it does not seem like you are pre-training the QFormer? Does this mean you are training it E2E? An E2E model as big as QFormer trained on such small datasets is not very sound.
- The authors say fewer tokens but do not talk about the increase in parameter count. I would like to see ore discussion on this please.
- Some audio captioning prior art missing from comparison [1,2].
- I am concerned why only audio clips with "No subtitles" were uses. Is the synchronization of time and subtitle correct? Also, human speech is an important sound and heavily present in audiocaps. If the authors believe not, then I think the paper should refocus on "environmental sounds" and not "audio".
- (Not a big weakness) I feel its important that synthetic data generation pipelines have some verification strategy (human or automatic).
- The freezing and unfreezing paradigm has been heavily employed in prior art in the DCASE challenge. The paper misses some citations. Please see DCASE captioning challenges that use BART like architectures.
- The fact that visual modality helps in audio captioning is not new and has been explored. Comparison or discussion with papers like SOUND-VECAPS [4] or [5] is missing.
- For GenAu, a discussion with Stable Audio is missing. I understand where at some places the authors mention DiT and how GenAu mitigates some limitations of DiT based architectures, but the introduction of time embeddings and the use of attention modules makes it very similar to StableAudio overall. A discussion would do justice.
- Table 4 does not compare with Stable Audio. Same training data + Stable Audio comparison is a must for a fair comparison.
- The table has missing comparison of baselines + their re-captioned data. I struggle to understand where the gains are coming from. The only ablation of GenAU w. U-Net is not sufficient. I also understand computational constraints but a couple of baselines with the data would do more justice to the results.

[1] https://ieeexplore.ieee.org/abstract/document/10448030.   
[2] https://arxiv.org/abs/2309.07372.   
[3] https://arxiv.org/abs/2406.11768.  
[4] https://arxiv.org/abs/2407.04416.   
[5] https://arxiv.org/abs/2309.11500.
Yes
No ethics concerns required.
No ethics review needed.
1. What does the rejection rate in line 282 refer to? Is the rejection related to downloads?

2. In Section 3.2, the authors mention that they use the captions of each video from the video-captioning model in Panda-70M. Does this imply that the "metadata" is essentially raw captions generated by another captioning system?

3. Can the authors provide more details about the collected video datasets? Specifically, what is the average length of each video sample?

4. How does GenAu perform if only one text encoder (either FLAN-T5 or CLAP) is used?

5. What is the performance of GenAu when trained on the proposed AudioReCap-XL dataset?

6. Could the authors provide some demo examples of GenAu?
1. The authors employ a Q-Former structure to compress audio representations from the pretrained HTSAT model, which significantly reduces system inference time. Additionally, to develop a more stable system, the paper adopts a two-stage training strategy: in stage one, only the Q-Former and CLAP-projector are trained on weakly labeled samples, while in stage two, the system is fully fine-tuned on the AudioCaps dataset.
2. The paper collects the majority of its data from videos, filtering out ambient clips that lack automatic transcripts. This is a straightforward yet interesting method to exclude speech and music content.
3. According to experimental results, both the captioning and generation systems achieve state-of-the-art (SoTA) performance.

Overall, this paper proposes an interesting approach by leveraging external metadata (e.g., captions or titles from visual information) to enhance audio captioning performance. Moreover, the introduction of the Q-Former module effectively reduces complexity and inference time. Finally, the authors present AutoReCap-XL, a dataset significantly larger than any existing audio-language datasets.
This paper introduces a high-quality and efficient audio captioning model, named AutoCap, which demonstrates improvements in generation quality while being four times faster than current state-of-the-art (SoTA) models. The authors further present AutoReCap-XL, a large-scale audio-language dataset comprising 57 million ambient clips paired with automatically generated captions. Additionally, the paper proposes an audio generation model named GenAu, which also achieves SoTA performance.
1. The paper lacks a detailed pipeline explaining how captions are analyzed and how speech or music-related content is filtered out. The authors should provide more clarification on this method.
2. The proposed audio captioning system appears to require external "metadata" to generate captions. However, the definition of this "metadata" is ambiguous throughout the paper. The authors should offer more details on what this metadata consists of and how it is used. If this metadata is not readily accessible from raw audio, it limits the applicability of the system to real-world scenarios.
3. According to Table 1, the comparison indicates that the proposed AutoCap does not achieve the best performance on several metrics, especially when only audio is used as input data.
4. For the audio generation system, the proposed model largely follows the architecture from Huang et al. (2023), incorporating the 1D-VAE and LDM modules. This feels more like an engineering effort, where the existing system is applied and scaled to larger datasets. Moreover, the model uses both FLAN-T5 and CLAP for conditional input, whereas previous models generally employ only one text encoder to achieve satisfactory results. The authors should explain why both encoders are necessary, along with experimental comparisons showing if using only one encoder leads to a drop in performance.
5. The paper lacks an evaluation of the effectiveness of the proposed AutoReCap-XL dataset.

Overall, the paper presents an interesting approach to improving the captioning system using external metadata. However, the explanation of certain methods and strategies needs more detail. Additionally, the metadata required by the captioning system may not be easily accessible from raw audio, making it difficult to apply the system to general tasks. The proposed generation model seems more like an engineering effort rather than a novel contribution. Lastly, the large-scale dataset introduced in the paper requires experimental validation to demonstrate its effectiveness in audio-language tasks. I am willing to change the score if the author can fulfil the experiments for the proposed two systems as well as the dataset.
This work introduces AutoCap, an efficient audio captioning model, and GenAu, a large-scale audio generation model, and AutoReCap-XL, the largest audio-text dataset with 57M clips.
@inproceedings{
anonymous2024taming,
title={Taming Data and Transformers for Audio Generation},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=lidVssyB7G},
note={under review}
}
Generating ambient sounds is a challenging task due to data scarcity and often
insufficient caption quality, making it difficult to employ large-scale generative
models for the task. In this work, we tackle this problem by introducing two
new models. First, we propose AutoCap, a high-quality and efficient automatic
audio captioning model. By using a compact audio representation and leveraging
audio metadata, AutoCap substantially enhances caption quality, reaching a CIDEr
score of 83.2, marking a 3.2% improvement from the best available captioning
model at four times faster inference speed. Second, we propose GenAu, a scalable
transformer-based audio generation architecture that we scale up to 1.25B param-
eters. Using AutoCap to generate caption clips from existing audio datasets, we
demonstrate the benefits of data scaling with synthetic captions as well as model
size scaling. When compared to state-of-the-art audio generators trained at similar
size and data scale, GenAu obtains significant improvements of 4.7% in FAD
score, 22.7% in IS, and 13.5% in CLAP score, indicating significantly improved
quality of generated audio compared to previous works. Moreover, we propose an
efficient and scalable pipeline for collecting audio datasets, enabling us to compile
57M ambient audio clips, forming AutoReCap-XL, the largest available audio-text
dataset, at 90 times the scale of existing ones. Our code, model checkpoints, and
dataset will be made publicly available upon acceptance.
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Audio Diffusion Models T2A
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applications to computer vision, audio, language, and other modalities
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Taming Data and Transformers for Audio Generation
ICLR 2025 Conference Submission
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