livetalking/app.py

# server.py
from flask import Flask, render_template,send_from_directory,request, jsonify
from flask_sockets import Sockets
import base64
import time
import json
import gevent
from gevent import pywsgi
from geventwebsocket.handler import WebSocketHandler
import os
import re
import numpy as np
from threading import Thread,Event
import multiprocessing

from aiohttp import web
import aiohttp
import aiohttp_cors
from aiortc import RTCPeerConnection, RTCSessionDescription
from aiortc.rtcrtpsender import RTCRtpSender
from webrtc import HumanPlayer

import argparse

import shutil
import asyncio
import string


app = Flask(__name__)
sockets = Sockets(app)
nerfreals = []
statreals = [] 

    
@sockets.route('/humanecho')
def echo_socket(ws):
    # 获取WebSocket对象
    #ws = request.environ.get('wsgi.websocket')
    # 如果没有获取到，返回错误信息
    if not ws:
        print('未建立连接！')
        return 'Please use WebSocket'
    # 否则，循环接收和发送消息
    else:
        print('建立连接！')
        while True:
            message = ws.receive()           
            
            if not message or len(message)==0:
                return '输入信息为空'
            else:                                
                nerfreal.put_msg_txt(message)


# def llm_response(message):
#     from llm.LLM import LLM
#     # llm = LLM().init_model('Gemini', model_path= 'gemini-pro',api_key='Your API Key', proxy_url=None)
#     # llm = LLM().init_model('ChatGPT', model_path= 'gpt-3.5-turbo',api_key='Your API Key')
#     llm = LLM().init_model('VllmGPT', model_path= 'THUDM/chatglm3-6b')
#     response = llm.chat(message)
#     print(response)
#     return response

def llm_response(message,nerfreal):
    start = time.perf_counter()
    from openai import OpenAI
    client = OpenAI(
        # 如果您没有配置环境变量，请在此处用您的API Key进行替换
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        # 填写DashScope SDK的base_url
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
    )
    end = time.perf_counter()
    print(f"llm Time init: {end-start}s")
    completion = client.chat.completions.create(
        model="qwen-plus",
        messages=[{'role': 'system', 'content': 'You are a helpful assistant.'},
                  {'role': 'user', 'content': message}],
        stream=True,
        # 通过以下设置，在流式输出的最后一行展示token使用信息
        stream_options={"include_usage": True}
    )
    result=""
    first = True
    for chunk in completion:
        if len(chunk.choices)>0:
            #print(chunk.choices[0].delta.content)
            if first:
                end = time.perf_counter()
                print(f"llm Time to first chunk: {end-start}s")
                first = False
            msg = chunk.choices[0].delta.content
            lastpos=0
            #msglist = re.split('[,.!;:，。！?]',msg)
            for i, char in enumerate(msg):
                if char in ",.!;:，。！？：；" :
                    result = result+msg[lastpos:i+1]
                    lastpos = i+1
                    if len(result)>10:
                        print(result)
                        nerfreal.put_msg_txt(result)
                        result=""
            result = result+msg[lastpos:]
    end = time.perf_counter()
    print(f"llm Time to last chunk: {end-start}s")
    nerfreal.put_msg_txt(result)            

@sockets.route('/humanchat')
def chat_socket(ws):
    # 获取WebSocket对象
    #ws = request.environ.get('wsgi.websocket')
    # 如果没有获取到，返回错误信息
    if not ws:
        print('未建立连接！')
        return 'Please use WebSocket'
    # 否则，循环接收和发送消息
    else:
        print('建立连接！')
        while True:
            message = ws.receive()           
            
            if len(message)==0:
                return '输入信息为空'
            else:
                res=llm_response(message)                           
                nerfreal.put_msg_txt(res)

#####webrtc###############################
pcs = set()

#@app.route('/offer', methods=['POST'])
async def offer(request):
    params = await request.json()
    offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"])

    sessionid = len(nerfreals)
    for index,value in enumerate(statreals):
        if value == 0:
            sessionid = index
            break
    if sessionid>=len(nerfreals):
        print('reach max session')
        return -1
    statreals[sessionid] = 1
    
    pc = RTCPeerConnection()
    pcs.add(pc)

