Audio-FLAN: New Large-Scale Dataset for Unified Audio Understanding and Generation
Researchers have introduced Audio-FLAN, a large-scale instruction-tuning dataset containing over 100 million instances across 80 diverse audio tasks spanning speech, music, and sound domains. The dataset addresses a gap in AI research by unifying audio understanding (transcription, comprehension) and generation (speech, music, sound synthesis) tasks, which have traditionally been treated separately. This development enables the creation of more capable audio-language models that can handle multiple audio domains in a zero-shot manner.
Audio-FLAN is a comprehensive instruction-tuning dataset designed to advance unified audio-language models by combining audio understanding and generation capabilities. The dataset encompasses over 100 million instances across 80 diverse tasks, covering speech, music, and sound domains. Recent progress in audio tokenization has made it feasible to integrate audio into large language models, but the lack of unified datasets has hindered development of models that handle both understanding and generation tasks seamlessly. The researchers argue that instruction tuning—a technique that has proven successful for text and vision models—has remained largely unexplored in audio. By making Audio-FLAN publicly available on HuggingFace and GitHub, the authors aim to facilitate research into zero-shot audio-language models capable of handling a wide range of audio processing tasks.
What's missing
The paper does not provide quantitative benchmarks or performance comparisons with existing audio models, baseline results on standard audio tasks, or details about the composition and distribution of tasks across the 80 audio domains. Additionally, potential limitations of the instruction-tuning approach for audio and computational requirements for training on this dataset are not discussed in the abstract.
What different sources said
- arXiv cs.AICenter
Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound
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