EchoMimic是由蚂蚁集团推出的一个 AI 驱动的口型同步技术项目，能够通过人像面部特征和音频来帮助人物“对口型”，生成逼真的动态肖像视频。
EchoMimic的技术亮点在于其创新的动画生成方法，它不仅能够通过音频和面部关键点单独驱动图像动画，还能结合这两种方式，通过音频信号和面部关键点的组合来生成逼真的“说话的头部”视频。
EchoMimic支持单独使用音频或面部标志点生成肖像视频，也支持将音频和人像照片相结合，实现更自然、流畅的对口型效果。
EchoMimic支持多语言，包括中文普通话、英语，以及适应唱歌等场景。
github项目地址：https://github.com/BadToBest/EchoMimic。
一、环境安装
1、python环境
建议安装python版本在3.10以上。
2、pip库安装
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
3、模型下载：
git lfs install
git clone https://huggingface.co/BadToBest/EchoMimic
二、功能测试
1、运行测试：
（1）python代码调用测试audio2video
import argparse
import os
import random
import platform
import subprocess
from datetime import datetime
from pathlib import Path
import cv2
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler
from omegaconf import OmegaConf
from PIL import Image
from src.models.unet_2d_condition import UNet2DConditionModel
from src.models.unet_3d_echo import EchoUNet3DConditionModel
from src.models.whisper.audio2feature import load_audio_model
from src.pipelines.pipeline_echo_mimic import Audio2VideoPipeline
from src.utils.util import save_videos_grid, crop_and_pad
from src.models.face_locator import FaceLocator
from moviepy.editor import VideoFileClip, AudioFileClip
from facenet_pytorch import MTCNN
# Check and add FFmpeg path if necessary
ffmpeg_path = os.getenv('FFMPEG_PATH')
if ffmpeg_path is None and platform.system() in ['Linux', 'Darwin']:
try:
result = subprocess.run(['which', 'ffmpeg'], capture_output=True, text=True)
if result.returncode == 0:
ffmpeg_path = result.stdout.strip()
print(f"FFmpeg is installed at: {ffmpeg_path}")
else:
print("FFmpeg is not installed. Please download ffmpeg-static and export to FFMPEG_PATH.")
print("For example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static")
except Exception as e:
print(f"Error finding ffmpeg: {e}")
else:
if ffmpeg_path and ffmpeg_path not in os.getenv('PATH'):
print("Adding FFMPEG_PATH to PATH")
os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}"
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/prompts/animation.yaml")
parser.add_argument("-W", type=int, default=512)
parser.add_argument("-H", type=int, default=512)
parser.add_argument("-L", type=int, default=1200)
parser.add_argument("--seed", type=int, default=420)
parser.add_argument("--facemusk_dilation_ratio", type=float, default=0.1)
parser.add_argument("--facecrop_dilation_ratio", type=float, default=0.5)
parser.add_argument("--context_frames", type=int, default=12)
parser.add_argument("--context_overlap", type=int, default=3)
parser.add_argument("--cfg", type=float, default=2.5)
parser.add_argument("--steps", type=int, default=30)
parser.add_argument("--sample_rate", type=int, default=16000)
parser.add_argument("--fps", type=int, default=24)
parser.add_argument("--device", type=str, default="cuda")
return parser.parse_args()
def select_face(det_bboxes, probs):
"""
Select the largest face with a detection probability above 0.8.
