grpc docs

funasr/runtime/python/grpc/Readme.md

@@ -5,7 +5,6 @@ The audio data is in streaming, the asr inference process is in offline.
 ## For the Server
 
 ### Prepare server environment
-#### Backend is modelscope pipeline (default)
 Install the modelscope and funasr
 
 ```shell
@@ -22,18 +21,6 @@ cd funasr/runtime/python/grpc
 pip install -r requirements_server.txt
 ```
 
-#### Backend is funasr_onnx (optional)
-
-Install [`funasr_onnx`](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime).
-
-```
-pip install funasr_onnx -i https://pypi.Python.org/simple
-```
-
-Export the model, more details ref to [export docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime).
-```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 onnx --quantize True
-```
 
 ### Generate protobuf file
 Run on server, the two generated pb files are both used for server and client
@@ -51,11 +38,6 @@ python -m grpc_tools.protoc  --proto_path=./proto -I ./proto    --python_out=. -
 python grpc_main_server.py --port 10095 --backend pipeline
 ```
 
-If you want run server with onnxruntime, please set `backend` and `onnx_dir`.
-```
-# Start server.
-python grpc_main_server.py --port 10095 --backend onnxruntime --onnx_dir /models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
-```
 
 ## For the client
 
@@ -87,9 +69,5 @@ python grpc_main_client_mic.py --host 127.0.0.1 --port 10095
 
 <div align="left"><img src="proto/workflow.png" width="400"/>
 
-## Reference
-We borrow from or refer to some code as:
-
-1)https://github.com/wenet-e2e/wenet/tree/main/runtime/core/grpc
-
-2)https://github.com/Open-Speech-EkStep/inference_service/blob/main/realtime_inference_service.py
+## Acknowledge
+1. This project is maintained by [FunASR community](https://github.com/alibaba-damo-academy/FunASR).