grpc_main_server.py 2.1 KB

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  1. import grpc
  2. from concurrent import futures
  3. import argparse
  4. import paraformer_pb2_grpc
  5. from grpc_server import ASRServicer
  6. def serve(args):
  7. server = grpc.server(futures.ThreadPoolExecutor(max_workers=10),
  8. # interceptors=(AuthInterceptor('Bearer mysecrettoken'),)
  9. )
  10. paraformer_pb2_grpc.add_ASRServicer_to_server(
  11. ASRServicer(args.user_allowed, args.model, args.sample_rate, args.backend, args.onnx_dir), server)
  12. port = "[::]:" + str(args.port)
  13. server.add_insecure_port(port)
  14. server.start()
  15. print("grpc server started!")
  16. server.wait_for_termination()
  17. if __name__ == '__main__':
  18. parser = argparse.ArgumentParser()
  19. parser.add_argument("--port",
  20. type=int,
  21. default=10095,
  22. required=True,
  23. help="grpc server port")
  24. parser.add_argument("--user_allowed",
  25. type=str,
  26. default="project1_user1|project1_user2|project2_user3",
  27. help="allowed user for grpc client")
  28. parser.add_argument("--model",
  29. type=str,
  30. default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
  31. help="model from modelscope")
  32. parser.add_argument("--sample_rate",
  33. type=int,
  34. default=16000,
  35. help="audio sample_rate from client")
  36. parser.add_argument("--backend",
  37. type=str,
  38. default="pipeline",
  39. choices=("pipeline", "onnxruntime"),
  40. help="backend, optional modelscope pipeline or onnxruntime")
  41. parser.add_argument("--onnx_dir",
  42. type=str,
  43. default="/nfs/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
  44. help="onnx model dir")
  45. args = parser.parse_args()
  46. serve(args)