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musereal.py
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musereal.py
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###############################################################################
# Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking
# email: [email protected]
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
###############################################################################
import math
import torch
import numpy as np
#from .utils import *
import subprocess
import os
import time
import torch.nn.functional as F
import cv2
import glob
import pickle
import copy
import queue
from queue import Queue
from threading import Thread, Event
import torch.multiprocessing as mp
from musetalk.utils.utils import get_file_type,get_video_fps,datagen
#from musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder
from musetalk.utils.blending import get_image,get_image_prepare_material,get_image_blending
from musetalk.utils.utils import load_all_model,load_diffusion_model,load_audio_model
from musetalk.whisper.audio2feature import Audio2Feature
from museasr import MuseASR
import asyncio
from av import AudioFrame, VideoFrame
from basereal import BaseReal
from tqdm import tqdm
def load_model():
# load model weights
audio_processor,vae, unet, pe = load_all_model()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
timesteps = torch.tensor([0], device=device)
pe = pe.half()
vae.vae = vae.vae.half()
#vae.vae.share_memory()
unet.model = unet.model.half()
#unet.model.share_memory()
return vae, unet, pe, timesteps, audio_processor
def load_avatar(avatar_id):
#self.video_path = '' #video_path
#self.bbox_shift = opt.bbox_shift
avatar_path = f"./data/avatars/{avatar_id}"
full_imgs_path = f"{avatar_path}/full_imgs"
coords_path = f"{avatar_path}/coords.pkl"
latents_out_path= f"{avatar_path}/latents.pt"
video_out_path = f"{avatar_path}/vid_output/"
mask_out_path =f"{avatar_path}/mask"
mask_coords_path =f"{avatar_path}/mask_coords.pkl"
avatar_info_path = f"{avatar_path}/avator_info.json"
# self.avatar_info = {
# "avatar_id":self.avatar_id,
# "video_path":self.video_path,
# "bbox_shift":self.bbox_shift
# }
input_latent_list_cycle = torch.load(latents_out_path) #,weights_only=True
with open(coords_path, 'rb') as f:
coord_list_cycle = pickle.load(f)
input_img_list = glob.glob(os.path.join(full_imgs_path, '*.[jpJP][pnPN]*[gG]'))
input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
frame_list_cycle = read_imgs(input_img_list)
with open(mask_coords_path, 'rb') as f:
mask_coords_list_cycle = pickle.load(f)
input_mask_list = glob.glob(os.path.join(mask_out_path, '*.[jpJP][pnPN]*[gG]'))
input_mask_list = sorted(input_mask_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
mask_list_cycle = read_imgs(input_mask_list)
return frame_list_cycle,mask_list_cycle,coord_list_cycle,mask_coords_list_cycle,input_latent_list_cycle
@torch.no_grad()
def warm_up(batch_size,model):
# 预热函数
print('warmup model...')
vae, unet, pe, timesteps, audio_processor = model
#batch_size = 16
#timesteps = torch.tensor([0], device=unet.device)
whisper_batch = np.ones((batch_size, 50, 384), dtype=np.uint8)
latent_batch = torch.ones(batch_size, 8, 32, 32).to(unet.device)
audio_feature_batch = torch.from_numpy(whisper_batch)
audio_feature_batch = audio_feature_batch.to(device=unet.device, dtype=unet.model.dtype)
audio_feature_batch = pe(audio_feature_batch)
latent_batch = latent_batch.to(dtype=unet.model.dtype)
pred_latents = unet.model(latent_batch,
timesteps,
encoder_hidden_states=audio_feature_batch).sample
vae.decode_latents(pred_latents)
def read_imgs(img_list):
frames = []
print('reading images...')
