mirror of
https://github.com/TMElyralab/MuseTalk.git
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feat: real-time infer (#286)
* feat: realtime infer * cchore: infer script
This commit is contained in:
Zhizhou Zhong
GitHub
@@ -1,8 +1,9 @@
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import os
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import cv2
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import math
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import copy
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import glob
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import torch
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import glob
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import shutil
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import pickle
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import argparse
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@@ -17,18 +18,16 @@ from musetalk.utils.audio_processor import AudioProcessor
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from musetalk.utils.utils import get_file_type, get_video_fps, datagen, load_all_model
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from musetalk.utils.preprocessing import get_landmark_and_bbox, read_imgs, coord_placeholder
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@torch.no_grad()
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def main(args):
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# Configure ffmpeg path
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if args.ffmpeg_path not in os.getenv('PATH'):
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print("Adding ffmpeg to PATH")
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os.environ["PATH"] = f"{args.ffmpeg_path}:{os.environ['PATH']}"
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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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@@ -37,164 +36,229 @@ def main(args):
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device=device
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)
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timesteps = torch.tensor([0], device=device)
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if args.use_float16 is True:
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# Convert models to half precision if float16 is enabled
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if args.use_float16:
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pe = pe.half()
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vae.vae = vae.vae.half()
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unet.model = unet.model.half()
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# Move models to specified device
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pe = pe.to(device)
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vae.vae = vae.vae.to(device)
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unet.model = unet.model.to(device)
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# Initialize audio processor and Whisper model
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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
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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 task_id in inference_config:
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video_path = inference_config[task_id]["video_path"]
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audio_path = inference_config[task_id]["audio_path"]
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bbox_shift = inference_config[task_id].get("bbox_shift", args.bbox_shift)
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input_basename = os.path.basename(video_path).split('.')[0]
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audio_basename = os.path.basename(audio_path).split('.')[0]
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output_basename = f"{input_basename}_{audio_basename}"
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result_img_save_path = os.path.join(args.result_dir, output_basename) # related to video & audio inputs
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crop_coord_save_path = os.path.join(result_img_save_path, input_basename+".pkl") # only related to video input
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os.makedirs(result_img_save_path,exist_ok =True)
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if args.output_vid_name is None:
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output_vid_name = os.path.join(args.result_dir, output_basename+".mp4")
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else:
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output_vid_name = os.path.join(args.result_dir, args.output_vid_name)
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############################################## extract frames from source video ##############################################
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if get_file_type(video_path)=="video":
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save_dir_full = os.path.join(args.result_dir, input_basename)
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os.makedirs(save_dir_full,exist_ok = True)
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cmd = f"ffmpeg -v fatal -i {video_path} -start_number 0 {save_dir_full}/%08d.png"
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os.system(cmd)
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input_img_list = sorted(glob.glob(os.path.join(save_dir_full, '*.[jpJP][pnPN]*[gG]')))
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fps = get_video_fps(video_path)
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elif get_file_type(video_path)=="image":
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input_img_list = [video_path, ]
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fps = args.fps
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elif os.path.isdir(video_path): # input img folder
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input_img_list = glob.glob(os.path.join(video_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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fps = args.fps
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else:
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raise ValueError(f"{video_path} should be a video file, an image file or a directory of images")
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############################################## extract audio feature ##############################################
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# Extract audio features
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whisper_input_features, librosa_length = audio_processor.get_audio_feature(audio_path)
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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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# 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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############################################## preprocess input image ##############################################
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if os.path.exists(crop_coord_save_path) and args.use_saved_coord:
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print("using extracted coordinates")
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with open(crop_coord_save_path,'rb') as f:
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coord_list = pickle.load(f)
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frame_list = read_imgs(input_img_list)
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else:
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print("extracting landmarks...time consuming")
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coord_list, frame_list = get_landmark_and_bbox(input_img_list, bbox_shift)
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with open(crop_coord_save_path, 'wb') as f:
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pickle.dump(coord_list, f)
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i = 0
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input_latent_list = []
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for bbox, frame in zip(coord_list, frame_list):
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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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crop_frame = frame[y1:y2, x1:x2]
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crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4)
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latents = vae.get_latents_for_unet(crop_frame)
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input_latent_list.append(latents)
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else: # v1
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fp = FaceParsing()
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# to smooth the first and the last frame
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frame_list_cycle = frame_list + frame_list[::-1]
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coord_list_cycle = coord_list + coord_list[::-1]
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input_latent_list_cycle = input_latent_list + input_latent_list[::-1]
