python-websocket funasr1.0 (#1310)

zhifu gao

2024-01-26 4f224c88068b66bcb6f81570da59d99c9bba8288

python-websocket funasr1.0 (#1310)

* fix add_file bug (#1296)

Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>

* funasr1.0 uniasr

* funasr1.0 uniasr

* update with main (#1305)

* v1.0.3

* update clients for 2pass

* update download tools

---------

Co-authored-by: 雾聪 <wucong.lyb@alibaba-inc.com>

* vad streaming return [beg, -1], [], [-1, end], [beg, end]]

* funasr1.0 websocket-python

* funasr1.0 websocket-python

---------

Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>
Co-authored-by: 雾聪 <wucong.lyb@alibaba-inc.com>

 examples/industrial_data_pretraining/ct_transformer_streaming/demo.py

@@ -5,7 +5,7 @@

from funasr import AutoModel

model = AutoModel(model="damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727", model_revision="v2.0.4")
model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727", model_revision="v2.0.4")

inputs = "跨境河流是养育沿岸|人民的生命之源长期以来为帮助下游地区防灾减灾中方技术人员|在上游地区极为恶劣的自然条件下克服巨大困难甚至冒着生命危险|向印方提供汛期水文资料处理紧急事件中方重视印方在跨境河流问题上的关切|愿意进一步完善双方联合工作机制|凡是|中方能做的我们|都会去做而且会做得更好我请印度朋友们放心中国在上游的|任何开发利用都会经过科学|规划和论证兼顾上下游的利益"
vads = inputs.split("|")

 funasr/auto/auto_model.py

@@ -88,7 +88,8 @@
class AutoModel:
    
    def __init__(self, **kwargs):
        tables.print()
        if kwargs.get("disable_log", False):
            tables.print()
        
        model, kwargs = self.build_model(**kwargs)
        

 runtime/python/websocket/funasr_wss_client.py

@@ -29,6 +29,14 @@
                    type=str,
                    default="5, 10, 5",
                    help="chunk")
parser.add_argument("--encoder_chunk_look_back",
                    type=int,
                    default=4,
                    help="chunk")
parser.add_argument("--decoder_chunk_look_back",
                    type=int,
                    default=0,
                    help="chunk")
parser.add_argument("--chunk_interval",
                    type=int,
                    default=10,
@@ -113,25 +121,36 @@
    fst_dict = {}
    hotword_msg = ""
    if args.hotword.strip() != "":
        f_scp = open(args.hotword)
        hot_lines = f_scp.readlines()
        for line in hot_lines:
            words = line.strip().split(" ")
            if len(words) < 2:
                print("Please checkout format of hotwords")
                continue
            try:
                fst_dict[" ".join(words[:-1])] = int(words[-1])
            except ValueError:
                print("Please checkout format of hotwords")
        hotword_msg=json.dumps(fst_dict)
        if os.path.exists(args.hotword):
            f_scp = open(args.hotword)
            hot_lines = f_scp.readlines()
            for line in hot_lines:
                words = line.strip().split(" ")
                if len(words) < 2:
                    print("Please checkout format of hotwords")
                    continue
                try:
                    fst_dict[" ".join(words[:-1])] = int(words[-1])
                except ValueError:
                    print("Please checkout format of hotwords")
            hotword_msg = json.dumps(fst_dict)
        else:
            hotword_msg = args.hotword