for img_path in tqdm(img_list):
frame = cv2.imread(img_path)
frames.append(frame)
return frames
def __mirror_index(size, index):
#size = len(self.coord_list_cycle)
turn = index // size
res = index % size
if turn % 2 == 0:
return res
else:
return size - res - 1
@torch.no_grad()
def inference(render_event,batch_size,input_latent_list_cycle,audio_feat_queue,audio_out_queue,res_frame_queue,
vae, unet, pe,timesteps): #vae, unet, pe,timesteps
# vae, unet, pe = load_diffusion_model()
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# timesteps = torch.tensor([0], device=device)
# pe = pe.half()
# vae.vae = vae.vae.half()
# unet.model = unet.model.half()
length = len(input_latent_list_cycle)
index = 0
count=0
counttime=0
print('start inference')
while render_event.is_set():
starttime=time.perf_counter()
try:
whisper_chunks = audio_feat_queue.get(block=True, timeout=1)
except queue.Empty:
continue
is_all_silence=True
audio_frames = []
for _ in range(batch_size*2):
frame,type = audio_out_queue.get()
audio_frames.append((frame,type))
if type==0:
is_all_silence=False
if is_all_silence:
for i in range(batch_size):
res_frame_queue.put((None,__mirror_index(length,index),audio_frames[i*2:i*2+2]))
index = index + 1
else:
# print('infer=======')
t=time.perf_counter()
whisper_batch = np.stack(whisper_chunks)
latent_batch = []
for i in range(batch_size):
idx = __mirror_index(length,index+i)
latent = input_latent_list_cycle[idx]
latent_batch.append(latent)
latent_batch = torch.cat(latent_batch, dim=0)
# for i, (whisper_batch,latent_batch) in enumerate(gen):
audio_feature_batch = torch.from_numpy(whisper_batch)
audio_feature_batch = audio_feature_batch.to(device=unet.device,
dtype=unet.model.dtype)
audio_feature_batch = pe(audio_feature_batch)
latent_batch = latent_batch.to(dtype=unet.model.dtype)
# print('prepare time:',time.perf_counter()-t)
# t=time.perf_counter()
pred_latents = unet.model(latent_batch,
timesteps,
encoder_hidden_states=audio_feature_batch).sample
# print('unet time:',time.perf_counter()-t)
# t=time.perf_counter()
recon = vae.decode_latents(pred_latents)
# infer_inqueue.put((whisper_batch,latent_batch,sessionid))
# recon,outsessionid = infer_outqueue.get()
# if outsessionid != sessionid:
# print('outsessionid:',outsessionid,' mysessionid:',sessionid)
# print('vae time:',time.perf_counter()-t)
#print('diffusion len=',len(recon))
counttime += (time.perf_counter() - t)
count += batch_size
#_totalframe += 1
if count>=100:
print(f"------actual avg infer fps:{count/counttime:.4f}")
count=0
counttime=0
for i,res_frame in enumerate(recon):
#self.__pushmedia(res_frame,loop,audio_track,video_track)
res_frame_queue.put((res_frame,__mirror_index(length,index),audio_frames[i*2:i*2+2]))
index = index + 1
#print('total batch time:',time.perf_counter()-starttime)
print('musereal inference processor stop')
class MuseReal(BaseReal):
@torch.no_grad()
def __init__(self, opt, model, avatar):
super().__init__(opt)
#self.opt = opt # shared with the trainer's opt to support in-place modification of rendering parameters.
self.W = opt.W
self.H = opt.H
self.fps = opt.fps # 20 ms per frame
self.batch_size = opt.batch_size
self.idx = 0
self.res_frame_queue = mp.Queue(self.batch_size*2)
self.vae, self.unet, self.pe, self.timesteps, self.audio_processor = model
self.frame_list_cycle,self.mask_list_cycle,self.coord_list_cycle,self.mask_coords_list_cycle, self.input_latent_list_cycle = avatar
#self.__loadavatar()
self.asr = MuseASR(opt,self,self.audio_processor)
self.asr.warm_up()
self.render_event = mp.Event()
def __del__(self):
print(f'musereal({self.sessionid}) delete')
def __mirror_index(self, index):
size = len(self.coord_list_cycle)
turn = index // size
res = index % size
if turn % 2 == 0:
return res
else:
return size - res - 1
def __warm_up(self):
self.asr.run_step()
whisper_chunks = self.asr.get_next_feat()
whisper_batch = np.stack(whisper_chunks)
latent_batch = []
for i in range(self.batch_size):
idx = self.__mirror_index(self.idx+i)
latent = self.input_latent_list_cycle[idx]
latent_batch.append(latent)