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############################################## inference batch by batch ##############################################
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print("start inference")
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video_num = len(whisper_chunks)
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batch_size = args.batch_size
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gen = datagen(whisper_chunks,input_latent_list_cycle,batch_size)
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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)/batch_size)))):
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audio_feature_batch = pe(whisper_batch)
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latent_batch = latent_batch.to(dtype=unet.model.dtype)
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# Load inference configuration
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inference_config = OmegaConf.load(args.inference_config)
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print("Loaded inference config:", inference_config)
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# Process each task
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for task_id in inference_config:
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try:
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# Get task configuration
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video_path = inference_config[task_id]["video_path"]
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audio_path = inference_config[task_id]["audio_path"]
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if "result_name" in inference_config[task_id]:
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args.output_vid_name = inference_config[task_id]["result_name"]
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pred_latents = unet.model(latent_batch, timesteps, encoder_hidden_states=audio_feature_batch).sample
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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_list.append(res_frame)
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############################################## pad to full image ##############################################
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print("pad talking image to original video")
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for i, res_frame in enumerate(tqdm(res_frame_list)):
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bbox = coord_list_cycle[i%(len(coord_list_cycle))]
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ori_frame = copy.deepcopy(frame_list_cycle[i%(len(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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# Set bbox_shift based on version
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if args.version == "v15":
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bbox_shift = 0 # v15 uses fixed bbox_shift
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else:
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bbox_shift = inference_config[task_id].get("bbox_shift", args.bbox_shift) # v1 uses config or default
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# Merge results
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combine_frame = get_image(ori_frame, res_frame, [x1, y1, x2, y2], fp=fp)
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cv2.imwrite(f"{result_img_save_path}/{str(i).zfill(8)}.png",combine_frame)
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# Set output paths
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input_basename = os.path.basename(video_path).split('.')[0]
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audio_basename = os.path.basename(audio_path).split('.')[0]
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output_basename = f"{input_basename}_{audio_basename}"
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# Create temporary directories
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temp_dir = os.path.join(args.result_dir, f"{args.version}")
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os.makedirs(temp_dir, exist_ok=True)
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# Set result save paths
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result_img_save_path = os.path.join(temp_dir, output_basename)
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crop_coord_save_path = os.path.join(args.result_dir, "../", input_basename+".pkl")
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os.makedirs(result_img_save_path, exist_ok=True)
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# Set output video paths
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if args.output_vid_name is None:
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output_vid_name = os.path.join(temp_dir, output_basename + ".mp4")
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else:
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output_vid_name = os.path.join(temp_dir, args.output_vid_name)
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output_vid_name_concat = os.path.join(temp_dir, output_basename + "_concat.mp4")
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# Extract frames from source video
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if get_file_type(video_path) == "video":
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save_dir_full = os.path.join(temp_dir, input_basename)
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os.makedirs(save_dir_full, exist_ok=True)
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cmd = f"ffmpeg -v fatal -i {video_path} -start_number 0 {save_dir_full}/%08d.png"
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os.system(cmd)
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input_img_list = sorted(glob.glob(os.path.join(save_dir_full, '*.[jpJP][pnPN]*[gG]')))
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fps = get_video_fps(video_path)
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elif get_file_type(video_path) == "image":
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input_img_list = [video_path]
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fps = args.fps
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elif os.path.isdir(video_path):
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input_img_list = glob.glob(os.path.join(video_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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fps = args.fps
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else:
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raise ValueError(f"{video_path} should be a video file, an image file or a directory of images")
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cmd_img2video = f"ffmpeg -y -v warning -r {fps} -f image2 -i {result_img_save_path}/%08d.png -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p -crf 18 temp.mp4"
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print(cmd_img2video)
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os.system(cmd_img2video)
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# Extract audio features
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whisper_input_features, librosa_length = audio_processor.get_audio_feature(audio_path)
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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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# Preprocess input images
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if os.path.exists(crop_coord_save_path) and args.use_saved_coord:
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print("Using saved coordinates")
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with open(crop_coord_save_path, 'rb') as f:
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coord_list = pickle.load(f)
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frame_list = read_imgs(input_img_list)
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else:
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print("Extracting landmarks... time-consuming operation")
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coord_list, frame_list = get_landmark_and_bbox(input_img_list, bbox_shift)
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with open(crop_coord_save_path, 'wb') as f:
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pickle.dump(coord_list, f)
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print(f"Number of frames: {len(frame_list)}")
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# Process each frame
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input_latent_list = []
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for bbox, frame in zip(coord_list, frame_list):
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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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crop_frame = frame[y1:y2, x1:x2]
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crop_frame = cv2.resize(crop_frame, (256,256), interpolation=cv2.INTER_LANCZOS4)
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latents = vae.get_latents_for_unet(crop_frame)
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input_latent_list.append(latents)
