mirror of
https://github.com/TMElyralab/MuseTalk.git
synced 2026-02-04 09:29:20 +08:00
410 lines
19 KiB
Python
410 lines
19 KiB
Python
import argparse
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import os
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from omegaconf import OmegaConf
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import numpy as np
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import cv2
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import torch
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import glob
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import pickle
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import sys
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from tqdm import tqdm
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import copy
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import json
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from transformers import WhisperModel
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from musetalk.utils.face_parsing import FaceParsing
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from musetalk.utils.utils import datagen
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from musetalk.utils.preprocessing import get_landmark_and_bbox, read_imgs
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from musetalk.utils.blending import get_image_prepare_material, get_image_blending
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from musetalk.utils.utils import load_all_model
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from musetalk.utils.audio_processor import AudioProcessor
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import shutil
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import threading
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import queue
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import time
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import subprocess
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def fast_check_ffmpeg():
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try:
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subprocess.run(["ffmpeg", "-version"], capture_output=True, check=True)
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return True
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except:
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return False
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def video2imgs(vid_path, save_path, ext='.png', cut_frame=10000000):
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cap = cv2.VideoCapture(vid_path)
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count = 0
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while True:
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if count > cut_frame:
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break
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ret, frame = cap.read()
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if ret:
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cv2.imwrite(f"{save_path}/{count:08d}.png", frame)
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count += 1
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else:
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break
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def osmakedirs(path_list):
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for path in path_list:
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os.makedirs(path) if not os.path.exists(path) else None
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@torch.no_grad()
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class Avatar:
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def __init__(self, avatar_id, video_path, bbox_shift, batch_size, preparation):
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self.avatar_id = avatar_id
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self.video_path = video_path
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self.bbox_shift = bbox_shift
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# 根据版本设置不同的基础路径
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if args.version == "v15":
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self.base_path = f"./results/{args.version}/avatars/{avatar_id}"
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else: # v1
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self.base_path = f"./results/avatars/{avatar_id}"
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self.avatar_path = self.base_path
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self.full_imgs_path = f"{self.avatar_path}/full_imgs"
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self.coords_path = f"{self.avatar_path}/coords.pkl"
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self.latents_out_path = f"{self.avatar_path}/latents.pt"
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self.video_out_path = f"{self.avatar_path}/vid_output/"
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self.mask_out_path = f"{self.avatar_path}/mask"
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self.mask_coords_path = f"{self.avatar_path}/mask_coords.pkl"
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self.avatar_info_path = f"{self.avatar_path}/avator_info.json"
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self.avatar_info = {
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"avatar_id": avatar_id,
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"video_path": video_path,
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"bbox_shift": bbox_shift,
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"version": args.version
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}
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self.preparation = preparation
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self.batch_size = batch_size
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self.idx = 0
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self.init()
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def init(self):
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if self.preparation:
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if os.path.exists(self.avatar_path):
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response = input(f"{self.avatar_id} exists, Do you want to re-create it ? (y/n)")
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if response.lower() == "y":
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shutil.rmtree(self.avatar_path)
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print("*********************************")
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print(f" creating avator: {self.avatar_id}")
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print("*********************************")
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osmakedirs([self.avatar_path, self.full_imgs_path, self.video_out_path, self.mask_out_path])
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self.prepare_material()
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else:
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self.input_latent_list_cycle = torch.load(self.latents_out_path)
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with open(self.coords_path, 'rb') as f:
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self.coord_list_cycle = pickle.load(f)
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input_img_list = glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]'))
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input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
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self.frame_list_cycle = read_imgs(input_img_list)
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with open(self.mask_coords_path, 'rb') as f:
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self.mask_coords_list_cycle = pickle.load(f)
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input_mask_list = glob.glob(os.path.join(self.mask_out_path, '*.[jpJP][pnPN]*[gG]'))
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input_mask_list = sorted(input_mask_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
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self.mask_list_cycle = read_imgs(input_mask_list)
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else:
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print("*********************************")
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print(f" creating avator: {self.avatar_id}")
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print("*********************************")
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osmakedirs([self.avatar_path, self.full_imgs_path, self.video_out_path, self.mask_out_path])
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self.prepare_material()
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else:
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if not os.path.exists(self.avatar_path):
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print(f"{self.avatar_id} does not exist, you should set preparation to True")
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sys.exit()
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with open(self.avatar_info_path, "r") as f:
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avatar_info = json.load(f)
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if avatar_info['bbox_shift'] != self.avatar_info['bbox_shift']:
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response = input(f" 【bbox_shift】 is changed, you need to re-create it ! (c/continue)")
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if response.lower() == "c":
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shutil.rmtree(self.avatar_path)
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print("*********************************")
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print(f" creating avator: {self.avatar_id}")
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print("*********************************")
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osmakedirs([self.avatar_path, self.full_imgs_path, self.video_out_path, self.mask_out_path])
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self.prepare_material()
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else:
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sys.exit()
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else:
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self.input_latent_list_cycle = torch.load(self.latents_out_path)
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with open(self.coords_path, 'rb') as f:
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self.coord_list_cycle = pickle.load(f)
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input_img_list = glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]'))
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input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
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self.frame_list_cycle = read_imgs(input_img_list)
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with open(self.mask_coords_path, 'rb') as f:
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self.mask_coords_list_cycle = pickle.load(f)
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input_mask_list = glob.glob(os.path.join(self.mask_out_path, '*.[jpJP][pnPN]*[gG]'))
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input_mask_list = sorted(input_mask_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
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self.mask_list_cycle = read_imgs(input_mask_list)
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def prepare_material(self):
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print("preparing data materials ... ...")
