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.

Steps

Step 1) Prepare server environment (on server).

Install modelscope and funasr with pip or with cuda-docker image.

Option 1: Install modelscope and funasr with pip

Option 2: or install with cuda-docker image as:

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

Get into funasr source code grpc directory.
git clone https://github.com/alibaba-damo-academy/FunASR cd FunASR/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. Optional. python -m pip install pyaudio webrtcvad
# 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