    @pc.on("connectionstatechange")
    async def on_connectionstatechange():
        print("Connection state is %s" % pc.connectionState)
        if pc.connectionState == "failed":
            await pc.close()
            pcs.discard(pc)
            statreals[sessionid] = 0
        if pc.connectionState == "closed":
            pcs.discard(pc)
            statreals[sessionid] = 0

    player = HumanPlayer(nerfreals[sessionid])
    audio_sender = pc.addTrack(player.audio)
    video_sender = pc.addTrack(player.video)
    capabilities = RTCRtpSender.getCapabilities("video")
    preferences = list(filter(lambda x: x.name == "H264", capabilities.codecs))
    preferences += list(filter(lambda x: x.name == "VP8", capabilities.codecs))
    preferences += list(filter(lambda x: x.name == "rtx", capabilities.codecs))
    transceiver = pc.getTransceivers()[1]
    transceiver.setCodecPreferences(preferences)

    await pc.setRemoteDescription(offer)

    answer = await pc.createAnswer()
    await pc.setLocalDescription(answer)

    #return jsonify({"sdp": pc.localDescription.sdp, "type": pc.localDescription.type})

    return web.Response(
        content_type="application/json",
        text=json.dumps(
            {"sdp": pc.localDescription.sdp, "type": pc.localDescription.type, "sessionid":sessionid}
        ),
    )

async def human(request):
    params = await request.json()

    sessionid = params.get('sessionid',0)
    if params.get('interrupt'):
        nerfreals[sessionid].pause_talk()

    if params['type']=='echo':
        nerfreals[sessionid].put_msg_txt(params['text'])
    elif params['type']=='chat':
        res=await asyncio.get_event_loop().run_in_executor(None, llm_response, params['text'],nerfreals[sessionid])                         
        #nerfreals[sessionid].put_msg_txt(res)

    return web.Response(
        content_type="application/json",
        text=json.dumps(
            {"code": 0, "data":"ok"}
        ),
    )

async def humanaudio(request):
    try:
        form= await request.post()
        sessionid = int(form.get('sessionid',0))
        fileobj = form["file"]
        filename=fileobj.filename
        filebytes=fileobj.file.read()
        nerfreals[sessionid].put_audio_file(filebytes)

        return web.Response(
            content_type="application/json",
            text=json.dumps(
                {"code": 0, "msg":"ok"}
            ),
        )
    except Exception as e:
        return web.Response(
            content_type="application/json",
            text=json.dumps(
                {"code": -1, "msg":"err","data": ""+e.args[0]+""}
            ),
        )

async def set_audiotype(request):
    params = await request.json()

    sessionid = params.get('sessionid',0)    
    nerfreals[sessionid].set_curr_state(params['audiotype'],params['reinit'])

    return web.Response(
        content_type="application/json",
        text=json.dumps(
            {"code": 0, "data":"ok"}
        ),
    )

async def record(request):
    params = await request.json()

    sessionid = params.get('sessionid',0)
    if params['type']=='start_record':
        # nerfreals[sessionid].put_msg_txt(params['text'])
        nerfreals[sessionid].start_recording("data/record_lasted.mp4")
    elif params['type']=='end_record':
        nerfreals[sessionid].stop_recording()
    return web.Response(
        content_type="application/json",
        text=json.dumps(
            {"code": 0, "data":"ok"}
        ),
    )

async def is_speaking(request):
    params = await request.json()

    sessionid = params.get('sessionid',0)
    return web.Response(
        content_type="application/json",
        text=json.dumps(
            {"code": 0, "data": nerfreals[sessionid].is_speaking()}
        ),
    )


async def on_shutdown(app):
    # close peer connections
    coros = [pc.close() for pc in pcs]
    await asyncio.gather(*coros)
    pcs.clear()

async def post(url,data):
    try:
        async with aiohttp.ClientSession() as session:
            async with session.post(url,data=data) as response:
                return await response.text()
    except aiohttp.ClientError as e:
        print(f'Error: {e}')

async def run(push_url):
    pc = RTCPeerConnection()
    pcs.add(pc)

    @pc.on("connectionstatechange")
    async def on_connectionstatechange():
        print("Connection state is %s" % pc.connectionState)
        if pc.connectionState == "failed":
            await pc.close()
            pcs.discard(pc)

    player = HumanPlayer(nerfreals[0])
    audio_sender = pc.addTrack(player.audio)
    video_sender = pc.addTrack(player.video)

    await pc.setLocalDescription(await pc.createOffer())
    answer = await post(push_url,pc.localDescription.sdp)
    await pc.setRemoteDescription(RTCSessionDescription(sdp=answer,type='answer'))
##########################################
# os.environ['MKL_SERVICE_FORCE_INTEL'] = '1'
# os.environ['MULTIPROCESSING_METHOD'] = 'forkserver'                                                    
if __name__ == '__main__':
    multiprocessing.set_start_method('spawn')
    parser = argparse.ArgumentParser()
    parser.add_argument('--pose', type=str, default="data/data_kf.json", help="transforms.json, pose source")
    parser.add_argument('--au', type=str, default="data/au.csv", help="eye blink area")
    parser.add_argument('--torso_imgs', type=str, default="", help="torso images path")

    parser.add_argument('-O', action='store_true', help="equals --fp16 --cuda_ray --exp_eye")

    parser.add_argument('--data_range', type=int, nargs='*', default=[0, -1], help="data range to use")
    parser.add_argument('--workspace', type=str, default='data/video')
    parser.add_argument('--seed', type=int, default=0)