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with open(self.avatar_info_path, "w") as f:
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json.dump(self.avatar_info, f)
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if os.path.isfile(self.video_path):
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video2imgs(self.video_path, self.full_imgs_path, ext='png')
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else:
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print(f"copy files in {self.video_path}")
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files = os.listdir(self.video_path)
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files.sort()
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files = [file for file in files if file.split(".")[-1] == "png"]
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for filename in files:
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shutil.copyfile(f"{self.video_path}/{filename}", f"{self.full_imgs_path}/{filename}")
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input_img_list = sorted(glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]')))
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print("extracting landmarks...")
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coord_list, frame_list = get_landmark_and_bbox(input_img_list, self.bbox_shift)
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input_latent_list = []
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idx = -1
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# maker if the bbox is not sufficient
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coord_placeholder = (0.0, 0.0, 0.0, 0.0)
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for bbox, frame in zip(coord_list, frame_list):
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idx = idx + 1
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if bbox == coord_placeholder:
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continue
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x1, y1, x2, y2 = bbox
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if args.version == "v15":
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y2 = y2 + args.extra_margin
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y2 = min(y2, frame.shape[0])
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coord_list[idx] = [x1, y1, x2, y2] # 更新coord_list中的bbox
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crop_frame = frame[y1:y2, x1:x2]
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resized_crop_frame = cv2.resize(crop_frame, (256, 256), interpolation=cv2.INTER_LANCZOS4)
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latents = vae.get_latents_for_unet(resized_crop_frame)
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input_latent_list.append(latents)
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self.frame_list_cycle = frame_list + frame_list[::-1]
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self.coord_list_cycle = coord_list + coord_list[::-1]
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self.input_latent_list_cycle = input_latent_list + input_latent_list[::-1]
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self.mask_coords_list_cycle = []
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self.mask_list_cycle = []
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for i, frame in enumerate(tqdm(self.frame_list_cycle)):
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cv2.imwrite(f"{self.full_imgs_path}/{str(i).zfill(8)}.png", frame)
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x1, y1, x2, y2 = self.coord_list_cycle[i]
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if args.version == "v15":
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mode = args.parsing_mode
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else:
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mode = "raw"
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mask, crop_box = get_image_prepare_material(frame, [x1, y1, x2, y2], fp=fp, mode=mode)
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cv2.imwrite(f"{self.mask_out_path}/{str(i).zfill(8)}.png", mask)
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self.mask_coords_list_cycle += [crop_box]
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self.mask_list_cycle.append(mask)
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with open(self.mask_coords_path, 'wb') as f:
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pickle.dump(self.mask_coords_list_cycle, f)
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with open(self.coords_path, 'wb') as f:
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pickle.dump(self.coord_list_cycle, f)
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torch.save(self.input_latent_list_cycle, os.path.join(self.latents_out_path))
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def process_frames(self, res_frame_queue, video_len, skip_save_images):
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print(video_len)
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while True:
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if self.idx >= video_len - 1:
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break
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try:
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start = time.time()
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res_frame = res_frame_queue.get(block=True, timeout=1)
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except queue.Empty:
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continue
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bbox = self.coord_list_cycle[self.idx % (len(self.coord_list_cycle))]
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ori_frame = copy.deepcopy(self.frame_list_cycle[self.idx % (len(self.frame_list_cycle))])
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x1, y1, x2, y2 = bbox
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try:
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res_frame = cv2.resize(res_frame.astype(np.uint8), (x2 - x1, y2 - y1))
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except:
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continue
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mask = self.mask_list_cycle[self.idx % (len(self.mask_list_cycle))]
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mask_crop_box = self.mask_coords_list_cycle[self.idx % (len(self.mask_coords_list_cycle))]
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combine_frame = get_image_blending(ori_frame,res_frame,bbox,mask,mask_crop_box)
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if skip_save_images is False:
