AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models (2411.18953v1)

Abstract: With the emergence of audio-LLMs, constructing large-scale paired audio-language datasets has become essential yet challenging for model development, primarily due to the time-intensive and labour-heavy demands involved. While LLMs have improved the efficiency of synthetic audio caption generation, current approaches struggle to effectively extract and incorporate detailed audio information. In this paper, we propose an automated pipeline that integrates audio-LLMs for fine-grained content extraction, LLMs for synthetic caption generation, and a contrastive language-audio pretraining (CLAP) model-based refinement process to improve the quality of captions. Specifically, we employ prompt chaining techniques in the content extraction stage to obtain accurate and fine-grained audio information, while we use the refinement process to mitigate potential hallucinations in the generated captions. Leveraging the AudioSet dataset and the proposed approach, we create AudioSetCaps, a dataset comprising 1.9 million audio-caption pairs, the largest audio-caption dataset at the time of writing. The models trained with AudioSetCaps achieve state-of-the-art performance on audio-text retrieval with R@1 scores of 46.3% for text-to-audio and 59.7% for audio-to-text retrieval and automated audio captioning with the CIDEr score of 84.8. As our approach has shown promising results with AudioSetCaps, we create another dataset containing 4.1 million synthetic audio-language pairs based on the Youtube-8M and VGGSound datasets. To facilitate research in audio-language learning, we have made our pipeline, datasets with 6 million audio-language pairs, and pre-trained models publicly available at https://github.com/JishengBai/AudioSetCaps.

An Analysis of AudioSetCaps: An Enriched Audio-Caption Dataset

The paper "AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Audio and LLMs" introduces an in-depth exploration into the creation and application of a large-scale audio-caption dataset, AudioSetCaps, whose formation utilizes advanced techniques integrating audio-LLMs (ALMs) and LLMs. This research addresses a central challenge in audio-language learning: constructing expansive paired datasets efficiently, which are critical for developing robust models capable of fine-grained audio understanding and reasoning.

Dataset Creation and Methodology

Central to this paper is the introduction of a novel automatic pipeline designed for generating audio captions. The pipeline harmonizes the capabilities of ALMs and LLMs to synthesize detailed and contextually relevant audio descriptions. The process is comprised of three phases:

1. Audio Content Extraction: This stage leverages large audio-LLMs (LALMs) to dissect and extract nuanced audio information, including speech characteristics and music attributes, vital for creating meaningful captions.
2. Caption Generation: Utilizing LLMs, this phase converts the abundantly extracted audio information into structured, coherent captions. Great precision is maintained in representing the audio characteristics without overtly hallucinating non-existent elements.
3. Caption Refinement: Employing the contrastive language-audio pretraining (CLAP) model, this stage refines generated captions, ensuring alignment with the actual audio content, thus enhancing data reliability.

Empirical Performance and Claims

The research demonstrates robust numerical outcomes with the models trained on AudioSetCaps. A notable achievement is their superior performance in audio-text retrieval tasks, achieving R@1 scores of 46.3% for text-to-audio and 59.7% for audio-to-text retrieval. Additionally, in the automated audio captioning (AAC) challenge, the dataset yields a CIDEr score of 84.8, underscoring the dataset's potential for improving the quality of audio-LLMs.

Moreover, the paper's extension to create another dataset based on the Youtube-8M and VGGSound datasets, culminating in over 6 million audio-language pairs, illustrates the scalability and adaptability of this approach.

Theoretical and Practical Implications

From a theoretical standpoint, the paper significantly contributes to advancing methodologies in audio-language processing by showcasing how synthetic data, when meticulously generated, can closely match or even surpass the efficacy of human-annotated data. The use of prompt chaining and LLMs in synthesizing comprehensive captions represents a sophisticated application of modern AI methodologies to a classic data bottleneck issue.

Practically, the open release of their pipeline, extensive datasets, and pre-trained models posits broad implications for facilitating research in audio-language interaction, allowing other researchers to build upon this foundation to explore diverse cross-modal tasks such as zero-shot classification and contextual audio recognition.

Future Prospects

The work presents opportunities for future research across several dimensions. Firstly, enhancing the pipeline for multimodal datasets beyond audio-centric data by incorporating comprehensive multimedia cues could significantly enrich the quality and diversity of generated captions. Furthermore, exploring the fusion of this pipeline with more advanced, dynamically adjustable LLM settings could potentially yield even richer descriptive contexts and better handle audio nuances.

In conclusion, this paper builds a notable contribution to the field of audio-language learning by presenting a powerful, scalable method to generate extensive and detailed audio-caption datasets. The implications of this work reach beyond traditional audio captioning, suggesting future developments in AI could increasingly rely on such automated, scalable data generation paradigms to overcome the challenges posed by large-scale dataset creation.