"""
if det_bboxes is None or probs is None:
return None
filtered_bboxes = [det_bboxes[i] for i in range(len(det_bboxes)) if probs[i] > 0.8]
if not filtered_bboxes:
return None
return max(filtered_bboxes, key=lambda x: (x[3] - x[1]) * (x[2] - x[0]))
def main():
args = parse_args()
config = OmegaConf.load(args.config)
weight_dtype = torch.float16 if config.weight_dtype == "fp16" else torch.float32
device = args.device
if "cuda" in device and not torch.cuda.is_available():
device = "cpu"
infer_config = OmegaConf.load(config.inference_config)
############# Initialize models #############
vae = AutoencoderKL.from_pretrained(config.pretrained_vae_path).to("cuda", dtype=weight_dtype)
reference_unet = UNet2DConditionModel.from_pretrained(config.pretrained_base_model_path, subfolder="unet").to(dtype=weight_dtype, device=device)
reference_unet.load_state_dict(torch.load(config.reference_unet_path, map_location="cpu"))
unet_kwargs = infer_config.unet_additional_kwargs or {}
denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
config.pretrained_base_model_path,
config.motion_module_path if os.path.exists(config.motion_module_path) else "",
subfolder="unet",
unet_additional_kwargs=unet_kwargs
).to(dtype=weight_dtype, device=device)
denoising_unet.load_state_dict(torch.load(config.denoising_unet_path, map_location="cpu"), strict=False)
face_locator = FaceLocator(320, conditioning_channels=1, block_out_channels=(16, 32, 96, 256)).to(dtype=weight_dtype, device="cuda")
face_locator.load_state_dict(torch.load(config.face_locator_path))
audio_processor = load_audio_model(model_path=config.audio_model_path, device=device)
face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device=device)
############# Initiate pipeline #############
scheduler = DDIMScheduler(**OmegaConf.to_container(infer_config.noise_scheduler_kwargs))
pipe = Audio2VideoPipeline(
vae=vae,
reference_unet=reference_unet,
denoising_unet=denoising_unet,
audio_guider=audio_processor,
face_locator=face_locator,
scheduler=scheduler,
).to("cuda", dtype=weight_dtype)
date_str = datetime.now().strftime("%Y%m%d")
time_str = datetime.now().strftime("%H%M")
save_dir_name = f"{time_str}--seed_{args.seed}-{args.W}x{args.H}"
save_dir = Path(f"output/{date_str}/{save_dir_name}")
save_dir.mkdir(exist_ok=True, parents=True)
for ref_image_path, audio_paths in config["test_cases"].items():
for audio_path in audio_paths:
seed = args.seed if args.seed is not None and args.seed > -1 else random.randint(100, 1000000)
generator = torch.manual_seed(seed)
ref_name = Path(ref_image_path).stem
audio_name = Path(audio_path).stem
final_fps = args.fps
#### Prepare face mask
face_img = cv2.imread(ref_image_path)
face_mask = np.zeros((face_img.shape[0], face_img.shape[1]), dtype='uint8')
det_bboxes, probs = face_detector.detect(face_img)
select_bbox = select_face(det_bboxes, probs)
if select_bbox is None:
face_mask[:, :] = 255
else:
xyxy = np.round(select_bbox[:4]).astype('int')
rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2]
r_pad = int((re - rb) * args.facemusk_dilation_ratio)
c_pad = int((ce - cb) * args.facemusk_dilation_ratio)
face_mask[rb - r_pad : re + r_pad, cb - c_pad : ce + c_pad] = 255
r_pad_crop = int((re - rb) * args.facecrop_dilation_ratio)
c_pad_crop = int((ce - cb) * args.facecrop_dilation_ratio)
crop_rect = [max(0, cb - c_pad_crop), max(0, rb - r_pad_crop), min(ce + c_pad_crop, face_img.shape[1]), min(re + r_pad_crop, face_img.shape[0])]
face_img = crop_and_pad(face_img, crop_rect)
face_mask = crop_and_pad(face_mask, crop_rect)
face_img = cv2.resize(face_img, (args.W, args.H))
face_mask = cv2.resize(face_mask, (args.W, args.H))
ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]])
face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(0).unsqueeze(0) / 255.0
video = pipe(
ref_image_pil,
audio_path,
face_mask_tensor,
width=args.W,
height=args.H,
duration=args.L,
num_inference_steps=args.steps,
cfg_scale=args.cfg,
generator=generator,
audio_sample_rate=args.sample_rate,
context_frames=args.context_frames,
fps=final_fps,
context_overlap=args.context_overlap
).videos
video_save_path = save_dir / f"{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4"
save_videos_grid(video, str(video_save_path), n_rows=1, fps=final_fps)
# Add audio to generated video
with_audio_path = save_dir / f"{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4"
video_clip = VideoFileClip(str(video_save_path))
audio_clip = AudioFileClip(audio_path)
final_video = video_clip.set_audio(audio_clip)
final_video.write_videofile(str(with_audio_path), codec="libx264", audio_codec="aac")
print(f"Saved video with audio to {with_audio_path}")
if __name__ == "__main__":
main()（2）python代码调用测试audio2pose
未完......
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