FunASR/funasr/runtime/python/grpc

Using paraformer with grpc

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. 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

pip install "modelscope[audio_asr]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip install --editable ./

Install the requirements

cd funasr/runtime/python/grpc
pip install -r requirements_server.txt

Backend is funasr_onnx (optional)

Install funasr_onnx.

pip install funasr_onnx -i https://pypi.Python.org/simple

Export the model, more details ref to export docs.

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

# paraformer_pb2.py and paraformer_pb2_grpc.py are already generated, 
# regenerate it only when you make changes to ./proto/paraformer.proto file.
python -m grpc_tools.protoc  --proto_path=./proto -I ./proto    --python_out=. --grpc_python_out=./ ./proto/paraformer.proto

Start grpc server

# Start server.
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

Install the requirements

git clone https://github.com/alibaba/FunASR.git && cd FunASR
cd funasr/runtime/python/grpc
pip install -r requirements_client.txt

Generate protobuf file

Run on server, the two generated pb files are both used for server and client

# paraformer_pb2.py and paraformer_pb2_grpc.py are already generated, 
# regenerate it only when you make changes to ./proto/paraformer.proto file.
python -m grpc_tools.protoc  --proto_path=./proto -I ./proto    --python_out=. --grpc_python_out=./ ./proto/paraformer.proto

Start grpc client

# Start client.
python grpc_main_client_mic.py --host 127.0.0.1 --port 10095

Workflow in desgin

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