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cv2.imwrite(f"{self.avatar_path}/tmp/{str(self.idx).zfill(8)}.png", combine_frame)
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self.idx = self.idx + 1
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@torch.no_grad()
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def inference(self, audio_path, out_vid_name, fps, skip_save_images):
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os.makedirs(self.avatar_path + '/tmp', exist_ok=True)
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print("start inference")
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############################################## extract audio feature ##############################################
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start_time = time.time()
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# Extract audio features
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whisper_input_features, librosa_length = audio_processor.get_audio_feature(audio_path, weight_dtype=weight_dtype)
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whisper_chunks = audio_processor.get_whisper_chunk(
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whisper_input_features,
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device,
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weight_dtype,
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whisper,
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librosa_length,
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fps=fps,
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audio_padding_length_left=args.audio_padding_length_left,
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audio_padding_length_right=args.audio_padding_length_right,
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)
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print(f"processing audio:{audio_path} costs {(time.time() - start_time) * 1000}ms")
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############################################## inference batch by batch ##############################################
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video_num = len(whisper_chunks)
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res_frame_queue = queue.Queue()
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self.idx = 0
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# Create a sub-thread and start it
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process_thread = threading.Thread(target=self.process_frames, args=(res_frame_queue, video_num, skip_save_images))
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process_thread.start()
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gen = datagen(whisper_chunks,
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self.input_latent_list_cycle,
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self.batch_size)
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start_time = time.time()
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res_frame_list = []
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for i, (whisper_batch, latent_batch) in enumerate(tqdm(gen, total=int(np.ceil(float(video_num) / self.batch_size)))):
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audio_feature_batch = pe(whisper_batch.to(device))
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latent_batch = latent_batch.to(device=device, dtype=unet.model.dtype)
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pred_latents = unet.model(latent_batch,
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timesteps,
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encoder_hidden_states=audio_feature_batch).sample
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pred_latents = pred_latents.to(device=device, dtype=vae.vae.dtype)
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recon = vae.decode_latents(pred_latents)
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for res_frame in recon:
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res_frame_queue.put(res_frame)
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# Close the queue and sub-thread after all tasks are completed
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process_thread.join()
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if args.skip_save_images is True:
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print('Total process time of {} frames without saving images = {}s'.format(
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video_num,
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time.time() - start_time))
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else:
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print('Total process time of {} frames including saving images = {}s'.format(
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video_num,
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time.time() - start_time))
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if out_vid_name is not None and args.skip_save_images is False:
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# optional
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cmd_img2video = f"ffmpeg -y -v warning -r {fps} -f image2 -i {self.avatar_path}/tmp/%08d.png -vcodec libx264 -vf format=yuv420p -crf 18 {self.avatar_path}/temp.mp4"
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print(cmd_img2video)
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os.system(cmd_img2video)
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output_vid = os.path.join(self.video_out_path, out_vid_name + ".mp4") # on
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cmd_combine_audio = f"ffmpeg -y -v warning -i {audio_path} -i {self.avatar_path}/temp.mp4 {output_vid}"
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print(cmd_combine_audio)
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os.system(cmd_combine_audio)
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os.remove(f"{self.avatar_path}/temp.mp4")
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shutil.rmtree(f"{self.avatar_path}/tmp")
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print(f"result is save to {output_vid}")
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print("\n")
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if __name__ == "__main__":
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'''
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This script is used to simulate online chatting and applies necessary pre-processing such as face detection and face parsing in advance. During online chatting, only UNet and the VAE decoder are involved, which makes MuseTalk real-time.