    ### training options
    parser.add_argument('--ckpt', type=str, default='data/pretrained/ngp_kf.pth')
   
    parser.add_argument('--num_rays', type=int, default=4096 * 16, help="num rays sampled per image for each training step")
    parser.add_argument('--cuda_ray', action='store_true', help="use CUDA raymarching instead of pytorch")
    parser.add_argument('--max_steps', type=int, default=16, help="max num steps sampled per ray (only valid when using --cuda_ray)")
    parser.add_argument('--num_steps', type=int, default=16, help="num steps sampled per ray (only valid when NOT using --cuda_ray)")
    parser.add_argument('--upsample_steps', type=int, default=0, help="num steps up-sampled per ray (only valid when NOT using --cuda_ray)")
    parser.add_argument('--update_extra_interval', type=int, default=16, help="iter interval to update extra status (only valid when using --cuda_ray)")
    parser.add_argument('--max_ray_batch', type=int, default=4096, help="batch size of rays at inference to avoid OOM (only valid when NOT using --cuda_ray)")

    ### loss set
    parser.add_argument('--warmup_step', type=int, default=10000, help="warm up steps")
    parser.add_argument('--amb_aud_loss', type=int, default=1, help="use ambient aud loss")
    parser.add_argument('--amb_eye_loss', type=int, default=1, help="use ambient eye loss")
    parser.add_argument('--unc_loss', type=int, default=1, help="use uncertainty loss")
    parser.add_argument('--lambda_amb', type=float, default=1e-4, help="lambda for ambient loss")

    ### network backbone options
    parser.add_argument('--fp16', action='store_true', help="use amp mixed precision training")
    
    parser.add_argument('--bg_img', type=str, default='white', help="background image")
    parser.add_argument('--fbg', action='store_true', help="frame-wise bg")
    parser.add_argument('--exp_eye', action='store_true', help="explicitly control the eyes")
    parser.add_argument('--fix_eye', type=float, default=-1, help="fixed eye area, negative to disable, set to 0-0.3 for a reasonable eye")
    parser.add_argument('--smooth_eye', action='store_true', help="smooth the eye area sequence")

    parser.add_argument('--torso_shrink', type=float, default=0.8, help="shrink bg coords to allow more flexibility in deform")

    ### dataset options
    parser.add_argument('--color_space', type=str, default='srgb', help="Color space, supports (linear, srgb)")
    parser.add_argument('--preload', type=int, default=0, help="0 means load data from disk on-the-fly, 1 means preload to CPU, 2 means GPU.")
    # (the default value is for the fox dataset)
    parser.add_argument('--bound', type=float, default=1, help="assume the scene is bounded in box[-bound, bound]^3, if > 1, will invoke adaptive ray marching.")
    parser.add_argument('--scale', type=float, default=4, help="scale camera location into box[-bound, bound]^3")
    parser.add_argument('--offset', type=float, nargs='*', default=[0, 0, 0], help="offset of camera location")
    parser.add_argument('--dt_gamma', type=float, default=1/256, help="dt_gamma (>=0) for adaptive ray marching. set to 0 to disable, >0 to accelerate rendering (but usually with worse quality)")
    parser.add_argument('--min_near', type=float, default=0.05, help="minimum near distance for camera")
    parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied (sigma)")
    parser.add_argument('--density_thresh_torso', type=float, default=0.01, help="threshold for density grid to be occupied (alpha)")
    parser.add_argument('--patch_size', type=int, default=1, help="[experimental] render patches in training, so as to apply LPIPS loss. 1 means disabled, use [64, 32, 16] to enable")

    parser.add_argument('--init_lips', action='store_true', help="init lips region")
    parser.add_argument('--finetune_lips', action='store_true', help="use LPIPS and landmarks to fine tune lips region")
    parser.add_argument('--smooth_lips', action='store_true', help="smooth the enc_a in a exponential decay way...")

    parser.add_argument('--torso', action='store_true', help="fix head and train torso")
    parser.add_argument('--head_ckpt', type=str, default='', help="head model")

