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| title: Light-MER | |
| emoji: 🎭 | |
| colorFrom: yellow | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: "5.23.0" | |
| app_file: app.py | |
| short_description: Sub-1B multimodal emotion recognition from video and audio | |
| python_version: "3.10" | |
| startup_duration_timeout: 10m | |
| # Light-MER: Lightweight Multimodal Emotion Recognition | |
| A sub-1B parameter multimodal emotion recognition model that processes video, audio, and text to recognize emotions. | |
| ## Model | |
| - **Paper**: [Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?](https://arxiv.org/abs/2607.12787) | |
| - **GitHub**: [GAIR-Lab/Light-MER](https://github.com/GAIR-Lab/Light-MER) | |
| - **Model**: [kevin233333/Light-MER](https://huggingface.co/kevin233333/Light-MER) | |
| ## How it works | |
| Light-MER uses a Qwen3-0.6B LLM backbone with CLIP-ViT-Base visual encoder and Chinese-HuBERT audio encoder. | |
| The model processes video frames, audio clips, and optional subtitles to perform emotion recognition and reasoning. | |
| ## Usage | |
| 1. Upload a video file (with or without audio) | |
| 2. Optionally provide a separate audio file or subtitle text | |
| 3. Ask a question about the emotional state (e.g., "Please infer the person's emotional state and provide your reasoning process.") | |
| 4. Click "Recognize Emotion" to get the model's response |