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'''
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parser = argparse.ArgumentParser()
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parser.add_argument("--version", type=str, default="v15", choices=["v1", "v15"], help="Version of MuseTalk: v1 or v15")
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parser.add_argument("--ffmpeg_path", type=str, default="./ffmpeg-4.4-amd64-static/", help="Path to ffmpeg executable")
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parser.add_argument("--gpu_id", type=int, default=0, help="GPU ID to use")
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parser.add_argument("--vae_type", type=str, default="sd-vae", help="Type of VAE model")
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parser.add_argument("--unet_config", type=str, default="./models/musetalk/musetalk.json", help="Path to UNet configuration file")
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parser.add_argument("--unet_model_path", type=str, default="./models/musetalk/pytorch_model.bin", help="Path to UNet model weights")
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parser.add_argument("--whisper_dir", type=str, default="./models/whisper", help="Directory containing Whisper model")
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parser.add_argument("--inference_config", type=str, default="configs/inference/realtime.yaml")
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parser.add_argument("--bbox_shift", type=int, default=0, help="Bounding box shift value")
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parser.add_argument("--result_dir", default='./results', help="Directory for output results")
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parser.add_argument("--extra_margin", type=int, default=10, help="Extra margin for face cropping")
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parser.add_argument("--fps", type=int, default=25, help="Video frames per second")
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parser.add_argument("--audio_padding_length_left", type=int, default=2, help="Left padding length for audio")
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parser.add_argument("--audio_padding_length_right", type=int, default=2, help="Right padding length for audio")
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parser.add_argument("--batch_size", type=int, default=20, help="Batch size for inference")
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parser.add_argument("--output_vid_name", type=str, default=None, help="Name of output video file")
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parser.add_argument("--use_saved_coord", action="store_true", help='Use saved coordinates to save time')
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parser.add_argument("--saved_coord", action="store_true", help='Save coordinates for future use')
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parser.add_argument("--parsing_mode", default='jaw', help="Face blending parsing mode")
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parser.add_argument("--left_cheek_width", type=int, default=90, help="Width of left cheek region")
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parser.add_argument("--right_cheek_width", type=int, default=90, help="Width of right cheek region")
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parser.add_argument("--skip_save_images",
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action="store_true",
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help="Whether skip saving images for better generation speed calculation",
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)
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args = parser.parse_args()
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# Configure ffmpeg path
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if not fast_check_ffmpeg():
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print("Adding ffmpeg to PATH")
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# Choose path separator based on operating system
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path_separator = ';' if sys.platform == 'win32' else ':'
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os.environ["PATH"] = f"{args.ffmpeg_path}{path_separator}{os.environ['PATH']}"
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if not fast_check_ffmpeg():
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print("Warning: Unable to find ffmpeg, please ensure ffmpeg is properly installed")
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# Set computing device
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device = torch.device(f"cuda:{args.gpu_id}" if torch.cuda.is_available() else "cpu")
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# Load model weights
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vae, unet, pe = load_all_model(
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unet_model_path=args.unet_model_path,
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vae_type=args.vae_type,
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unet_config=args.unet_config,
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device=device
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)
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timesteps = torch.tensor([0], device=device)
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pe = pe.half().to(device)
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vae.vae = vae.vae.half().to(device)
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unet.model = unet.model.half().to(device)
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# Initialize audio processor and Whisper model
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audio_processor = AudioProcessor(feature_extractor_path=args.whisper_dir)
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weight_dtype = unet.model.dtype
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whisper = WhisperModel.from_pretrained(args.whisper_dir)
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whisper = whisper.to(device=device, dtype=weight_dtype).eval()
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whisper.requires_grad_(False)
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# Initialize face parser with configurable parameters based on version
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if args.version == "v15":
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fp = FaceParsing(
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left_cheek_width=args.left_cheek_width,
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right_cheek_width=args.right_cheek_width
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)
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else: # v1
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fp = FaceParsing()
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inference_config = OmegaConf.load(args.inference_config)
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print(inference_config)
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for avatar_id in inference_config:
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data_preparation = inference_config[avatar_id]["preparation"]
|
|
video_path = inference_config[avatar_id]["video_path"]
|
|
if args.version == "v15":
|
|
bbox_shift = 0
|
|
else:
|
|
bbox_shift = inference_config[avatar_id]["bbox_shift"]
|
|
avatar = Avatar(
|
|
avatar_id=avatar_id,
|
|
video_path=video_path,
|
|
bbox_shift=bbox_shift,
|
|
batch_size=args.batch_size,
|
|
preparation=data_preparation)
|
|
|
|
audio_clips = inference_config[avatar_id]["audio_clips"]
|
|
for audio_num, audio_path in audio_clips.items():
|
|
print("Inferring using:", audio_path)
|
|
avatar.inference(audio_path,
|
|
audio_num,
|
|
args.fps,
|
|
args.skip_save_images)
|