    use_itn=True
    use_itn = True
    if args.use_itn == 0:
        use_itn=False
    
    message = json.dumps({"mode": args.mode, "chunk_size": args.chunk_size, "chunk_interval": args.chunk_interval,
                          "wav_name": "microphone", "is_speaking": True, "hotwords":hotword_msg, "itn": use_itn})
    message = json.dumps({"mode": args.mode,
                          "chunk_size": args.chunk_size,
                          "chunk_interval": args.chunk_interval,
                          "encoder_chunk_look_back": args.encoder_chunk_look_back,
                          "decoder_chunk_look_back": args.decoder_chunk_look_back,
                          "wav_name": "microphone",
                          "is_speaking": True,
                          "hotwords": hotword_msg,
                          "itn": use_itn,
                          })
    #voices.put(message)
    await websocket.send(message)
    while True:
@@ -154,18 +173,21 @@
    fst_dict = {}
    hotword_msg = ""
    if args.hotword.strip() != "":
        f_scp = open(args.hotword)
        hot_lines = f_scp.readlines()
        for line in hot_lines:
            words = line.strip().split(" ")
            if len(words) < 2:
                print("Please checkout format of hotwords")
                continue
            try:
                fst_dict[" ".join(words[:-1])] = int(words[-1])
            except ValueError:
                print("Please checkout format of hotwords")
        hotword_msg=json.dumps(fst_dict)
        if os.path.exists(args.hotword):
            f_scp = open(args.hotword)
            hot_lines = f_scp.readlines()
            for line in hot_lines:
                words = line.strip().split(" ")
                if len(words) < 2:
                    print("Please checkout format of hotwords")
                    continue
                try:
                    fst_dict[" ".join(words[:-1])] = int(words[-1])
                except ValueError:
                    print("Please checkout format of hotwords")
            hotword_msg = json.dumps(fst_dict)
        else:
            hotword_msg = args.hotword
        print (hotword_msg)

    sample_rate = args.audio_fs
@@ -203,8 +225,17 @@
        # print(stride)

        # send first time
        message = json.dumps({"mode": args.mode, "chunk_size": args.chunk_size, "chunk_interval": args.chunk_interval, "audio_fs":sample_rate,
                          "wav_name": wav_name, "wav_format": wav_format, "is_speaking": True, "hotwords":hotword_msg, "itn": use_itn})
        message = json.dumps({"mode": args.mode,
                              "chunk_size": args.chunk_size,
                              "chunk_interval": args.chunk_interval,
                              "encoder_chunk_look_back": args.encoder_chunk_look_back,
                              "decoder_chunk_look_back": args.decoder_chunk_look_back,
                              "audio_fs":sample_rate,
                              "wav_name": wav_name,
                              "wav_format": wav_format,
                              "is_speaking": True,
                              "hotwords": hotword_msg,
                              "itn": use_itn})

        #voices.put(message)
        await websocket.send(message)

 runtime/python/websocket/funasr_wss_server.py

@@ -7,14 +7,7 @@
import numpy as np
import argparse
import ssl
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger

tracemalloc.start()

logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)

parser = argparse.ArgumentParser()
parser.add_argument("--host",
@@ -29,24 +22,44 @@
                    help="grpc server port")
parser.add_argument("--asr_model",
                    type=str,
                    default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
                    default="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
                    help="model from modelscope")
parser.add_argument("--asr_model_revision",
                    type=str,
                    default="v2.0.4",
                    help="")
parser.add_argument("--asr_model_online",
                    type=str,
                    default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
                    default="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
                    help="model from modelscope")
parser.add_argument("--asr_model_online_revision",
                    type=str,
                    default="v2.0.4",
                    help="")
parser.add_argument("--vad_model",
                    type=str,
                    default="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                    default="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                    help="model from modelscope")
parser.add_argument("--vad_model_revision",
                    type=str,
                    default="v2.0.4",
                    help="")
parser.add_argument("--punc_model",
                    type=str,
                    default="damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
                    default="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
                    help="model from modelscope")
parser.add_argument("--punc_model_revision",
                    type=str,
                    default="v2.0.4",
                    help="")
parser.add_argument("--ngpu",
                    type=int,
                    default=1,
                    help="0 for cpu, 1 for gpu")
parser.add_argument("--device",
                    type=str,
                    default="cuda",
                    help="cuda, cpu")
parser.add_argument("--ncpu",
                    type=int,
                    default=4,
@@ -68,213 +81,232 @@
websocket_users = set()

print("model loading")
from funasr import AutoModel

# asr
inference_pipeline_asr = pipeline(
    task=Tasks.auto_speech_recognition,
    model=args.asr_model,
    ngpu=args.ngpu,
    ncpu=args.ncpu,
    model_revision=None)


