add files

funasr_local/runtime/python/grpc/.gitignore

@@ -0,0 +1 @@
+**/__pycache__

funasr_local/runtime/python/grpc/Readme.md

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+# Service with grpc-python
+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
+
+```shell
+pip install -U modelscope funasr
+# For the users in China, you could install with the command:
+# pip install -U modelscope funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
+git clone https://github.com/alibaba/FunASR.git && cd FunASR
+```
+
+Install the requirements
+
+```shell
+cd funasr/runtime/python/grpc
+pip install -r requirements_server.txt
+```
+
+#### Backend is funasr_onnx (optional)
+
+Install [`funasr_onnx`](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime).
+
+```
+pip install funasr_onnx -i https://pypi.Python.org/simple
+```
+
+Export the model, more details ref to [export docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime).
+```shell
+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
+
+```shell
+# 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
+
+```shell
+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
+
+```shell
+# 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
+
+<div align="left"><img src="proto/workflow.png" width="400"/>
+
+## 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

funasr_local/runtime/python/grpc/grpc_client.py

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+import queue
+import paraformer_pb2
+
+def transcribe_audio_bytes(stub, chunk, user='zksz', language='zh-CN', speaking = True, isEnd = False):
+    req = paraformer_pb2.Request()
+    if chunk is not None:
+        req.audio_data = chunk
+    req.user = user
+    req.language = language
+    req.speaking = speaking
+    req.isEnd = isEnd
+    my_queue = queue.SimpleQueue()
+    my_queue.put(req) 
+    return  stub.Recognize(iter(my_queue.get, None))
+
+
+

funasr_local/runtime/python/grpc/grpc_main_client.py

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+import grpc
+import json
+import time
+import asyncio
+import soundfile as sf
+import argparse
+
+from grpc_client import transcribe_audio_bytes
+from paraformer_pb2_grpc import ASRStub
+
+# send the audio data once
+async def grpc_rec(wav_scp, grpc_uri, asr_user, language):
+    with grpc.insecure_channel(grpc_uri) as channel:
+        stub = ASRStub(channel)
+        for line in wav_scp:
+            wav_file = line.split()[1]
+            wav, _ = sf.read(wav_file, dtype='int16')
+            
+            b = time.time()
+            response = transcribe_audio_bytes(stub, wav.tobytes(), user=asr_user, language=language, speaking=False, isEnd=False)
+            resp = response.next()
+            text = ''
+            if 'decoding' == resp.action:
+                resp = response.next()
+                if 'finish' == resp.action:
+                    text = json.loads(resp.sentence)['text']
+            response = transcribe_audio_bytes(stub, None, user=asr_user, language=language, speaking=False, isEnd=True)
+            res= {'text': text, 'time': time.time() - b}
+            print(res)
+
+async def test(args):
+    wav_scp = open(args.wav_scp, "r").readlines()
+    uri = '{}:{}'.format(args.host, args.port)
+    res = await grpc_rec(wav_scp, uri, args.user_allowed, language = 'zh-CN')
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host",
+                        type=str,
+                        default="127.0.0.1",
+                        required=False,
+                        help="grpc server host ip")
+    parser.add_argument("--port",
+                        type=int,
+                        default=10108,
+                        required=False,
+                        help="grpc server port")              
+    parser.add_argument("--user_allowed",
+                        type=str,
+                        default="project1_user1",
+                        help="allowed user for grpc client")
+    parser.add_argument("--sample_rate",
+                        type=int,
+                        default=16000,
+                        help="audio sample_rate from client") 
+    parser.add_argument("--wav_scp",
+                        type=str,
+                        required=True,
+                        help="audio wav scp")                    
+    args = parser.parse_args()
+    
+    asyncio.run(test(args))