    ### GUI options
    parser.add_argument('--gui', action='store_true', help="start a GUI")
    parser.add_argument('--W', type=int, default=450, help="GUI width")
    parser.add_argument('--H', type=int, default=450, help="GUI height")
    parser.add_argument('--radius', type=float, default=3.35, help="default GUI camera radius from center")
    parser.add_argument('--fovy', type=float, default=21.24, help="default GUI camera fovy")
    parser.add_argument('--max_spp', type=int, default=1, help="GUI rendering max sample per pixel")

    ### else
    parser.add_argument('--att', type=int, default=2, help="audio attention mode (0 = turn off, 1 = left-direction, 2 = bi-direction)")
    parser.add_argument('--aud', type=str, default='', help="audio source (empty will load the default, else should be a path to a npy file)")
    parser.add_argument('--emb', action='store_true', help="use audio class + embedding instead of logits")

    parser.add_argument('--ind_dim', type=int, default=4, help="individual code dim, 0 to turn off")
    parser.add_argument('--ind_num', type=int, default=10000, help="number of individual codes, should be larger than training dataset size")

    parser.add_argument('--ind_dim_torso', type=int, default=8, help="individual code dim, 0 to turn off")

    parser.add_argument('--amb_dim', type=int, default=2, help="ambient dimension")
    parser.add_argument('--part', action='store_true', help="use partial training data (1/10)")
    parser.add_argument('--part2', action='store_true', help="use partial training data (first 15s)")

    parser.add_argument('--train_camera', action='store_true', help="optimize camera pose")
    parser.add_argument('--smooth_path', action='store_true', help="brute-force smooth camera pose trajectory with a window size")
    parser.add_argument('--smooth_path_window', type=int, default=7, help="smoothing window size")

    # asr
    parser.add_argument('--asr', action='store_true', help="load asr for real-time app")
    parser.add_argument('--asr_wav', type=str, default='', help="load the wav and use as input")
    parser.add_argument('--asr_play', action='store_true', help="play out the audio")

    #parser.add_argument('--asr_model', type=str, default='deepspeech')
    parser.add_argument('--asr_model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto') #
    # parser.add_argument('--asr_model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
    # parser.add_argument('--asr_model', type=str, default='facebook/hubert-large-ls960-ft')

    parser.add_argument('--asr_save_feats', action='store_true')
    # audio FPS
    parser.add_argument('--fps', type=int, default=50)
    # sliding window left-middle-right length (unit: 20ms)
    parser.add_argument('-l', type=int, default=10)
    parser.add_argument('-m', type=int, default=8)
    parser.add_argument('-r', type=int, default=10)

    parser.add_argument('--fullbody', action='store_true', help="fullbody human")
    parser.add_argument('--fullbody_img', type=str, default='data/fullbody/img')
    parser.add_argument('--fullbody_width', type=int, default=580)
    parser.add_argument('--fullbody_height', type=int, default=1080)
    parser.add_argument('--fullbody_offset_x', type=int, default=0)
    parser.add_argument('--fullbody_offset_y', type=int, default=0)

    #musetalk opt
    parser.add_argument('--avatar_id', type=str, default='avator_1')
    parser.add_argument('--bbox_shift', type=int, default=5)
    parser.add_argument('--batch_size', type=int, default=16)

    # parser.add_argument('--customvideo', action='store_true', help="custom video")
    # parser.add_argument('--customvideo_img', type=str, default='data/customvideo/img')
    # parser.add_argument('--customvideo_imgnum', type=int, default=1)

    parser.add_argument('--customvideo_config', type=str, default='')

    parser.add_argument('--tts', type=str, default='edgetts') #xtts gpt-sovits cosyvoice
    parser.add_argument('--REF_FILE', type=str, default=None)
    parser.add_argument('--REF_TEXT', type=str, default=None)
    parser.add_argument('--TTS_SERVER', type=str, default='http://127.0.0.1:9880') # http://localhost:9000
    # parser.add_argument('--CHARACTER', type=str, default='test')
    # parser.add_argument('--EMOTION', type=str, default='default')

    parser.add_argument('--model', type=str, default='ernerf') #musetalk wav2lip

    parser.add_argument('--transport', type=str, default='rtcpush') #rtmp webrtc rtcpush
    parser.add_argument('--push_url', type=str, default='http://localhost:1985/rtc/v1/whip/?app=live&stream=livestream') #rtmp://localhost/live/livestream

    parser.add_argument('--max_session', type=int, default=1)  #multi session count
    parser.add_argument('--listenport', type=int, default=8010)

