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## 特别鸣谢

本程序中的预处理及后处理代码,来自于:https://github.com/chenkui164/FastASR
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## 线程数与性能关系

测试环境Rocky Linux 8,仅测试cpp版本结果(未测python版本)

简述:
在3台配置不同的机器上分别编译并测试,在fftw和onnxruntime版本都相同的前提下,识别同一个30分钟的音频文件,分别测试不同onnx线程数量的表现。

![线程数关系](images/threadnum.png "Windows ASR")

目前可以总结出大致规律:

并非onnx线程数越多越好
2线程比1线程提升显著,线程再多则提升较小
线程数等于CPU物理核心数时效率最好
实操建议:

大部分场景用3-4线程性价比最高
低配机器用2线程合适



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##  演示

![Windows演示](images/demo.png "Windows ASR")

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## 注意
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本程序只支持 采样率16000hz, 位深16bit的 **单声道** 音频。
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## 快速使用

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### Windows
 
 安装Vs2022 打开cpp_onnx目录下的cmake工程,直接 build即可。 本仓库已经准备好所有相关依赖库。
 
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 Windows下已经预置fftw3、onnxruntime及openblas库


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### Linux
See the bottom of this page: Building Guidance
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###  运行程序

tester  /path/to/models/dir /path/to/wave/file

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 例如: tester /data/models  /data/test.wav
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/data/models 需要包括如下两个文件: model.onnx 和vocab.txt
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```

```
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## 支持平台
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- Windows
- Linux/Unix
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## 依赖
- fftw3
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- onnxruntime
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## 导出onnx格式模型文件
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安装 modelscope与FunASR,依赖:torch,torchaudio,安装过程[详细参考文档](https://github.com/alibaba-damo-academy/FunASR/wiki)
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```shell
pip install "modelscope[audio_asr]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip install --editable ./
```
导出onnx模型,[详见](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export),参考示例,从modelscope中模型导出:

```
python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
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```
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## Building Guidance for Linux/Unix
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```
git clone https://github.com/RapidAI/RapidASR.git
cd RapidASR/cpp_onnx/
mkdir build
cd build
# download an appropriate onnxruntime from https://github.com/microsoft/onnxruntime/releases/tag/v1.14.0
# here we get a copy of onnxruntime for linux 64
wget https://github.com/microsoft/onnxruntime/releases/download/v1.14.0/onnxruntime-linux-x64-1.14.0.tgz
#ls 
# onnxruntime-linux-x64-1.14.0  onnxruntime-linux-x64-1.14.0.tgz

#install fftw3-dev
apt install libfftw3-dev

# build 
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 cmake  -DCMAKE_BUILD_TYPE=release .. -DONNXRUNTIME_DIR=/mnt/c/Users/ma139/RapidASR/cpp_onnx/build/onnxruntime-linux-x64-1.14.0
 make
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 # then in the subfolder tester of current direcotry, you will see a program, tester
 
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````
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### The structure of a qualified onnxruntime package.
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```
onnxruntime_xxx
├───include
└───lib
```