funasr_local/runtime/python/grpc/grpc_main_client_mic.py

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+import pyaudio
+import grpc
+import json
+import webrtcvad
+import time
+import asyncio
+import argparse
+
+from grpc_client import transcribe_audio_bytes
+from paraformer_pb2_grpc import ASRStub
+
+async def deal_chunk(sig_mic):
+    global stub,SPEAKING,asr_user,language,sample_rate
+    if vad.is_speech(sig_mic, sample_rate): #speaking
+        SPEAKING = True
+        response = transcribe_audio_bytes(stub, sig_mic, user=asr_user, language=language, speaking = True, isEnd = False) #speaking, send audio to server.
+    else: #silence   
+        begin_time = 0
+        if SPEAKING: #means we have some audio recorded, send recognize order to server.
+            SPEAKING = False
+            begin_time = int(round(time.time() * 1000))            
+            response = transcribe_audio_bytes(stub, None, user=asr_user, language=language, speaking = False, isEnd = False) #speak end, call server for recognize one sentence
+            resp = response.next()           
+            if "decoding" == resp.action:   
+                resp = response.next() #TODO, blocking operation may leads to miss some audio clips. C++ multi-threading is preferred.
+                if "finish" == resp.action:        
+                    end_time = int(round(time.time() * 1000))
+                    print (json.loads(resp.sentence))
+                    print ("delay in ms: %d " % (end_time - begin_time))
+                else:
+                    pass
+        
+
+async def record(host,port,sample_rate,mic_chunk,record_seconds,asr_user,language):
+    with grpc.insecure_channel('{}:{}'.format(host, port)) as channel:
+        global stub
+        stub = ASRStub(channel)
+        for i in range(0, int(sample_rate / mic_chunk * record_seconds)):
+     
+            sig_mic = stream.read(mic_chunk,exception_on_overflow = False) 
+            await asyncio.create_task(deal_chunk(sig_mic))
+
+        #end grpc
+        response = transcribe_audio_bytes(stub, None, user=asr_user, language=language, speaking = False, isEnd = True)
+        print (response.next().action)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host",
+                        type=str,
+                        default="127.0.0.1",
+                        required=True,
+                        help="grpc server host ip")
+                        
+    parser.add_argument("--port",
+                        type=int,
+                        default=10095,
+                        required=True,
+                        help="grpc server port")              
+                        
+    parser.add_argument("--user_allowed",
+                        type=str,
+                        default="project1_user1",
+                        help="allowed user for grpc client")
+                        
+    parser.add_argument("--sample_rate",
+                        type=int,
+                        default=16000,
+                        help="audio sample_rate from client")    
+
+    parser.add_argument("--mic_chunk",
+                        type=int,
+                        default=160,
+                        help="chunk size for mic")  
+
+    parser.add_argument("--record_seconds",
+                        type=int,
+                        default=120,
+                        help="run specified seconds then exit ")                       
+
+    args = parser.parse_args()
+    
+
+    SPEAKING = False
+    asr_user = args.user_allowed
+    sample_rate = args.sample_rate
+    language = 'zh-CN'  
+    
+
+    vad = webrtcvad.Vad()
+    vad.set_mode(1)
+
+    FORMAT = pyaudio.paInt16
+    CHANNELS = 1
+    p = pyaudio.PyAudio()
+    
+    stream = p.open(format=FORMAT,
+                channels=CHANNELS,
+                rate=args.sample_rate,
+                input=True,
+                frames_per_buffer=args.mic_chunk)
+                
+    print("* recording")
+    asyncio.run(record(args.host,args.port,args.sample_rate,args.mic_chunk,args.record_seconds,args.user_allowed,language))
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
+    print("recording stop")
+
+    
+

