229 lines
11 KiB
Python
229 lines
11 KiB
Python
# server.py
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from flask import Flask, request, jsonify
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from flask_sockets import Sockets
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import base64
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import time
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import json
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import gevent
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from gevent import pywsgi
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from geventwebsocket.handler import WebSocketHandler
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import os
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import re
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import numpy as np
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from threading import Thread
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import multiprocessing
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import argparse
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from nerf_triplane.provider import NeRFDataset_Test
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from nerf_triplane.utils import *
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from nerf_triplane.network import NeRFNetwork
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from nerfreal import NeRFReal
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import shutil
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import asyncio
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import edge_tts
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app = Flask(__name__)
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sockets = Sockets(app)
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global nerfreal
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async def main(voicename: str, text: str, render):
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communicate = edge_tts.Communicate(text, voicename)
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#with open(OUTPUT_FILE, "wb") as file:
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async for chunk in communicate.stream():
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if chunk["type"] == "audio":
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render.push_audio(chunk["data"])
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#file.write(chunk["data"])
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elif chunk["type"] == "WordBoundary":
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pass
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def txt_to_audio(text_):
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audio_list = []
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#audio_path = 'data/audio/aud_0.wav'
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voicename = "zh-CN-YunxiaNeural"
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text = text_
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asyncio.get_event_loop().run_until_complete(main(voicename,text,nerfreal))
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#audio_process(audio_path)
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@sockets.route('/humanecho')
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def echo_socket(ws):
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# 获取WebSocket对象
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#ws = request.environ.get('wsgi.websocket')
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# 如果没有获取到，返回错误信息
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if not ws:
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print('未建立连接！')
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return 'Please use WebSocket'
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# 否则，循环接收和发送消息
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else:
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print('建立连接！')
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while True:
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message = ws.receive()
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if len(message)==0:
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return '输入信息为空'
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else:
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txt_to_audio(message)
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def render():
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nerfreal.render()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--pose', type=str, default="data/data_kf.json", help="transforms.json, pose source")
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parser.add_argument('-O', action='store_true', help="equals --fp16 --cuda_ray --exp_eye")
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parser.add_argument('--data_range', type=int, nargs='*', default=[0, -1], help="data range to use")
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parser.add_argument('--workspace', type=str, default='data/video')
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parser.add_argument('--seed', type=int, default=0)
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### training options
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parser.add_argument('--ckpt', type=str, default='data/pretrained/ngp_kf.pth')
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parser.add_argument('--num_rays', type=int, default=4096 * 16, help="num rays sampled per image for each training step")
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parser.add_argument('--cuda_ray', action='store_true', help="use CUDA raymarching instead of pytorch")
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parser.add_argument('--max_steps', type=int, default=16, help="max num steps sampled per ray (only valid when using --cuda_ray)")
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parser.add_argument('--num_steps', type=int, default=16, help="num steps sampled per ray (only valid when NOT using --cuda_ray)")
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parser.add_argument('--upsample_steps', type=int, default=0, help="num steps up-sampled per ray (only valid when NOT using --cuda_ray)")
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parser.add_argument('--update_extra_interval', type=int, default=16, help="iter interval to update extra status (only valid when using --cuda_ray)")
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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)")
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### loss set
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parser.add_argument('--warmup_step', type=int, default=10000, help="warm up steps")
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parser.add_argument('--amb_aud_loss', type=int, default=1, help="use ambient aud loss")
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parser.add_argument('--amb_eye_loss', type=int, default=1, help="use ambient eye loss")
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parser.add_argument('--unc_loss', type=int, default=1, help="use uncertainty loss")
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parser.add_argument('--lambda_amb', type=float, default=1e-4, help="lambda for ambient loss")
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### network backbone options
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parser.add_argument('--fp16', action='store_true', help="use amp mixed precision training")
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parser.add_argument('--bg_img', type=str, default='white', help="background image")
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parser.add_argument('--fbg', action='store_true', help="frame-wise bg")
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parser.add_argument('--exp_eye', action='store_true', help="explicitly control the eyes")
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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")
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parser.add_argument('--smooth_eye', action='store_true', help="smooth the eye area sequence")
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parser.add_argument('--torso_shrink', type=float, default=0.8, help="shrink bg coords to allow more flexibility in deform")
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### dataset options
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parser.add_argument('--color_space', type=str, default='srgb', help="Color space, supports (linear, srgb)")
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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.")
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# (the default value is for the fox dataset)
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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.")
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parser.add_argument('--scale', type=float, default=4, help="scale camera location into box[-bound, bound]^3")
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parser.add_argument('--offset', type=float, nargs='*', default=[0, 0, 0], help="offset of camera location")
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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)")
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parser.add_argument('--min_near', type=float, default=0.05, help="minimum near distance for camera")
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parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied (sigma)")
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parser.add_argument('--density_thresh_torso', type=float, default=0.01, help="threshold for density grid to be occupied (alpha)")
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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")
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parser.add_argument('--init_lips', action='store_true', help="init lips region")
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parser.add_argument('--finetune_lips', action='store_true', help="use LPIPS and landmarks to fine tune lips region")
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parser.add_argument('--smooth_lips', action='store_true', help="smooth the enc_a in a exponential decay way...")