model_asr = AutoModel(model=args.asr_model,
                      model_revision=args.asr_model_revision,
                      ngpu=args.ngpu,
                      ncpu=args.ncpu,
                      device=args.device,
                      disable_pbar=True,
                      disable_log=True,
                      )
# asr
model_asr_streaming = AutoModel(model=args.asr_model_online,
                                model_revision=args.asr_model_online_revision,
                                ngpu=args.ngpu,
                                ncpu=args.ncpu,
                                device=args.device,
                                disable_pbar=True,
                                disable_log=True,
                                )
# vad
inference_pipeline_vad = pipeline(
    task=Tasks.voice_activity_detection,
    model=args.vad_model,
    model_revision=None,
    mode='online',
    ngpu=args.ngpu,
    ncpu=args.ncpu,
)
model_vad = AutoModel(model=args.vad_model,
                      model_revision=args.vad_model_revision,
                      ngpu=args.ngpu,
                      ncpu=args.ncpu,
                      device=args.device,
                      disable_pbar=True,
                      disable_log=True,
                      # chunk_size=60,
                      )

if args.punc_model != "":
    inference_pipeline_punc = pipeline(
        task=Tasks.punctuation,
        model=args.punc_model,
        model_revision="v1.0.2",
        ngpu=args.ngpu,
        ncpu=args.ncpu,
    )
    model_punc = AutoModel(model=args.punc_model,
                           model_revision=args.punc_model_revision,
                           ngpu=args.ngpu,
                           ncpu=args.ncpu,
                           device=args.device,
                           disable_pbar=True,
                           disable_log=True,
                           )
else:
    inference_pipeline_punc = None
    model_punc = None

inference_pipeline_asr_online = pipeline(
    task=Tasks.auto_speech_recognition,
    model=args.asr_model_online,
    ngpu=args.ngpu,
    ncpu=args.ncpu,
    model_revision='v1.0.7',
    update_model='v1.0.7',
    mode='paraformer_streaming')


print("model loaded! only support one client at the same time now!!!!")

async def ws_reset(websocket):
    print("ws reset now, total num is ",len(websocket_users))
    websocket.param_dict_asr_online = {"cache": dict()}
    websocket.param_dict_vad = {'in_cache': dict(), "is_final": True}
    websocket.param_dict_asr_online["is_final"]=True
    # audio_in=b''.join(np.zeros(int(16000),dtype=np.int16))
    # inference_pipeline_vad(audio_in=audio_in, param_dict=websocket.param_dict_vad)
    # inference_pipeline_asr_online(audio_in=audio_in, param_dict=websocket.param_dict_asr_online)
    await websocket.close()
    
    
    print("ws reset now, total num is ",len(websocket_users))

    websocket.status_dict_asr_online["cache"] = {}
    websocket.status_dict_asr_online["is_final"] = True
    websocket.status_dict_vad["cache"] = {}
    websocket.status_dict_vad["is_final"] = True
    websocket.status_dict_punc["cache"] = {}
	
    await websocket.close()


async def clear_websocket():
   for websocket in websocket_users:
       await ws_reset(websocket)
   websocket_users.clear()
 
 
       
    for websocket in websocket_users:
        await ws_reset(websocket)
    websocket_users.clear()



async def ws_serve(websocket, path):
    frames = []
    frames_asr = []
    frames_asr_online = []
    global websocket_users
    await clear_websocket()
    websocket_users.add(websocket)
    websocket.param_dict_asr = {}
    websocket.param_dict_asr_online = {"cache": dict()}
    websocket.param_dict_vad = {'in_cache': dict(), "is_final": False}
    websocket.param_dict_punc = {'cache': list()}
    websocket.vad_pre_idx = 0
    speech_start = False
    speech_end_i = -1
    websocket.wav_name = "microphone"
    websocket.mode = "2pass"
    print("new user connected", flush=True)

    try:
        async for message in websocket:
            if isinstance(message, str):
                messagejson = json.loads(message)
        
