import spaces import torch, uuid import os, sys, shutil, platform from src.facerender.pirender_animate import AnimateFromCoeff_PIRender 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.init_path import init_path 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', lazy_load=False): if torch.cuda.is_available(): device = "cuda" elif platform.system() == 'Darwin': # macos device = "mps" else: device = "cpu" self.device = device os.environ['TORCH_HOME']= checkpoint_path self.checkpoint_path = checkpoint_path self.config_path = config_path @spaces.GPU def test(self, source_image, driven_audio, preprocess='crop', still_mode=False, use_enhancer=False, batch_size=1, size=256, pose_style = 0, facerender='facevid2vid', exp_scale=1.0, use_ref_video = False, ref_video = None, ref_info = None, use_idle_mode = False, length_of_audio = 0, use_blink=True, result_dir='./results/'): self.sadtalker_paths = init_path(self.checkpoint_path, self.config_path, size, False, preprocess) print(self.sadtalker_paths) self.audio_to_coeff = Audio2Coeff(self.sadtalker_paths, self.device) self.preprocess_model = CropAndExtract(self.sadtalker_paths, self.device) if facerender == 'facevid2vid' and self.device != 'mps': self.animate_from_coeff = AnimateFromCoeff(self.sadtalker_paths, self.device) elif facerender == 'pirender' or self.device == 'mps': self.animate_from_coeff = AnimateFromCoeff_PIRender(self.sadtalker_paths, self.device) facerender = 'pirender' else: raise(RuntimeError('Unknown model: {}'.format(facerender))) 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 driven_audio is not None and 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) elif use_idle_mode: audio_path = os.path.join(input_dir, 'idlemode_'+str(length_of_audio)+'.wav') ## generate audio from this new audio_path from pydub import AudioSegment one_sec_segment = AudioSegment.silent(duration=1000*length_of_audio) #duration in milliseconds one_sec_segment.export(audio_path, format="wav") else: print(use_ref_video, ref_info) assert use_ref_video == True and ref_info == 'all' if use_ref_video and ref_info == 'all': # full ref mode ref_video_videoname = os.path.basename(ref_video) audio_path = os.path.join(save_dir, ref_video_videoname+'.wav') print('new audiopath:',audio_path) # if ref_video contains audio, set the audio from ref_video. cmd = r"ffmpeg -y -hide_banner -loglevel error -i %s %s"%(ref_video, audio_path) os.system(cmd) os.makedirs(save_dir, exist_ok=True) #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, crop_info = self.preprocess_model.generate(pic_path, first_frame_dir, preprocess, True, size) if first_coeff_path is None: raise AttributeError("No face is detected") if use_ref_video: print('using ref video for genreation') ref_video_videoname = os.path.splitext(os.path.split(ref_video)[-1])[0] ref_video_frame_dir = os.path.join(save_dir, ref_video_videoname) os.makedirs(ref_video_frame_dir, exist_ok=True) print('3DMM Extraction for the reference video providing pose') ref_video_coeff_path, _, _ = self.preprocess_model.generate(ref_video, ref_video_frame_dir, preprocess, source_image_flag=False) else: ref_video_coeff_path = None if use_ref_video: if ref_info == 'pose': ref_pose_coeff_path = ref_video_coeff_path ref_eyeblink_coeff_path = None elif ref_info == 'blink': ref_pose_coeff_path = None ref_eyeblink_coeff_path = ref_video_coeff_path elif ref_info == 'pose+blink': ref_pose_coeff_path = ref_video_coeff_path ref_eyeblink_coeff_path = ref_video_coeff_path elif ref_info == 'all': ref_pose_coeff_path = None ref_eyeblink_coeff_path = None else: raise('error in refinfo') else: ref_pose_coeff_path = None ref_eyeblink_coeff_path = None #audio2ceoff if use_ref_video and ref_info == 'all': coeff_path = ref_video_coeff_path # self.audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path) else: batch = get_data(first_coeff_path, audio_path, self.device, ref_eyeblink_coeff_path=ref_eyeblink_coeff_path, still=still_mode, \ idlemode=use_idle_mode, length_of_audio=length_of_audio, use_blink=use_blink) # longer audio? coeff_path = self.audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path) #coeff2video data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, still_mode=still_mode, \ preprocess=preprocess, size=size, expression_scale = exp_scale, facemodel=facerender) return_path = self.animate_from_coeff.generate(data, save_dir, pic_path, crop_info, enhancer='gfpgan' if use_enhancer else None, preprocess=preprocess, img_size=size) video_name = data['video_name'] print(f'The generated video is named {video_name} in {save_dir}') del self.preprocess_model del self.audio_to_coeff del self.animate_from_coeff if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.synchronize() import gc; gc.collect() return return_path