    opt = parser.parse_args()
    #app.config.from_object(opt)
    #print(app.config)
    opt.customopt = []
    if opt.customvideo_config!='':
        with open(opt.customvideo_config,'r') as file:
            opt.customopt = json.load(file)

    if opt.model == 'ernerf':
        from ernerf.nerf_triplane.provider import NeRFDataset_Test
        from ernerf.nerf_triplane.utils import *
        from ernerf.nerf_triplane.network import NeRFNetwork
        from nerfreal import NeRFReal
        # assert test mode
        opt.test = True
        opt.test_train = False
        #opt.train_camera =True
        # explicit smoothing
        opt.smooth_path = True
        opt.smooth_lips = True

        assert opt.pose != '', 'Must provide a pose source'

        # if opt.O:
        opt.fp16 = True
        opt.cuda_ray = True
        opt.exp_eye = True
        opt.smooth_eye = True

        if opt.torso_imgs=='': #no img,use model output
            opt.torso = True

        # assert opt.cuda_ray, "Only support CUDA ray mode."
        opt.asr = True

        if opt.patch_size > 1:
            # assert opt.patch_size > 16, "patch_size should > 16 to run LPIPS loss."
            assert opt.num_rays % (opt.patch_size ** 2) == 0, "patch_size ** 2 should be dividable by num_rays."
        seed_everything(opt.seed)
        print(opt)

        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        model = NeRFNetwork(opt)

        criterion = torch.nn.MSELoss(reduction='none')
        metrics = [] # use no metric in GUI for faster initialization...
        print(model)
        trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace, criterion=criterion, fp16=opt.fp16, metrics=metrics, use_checkpoint=opt.ckpt)

        test_loader = NeRFDataset_Test(opt, device=device).dataloader()
        model.aud_features = test_loader._data.auds
        model.eye_areas = test_loader._data.eye_area

        # we still need test_loader to provide audio features for testing.
        for _ in range(opt.max_session):
            nerfreal = NeRFReal(opt, trainer, test_loader)
            nerfreals.append(nerfreal)
    elif opt.model == 'musetalk':
        from musereal import MuseReal
        print(opt)
        for _ in range(opt.max_session):
            nerfreal = MuseReal(opt)
            nerfreals.append(nerfreal)
    elif opt.model == 'wav2lip':
        from lipreal import LipReal
        print(opt)
        for _ in range(opt.max_session):
            nerfreal = LipReal(opt)
            nerfreals.append(nerfreal)
    
    for _ in range(opt.max_session):
        statreals.append(0)

    if opt.transport=='rtmp':
        thread_quit = Event()
        rendthrd = Thread(target=nerfreals[0].render,args=(thread_quit,))
        rendthrd.start()

    #############################################################################
    appasync = web.Application()
    appasync.on_shutdown.append(on_shutdown)
    appasync.router.add_post("/offer", offer)
    appasync.router.add_post("/human", human)
    appasync.router.add_post("/humanaudio", humanaudio)
    appasync.router.add_post("/set_audiotype", set_audiotype)
    appasync.router.add_post("/record", record)
    appasync.router.add_post("/is_speaking", is_speaking)
    appasync.router.add_static('/',path='web')

    # Configure default CORS settings.
    cors = aiohttp_cors.setup(appasync, defaults={
            "*": aiohttp_cors.ResourceOptions(
                allow_credentials=True,
                expose_headers="*",
                allow_headers="*",
            )
        })
    # Configure CORS on all routes.
    for route in list(appasync.router.routes()):
        cors.add(route)

    pagename='webrtcapi.html'
    if opt.transport=='rtmp':
        pagename='echoapi.html'
    elif opt.transport=='rtcpush':
        pagename='rtcpushapi.html'
    print('start http server; http://<serverip>:'+str(opt.listenport)+'/'+pagename)
    def run_server(runner):
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        loop.run_until_complete(runner.setup())
        site = web.TCPSite(runner, '0.0.0.0', opt.listenport)
        loop.run_until_complete(site.start())
        if opt.transport=='rtcpush':
            loop.run_until_complete(run(opt.push_url))
        loop.run_forever()    
    #Thread(target=run_server, args=(web.AppRunner(appasync),)).start()
    run_server(web.AppRunner(appasync))

    #app.on_shutdown.append(on_shutdown)
    #app.router.add_post("/offer", offer)

    # print('start websocket server')
    # server = pywsgi.WSGIServer(('0.0.0.0', 8000), app, handler_class=WebSocketHandler)
    # server.serve_forever()