latent_batch = torch.cat(latent_batch, dim=0)
print('infer=======')
# for i, (whisper_batch,latent_batch) in enumerate(gen):
audio_feature_batch = torch.from_numpy(whisper_batch)
audio_feature_batch = audio_feature_batch.to(device=self.unet.device,
dtype=self.unet.model.dtype)
audio_feature_batch = self.pe(audio_feature_batch)
latent_batch = latent_batch.to(dtype=self.unet.model.dtype)
pred_latents = self.unet.model(latent_batch,
self.timesteps,
encoder_hidden_states=audio_feature_batch).sample
recon = self.vae.decode_latents(pred_latents)
def process_frames(self,quit_event,loop=None,audio_track=None,video_track=None):
while not quit_event.is_set():
try:
res_frame,idx,audio_frames = self.res_frame_queue.get(block=True, timeout=1)
except queue.Empty:
continue
if audio_frames[0][1]!=0 and audio_frames[1][1]!=0: #全为静音数据，只需要取fullimg
self.speaking = False
audiotype = audio_frames[0][1]
if self.custom_index.get(audiotype) is not None: #有自定义视频
mirindex = self.mirror_index(len(self.custom_img_cycle[audiotype]),self.custom_index[audiotype])
combine_frame = self.custom_img_cycle[audiotype][mirindex]
self.custom_index[audiotype] += 1
# if not self.custom_opt[audiotype].loop and self.custom_index[audiotype]>=len(self.custom_img_cycle[audiotype]):
# self.curr_state = 1 #当前视频不循环播放，切换到静音状态
else:
combine_frame = self.frame_list_cycle[idx]
else:
self.speaking = True
bbox = self.coord_list_cycle[idx]
ori_frame = copy.deepcopy(self.frame_list_cycle[idx])
x1, y1, x2, y2 = bbox
try:
res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1))
except:
continue
mask = self.mask_list_cycle[idx]
mask_crop_box = self.mask_coords_list_cycle[idx]
#combine_frame = get_image(ori_frame,res_frame,bbox)
#t=time.perf_counter()
combine_frame = get_image_blending(ori_frame,res_frame,bbox,mask,mask_crop_box)
#print('blending time:',time.perf_counter()-t)
image = combine_frame #(outputs['image'] * 255).astype(np.uint8)
new_frame = VideoFrame.from_ndarray(image, format="bgr24")
asyncio.run_coroutine_threadsafe(video_track._queue.put(new_frame), loop)
self.record_video_data(image)
#self.recordq_video.put(new_frame)
for audio_frame in audio_frames:
frame,type = audio_frame
frame = (frame * 32767).astype(np.int16)
new_frame = AudioFrame(format='s16', layout='mono', samples=frame.shape[0])
new_frame.planes[0].update(frame.tobytes())
new_frame.sample_rate=16000
# if audio_track._queue.qsize()>10:
# time.sleep(0.1)
asyncio.run_coroutine_threadsafe(audio_track._queue.put(new_frame), loop)
self.record_audio_data(frame)
#self.recordq_audio.put(new_frame)
print('musereal process_frames thread stop')
def render(self,quit_event,loop=None,audio_track=None,video_track=None):
#if self.opt.asr:
# self.asr.warm_up()
self.tts.render(quit_event)
self.init_customindex()
process_thread = Thread(target=self.process_frames, args=(quit_event,loop,audio_track,video_track))
process_thread.start()
self.render_event.set() #start infer process render
Thread(target=inference, args=(self.render_event,self.batch_size,self.input_latent_list_cycle,
self.asr.feat_queue,self.asr.output_queue,self.res_frame_queue,
self.vae, self.unet, self.pe,self.timesteps)).start() #mp.Process
count=0
totaltime=0
_starttime=time.perf_counter()
#_totalframe=0
while not quit_event.is_set(): #todo
# update texture every frame
# audio stream thread...
t = time.perf_counter()
self.asr.run_step()
#self.test_step(loop,audio_track,video_track)
# totaltime += (time.perf_counter() - t)
# count += self.opt.batch_size
# if count>=100:
# print(f"------actual avg infer fps:{count/totaltime:.4f}")
# count=0
# totaltime=0
if video_track._queue.qsize()>=1.5*self.opt.batch_size:
print('sleep qsize=',video_track._queue.qsize())
time.sleep(0.04*video_track._queue.qsize()*0.8)
# if video_track._queue.qsize()>=5:
# print('sleep qsize=',video_track._queue.qsize())
# time.sleep(0.04*video_track._queue.qsize()*0.8)
# delay = _starttime+_totalframe*0.04-time.perf_counter() #40ms
# if delay > 0:
# time.sleep(delay)
self.render_event.clear() #end infer process render
print('musereal thread stop')