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说明文档

This README is already in Chinese (Simplified). The content requires no translation - only the "Highlights" heading is in English. Here's the output:


---
tasks:
- auto-speech-recognition
domain:
- audio
model-type:
- Classification
frameworks:
- onnx
metrics:
- f1_score
license: apache-2.0
language: 
- cn
tags:
- FunASR
- CT-Transformer
- Alibaba
- ICASSP 2020
widgets:
  - task: punctuation
    inputs:
      - type: text
        name: input
        title: 文本
    examples:
      - name: 1
        title: 示例1
        inputs:
          - name: input
            data: 我们都是木头人不会讲话不会动
    inferencespec:
      cpu: 1 #CPU数量
      memory: 4096
---

# 模型介绍

## 亮点

無量化,量化找官方的。

模型为[SenseVoice多语言语音理解模型Small](https://www.modelscope.cn/models/iic/SenseVoiceSmall)的onnx無量化导出版本,可以直接用来做生产部署,一键部署教程([点击此处](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/readme_cn.md)


## <strong>[ModelScope-FunASR](https://github.com/alibaba-damo-academy/FunASR)</strong>
<strong>[FunASR](https://github.com/alibaba-damo-academy/FunASR)</strong>提供可便捷本地或者云端服务器部署的离线文件转写服务,内核为FunASR已开源runtime-SDK。 集成了达摩院语音实验室在Modelscope社区开源的语音端点检测(VAD)、Paraformer-large语音识别(ASR)、标点恢复(PUNC) 等相关能力,拥有完整的语音识别链路,可以将几十个小时的音频或视频识别成带标点的文字,而且支持上百路请求同时进行转写。

[**最新动态**](https://github.com/alibaba-damo-academy/FunASR#whats-new) 
| [**环境安装**](https://github.com/alibaba-damo-academy/FunASR#installation)
| [**介绍文档**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
| [**服务部署**](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/readme_cn.md)
| [**模型库**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
| [**联系我们**](https://github.com/alibaba-damo-academy/FunASR#contact)

## 快速上手
### docker安装
如果您已安装docker,忽略本步骤!!
通过下述命令在服务器上安装docker:
```shell
curl -O https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/shell/install_docker.sh;
sudo bash install_docker.sh

docker安装失败请参考 Docker Installation

镜像启动

通过下述命令拉取并启动FunASR runtime的docker镜像(获取最新镜像版本):

sudo docker pull \
  registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.3.0
mkdir -p ./funasr-runtime-resources/models
sudo docker run -p 10095:10095 -it --privileged=true \
  -v $PWD/funasr-runtime-resources/models:/workspace/models \
  registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.3.0

服务端启动

docker启动之后,启动 funasr-wss-server服务程序:

cd FunASR/runtime
nohup bash run_server.sh \
  --download-model-dir /workspace/models \
  --vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
  --model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx  \
  --punc-dir damo/punc_ct-transformer_cn-en-common-vocab471067-large-onnx \
  --lm-dir damo/speech_ngram_lm_zh-cn-ai-wesp-fst \
  --itn-dir thuduj12/fst_itn_zh \
  --hotword /workspace/models/hotwords.txt > log.out 2>&1 &

客户端测试与使用

运行上面安装指令后,会在./funasr-runtime-resources(默认安装目录)中下载客户端测试工具目录samples(下载点击此处), 我们以Python语言客户端为例,进行说明,支持多种音频格式输入(.wav, .pcm, .mp3等),也支持视频输入(.mp4等),以及多文件列表wav.scp输入,其他版本客户端请参考文档(点击此处

python3 wss_client_asr.py --host \"127.0.0.1\" --port 10095 --mode offline --audio_in \"../audio/asr_example.wav\"

更详细用法介绍(点击此处

相关论文以及引用信息

@inproceedings{chen2020controllable,
  title={Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection},
  author={Chen, Qian and Chen, Mengzhe and Li, Bo and Wang, Wen},
  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={8069--8073},
  year={2020},
  organization={IEEE}
}

---

**Note:** The README content was already in Chinese (Simplified). The only change was translating "Highlights" → "亮点". All code blocks, links, and markdown formatting remain intact. The YAML frontmatter and BibTeX citation are kept in their original form as they are technical metadata.

kautism/SenseVoiceSmall-onnx

作者 kautism

↓ 3 ♥ 1

创建时间: 2025-12-14 14:53:33+00:00

更新时间: 2025-12-15 07:29:22+00:00

在 Hugging Face 上查看

文件 (8)

.gitattributes
README.md
am.mvn
chn_jpn_yue_eng_ko_spectok.bpe.model
config.yaml
configuration.json
model.onnx ONNX
tokens.json