From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

---
 funasr/export/README.md |  100 ++++++++++++++++++++++++++++++++++++++++----------
 1 files changed, 80 insertions(+), 20 deletions(-)

diff --git a/funasr/export/README.md b/funasr/export/README.md
index a4d61bb..c46a799 100644
--- a/funasr/export/README.md
+++ b/funasr/export/README.md
@@ -1,33 +1,93 @@
+# Export models
 
 ## Environments
-    funasr 0.1.7
-    python 3.7
-    torch 1.11.0
-    modelscope 1.2.0
+### Install modelscope and funasr
 
-## Install modelscope and funasr
-
-The installation is the same as [funasr](../../README.md)
-
-## Export onnx format model
-Export model from modelscope
+The installation is the same as [funasr](https://github.com/alibaba-damo-academy/FunASR/blob/main/README.md#installation)
 ```shell
-python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
+# pip3 install torch torchaudio
+pip install -U modelscope funasr
+# For the users in China, you could install with the command:
+# pip install -U modelscope funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
 ```
-Export model from local path
+### Install the quantization tools
 ```shell
-python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
+pip install torch-quant # Optional, for torchscript quantization
+pip install onnx onnxruntime # Optional, for onnx quantization
 ```
 
-## Export torchscripts format model
-Export model from modelscope
-```shell
-python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" false
-```
+## Usage
+   `Tips`: torch>=1.11.0
+
+   ```shell
+   python -m funasr.export.export_model \
+       --model-name [model_name] \
+       --export-dir [export_dir] \
+       --type [onnx, torch] \
+       --quantize [true, false] \
+       --fallback-num [fallback_num]
+   ```
+   `model-name`: the model is to export. It could be the models from modelscope, or local finetuned model(named: model.pb).
+
+   `export-dir`: the dir where the onnx is export.
+
+   `type`: `onnx` or `torch`, export onnx format model or torchscript format model.
+
+   `quantize`: `true`, export quantized model at the same time; `false`, export fp32 model only.
+
+   `fallback-num`: specify the number of fallback layers to perform automatic mixed precision quantization.
 
 
-Export model from local path
+### Export onnx format model
+#### Export model from modelscope
 ```shell
-python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" false
+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 false
 ```
+#### Export model from local path
+The model'name must be `model.pb`
+```shell
+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 --quantize false
+```
+#### Test onnx model
+Ref to [test](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export/test)
+
+### Export torchscripts format model
+#### Export model from modelscope
+```shell
+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 --quantize false
+```
+
+#### Export model from local path
+The model'name must be `model.pb`
+```shell
+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 --quantize false
+```
+#### Test onnx model
+Ref to [test](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export/test)
+
+## Runtime
+### ONNXRuntime
+#### ONNXRuntime-python
+Ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/onnxruntime_python.html)
+#### ONNXRuntime-cpp
+Ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/onnxruntime_cpp.html)
+### Libtorch
+#### Libtorch-python
+Ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/libtorch_python.html)
+#### Libtorch-cpp
+Undo
+## Performance Benchmark
+
+### Paraformer on CPU
+
+[onnx runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_onnx.md)
+
+[libtorch runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_libtorch.md)
+
+### Paraformer on GPU
+[nv-triton](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/triton_gpu)
+
+
+## Acknowledge
+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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