funasr_local/runtime/python/grpc/grpc_main_server.py

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

funasr_local/runtime/python/grpc/grpc_server.py

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+from concurrent import futures
+import grpc
+import json
+import time
+
+import paraformer_pb2_grpc
+from paraformer_pb2 import Response
+
+
+class ASRServicer(paraformer_pb2_grpc.ASRServicer):
+    def __init__(self, user_allowed, model, sample_rate, backend, onnx_dir, vad_model='', punc_model=''):
+        print("ASRServicer init")
+        self.backend = backend
+        self.init_flag = 0
+        self.client_buffers = {}
+        self.client_transcription = {}
+        self.auth_user = user_allowed.split("|")
+        if self.backend == "pipeline":
+            try:
+                from modelscope.pipelines import pipeline
+                from modelscope.utils.constant import Tasks
+            except ImportError:
+                raise ImportError(f"Please install modelscope")
+            self.inference_16k_pipeline = pipeline(task=Tasks.auto_speech_recognition, model=model, vad_model=vad_model, punc_model=punc_model)
+        elif self.backend == "onnxruntime":
+            try:
+                from funasr_local_onnx import Paraformer
+            except ImportError:
+                raise ImportError(f"Please install onnxruntime environment")
+            self.inference_16k_pipeline = Paraformer(model_dir=onnx_dir)
+        self.sample_rate = sample_rate
+
+    def clear_states(self, user):
+        self.clear_buffers(user)
+        self.clear_transcriptions(user)
+
+    def clear_buffers(self, user):
+        if user in self.client_buffers:
+            del self.client_buffers[user]
+
+    def clear_transcriptions(self, user):
+        if user in self.client_transcription:
+            del self.client_transcription[user]
+
+    def disconnect(self, user):
+        self.clear_states(user)
+        print("Disconnecting user: %s" % str(user))
+
+    def Recognize(self, request_iterator, context):
+        
+            
+        for req in request_iterator:
+            if req.user not in self.auth_user:
+                result = {}
+                result["success"] = False
+                result["detail"] = "Not Authorized user: %s " % req.user
+                result["text"] = ""
+                yield Response(sentence=json.dumps(result), user=req.user, action="terminate", language=req.language)
+            elif req.isEnd: #end grpc
+                print("asr end")
+                self.disconnect(req.user)
+                result = {}
+                result["success"] = True
+                result["detail"] = "asr end"
+                result["text"] = ""
+                yield Response(sentence=json.dumps(result), user=req.user, action="terminate",language=req.language)
+            elif req.speaking: #continue speaking
+                if req.audio_data is not None and len(req.audio_data) > 0:
+                    if req.user in self.client_buffers:
+                        self.client_buffers[req.user] += req.audio_data #append audio
+                    else:
+                        self.client_buffers[req.user] = req.audio_data
+                result = {}
+                result["success"] = True
+                result["detail"] = "speaking"
+                result["text"] = ""
+                yield Response(sentence=json.dumps(result), user=req.user, action="speaking", language=req.language)
+            elif not req.speaking: #silence
+                if req.user not in self.client_buffers:
+                    result = {}
+                    result["success"] = True
+                    result["detail"] = "waiting_for_more_voice"
+                    result["text"] = ""
+                    yield Response(sentence=json.dumps(result), user=req.user, action="waiting", language=req.language)
+                else:
+                    begin_time = int(round(time.time() * 1000))
+                    tmp_data = self.client_buffers[req.user]
+                    self.clear_states(req.user)
+                    result = {}
+                    result["success"] = True
+                    result["detail"] = "decoding data: %d bytes" % len(tmp_data)
+                    result["text"] = ""
+                    yield Response(sentence=json.dumps(result), user=req.user, action="decoding", language=req.language)
+                    if len(tmp_data) < 9600: #min input_len for asr model , 300ms
+                        end_time = int(round(time.time() * 1000))
+                        delay_str = str(end_time - begin_time)
+                        result = {}
+                        result["success"] = True
+                        result["detail"] = "waiting_for_more_voice"
+                        result["server_delay_ms"] = delay_str
+                        result["text"] = ""
+                        print ("user: %s , delay(ms): %s, info: %s " % (req.user, delay_str, "waiting_for_more_voice"))
+                        yield Response(sentence=json.dumps(result), user=req.user, action="waiting", language=req.language)
+                    else:
+                        if self.backend == "pipeline":
+                            asr_result = self.inference_16k_pipeline(audio_in=tmp_data, audio_fs = self.sample_rate)
+                            if "text" in asr_result:
+                                asr_result = asr_result['text']
+                            else:
+                                asr_result = ""
+                        elif self.backend == "onnxruntime":
+                            from funasr_local_onnx.utils.frontend import load_bytes
+                            array = load_bytes(tmp_data)
+                            asr_result = self.inference_16k_pipeline(array)[0]
+                        end_time = int(round(time.time() * 1000))
+                        delay_str = str(end_time - begin_time)
+                        print ("user: %s , delay(ms): %s, text: %s " % (req.user, delay_str, asr_result))
+                        result = {}
+                        result["success"] = True
+                        result["detail"] = "finish_sentence"
+                        result["server_delay_ms"] = delay_str
+                        result["text"] = asr_result
+                        yield Response(sentence=json.dumps(result), user=req.user, action="finish", language=req.language)
+            else:
+                result = {}
+                result["success"] = False 
+                result["detail"] = "error, no condition matched! Unknown reason."
+                result["text"] = ""
+                self.disconnect(req.user)
+                yield Response(sentence=json.dumps(result), user=req.user, action="terminate", language=req.language)
+                
+

