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+
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+## Environments
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+ torch >= 1.11.0
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+ modelscope >= 1.2.0
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+ torch-quant >= 0.4.0 (required for exporting quantized torchscript format model)
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+ # pip install torch-quant -i https://pypi.org/simple
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+
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+## Install modelscope and funasr
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+
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+The installation is the same as [funasr](https://github.com/alibaba-damo-academy/FunASR/blob/main/README.md#installation)
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+
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+## Export model
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+ `Tips`: torch>=1.11.0
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+
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+ ```shell
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+ python -m funasr.export.export_model \
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+ --model-name [model_name] \
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+ --export-dir [export_dir] \
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+ --type [onnx, torch] \
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+ --quantize [true, false] \
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+ --fallback-num [fallback_num]
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+ ```
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+ `model-name`: the model is to export. It could be the models from modelscope, or local finetuned model(named: model.pb).
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+
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+ `export-dir`: the dir where the onnx is export.
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+
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+ `type`: `onnx` or `torch`, export onnx format model or torchscript format model.
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+
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+ `quantize`: `true`, export quantized model at the same time; `false`, export fp32 model only.
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+
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+ `fallback-num`: specify the number of fallback layers to perform automatic mixed precision quantization.
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+
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+## Performance Benchmark of Runtime
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+
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+### Paraformer on CPU
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+
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+[onnx runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_onnx.md)
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+
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+[libtorch runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_libtorch.md)
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+
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+### Paraformer on GPU
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+[nv-triton](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/triton_gpu)
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+
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+## For example
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+### Export onnx format model
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+Export model from modelscope
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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
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+```
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+Export model from local path, the model'name must be `model.pb`.
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+```shell
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+python -m funasr.export.export_model --model-name /mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type onnx
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+```
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+
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+### Export torchscripts format model
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+Export model from modelscope
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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 torch
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+```
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+
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+Export model from local path, the model'name must be `model.pb`.
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+```shell
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+python -m funasr.export.export_model --model-name /mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch
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+```
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+
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+## Acknowledge
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+Torch model quantization is supported by [BladeDISC](https://github.com/alibaba/BladeDISC), an end-to-end DynamIc Shape Compiler project for machine learning workloads. BladeDISC provides general, transparent, and ease of use performance optimization for TensorFlow/PyTorch workloads on GPGPU and CPU backends. If you are interested, please contact us.
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+
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游雁