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parser.add_argument('--torso', action='store_true', help="fix head and train torso")
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parser.add_argument('--head_ckpt', type=str, default='', help="head model")
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### GUI options
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parser.add_argument('--gui', action='store_true', help="start a GUI")
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parser.add_argument('--W', type=int, default=450, help="GUI width")
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parser.add_argument('--H', type=int, default=450, help="GUI height")
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parser.add_argument('--radius', type=float, default=3.35, help="default GUI camera radius from center")
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parser.add_argument('--fovy', type=float, default=21.24, help="default GUI camera fovy")
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parser.add_argument('--max_spp', type=int, default=1, help="GUI rendering max sample per pixel")
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### else
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parser.add_argument('--att', type=int, default=2, help="audio attention mode (0 = turn off, 1 = left-direction, 2 = bi-direction)")
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parser.add_argument('--aud', type=str, default='', help="audio source (empty will load the default, else should be a path to a npy file)")
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parser.add_argument('--emb', action='store_true', help="use audio class + embedding instead of logits")
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parser.add_argument('--ind_dim', type=int, default=4, help="individual code dim, 0 to turn off")
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parser.add_argument('--ind_num', type=int, default=10000, help="number of individual codes, should be larger than training dataset size")
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parser.add_argument('--ind_dim_torso', type=int, default=8, help="individual code dim, 0 to turn off")
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parser.add_argument('--amb_dim', type=int, default=2, help="ambient dimension")
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parser.add_argument('--part', action='store_true', help="use partial training data (1/10)")
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parser.add_argument('--part2', action='store_true', help="use partial training data (first 15s)")
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parser.add_argument('--train_camera', action='store_true', help="optimize camera pose")
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parser.add_argument('--smooth_path', action='store_true', help="brute-force smooth camera pose trajectory with a window size")
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parser.add_argument('--smooth_path_window', type=int, default=7, help="smoothing window size")
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# asr
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parser.add_argument('--asr', action='store_true', help="load asr for real-time app")
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parser.add_argument('--asr_wav', type=str, default='', help="load the wav and use as input")
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parser.add_argument('--asr_play', action='store_true', help="play out the audio")
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#parser.add_argument('--asr_model', type=str, default='deepspeech')
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parser.add_argument('--asr_model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto')
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# parser.add_argument('--asr_model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
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parser.add_argument('--push_url', type=str, default='rtmp://localhost/live/livestream')
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parser.add_argument('--asr_save_feats', action='store_true')
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# audio FPS
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parser.add_argument('--fps', type=int, default=50)
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# sliding window left-middle-right length (unit: 20ms)
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parser.add_argument('-l', type=int, default=10)
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parser.add_argument('-m', type=int, default=50)
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parser.add_argument('-r', type=int, default=10)
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opt = parser.parse_args()
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# assert test mode
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opt.test = True
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opt.test_train = False
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#opt.train_camera =True
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# explicit smoothing
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opt.smooth_path = True
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opt.smooth_eye = True
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opt.smooth_lips = True
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assert opt.pose != '', 'Must provide a pose source'
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# if opt.O:
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opt.fp16 = True
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opt.exp_eye = True
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opt.cuda_ray = True
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opt.torso = True
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# assert opt.cuda_ray, "Only support CUDA ray mode."
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opt.asr = True
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if opt.patch_size > 1:
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# assert opt.patch_size > 16, "patch_size should > 16 to run LPIPS loss."
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assert opt.num_rays % (opt.patch_size ** 2) == 0, "patch_size ** 2 should be dividable by num_rays."
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seed_everything(opt.seed)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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model = NeRFNetwork(opt)
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criterion = torch.nn.MSELoss(reduction='none')
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metrics = [] # use no metric in GUI for faster initialization...
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print(model)
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trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace, criterion=criterion, fp16=opt.fp16, metrics=metrics, use_checkpoint=opt.ckpt)
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test_loader = NeRFDataset_Test(opt, device=device).dataloader()
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model.aud_features = test_loader._data.auds
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model.eye_areas = test_loader._data.eye_area
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# we still need test_loader to provide audio features for testing.
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nerfreal = NeRFReal(opt, trainer, test_loader)
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#txt_to_audio('我是中国人,我来自北京')
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rendthrd = Thread(target=render)
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rendthrd.start()
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#############################################################################
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print('start websocket server')
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server = pywsgi.WSGIServer(('0.0.0.0', 8000), app, handler_class=WebSocketHandler)
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server.serve_forever()
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