funasr_local/runtime/python/grpc/paraformer_pb2.py

@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: paraformer.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x10paraformer.proto\x12\nparaformer\"^\n\x07Request\x12\x12\n\naudio_data\x18\x01 \x01(\x0c\x12\x0c\n\x04user\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x10\n\x08speaking\x18\x04 \x01(\x08\x12\r\n\x05isEnd\x18\x05 \x01(\x08\"L\n\x08Response\x12\x10\n\x08sentence\x18\x01 \x01(\t\x12\x0c\n\x04user\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x0e\n\x06\x61\x63tion\x18\x04 \x01(\t2C\n\x03\x41SR\x12<\n\tRecognize\x12\x13.paraformer.Request\x1a\x14.paraformer.Response\"\x00(\x01\x30\x01\x42\x16\n\x07\x65x.grpc\xa2\x02\nparaformerb\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'paraformer_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\007ex.grpc\242\002\nparaformer'
+  _REQUEST._serialized_start=32
+  _REQUEST._serialized_end=126
+  _RESPONSE._serialized_start=128
+  _RESPONSE._serialized_end=204
+  _ASR._serialized_start=206
+  _ASR._serialized_end=273
+# @@protoc_insertion_point(module_scope)

funasr_local/runtime/python/grpc/paraformer_pb2_grpc.py

@@ -0,0 +1,66 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
+import paraformer_pb2 as paraformer__pb2
+
+
+class ASRStub(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def __init__(self, channel):
+        """Constructor.
+
+        Args:
+            channel: A grpc.Channel.
+        """
+        self.Recognize = channel.stream_stream(
+                '/paraformer.ASR/Recognize',
+                request_serializer=paraformer__pb2.Request.SerializeToString,
+                response_deserializer=paraformer__pb2.Response.FromString,
+                )
+
+
+class ASRServicer(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def Recognize(self, request_iterator, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+
+def add_ASRServicer_to_server(servicer, server):
+    rpc_method_handlers = {
+            'Recognize': grpc.stream_stream_rpc_method_handler(
+                    servicer.Recognize,
+                    request_deserializer=paraformer__pb2.Request.FromString,
+                    response_serializer=paraformer__pb2.Response.SerializeToString,
+            ),
+    }
+    generic_handler = grpc.method_handlers_generic_handler(
+            'paraformer.ASR', rpc_method_handlers)
+    server.add_generic_rpc_handlers((generic_handler,))
+
+
+ # This class is part of an EXPERIMENTAL API.
+class ASR(object):
+    """Missing associated documentation comment in .proto file."""
+
+    @staticmethod
+    def Recognize(request_iterator,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.stream_stream(request_iterator, target, '/paraformer.ASR/Recognize',
+            paraformer__pb2.Request.SerializeToString,
+            paraformer__pb2.Response.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)

funasr_local/runtime/python/grpc/proto/Readme.md

@@ -0,0 +1,25 @@
+```	
+service ASR { //grpc service
+  rpc Recognize (stream Request) returns (stream Response) {} //Stub
+}
+
+message Request { //request data
+  bytes audio_data = 1; //audio data in bytes.
+  string user = 2; //user allowed.
+  string language = 3; //language, zh-CN for now.
+  bool speaking = 4; //flag for speaking. 
+  bool isEnd = 5; //flag for end. set isEnd to true when you stop asr:
+  //vad:is_speech then speaking=True & isEnd = False, audio data will be appended for the specfied user.
+  //vad:silence then speaking=False & isEnd = False, clear audio buffer and do asr inference.
+}
+
+message Response { //response data.
+  string sentence = 1; //json, includes flag for success and asr text .
+  string user = 2; //same to request user.
+  string language = 3; //same to request language.
+  string action = 4; //server status:
+  //terminate：asr stopped; 
+  //speaking：user is speaking, audio data is appended; 
+  //decoding: server is decoding; 
+  //finish: get asr text, most used.
+}

funasr_local/runtime/python/grpc/proto/paraformer.proto

@@ -0,0 +1,38 @@
+// Copyright (c) 2021 Ximalaya Speech Team (Xiang Lyu)
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+syntax = "proto3";
+
+option java_package = "ex.grpc";
+option objc_class_prefix = "paraformer";
+
+package paraformer;
+
+service ASR {
+  rpc Recognize (stream Request) returns (stream Response) {}
+}
+
+message Request {
+  bytes audio_data = 1;
+  string user = 2;
+  string language = 3;
+  bool speaking = 4;
+  bool isEnd = 5;
+}
+
+message Response {
+  string sentence = 1;
+  string user = 2;
+  string language = 3;
+  string action = 4;
+}

funasr_local/runtime/python/grpc/requirements_client.txt

@@ -0,0 +1,4 @@
+pyaudio
+webrtcvad
+grpcio
+grpcio-tools

funasr_local/runtime/python/grpc/requirements_server.txt

@@ -0,0 +1,2 @@
+grpcio
+grpcio-tools