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import torch
from time import gmtime, strftime
import os, sys, shutil
from argparse import ArgumentParser
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
import uuid
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")
from modules.text2speech import text2speech
class SadTalker():
def __init__(self, checkpoint_path='checkpoints'):
if torch.cuda.is_available() :
device = "cuda"
else:
device = "cpu"
# current_code_path = sys.argv[0]
# modules_path = os.path.split(current_code_path)[0]
current_root_path = './'
os.environ['TORCH_HOME']=os.path.join(current_root_path, 'checkpoints')
path_of_lm_croper = os.path.join(current_root_path, 'checkpoints', 'shape_predictor_68_face_landmarks.dat')
path_of_net_recon_model = os.path.join(current_root_path, 'checkpoints', 'epoch_20.pth')
dir_of_BFM_fitting = os.path.join(current_root_path, 'checkpoints', 'BFM_Fitting')
wav2lip_checkpoint = os.path.join(current_root_path, 'checkpoints', 'wav2lip.pth')
audio2pose_checkpoint = os.path.join(current_root_path, 'checkpoints', 'auido2pose_00140-model.pth')
audio2pose_yaml_path = os.path.join(current_root_path, 'config', 'auido2pose.yaml')
audio2exp_checkpoint = os.path.join(current_root_path, 'checkpoints', 'auido2exp_00300-model.pth')
audio2exp_yaml_path = os.path.join(current_root_path, 'config', 'auido2exp.yaml')
free_view_checkpoint = os.path.join(current_root_path, 'checkpoints', 'facevid2vid_00189-model.pth.tar')
mapping_checkpoint = os.path.join(current_root_path, 'checkpoints', 'mapping_00229-model.pth.tar')
facerender_yaml_path = os.path.join(current_root_path, 'config', '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()) # strftime("%Y_%m_%d_%H.%M.%S")
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 = 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)
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)
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')
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