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cmd_combine_audio = f"ffmpeg -y -v warning -i {audio_path} -i temp.mp4 {output_vid_name}"
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print(cmd_combine_audio)
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os.system(cmd_combine_audio)
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os.remove("temp.mp4")
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shutil.rmtree(result_img_save_path)
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print(f"result is save to {output_vid_name}")
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# Smooth first and last frames
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frame_list_cycle = frame_list + frame_list[::-1]
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coord_list_cycle = coord_list + coord_list[::-1]
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input_latent_list_cycle = input_latent_list + input_latent_list[::-1]
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# Batch inference
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print("Starting inference")
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video_num = len(whisper_chunks)
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batch_size = args.batch_size
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gen = datagen(
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whisper_chunks=whisper_chunks,
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vae_encode_latents=input_latent_list_cycle,
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batch_size=batch_size,
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delay_frame=0,
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device=device,
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)
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res_frame_list = []
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total = int(np.ceil(float(video_num) / batch_size))
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# Execute inference
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for i, (whisper_batch, latent_batch) in enumerate(tqdm(gen, total=total)):
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audio_feature_batch = pe(whisper_batch)
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latent_batch = latent_batch.to(dtype=unet.model.dtype)
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pred_latents = unet.model(latent_batch, timesteps, encoder_hidden_states=audio_feature_batch).sample
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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_list.append(res_frame)
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# Pad generated images to original video size
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print("Padding generated images to original video size")
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for i, res_frame in enumerate(tqdm(res_frame_list)):
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bbox = coord_list_cycle[i%(len(coord_list_cycle))]
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ori_frame = copy.deepcopy(frame_list_cycle[i%(len(frame_list_cycle))])
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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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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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# Merge results with version-specific parameters
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if args.version == "v15":
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combine_frame = get_image(ori_frame, res_frame, [x1, y1, x2, y2], mode=args.parsing_mode, fp=fp)
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else:
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combine_frame = get_image(ori_frame, res_frame, [x1, y1, x2, y2], fp=fp)
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cv2.imwrite(f"{result_img_save_path}/{str(i).zfill(8)}.png", combine_frame)
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# Save prediction results
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temp_vid_path = f"{temp_dir}/temp_{input_basename}_{audio_basename}.mp4"
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cmd_img2video = f"ffmpeg -y -v warning -r {fps} -f image2 -i {result_img_save_path}/%08d.png -vcodec libx264 -vf format=yuv420p -crf 18 {temp_vid_path}"
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print("Video generation command:", cmd_img2video)
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os.system(cmd_img2video)
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cmd_combine_audio = f"ffmpeg -y -v warning -i {audio_path} -i {temp_vid_path} {output_vid_name}"
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print("Audio combination command:", cmd_combine_audio)
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os.system(cmd_combine_audio)
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# Clean up temporary files
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shutil.rmtree(result_img_save_path)
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os.remove(temp_vid_path)
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shutil.rmtree(save_dir_full)
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if not args.saved_coord:
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os.remove(crop_coord_save_path)
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print(f"Results saved to {output_vid_name}")
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except Exception as e:
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print("Error occurred during processing:", e)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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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("--inference_config", type=str, default="configs/inference/test_img.yaml")
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parser.add_argument("--bbox_shift", type=int, default=0)
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parser.add_argument("--result_dir", default='./results', help="path to output")
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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("--batch_size", type=int, default=8)
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parser.add_argument("--output_vid_name", type=str, default=None)
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parser.add_argument("--use_saved_coord",
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action="store_true",
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help='use saved coordinate to save time')
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parser.add_argument("--use_float16",
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action="store_true",
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help="Whether use float16 to speed up inference",
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)
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parser.add_argument("--fps", type=int, default=25, help="Video frames per second")
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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("--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/config.json", help="Path to UNet configuration file")
|
||||
parser.add_argument("--unet_model_path", type=str, default="./models/musetalkV15/unet.pth", help="Path to UNet model weights")
|
||||
parser.add_argument("--whisper_dir", type=str, default="./models/whisper", help="Directory containing Whisper model")
|
||||
parser.add_argument("--inference_config", type=str, default="configs/inference/test_img.yaml", help="Path to inference configuration file")
|
||||
parser.add_argument("--bbox_shift", type=int, default=0, help="Bounding box shift value")
|
||||
parser.add_argument("--result_dir", default='./results', help="Directory for output results")
|
||||
parser.add_argument("--extra_margin", type=int, default=10, help="Extra margin for face cropping")
|
||||
parser.add_argument("--fps", type=int, default=25, help="Video frames per second")
|
||||
parser.add_argument("--audio_padding_length_left", type=int, default=2, help="Left padding length for audio")
|
||||
parser.add_argument("--audio_padding_length_right", type=int, default=2, help="Right padding length for audio")
|
||||
parser.add_argument("--batch_size", type=int, default=8, help="Batch size for inference")
|
||||
parser.add_argument("--output_vid_name", type=str, default=None, help="Name of output video file")
|
||||
parser.add_argument("--use_saved_coord", action="store_true", help='Use saved coordinates to save time')
|
||||
parser.add_argument("--saved_coord", action="store_true", help='Save coordinates for future use')
|
||||
parser.add_argument("--use_float16", action="store_true", help="Use float16 for faster inference")
|
||||
parser.add_argument("--parsing_mode", default='jaw', help="Face blending parsing mode")
|
||||
parser.add_argument("--left_cheek_width", type=int, default=90, help="Width of left cheek region")
|
||||
parser.add_argument("--right_cheek_width", type=int, default=90, help="Width of right cheek region")
|
||||
parser.add_argument("--version", type=str, default="v15", choices=["v1", "v15"], help="Model version to use")
|
||||
args = parser.parse_args()
|
||||
main(args)
|
||||
|
||||
Reference in New Issue
Block a user