Official Implementation of GLAP - General Language Audio Pretraining
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Updated
May 14, 2026 - Python
Official Implementation of GLAP - General Language Audio Pretraining
[ACL 2026 Oral] Official code, UIQ benchmark, and pretrained checkpoints for "Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval" (Yoo et al., Sogang University)
List of academic resources on Language-Based Audio Retrieval
环境声音理解工具包:声学场景分类 + 声音事件检测,接入大模型做场景描述与音频-文本检索(numpy/scipy 核心,离线优先)
把统一声学编码器接入大语言模型的声学理解框架:声学场景理解、音频-文本检索与描述生成(纯 NumPy 内核,离线优先)
音频语言模型框架:把音频编码器 / 离散音频 token 接入 LLM,实现音频理解、描述、问答与检索(纯 numpy 核心,可选 torch)
Provide a fast, accurate audio tokenizer using a single linear layer for improved audio understanding and generation without relying on VAE models.
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