Vibrato: A Complete System One Model for Emotion

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.”

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⚡ 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

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📦 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.

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🚀 Quickstart

1. Installation

pip install onnxruntime tokenizers numpy

2. Run Inference

Clone or download this repo, then execute:

python run_inference.py

Or inside 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]

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🔗 Links

• GitHub Repository: corolin/vibrato
• ModelScope: corolin/vibrato
• Global Landing Page: https://vibrato.syrkos.com
• China Landing Page: https://vibrato.syrkos.cn