fix gpt_voits
This commit is contained in:
lipku
parent
847fd202df
commit
d52ea3133a
35
app.py
35
app.py
|
|
@ -85,6 +85,30 @@ def xtts(text, speaker, language, server_url, stream_chunk_size) -> Iterator[byt
|
||||||
|
|
||||||
print("xtts response.elapsed:", res.elapsed)
|
print("xtts response.elapsed:", res.elapsed)
|
||||||
|
|
||||||
|
def gpt_sovits(text, speaker, language, server_url, stream_chunk_size) -> Iterator[bytes]:
|
||||||
|
start = time.perf_counter()
|
||||||
|
speaker["text"] = text
|
||||||
|
speaker["language"] = language
|
||||||
|
speaker["stream_chunk_size"] = stream_chunk_size # you can reduce it to get faster response, but degrade quality
|
||||||
|
res = requests.get(f"{server_url}&text="+text,stream=True)
|
||||||
|
end = time.perf_counter()
|
||||||
|
print(f"gpt_sovits Time to make POST: {end-start}s")
|
||||||
|
|
||||||
|
if res.status_code != 200:
|
||||||
|
print("Error:", res.text)
|
||||||
|
return
|
||||||
|
|
||||||
|
first = True
|
||||||
|
for chunk in res.iter_content(chunk_size=960):
|
||||||
|
if first:
|
||||||
|
end = time.perf_counter()
|
||||||
|
print(f"gpt_sovits Time to first chunk: {end-start}s")
|
||||||
|
first = False
|
||||||
|
if chunk:
|
||||||
|
yield chunk
|
||||||
|
|
||||||
|
print("gpt_sovits response.elapsed:", res.elapsed)
|
||||||
|
|
||||||
def stream_xtts(audio_stream,render):
|
def stream_xtts(audio_stream,render):
|
||||||
for chunk in audio_stream:
|
for chunk in audio_stream:
|
||||||
if chunk is not None:
|
if chunk is not None:
|
||||||
|
|
@ -97,6 +121,17 @@ def txt_to_audio(text_):
|
||||||
t = time.time()
|
t = time.time()
|
||||||
asyncio.get_event_loop().run_until_complete(main(voicename,text,nerfreal))
|
asyncio.get_event_loop().run_until_complete(main(voicename,text,nerfreal))
|
||||||
print(f'-------edge tts time:{time.time()-t:.4f}s')
|
print(f'-------edge tts time:{time.time()-t:.4f}s')
|
||||||
|
elif tts_type == "gpt": #gpt_sovits
|
||||||
|
stream_xtts(
|
||||||
|
gpt_sovits(
|
||||||
|
text_,
|
||||||
|
gspeaker,
|
||||||
|
"zh-cn", #en args.language,
|
||||||
|
"http://127.0.0.1:9880/tts_ava?ava=maimai&streaming_mode=true", #args.server_url,
|
||||||
|
"20" #args.stream_chunk_size
|
||||||
|
),
|
||||||
|
nerfreal
|
||||||
|
)
|
||||||
else: #xtts
|
else: #xtts
|
||||||
stream_xtts(
|
stream_xtts(
|
||||||
xtts(
|
xtts(
|
||||||
|
|
|
||||||
394
app2.py
394
app2.py
|
|
@ -1,394 +0,0 @@
|
||||||
# server.py
|
|
||||||
from flask import Flask, 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
|
|
||||||
import multiprocessing
|
|
||||||
|
|
||||||
import argparse
|
|
||||||
from nerf_triplane.provider import NeRFDataset_Test
|
|
||||||
from nerf_triplane.utils import *
|
|
||||||
from nerf_triplane.network import NeRFNetwork
|
|
||||||
from nerfreal import NeRFReal
|
|
||||||
|
|
||||||
import shutil
|
|
||||||
import asyncio
|
|
||||||
import edge_tts
|
|
||||||
from typing import Iterator
|
|
||||||
|
|
||||||
import requests
|
|
||||||
|
|
||||||
app = Flask(__name__)
|
|
||||||
sockets = Sockets(app)
|
|
||||||
global nerfreal
|
|
||||||
global tts_type
|
|
||||||
global gspeaker
|
|
||||||
|
|
||||||
|
|
||||||
async def main(voicename: str, text: str, render):
|
|
||||||
communicate = edge_tts.Communicate(text, voicename)
|
|
||||||
|
|
||||||
#with open(OUTPUT_FILE, "wb") as file:
|
|
||||||
first = True
|
|
||||||
async for chunk in communicate.stream():
|
|
||||||
if first:
|
|
||||||
#render.before_push_audio()
|
|
||||||
first = False
|
|
||||||
if chunk["type"] == "audio":
|
|
||||||
render.push_audio(chunk["data"])
|
|
||||||
#file.write(chunk["data"])
|
|
||||||
elif chunk["type"] == "WordBoundary":
|
|
||||||
pass
|
|
||||||
|
|
||||||
def get_speaker(ref_audio,server_url):
|
|
||||||
files = {"wav_file": ("reference.wav", open(ref_audio, "rb"))}
|
|
||||||
response = requests.post(f"{server_url}/clone_speaker", files=files)
|
|
||||||
return response.json()
|
|
||||||
|
|
||||||
def xtts(text, speaker, language, server_url, stream_chunk_size) -> Iterator[bytes]:
|
|
||||||
start = time.perf_counter()
|
|
||||||
speaker["text"] = text
|
|
||||||
speaker["language"] = language
|
|
||||||
speaker["stream_chunk_size"] = stream_chunk_size # you can reduce it to get faster response, but degrade quality
|
|
||||||
res = requests.post(
|
|
||||||
f"{server_url}/tts_stream",
|
|
||||||
json=speaker,
|
|
||||||
stream=True,
|
|
||||||
)
|
|
||||||
end = time.perf_counter()
|
|
||||||