                if "is_speaking" in messagejson:
                    websocket.is_speaking = messagejson["is_speaking"]
                    websocket.param_dict_asr_online["is_final"] = not websocket.is_speaking
                if "chunk_interval" in messagejson:
                    websocket.chunk_interval = messagejson["chunk_interval"]
                if "wav_name" in messagejson:
                    websocket.wav_name = messagejson.get("wav_name")
                if "chunk_size" in messagejson:
                    websocket.param_dict_asr_online["chunk_size"] = messagejson["chunk_size"]
                if "encoder_chunk_look_back" in messagejson:
                    websocket.param_dict_asr_online["encoder_chunk_look_back"] = messagejson["encoder_chunk_look_back"]
                if "decoder_chunk_look_back" in messagejson:
                    websocket.param_dict_asr_online["decoder_chunk_look_back"] = messagejson["decoder_chunk_look_back"]
                if "mode" in messagejson:
                    websocket.mode = messagejson["mode"]
            if len(frames_asr_online) > 0 or len(frames_asr) > 0 or not isinstance(message, str):
                if not isinstance(message, str):
                    frames.append(message)
                    duration_ms = len(message)//32
                    websocket.vad_pre_idx += duration_ms
        
                    # asr online
                    frames_asr_online.append(message)
                    websocket.param_dict_asr_online["is_final"] = speech_end_i != -1
                    if len(frames_asr_online) % websocket.chunk_interval == 0 or websocket.param_dict_asr_online["is_final"]:
                        if websocket.mode == "2pass" or websocket.mode == "online":
                            audio_in = b"".join(frames_asr_online)
                            await async_asr_online(websocket, audio_in)
                        frames_asr_online = []
                    if speech_start:
                        frames_asr.append(message)
                    # vad online
                    speech_start_i, speech_end_i = await async_vad(websocket, message)
                    if speech_start_i != -1:
                        speech_start = True
                        beg_bias = (websocket.vad_pre_idx-speech_start_i)//duration_ms
                        frames_pre = frames[-beg_bias:]
                        frames_asr = []
                        frames_asr.extend(frames_pre)
                # asr punc offline
                if speech_end_i != -1 or not websocket.is_speaking:
                    # print("vad end point")
                    if websocket.mode == "2pass" or websocket.mode == "offline":
                        audio_in = b"".join(frames_asr)
                        await async_asr(websocket, audio_in)
                    frames_asr = []
                    speech_start = False
                    # frames_asr_online = []
                    # websocket.param_dict_asr_online = {"cache": dict()}
                    if not websocket.is_speaking:
                        websocket.vad_pre_idx = 0
                        frames = []
                        websocket.param_dict_vad = {'in_cache': dict()}
                    else:
                        frames = frames[-20:]

     
    except websockets.ConnectionClosed:
        print("ConnectionClosed...", websocket_users,flush=True)
        await ws_reset(websocket)
        websocket_users.remove(websocket)
    except websockets.InvalidState:
        print("InvalidState...")
    except Exception as e:
        print("Exception:", e)
    frames = []
    frames_asr = []
    frames_asr_online = []
    global websocket_users
    await clear_websocket()
    websocket_users.add(websocket)
    websocket.status_dict_asr = {}
    websocket.status_dict_asr_online = {"cache": {}, "is_final": False}
    websocket.status_dict_vad = {'cache': {}, "is_final": False}
    websocket.status_dict_punc = {'cache': {}}
    websocket.chunk_interval = 10
    websocket.vad_pre_idx = 0
    speech_start = False
    speech_end_i = -1
    websocket.wav_name = "microphone"
    websocket.mode = "2pass"
    print("new user connected", flush=True)
	
    try:
        async for message in websocket:
            if isinstance(message, str):
                messagejson = json.loads(message)
				
