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| import torch, uuid | |
| from time import gmtime, strftime | |
| import os, sys, shutil | |
| from src.utils.preprocess import CropAndExtract | |
| from src.test_audio2coeff import Audio2Coeff | |
| from src.facerender.animate import AnimateFromCoeff | |
| from src.generate_batch import get_data | |
| from src.generate_facerender_batch import get_facerender_data | |
| from src.utils.text2speech import text2speech | |
| from pydub import AudioSegment | |
| def mp3_to_wav(mp3_filename,wav_filename,frame_rate): | |
| mp3_file = AudioSegment.from_file(file=mp3_filename) | |
| mp3_file.set_frame_rate(frame_rate).export(wav_filename,format="wav") | |
| class SadTalker(): | |
| def __init__(self, checkpoint_path='checkpoints', config_path='src/config'): | |
| if torch.cuda.is_available() : | |
| device = "cuda" | |
| else: | |
| device = "cpu" | |
| os.environ['TORCH_HOME']= checkpoint_path | |
| path_of_lm_croper = os.path.join( checkpoint_path, 'shape_predictor_68_face_landmarks.dat') | |
| path_of_net_recon_model = os.path.join( checkpoint_path, 'epoch_20.pth') | |
| dir_of_BFM_fitting = os.path.join( checkpoint_path, 'BFM_Fitting') | |
| wav2lip_checkpoint = os.path.join( checkpoint_path, 'wav2lip.pth') | |
| audio2pose_checkpoint = os.path.join( checkpoint_path, 'auido2pose_00140-model.pth') | |
| audio2pose_yaml_path = os.path.join( config_path, 'auido2pose.yaml') | |
| audio2exp_checkpoint = os.path.join( checkpoint_path, 'auido2exp_00300-model.pth') | |
| audio2exp_yaml_path = os.path.join( config_path, 'auido2exp.yaml') | |
| free_view_checkpoint = os.path.join( checkpoint_path, 'facevid2vid_00189-model.pth.tar') | |
| mapping_checkpoint = os.path.join( checkpoint_path, 'mapping_00229-model.pth.tar') | |
| facerender_yaml_path = os.path.join( config_path, 'facerender.yaml') | |
| #init model | |
| print(path_of_lm_croper) | |
| self.preprocess_model = CropAndExtract(path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, device) | |
| print(audio2pose_checkpoint) | |
| self.audio_to_coeff = Audio2Coeff(audio2pose_checkpoint, audio2pose_yaml_path, | |
| audio2exp_checkpoint, audio2exp_yaml_path, wav2lip_checkpoint, device) | |
| print(free_view_checkpoint) | |
| self.animate_from_coeff = AnimateFromCoeff(free_view_checkpoint, mapping_checkpoint, | |
| facerender_yaml_path, device) | |
| self.device = device | |
| def test(self, source_image, driven_audio, still_mode, use_enhancer, result_dir='./'): | |
| time_tag = str(uuid.uuid4()) | |
| save_dir = os.path.join(result_dir, time_tag) | |
| os.makedirs(save_dir, exist_ok=True) | |
| input_dir = os.path.join(save_dir, 'input') | |
| os.makedirs(input_dir, exist_ok=True) | |
| print(source_image) | |
| pic_path = os.path.join(input_dir, os.path.basename(source_image)) | |
| shutil.move(source_image, input_dir) | |
| if os.path.isfile(driven_audio): | |
| audio_path = os.path.join(input_dir, os.path.basename(driven_audio)) | |
| #### mp3 to wav | |
| if '.mp3' in audio_path: | |
| mp3_to_wav(driven_audio, audio_path.replace('.mp3', '.wav'), 16000) | |
| audio_path = audio_path.replace('.mp3', '.wav') | |
| else: | |
| shutil.move(driven_audio, input_dir) | |
| else: | |
| text2speech | |
| os.makedirs(save_dir, exist_ok=True) | |
| pose_style = 0 | |
| #crop image and extract 3dmm from image | |
| 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, original_size = self.preprocess_model.generate(pic_path, first_frame_dir) | |
| if first_coeff_path is None: | |
| raise AttributeError("No face is detected") | |
| #audio2ceoff | |
| batch = get_data(first_coeff_path, audio_path, self.device) # longer audio? | |
| coeff_path = self.audio_to_coeff.generate(batch, save_dir, pose_style) | |
| #coeff2video | |
| batch_size = 4 | |
| data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, still_mode=still_mode) | |
| self.animate_from_coeff.generate(data, save_dir, enhancer='gfpgan' if use_enhancer else None, original_size=original_size) | |
| video_name = data['video_name'] | |
| print(f'The generated video is named {video_name} in {save_dir}') | |
| torch.cuda.empty_cache() | |
| torch.cuda.synchronize() | |
| import gc; gc.collect() | |
| if use_enhancer: | |
| return os.path.join(save_dir, video_name+'_enhanced.mp4'), os.path.join(save_dir, video_name+'_enhanced.mp4') | |
| else: | |
| return os.path.join(save_dir, video_name+'.mp4'), os.path.join(save_dir, video_name+'.mp4') | |