fixbug hotwords

游雁

2024-06-24 1596f6f414f6f41da66506debb1dff19fffeb3ec

 runtime/python/websocket/funasr_wss_server.py

@@ -7,137 +7,146 @@
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",
                    type=str,
                    default="0.0.0.0",
                    required=False,
                    help="host ip, localhost, 0.0.0.0")
parser.add_argument("--port",
                    type=int,
                    default=10095,
                    required=False,
                    help="grpc server port")
parser.add_argument("--asr_model",
                    type=str,
                    default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
                    help="model from modelscope")
parser.add_argument("--asr_model_online",
                    type=str,
                    default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
                    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="damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
                    help="model from modelscope")
parser.add_argument("--ngpu",
                    type=int,
                    default=1,
                    help="0 for cpu, 1 for gpu")
parser.add_argument("--ncpu",
                    type=int,
                    default=4,
                    help="cpu cores")
parser.add_argument("--certfile",
                    type=str,
                    default="../ssl_key/server.crt",
                    required=False,
                    help="certfile for ssl")
parser.add_argument(
    "--host", type=str, default="0.0.0.0", required=False, help="host ip, localhost, 0.0.0.0"
)
parser.add_argument("--port", type=int, default=10095, required=False, help="grpc server port")
parser.add_argument(
    "--asr_model",
    type=str,
    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="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="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="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, help="cpu cores")
parser.add_argument(
    "--certfile",
    type=str,
    default="../../ssl_key/server.crt",
    required=False,
    help="certfile for ssl",
)

parser.add_argument("--keyfile",
                    type=str,
                    default="../ssl_key/server.key",
                    required=False,
                    help="keyfile for ssl")
parser.add_argument(
    "--keyfile",
    type=str,
    default="../../ssl_key/server.key",
    required=False,
    help="keyfile for ssl",
)
args = parser.parse_args()


websocket_users = set()

print("model loading")
from funasr import AutoModel

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


    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_vad = AutoModel(
    model=args.vad_model,
    model_revision=None,
    mode='online',
    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_punc = AutoModel(
        model=args.punc_model,
        model_revision="v1.0.2",
        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)
    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()
    # 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.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
@@ -149,43 +158,65 @@
        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
                    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.param_dict_asr_online["chunk_size"] = messagejson["chunk_size"]
                    chunk_size = messagejson["chunk_size"]
                    if isinstance(chunk_size, str):
                        chunk_size = chunk_size.split(",")
                    websocket.status_dict_asr_online["chunk_size"] = [int(x) for x in chunk_size]
                if "encoder_chunk_look_back" in messagejson:
                    websocket.param_dict_asr_online["encoder_chunk_look_back"] = messagejson["encoder_chunk_look_back"]
                    websocket.status_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"]
                    websocket.status_dict_asr_online["decoder_chunk_look_back"] = messagejson[
                        "decoder_chunk_look_back"
                    ]
                if "hotword" in messagejson:
                    websocket.status_dict_asr["hotword"] = messagejson["hotwords"]
                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
                    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"]:
                    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)
                            await async_asr_online(websocket, audio_in)
                            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
                    speech_start_i, speech_end_i = await async_vad(websocket, message)
                    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
                        beg_bias = (websocket.vad_pre_idx - speech_start_i) // duration_ms
                        frames_pre = frames[-beg_bias:]
                        frames_asr = []
                        frames_asr.extend(frames_pre)
@@ -194,21 +225,23 @@
                    # print("vad end point")
                    if websocket.mode == "2pass" or websocket.mode == "offline":
                        audio_in = b"".join(frames_asr)
                        await async_asr(websocket, audio_in)
                        try:
                            await async_asr(websocket, audio_in)
                        except:
                            print("error in asr offline")
                    frames_asr = []
                    speech_start = False
                    # frames_asr_online = []
                    # websocket.param_dict_asr_online = {"cache": dict()}
                    frames_asr_online = []
                    websocket.status_dict_asr_online["cache"] = {}
                    if not websocket.is_speaking:
                        websocket.vad_pre_idx = 0
                        frames = []
                        websocket.param_dict_vad = {'in_cache': dict()}
                        websocket.status_dict_vad["cache"] = {}
                    else:
                        frames = frames[-20:]

     
    except websockets.ConnectionClosed:
        print("ConnectionClosed...", websocket_users,flush=True)
        print("ConnectionClosed...", websocket_users, flush=True)
        await ws_reset(websocket)
        websocket_users.remove(websocket)
    except websockets.InvalidState:
@@ -219,62 +252,83 @@

async def async_vad(websocket, audio_in):

    segments_result = inference_pipeline_vad(audio_in=audio_in, param_dict=websocket.param_dict_vad)
    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["text"]) > 1:

    if len(segments_result) == 0 or len(segments_result) > 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]
    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):
        # 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.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)
            #     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:

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)
    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()