                if "is_speaking" in messagejson:
                    websocket.is_speaking = messagejson["is_speaking"]
                    websocket.status_dict_asr_online["is_final"] = not websocket.is_speaking
                if "chunk_interval" in messagejson:
                    websocket.chunk_interval = messagejson["chunk_interval"]
                if "wav_name" in messagejson:
                    websocket.wav_name = messagejson.get("wav_name")
                if "chunk_size" in messagejson:
                    websocket.status_dict_asr_online["chunk_size"] = messagejson["chunk_size"]
                if "encoder_chunk_look_back" in messagejson:
                    websocket.status_dict_asr_online["encoder_chunk_look_back"] = messagejson["encoder_chunk_look_back"]
                if "decoder_chunk_look_back" in messagejson:
                    websocket.status_dict_asr_online["decoder_chunk_look_back"] = messagejson["decoder_chunk_look_back"]
                if "hotword" in messagejson:
                    websocket.status_dict_asr["hotword"] = messagejson["hotword"]
                if "mode" in messagejson:
                    websocket.mode = messagejson["mode"]
			
            websocket.status_dict_vad["chunk_size"] = int(websocket.status_dict_asr_online["chunk_size"][1]*60/websocket.chunk_interval)
            if len(frames_asr_online) > 0 or len(frames_asr) > 0 or not isinstance(message, str):
                if not isinstance(message, str):
                    frames.append(message)
                    duration_ms = len(message)//32
                    websocket.vad_pre_idx += duration_ms
					
                    # asr online
                    frames_asr_online.append(message)
                    websocket.status_dict_asr_online["is_final"] = speech_end_i != -1
                    if len(frames_asr_online) % websocket.chunk_interval == 0 or websocket.status_dict_asr_online["is_final"]:
                        if websocket.mode == "2pass" or websocket.mode == "online":
                            audio_in = b"".join(frames_asr_online)
                            try:
                                await async_asr_online(websocket, audio_in)
                            except:
                                print(f"error in asr streaming, {websocket.status_dict_asr_online}")
                        frames_asr_online = []
                    if speech_start:
                        frames_asr.append(message)
                    # vad online
                    try:
                        speech_start_i, speech_end_i = await async_vad(websocket, message)
                    except:
                        print("error in vad")
                    if speech_start_i != -1:
                        speech_start = True
                        beg_bias = (websocket.vad_pre_idx-speech_start_i)//duration_ms
                        frames_pre = frames[-beg_bias:]
                        frames_asr = []
                        frames_asr.extend(frames_pre)
                # asr punc offline
                if speech_end_i != -1 or not websocket.is_speaking:
                    # print("vad end point")
                    if websocket.mode == "2pass" or websocket.mode == "offline":
                        audio_in = b"".join(frames_asr)
                        try:
                            await async_asr(websocket, audio_in)
                        except:
                            print("error in asr offline")
                    frames_asr = []
                    speech_start = False
                    frames_asr_online = []
                    websocket.status_dict_asr_online["cache"] = {}
                    if not websocket.is_speaking:
                        websocket.vad_pre_idx = 0
                        frames = []
                        websocket.status_dict_vad["cache"] = {}
                    else:
                        frames = frames[-20:]
	
	
    except websockets.ConnectionClosed:
        print("ConnectionClosed...", websocket_users,flush=True)
        await ws_reset(websocket)
        websocket_users.remove(websocket)
    except websockets.InvalidState:
        print("InvalidState...")
    except Exception as e:
        print("Exception:", e)


async def async_vad(websocket, audio_in):

    segments_result = inference_pipeline_vad(audio_in=audio_in, param_dict=websocket.param_dict_vad)

    speech_start = -1
    speech_end = -1
    
    if len(segments_result) == 0 or len(segments_result["text"]) > 1:
        return speech_start, speech_end
    if segments_result["text"][0][0] != -1:
        speech_start = segments_result["text"][0][0]
    if segments_result["text"][0][1] != -1:
        speech_end = segments_result["text"][0][1]
    return speech_start, speech_end
	
    segments_result = model_vad.generate(input=audio_in, **websocket.status_dict_vad)[0]["value"]
    # print(segments_result)
	
    speech_start = -1
    speech_end = -1
	
    if len(segments_result) == 0 or len(segments_result) > 1:
        return speech_start, speech_end
    if segments_result[0][0] != -1:
        speech_start = segments_result[0][0]
    if segments_result[0][1] != -1:
        speech_end = segments_result[0][1]
    return speech_start, speech_end


