快速使用

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。

Windows下已经预置fftw3及onnxruntime库

Linux

See the bottom of this page: Building Guidance

运行程序

tester /path/to/models_dir /path/to/wave_file quantize(true or false)

例如： tester /data/models /data/test.wav false

/data/models 需要包括如下三个文件: config.yaml, am.mvn, model.onnx(or model_quant.onnx)

支持平台

• Windows
• Linux/Unix

依赖

• fftw3
• openblas
• onnxruntime

导出onnx格式模型文件

安装 modelscope与FunASR，依赖：torch，torchaudio，安装过程详细参考文档
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，参考示例，从modelscope中模型导出：

python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type onnx --quantize True

Building Guidance for Linux/Unix

git clone https://github.com/alibaba-damo-academy/FunASR.git && cd funasr/runtime/onnxruntime
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
tar -zxvf onnxruntime-linux-x64-1.14.0.tgz
# ls
# onnxruntime-linux-x64-1.14.0  onnxruntime-linux-x64-1.14.0.tgz

#install fftw3-dev
ubuntu: apt install libfftw3-dev
centos: yum install fftw fftw-devel

#install openblas
bash ./third_party/install_openblas.sh

# build
 cmake  -DCMAKE_BUILD_TYPE=release .. -DONNXRUNTIME_DIR=/path/to/onnxruntime-linux-x64-1.14.0
 make

 # then in the subfolder tester of current direcotry, you will see a program, tester

````

### The structure of a qualified onnxruntime package.

onnxruntime_xxx
├───include
└───lib
```

注意

本程序只支持 采样率16000hz, 位深16bit的 单声道 音频。

Acknowledge

1. This project is maintained by FunASR community.
2. We acknowledge mayong for contributing the onnxruntime(cpp api).
3. We borrowed a lot of code from FastASR for audio frontend and text-postprocess.