import grpc
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from concurrent import futures
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import argparse
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import paraformer_pb2_grpc
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from grpc_server import ASRServicer
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def serve(args):
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=10),
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# interceptors=(AuthInterceptor('Bearer mysecrettoken'),)
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)
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paraformer_pb2_grpc.add_ASRServicer_to_server(
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ASRServicer(args.user_allowed, args.model, args.sample_rate, args.backend, args.onnx_dir), server)
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port = "[::]:" + str(args.port)
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server.add_insecure_port(port)
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server.start()
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print("grpc server started!")
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server.wait_for_termination()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--port",
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type=int,
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default=10095,
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required=True,
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help="grpc server port")
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parser.add_argument("--user_allowed",
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type=str,
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default="project1_user1|project1_user2|project2_user3",
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help="allowed user for grpc client")
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parser.add_argument("--model",
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type=str,
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default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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help="model from modelscope")
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parser.add_argument("--sample_rate",
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type=int,
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default=16000,
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help="audio sample_rate from client")
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parser.add_argument("--backend",
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type=str,
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default="pipeline",
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choices=("pipeline", "onnxruntime"),
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help="backend, optional modelscope pipeline or onnxruntime")
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parser.add_argument("--onnx_dir",
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type=str,
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default="/nfs/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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help="onnx model dir")
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args = parser.parse_args()
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serve(args)
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