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.
Step 1) Prepare server environment (on server). #Optional, modelscope cuda docker is preferred. CID=`docker run --network host -d -it --gpus '"device=0"' registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.2.0` echo $CID docker exec -it $CID /bin/bash cd /opt/conda/lib/python3.7/site-packages/funasr/runtime/python/grpc
Step 2) Generate protobuf file (for server and client). #Optional, paraformer_pb2.py and paraformer_pb2_grpc.py are already generated. python -m grpc_tools.protoc --proto_path=./proto -I ./proto --python_out=. --grpc_python_out=./ ./proto/paraformer.proto
Step 3) Start grpc server (on server). python grpc_main_server.py --port 10095
Step 4) Start grpc client (on client with microphone). #Install dependency. python -m pip install pyaudio webrtcvad #Start client. python grpc_main_client_mic.py --host 127.0.0.1 --port 10095
We borrow or refer to some code from:
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
