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---
license: apache-2.0
language:
- zh
- en
base_model:
- google-bert/bert-base-multilingual-cased
tags:
- agent
---
<div align="center">
<h1>FireRedChat-turn-detector</h1>
</div>
<div align="center">
<a href="https://fireredteam.github.io/demos/firered_chat/">Demo</a> •
<a href="https://arxiv.org/pdf/2509.06502">Paper</a> •
<a href="https://huggingface.co/FireRedTeam">Huggingface</a>
</div>
## Descriptions
Compact end-of-turn detection used in FireRedChat. [livekit plugin available here](https://github.com/fireredchat-submodules/livekit-plugins-fireredchat-turn-detector)
- chinese_best_model_q8.onnx: FireRedChat turn-detector model (Chinese only)
- multilingual_best_model_q8.onnx: FireRedChat turn-detector model (Chinese and English)
## Roadmap
- [x] 2025/09
- [x] Release the onnx checkpoints and livekit plugin.
## Usage
```python
import numpy as np
import onnxruntime as ort
from transformers import AutoTokenizer
def softmax(x):
exp_x = np.exp(x - np.max(x, axis=1, keepdims=True))
return exp_x / np.sum(exp_x, axis=1, keepdims=True)
session = ort.InferenceSession(
"chinese_best_model_q8.onnx", providers=["CPUExecutionProvider"]
)
tokenizer = AutoTokenizer.from_pretrained(
"./tokenizer",
local_files_only=True,
truncation_side="left"
)
text = "这是一句没有标点的文本"
inputs = tokenizer(
text,
truncation=True,
padding='max_length',
add_special_tokens=False,
return_tensors="np",
max_length=128,
)
# Run inference
outputs = session.run(None,
{
"input_ids": inputs["input_ids"].astype("int64"),
"attention_mask": inputs["attention_mask"].astype("int64")
})
eou_probability = softmax(outputs[0]).flatten()[-1]
print(eou_probability, eou_probability>0.5)
```
### Acknowledgment
- Base model: google-bert/bert-base-multilingual-cased (license: "apache-2.0") |