print(f"xtts Time to make POST: {end-start}s")
|
|
||||||
|
|
||||||
if res.status_code != 200:
|
|
||||||
print("Error:", res.text)
|
|
||||||
return
|
|
||||||
|
|
||||||
first = True
|
|
||||||
for chunk in res.iter_content(chunk_size=960):
|
|
||||||
if first:
|
|
||||||
end = time.perf_counter()
|
|
||||||
print(f"xtts Time to first chunk: {end-start}s")
|
|
||||||
first = False
|
|
||||||
if chunk:
|
|
||||||
yield chunk
|
|
||||||
|
|
||||||
print("xtts response.elapsed:", res.elapsed)
|
|
||||||
|
|
||||||
def gpt_sovits(text, speaker, language, server_url, stream_chunk_size) -> Iterator[bytes]:
|
|
||||||
start = time.perf_counter()
|
|
||||||
speaker["text"] = text
|
|
||||||
speaker["language"] = language
|
|
||||||
speaker["stream_chunk_size"] = stream_chunk_size # you can reduce it to get faster response, but degrade quality
|
|
||||||
res = requests.get(f"{server_url}&text="+text,stream=True)
|
|
||||||
end = time.perf_counter()
|
|
||||||
print(f"xtts Time to make POST: {end-start}s")
|
|
||||||
|
|
||||||
if res.status_code != 200:
|
|
||||||
print("Error:", res.text)
|
|
||||||
return
|
|
||||||
|
|
||||||
first = True
|
|
||||||
for chunk in res.iter_content(chunk_size=960):
|
|
||||||
if first:
|
|
||||||
end = time.perf_counter()
|
|
||||||
print(f"xtts Time to first chunk: {end-start}s")
|
|
||||||
first = False
|
|
||||||
if chunk:
|
|
||||||
yield chunk
|
|
||||||
|
|
||||||
print("xtts response.elapsed:", res.elapsed)
|
|
||||||
|
|
||||||
def stream_xtts(audio_stream,render):
|
|
||||||
for chunk in audio_stream:
|
|
||||||
if chunk is not None:
|
|
||||||
render.push_audio(chunk)
|
|
||||||
|
|
||||||
def txt_to_audio(text_):
|
|
||||||
if tts_type == "edgetts":
|
|
||||||
voicename = "zh-CN-YunxiaNeural"
|
|
||||||
text = text_
|
|
||||||
t = time.time()
|
|
||||||
asyncio.get_event_loop().run_until_complete(main(voicename,text,nerfreal))
|
|
||||||
print(f'-------edge tts time:{time.time()-t:.4f}s')
|
|
||||||
elif tts_type == "gpt": #xtts
|
|
||||||
stream_xtts(
|
|
||||||
gpt_sovits(
|
|
||||||
text_,
|
|
||||||
gspeaker,
|
|
||||||
"zh-cn", #en args.language,
|
|
||||||
"http://127.0.0.1:9880/tts_ava?ava=maimai&streaming_mode=true", #args.server_url,
|
|
||||||
"20" #args.stream_chunk_size
|
|
||||||
),
|
|
||||||
nerfreal
|
|
||||||
)
|
|
||||||
else :#xtts
|
|
||||||
stream_xtts(
|
|
||||||
xtts(
|
|
||||||
text_,
|
|
||||||
gspeaker,
|
|
||||||
"zh-cn", #en args.language,
|
|
||||||
"http://localhost:9000", #args.server_url,
|
|
||||||
"20" #args.stream_chunk_size
|
|
||||||
),
|
|
||||||
nerfreal
|
|
||||||
)
|
|
||||||
@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:
|
|
||||||
txt_to_audio(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
|
|
||||||
|
|
||||||
@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)
|
|
||||||
txt_to_audio(res)
|
|
||||||
|
|
||||||
@sockets.route('/chat')
|
|
||||||
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:
|
|
||||||
txt_to_audio(message)
|
|
||||||
|
|
||||||
def render():
|
|
||||||
nerfreal.render()
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
|
|
||||||
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('--push_url', type=str, default='rtmp://localhost/live/livestream')
|
|
||||||
|
|
||||||
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)
|
|
||||||
|
|
||||||
parser.add_argument('--tts', type=str, default='edgetts') #xtts
|
|
||||||
parser.add_argument('--ref_file', type=str, default=None)
|
|
||||||
parser.add_argument('--xtts_server', type=str, default='http://localhost:9000')
|
|
||||||
|
|
||||||
opt = parser.parse_args()
|
|
||||||
app.config.from_object(opt)
|
|
||||||
#print(app.config['xtts_server'])
|
|
||||||
|
|
||||||
tts_type = opt.tts
|
|
||||||
if tts_type == "xtts":
|
|
||||||
print("Computing the latents for a new reference...")
|
|
||||||
gspeaker = get_speaker(opt.ref_file, opt.xtts_server)
|
|
||||||
|
|
||||||
# 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.
|
|
||||||
nerfreal = NeRFReal(opt, trainer, test_loader)
|
|
||||||
#txt_to_audio('我是中国人,我来自北京')
|
|
||||||
rendthrd = Thread(target=render)
|
|
||||||
rendthrd.start()
|
|
||||||
|
|
||||||
#############################################################################
|
|
||||||
print('start websocket server')
|
|
||||||
|
|
||||||
server = pywsgi.WSGIServer(('0.0.0.0', 8000), app, handler_class=WebSocketHandler)
|
|
||||||
server.serve_forever()
|
|
||||||
|
|
||||||
|
|
||||||
Loading…
Reference in New Issue