腾讯混元联合上海交大、南洋理工等机构推出MMAE（Massive Multitask Audio Editing Benchmark），这是首个全面评估AI语音/音频编辑能力的基准。MMAE要求模型理解现有音频并按自然语言指令精确修改，而非简单生成。当前模型在该基准上的精确匹配率（EMR）低于5%，暴露了可靠音频编辑的短板。MMAE包含2000个真实场景高保真样本、17741条细粒度评估项，覆盖声音/音乐/语音及混合共7种模态、6种任务复杂度（基础修改到多跳推理及多轮编辑）、8种操作类型（局部到全局）。论文、代码、数据集和演示已公开。
过去一年语音和音乐生成很热，但音频编辑还没人正经测过，腾讯这个基准把现状血淋淋地摆出来了，不到5%的准确率意味着整个方向都还在起步期。
Can AI truly edit audio, not just generate it? 🎧
Tencent Hy, in collaboration with SJTU, SII, NTU, TJU, ZODA, PKU, FDU, and other collaborators, introduces MMAE.
MMAE--A Massive Multitask Audio Editing Benchmark, is the first comprehensive evaluation benchmark for speech and audio "Banana🍌"
Instead of simply requiring the AI to "generate" audio, it demands that the AI understand an existing audio clip and precisely modify it according to natural language instructions—altering what needs to be changed while leaving the rest untouched.
Current models show an Exact Match Rate (EMR) below 5%, revealing a major gap in reliable audio editing.
MMAE includes:
✅ 2,000 high-fidelity samples from real-world scenarios
✅ 17,741 fine-grained rubric evaluation items
✅ 7 modality settings across sound, music, speech and their mixtures
✅ 6 task complexity from basic modifications to multi-hop reasoning and multi-round editing
✅ 8 operation types across local and global granularities
How to use:
arXiv: http://arxiv.org/abs/2606.07229
GitHub: https://github.com/ddlBoJack/MMAE
HuggingFace: https://huggingface.co/datasets/BoJack/MMAE
Demo: https://youtu.be/6At5nTWhlXI
来源：Tencent Hy · x.com