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Delete animation/SadTalker directory

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Justin John
2023-10-13 08:07:28 +05:30
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parent 3fe3d12bb2
commit e319fd2f94
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animation/SadTalker/src/gradio_demo.py
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import torch, uuid
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.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"
else:
device = "cpu"
self.device = device
os.environ['TORCH_HOME']= checkpoint_path
self.checkpoint_path = checkpoint_path
self.config_path = config_path
def test(self, source_image, driven_audio, preprocess='crop',
still_mode=False, use_enhancer=False, batch_size=1, size=256,
pose_style = 0, 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)
self.animate_from_coeff = AnimateFromCoeff(self.sadtalker_paths, self.device)
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)
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