mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
73 lines
2.0 KiB
Markdown
73 lines
2.0 KiB
Markdown
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## Demo
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```shell
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tester /path/models_dir /path/wave_file quantize(true or false)
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```
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The structure of /path/models_dir
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```
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config.yaml, am.mvn, model.onnx(or model_quant.onnx)
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```
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## Steps
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### Export onnx
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#### Install [modelscope and funasr](https://github.com/alibaba-damo-academy/FunASR#installation)
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```shell
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pip3 install torch torchaudio
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pip install -U modelscope
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pip install -U funasr
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```
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#### Export [onnx model](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export)
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```shell
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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
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```
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### Building for Linux/Unix
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#### Download onnxruntime
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```shell
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# download an appropriate onnxruntime from https://github.com/microsoft/onnxruntime/releases/tag/v1.14.0
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# here we get a copy of onnxruntime for linux 64
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wget https://github.com/microsoft/onnxruntime/releases/download/v1.14.0/onnxruntime-linux-x64-1.14.0.tgz
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tar -zxvf onnxruntime-linux-x64-1.14.0.tgz
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```
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#### Install fftw3
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```shell
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sudo apt install libfftw3-dev #ubuntu
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# sudo yum install fftw fftw-devel #centos
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```
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#### Install openblas
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```shell
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sudo apt-get install libopenblas-dev #ubuntu
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# sudo yum -y install openblas-devel #centos
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```
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#### Build runtime
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```shell
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git clone https://github.com/alibaba-damo-academy/FunASR.git && cd funasr/runtime/onnxruntime
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mkdir build && cd build
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cmake -DCMAKE_BUILD_TYPE=release .. -DONNXRUNTIME_DIR=/path/to/onnxruntime-linux-x64-1.14.0
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make
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```
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#### The structure of a qualified onnxruntime package.
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```
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onnxruntime_xxx
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├───include
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└───lib
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```
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### Building for Windows
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Ref to win/
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## Acknowledge
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1. This project is maintained by [FunASR community](https://github.com/alibaba-damo-academy/FunASR).
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2. We acknowledge [mayong](https://github.com/RapidAI/RapidASR/tree/main/cpp_onnx) for contributing the onnxruntime(cpp api).
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3. We borrowed a lot of code from [FastASR](https://github.com/chenkui164/FastASR) for audio frontend and text-postprocess.
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