async def async_asr(websocket, audio_in):
            if len(audio_in) > 0:
                # print(len(audio_in))
                rec_result = inference_pipeline_asr(audio_in=audio_in,
                                                    param_dict=websocket.param_dict_asr)
                # print(rec_result)
                if inference_pipeline_punc is not None and 'text' in rec_result and len(rec_result["text"])>0:
                    rec_result = inference_pipeline_punc(text_in=rec_result['text'],
                                                         param_dict=websocket.param_dict_punc)
                    # print("offline", rec_result)
                if 'text' in rec_result:
                    mode = "2pass-offline" if "2pass" in websocket.mode else websocket.mode
                    message = json.dumps({"mode": mode, "text": rec_result["text"], "wav_name": websocket.wav_name,"is_final":websocket.is_speaking})
                    await websocket.send(message)
    if len(audio_in) > 0:
        # print(len(audio_in))
        rec_result = model_asr.generate(input=audio_in, **websocket.status_dict_asr)[0]
        # print("offline_asr, ", rec_result)
        if model_punc is not None and len(rec_result["text"])>0:
            # print("offline, before punc", rec_result, "cache", websocket.status_dict_punc)
            rec_result = model_punc.generate(input=rec_result['text'], **websocket.status_dict_punc)[0]
            # print("offline, after punc", rec_result)
        if len(rec_result["text"])>0:
            # print("offline", rec_result)
            mode = "2pass-offline" if "2pass" in websocket.mode else websocket.mode
            message = json.dumps({"mode": mode, "text": rec_result["text"], "wav_name": websocket.wav_name,"is_final":websocket.is_speaking})
            await websocket.send(message)


async def async_asr_online(websocket, audio_in):
    if len(audio_in) > 0:
        # print(websocket.param_dict_asr_online.get("is_final", False))
        rec_result = inference_pipeline_asr_online(audio_in=audio_in,
                                                   param_dict=websocket.param_dict_asr_online)
        # print(rec_result)
        if websocket.mode == "2pass" and websocket.param_dict_asr_online.get("is_final", False):
            return
            #     websocket.param_dict_asr_online["cache"] = dict()
        if "text" in rec_result:
            if rec_result["text"] != "sil" and rec_result["text"] != "waiting_for_more_voice":
                # print("online", rec_result)
                mode = "2pass-online" if "2pass" in websocket.mode else websocket.mode
                message = json.dumps({"mode": mode, "text": rec_result["text"], "wav_name": websocket.wav_name,"is_final":websocket.is_speaking})
                await websocket.send(message)
    if len(audio_in) > 0:
        # print(websocket.status_dict_asr_online.get("is_final", False))
        rec_result = model_asr_streaming.generate(input=audio_in, **websocket.status_dict_asr_online)[0]
        # print("online, ", rec_result)
        if websocket.mode == "2pass" and websocket.status_dict_asr_online.get("is_final", False):
            return
            #     websocket.status_dict_asr_online["cache"] = dict()
        if len(rec_result["text"]):
            mode = "2pass-online" if "2pass" in websocket.mode else websocket.mode
            message = json.dumps({"mode": mode, "text": rec_result["text"], "wav_name": websocket.wav_name,"is_final":websocket.is_speaking})
            await websocket.send(message)

if len(args.certfile)>0:
    ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
    
    # Generate with Lets Encrypt, copied to this location, chown to current user and 400 permissions
    ssl_cert = args.certfile
    ssl_key = args.keyfile
    
    ssl_context.load_cert_chain(ssl_cert, keyfile=ssl_key)
    start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None,ssl=ssl_context)
    ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
	
    # Generate with Lets Encrypt, copied to this location, chown to current user and 400 permissions
    ssl_cert = args.certfile
    ssl_key = args.keyfile
	
    ssl_context.load_cert_chain(ssl_cert, keyfile=ssl_key)
    start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None,ssl=ssl_context)
else:
    start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None)
    start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None)
asyncio.get_event_loop().run_until_complete(start_server)
asyncio.get_event_loop().run_forever()