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| | | ## using paraformer with grpc |
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| | | |
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| | | # Using paraformer with grpc |
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| | | We can send streaming audio data to server in real-time with grpc client every 10 ms e.g., and get transcribed text when stop speaking. |
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| | | The audio data is in streaming, the asr inference process is in offline. |
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| | | Step 1) Generate protobuf file for grpc |
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| | | ## For the Server |
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| | | |
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| | | ### Prepare server environment |
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| | | #### Backend is modelscope pipeline (default) |
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| | | Install the modelscope and funasr |
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| | | |
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| | | ```shell |
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| | | pip install "modelscope[audio_asr]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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| | | git clone https://github.com/alibaba/FunASR.git && cd FunASR |
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| | | pip install --editable ./ |
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| | | ``` |
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| | | |
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| | | Install the requirements |
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| | | ```shell |
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| | | cd funasr/runtime/python/grpc |
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| | | pip install -r requirements_server.txt |
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| | | ``` |
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| | | |
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| | | #### Backend is funasr_onnx (optional) |
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| | | Install [`funasr_onnx`](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime). |
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| | | |
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| | | ``` |
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| | | pip install funasr_onnx -i https://pypi.Python.org/simple |
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| | | ``` |
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| | | Export the model, more details ref to [export docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime). |
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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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| | | |
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| | | ### Generate protobuf file |
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| | | Run on server, the two generated pb files are both used for server and client |
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| | | |
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| | | ```shell |
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| | | # paraformer_pb2.py and paraformer_pb2_grpc.py are already generated, |
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| | | # regenerate it only when you make changes to ./proto/paraformer.proto file. |
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| | | python -m grpc_tools.protoc --proto_path=./proto -I ./proto --python_out=. --grpc_python_out=./ ./proto/paraformer.proto |
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| | | ``` |
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| | | |
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| | | Step 2) start grpc server |
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| | | ### Start grpc server |
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| | | |
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| | | ``` |
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| | | # Start server. |
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| | | python grpc_main_server.py --port 10095 --backend pipeline |
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| | | ``` |
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| | | If you want run server with onnxruntime, please set `backend` and `onnx_dir`. |
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| | | ``` |
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| | | # Start server. |
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| | | python grpc_main_server.py --port 10095 --backend onnxruntime --onnx_dir /models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch |
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| | | ``` |
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| | | |
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| | | ## For the client |
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| | | |
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| | | ### Install the requirements |
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| | | |
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| | | ```shell |
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| | | git clone https://github.com/alibaba/FunASR.git && cd FunASR |
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| | | cd funasr/runtime/python/grpc |
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| | | pip install -r requirements_client.txt |
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| | | ``` |
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| | | |
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| | | ### Generate protobuf file |
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| | | Run on server, the two generated pb files are both used for server and client |
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| | | |
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| | | ```shell |
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| | | # paraformer_pb2.py and paraformer_pb2_grpc.py are already generated, |
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| | | # regenerate it only when you make changes to ./proto/paraformer.proto file. |
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| | | python -m grpc_tools.protoc --proto_path=./proto -I ./proto --python_out=. --grpc_python_out=./ ./proto/paraformer.proto |
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| | | ``` |
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| | | |
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| | | ### Start grpc client |
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| | | ``` |
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| | | # Start client. |
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| | | python grpc_main_client_mic.py --host 127.0.0.1 --port 10095 |
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| | | ``` |
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| | | |
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| | | |
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| | | Step 3) start grpc client |
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| | | ## Workflow in desgin |
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| | | |
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| | | <div align="left"><img src="proto/workflow.png" width="400"/> |
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| | | |
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| | | ## Reference |
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| | | We borrow from or refer to some code as: |
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| | | 1)https://github.com/wenet-e2e/wenet/tree/main/runtime/core/grpc |
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| | | 2)https://github.com/Open-Speech-EkStep/inference_service/blob/main/realtime_inference_service.py |
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