前沿音频模型ASMR标注测试：最高F1仅0.325
一名正在训练 text2asmr 文本生成 ASMR 模型的开发者，用相同提示词和20个标签，让八个前沿音频模型在480段片段上做声音标注，报告称它们在细粒度触发音上普遍表现不佳。Gemini 3.1 Pro 相对人工标签的触发音 F1 最高，为0.325；以频谱图作为输入的 Claude Opus 5.5 和 Grok 4.7 分别为0.121和0.110。Gemini 3.1 Pro 在干净的 FSD50K/ESC-50 刷擦片段中有92%标出刷擦声，但在真实 ASMR 片段上的刷擦 F1 只有0.18，耳语 F1 为0.70至0.79；作者称 Gemini 3.1 Pro 每条片段成本0.0088美元，MiMo 为0.0008美元，结果来自单篇 r/MachineLearning 帖子，未经独立验证。
该报告提供了较具体的证据，显示领先音频模型难以标注细粒度拟音，且作者正招募志愿者帮助标注训练 text2asmr 所需的大约12.6万段录音。
I'm training text2asmr, a text-to-ASMR model (whispering plus trigger sounds like tapping, crinkling, brushing). The generator needs every training clip tagged with the sounds in it, and there are about 126k recordings, so I tried frontier models as labellers. They were much worse than I expected, so here are the numbers.
Setup. Every judge got the same prompt and the same 20 labels on 480 clips:
- External: sound-effect clips from FSD50K and ESC-50 that humans had already labelled with one class, 40 per class. This measures recall.
- People: 6-second ASMR clips labelled on our site. A class counts as present when at least half of a clip's labellers ticked it. This measures F1.
| Judge | Trigger F1 vs people |
|---|---|
| Gemini 3.1 Pro | 0.325 |
| Gemini 2.5 Pro | 0.290 |
| GPT Audio 1.5 | 0.288 |
| GPT Audio | 0.264 |
| MiMo v2.6 Pro | 0.243 |
| Gemini 3.8 Flash | 0.212 |
| Claude Opus 5.5 (spectrogram image) | 0.121 |
| Grok 4.7 (spectrogram image) | 0.110 |
#Gemini 3.1 Pro#Gemini 2.5 Pro#Gemini 3.8 Flash#GPT Audio 1.5#GPT Audio#MiMo v2.6 Pro#Claude Opus 5.5#Grok 4.7#text2asmr#CLAP-ASMR