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language:
- zh
- en
tags:
- affective-computing
- emotion-analysis
- system-one
- pad-model
- onnx
- pytorch
license: mit
pipeline_tag: text-classification
---
# Vibrato: A Complete System One Model for Emotion
<p align="center">
<a href="https://github.com/corolin/vibrato"><img src="https://img.shields.io/badge/GitHub-corolin/vibrato-blue?logo=github"></a>
<a href="https://vibrato.syrkos.com"><img src="https://img.shields.io/badge/🌐_Website-Global-blue.svg"></a>
<a href="https://vibrato.syrkos.cn"><img src="https://img.shields.io/badge/🇨🇳_Website-China-red.svg"></a>
<a href="https://modelscope.cn/models/corolin/vibrato"><img src="https://img.shields.io/badge/ModelScope-魔搭-624aff.svg"></a>
<img src="https://img.shields.io/badge/License-MIT-green.svg">
<img src="https://img.shields.io/badge/ONNX-Ready-orange.svg">
</p>
**Vibrato** is a standalone, general-purpose **System One (Jev-class) model** that performs emotion reading as typed decisions. In a single forward pass over a sequence of dialog messages, Vibrato emits **calibrated probability distributions**:
- **3 × 9-bin Likert PAD distributions** (Pleasure, Arousal, Dominance)
- **OCC-22 ranked emotion categories**
- **Pragmatic behavioral flags (Nouls)**: `suppressed` (irony/repression) & `directed_at_me` (attribution target)
- **Confidence score** for automatic escalation to heavy LLMs
> *“In a string instrument, vibrato is how emotion travels through the note.”*
---
## ⚡ Performance Matrix
| Deployment | Hardware | Latency | Memory / Disk | Use Case |
|---|---|---|---|---|
| **Vibrato v6 int8 (Fast Head)** | CPU (1 thread) | **2.97 ~ 4.39 ms** | 2.2 MB | High-throughput streaming, zero external backbone |
| **Vibrato + GPU Head** | GPU | **1.92 ms** | 7.4 MB | Real-time chat servers |
| **e5s Bundle (All-in-one)** | CPU (1 thread) | **~15 ms** | 85 MB | Full offline affective computing, desktop AI |
| **Full BGE-M3 Backbone** | GPU | **~16 ms** | 569 MB | Maximum subtlety on affective nuance |
---
## 📦 Model Artifacts in this Repository
- `models/vibrato_v6_int8.onnx`: 1.86M judgment head quantized to INT8 (2.2 MB).
- `models/vibrato_v6_fp32.onnx`: Full precision judgment head (7.4 MB, bitwise-identical to PyTorch).
- `models/vibrato_e5s_int8.onnx`: Adapted judgment head for e5-small (2.1 MB).
- `models/e5s_int8.onnx`: Multilingual e5-small embedding backbone quantized to INT8 (118 MB).
- `checkpoints/vibrato_v6.pt` & `checkpoints/vibrato_e5s.pt`: Original PyTorch model weights.
- `vocab.json` & `tokenizer/`: Char-level mapping and subword tokenizer.
---
## 🚀 Quickstart
### 1. Installation
```bash
pip install onnxruntime tokenizers numpy
```
### 2. Run Inference
Clone or download this repo, then execute:
```bash
python run_inference.py
```
Or inside Python:
```python
from run_inference import load_e5s, score_messages
vocab, tok, head, backbone = load_e5s()
messages = [
{"role": "user", "message": "今天累死啦，快抱抱"}
]
pad, family, nouls, conf = score_messages(messages, vocab, head, backbone=backbone, tok=tok)
print("PAD:", pad) # [Pleasure, Arousal, Dominance] in [-1.0, +1.0]
print("Family:", family) # e.g., 'affectionate'
print("Nouls:", nouls) # {'suppressed': bool, 'directed_at_me': bool}
print("Confidence:", conf) # [0.0, 1.0]
```
---
## 🔗 Links
- **GitHub Repository**: [corolin/vibrato](https://github.com/corolin/vibrato)
- **ModelScope**: [corolin/vibrato](https://modelscope.cn/models/corolin/vibrato)
- **Global Landing Page**: [https://vibrato.syrkos.com](https://vibrato.syrkos.com)
- **China Landing Page**: [https://vibrato.syrkos.cn](https://vibrato.syrkos.cn)
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