| import torch, uuid
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| import os, sys, shutil
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| from src.utils.preprocess import CropAndExtract
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| from src.test_audio2coeff import Audio2Coeff
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| from src.facerender.animate import AnimateFromCoeff
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| from src.generate_batch import get_data
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| from src.generate_facerender_batch import get_facerender_data
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|
|
| from pydub import AudioSegment
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|
|
| def mp3_to_wav(mp3_filename,wav_filename,frame_rate):
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| mp3_file = AudioSegment.from_file(file=mp3_filename)
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| mp3_file.set_frame_rate(frame_rate).export(wav_filename,format="wav")
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|
|
|
|
| class SadTalker():
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|
|
| def __init__(self, checkpoint_path='checkpoints', config_path='src/config', lazy_load=False):
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|
|
| if torch.cuda.is_available() :
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| device = "cuda"
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| else:
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| device = "cpu"
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|
|
| self.device = device
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|
|
| os.environ['TORCH_HOME']= checkpoint_path
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|
|
| self.checkpoint_path = checkpoint_path
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| self.config_path = config_path
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|
|
| self.path_of_lm_croper = os.path.join( checkpoint_path, 'shape_predictor_68_face_landmarks.dat')
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| self.path_of_net_recon_model = os.path.join( checkpoint_path, 'epoch_20.pth')
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| self.dir_of_BFM_fitting = os.path.join( checkpoint_path, 'BFM_Fitting')
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| self.wav2lip_checkpoint = os.path.join( checkpoint_path, 'wav2lip.pth')
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|
|
| self.audio2pose_checkpoint = os.path.join( checkpoint_path, 'auido2pose_00140-model.pth')
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| self.audio2pose_yaml_path = os.path.join( config_path, 'auido2pose.yaml')
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|
|
| self.audio2exp_checkpoint = os.path.join( checkpoint_path, 'auido2exp_00300-model.pth')
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| self.audio2exp_yaml_path = os.path.join( config_path, 'auido2exp.yaml')
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|
|
| self.free_view_checkpoint = os.path.join( checkpoint_path, 'facevid2vid_00189-model.pth.tar')
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|
|
| self.lazy_load = lazy_load
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|
|
| if not self.lazy_load:
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|
|
|
|
| print(self.audio2pose_checkpoint)
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| self.audio_to_coeff = Audio2Coeff(self.audio2pose_checkpoint, self.audio2pose_yaml_path,
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| self.audio2exp_checkpoint, self.audio2exp_yaml_path, self.wav2lip_checkpoint, self.device)
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|
|
| print(self.path_of_lm_croper)
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| self.preprocess_model = CropAndExtract(self.path_of_lm_croper, self.path_of_net_recon_model, self.dir_of_BFM_fitting, self.device)
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|
|
| def test(self, source_image, driven_audio, preprocess='crop', still_mode=False, use_enhancer=False, result_dir='./results/'):
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|
|
|
|
|
|
| if self.lazy_load:
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|
|
|
|
| print(self.audio2pose_checkpoint)
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| self.audio_to_coeff = Audio2Coeff(self.audio2pose_checkpoint, self.audio2pose_yaml_path,
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| self.audio2exp_checkpoint, self.audio2exp_yaml_path, self.wav2lip_checkpoint, self.device)
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|
|
| print(self.path_of_lm_croper)
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| self.preprocess_model = CropAndExtract(self.path_of_lm_croper, self.path_of_net_recon_model, self.dir_of_BFM_fitting, self.device)
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|
|
| if preprocess == 'full':
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| self.mapping_checkpoint = os.path.join(self.checkpoint_path, 'mapping_00109-model.pth.tar')
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| self.facerender_yaml_path = os.path.join(self.config_path, 'facerender_still.yaml')
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| else:
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| self.mapping_checkpoint = os.path.join(self.checkpoint_path, 'mapping_00229-model.pth.tar')
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| self.facerender_yaml_path = os.path.join(self.config_path, 'facerender.yaml')
|
|
|
| print(self.free_view_checkpoint)
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| self.animate_from_coeff = AnimateFromCoeff(self.free_view_checkpoint, self.mapping_checkpoint,
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| self.facerender_yaml_path, self.device)
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|
|
| time_tag = str(uuid.uuid4())
|
| save_dir = os.path.join(result_dir, time_tag)
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| os.makedirs(save_dir, exist_ok=True)
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|
|
| input_dir = os.path.join(save_dir, 'input')
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| os.makedirs(input_dir, exist_ok=True)
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|
|
| print(source_image)
|
| pic_path = os.path.join(input_dir, os.path.basename(source_image))
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| shutil.move(source_image, input_dir)
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|
|
| if os.path.isfile(driven_audio):
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| audio_path = os.path.join(input_dir, os.path.basename(driven_audio))
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|
|
|
|
| if '.mp3' in audio_path:
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| mp3_to_wav(driven_audio, audio_path.replace('.mp3', '.wav'), 16000)
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| audio_path = audio_path.replace('.mp3', '.wav')
|
| else:
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| shutil.move(driven_audio, input_dir)
|
| else:
|
| raise AttributeError("error audio")
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|
|
|
|
| os.makedirs(save_dir, exist_ok=True)
|
| pose_style = 0
|
|
|
| first_frame_dir = os.path.join(save_dir, 'first_frame_dir')
|
| os.makedirs(first_frame_dir, exist_ok=True)
|
| first_coeff_path, crop_pic_path, crop_info = self.preprocess_model.generate(pic_path, first_frame_dir, preprocess)
|
|
|
| if first_coeff_path is None:
|
| raise AttributeError("No face is detected")
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|
|
|
|
| batch = get_data(first_coeff_path, audio_path, self.device, ref_eyeblink_coeff_path=None, still=still_mode)
|
| coeff_path = self.audio_to_coeff.generate(batch, save_dir, pose_style)
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|
|
| batch_size = 2
|
| data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, still_mode=still_mode, preprocess=preprocess)
|
| return_path = self.animate_from_coeff.generate(data, save_dir, pic_path, crop_info, enhancer='gfpgan' if use_enhancer else None, preprocess=preprocess)
|
| video_name = data['video_name']
|
| print(f'The generated video is named {video_name} in {save_dir}')
|
|
|
| if self.lazy_load:
|
| del self.preprocess_model
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| del self.audio_to_coeff
|
| del self.animate_from_coeff
|
|
|
| if torch.cuda.is_available():
|
| torch.cuda.empty_cache()
|
| torch.cuda.synchronize()
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|
|
| import gc; gc.collect()
|
|
|
| return return_path
|
|
|
| |
