SadTalker / app.py
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import os
import platform
import uuid
import shutil
from pydub import AudioSegment
import spaces
import torch
import gradio as gr
from huggingface_hub import snapshot_download
from examples.get_examples import get_examples
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
checkpoint_path = 'checkpoints'
config_path = 'src/config'
device = "cuda" if torch.cuda.is_available(
) else "mps" if platform.system() == 'Darwin' else "cpu"
os.environ['TORCH_HOME'] = checkpoint_path
snapshot_download(repo_id='vinthony/SadTalker-V002rc',
local_dir=checkpoint_path, local_dir_use_symlinks=True)
def mp3_to_wav(mp3_filename, wav_filename, frame_rate):
AudioSegment.from_file(file=mp3_filename).set_frame_rate(
frame_rate).export(wav_filename, format="wav")
@spaces.GPU()
def generate_video(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/'):
# Initialize models and paths
sadtalker_paths = init_path(
checkpoint_path, config_path, size, False, preprocess)
audio_to_coeff = Audio2Coeff(sadtalker_paths, device)
preprocess_model = CropAndExtract(sadtalker_paths, device)
animate_from_coeff = AnimateFromCoeff(sadtalker_paths, device) if facerender == 'facevid2vid' and device != 'mps' \
else AnimateFromCoeff_PIRender(sadtalker_paths, device)
# Create directories for saving results
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)
# Process source image
pic_path = os.path.join(input_dir, os.path.basename(source_image))
shutil.move(source_image, input_dir)
# Process driven audio
if driven_audio and os.path.isfile(driven_audio):
audio_path = os.path.join(input_dir, os.path.basename(driven_audio))
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')
AudioSegment.silent(
duration=1000*length_of_audio).export(audio_path, format="wav")
else:
assert use_ref_video and ref_info == 'all'
# Process reference video
if use_ref_video and ref_info == 'all':
ref_video_videoname = os.path.splitext(os.path.split(ref_video)[-1])[0]
audio_path = os.path.join(save_dir, ref_video_videoname+'.wav')
os.system(
f"ffmpeg -y -hide_banner -loglevel error -i {ref_video} {audio_path}")
ref_video_frame_dir = os.path.join(save_dir, ref_video_videoname)
os.makedirs(ref_video_frame_dir, exist_ok=True)
ref_video_coeff_path, _, _ = preprocess_model.generate(
ref_video, ref_video_frame_dir, preprocess, source_image_flag=False)
else:
ref_video_coeff_path = None
# Preprocess source 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 = preprocess_model.generate(
pic_path, first_frame_dir, preprocess, True, size)
if first_coeff_path is None:
raise AttributeError("No face is detected")
# Determine reference coefficients
ref_pose_coeff_path, ref_eyeblink_coeff_path = None, None
if use_ref_video:
if ref_info == 'pose':
ref_pose_coeff_path = ref_video_coeff_path
elif ref_info == 'blink':
ref_eyeblink_coeff_path = ref_video_coeff_path
elif ref_info == 'pose+blink':
ref_pose_coeff_path = ref_eyeblink_coeff_path = ref_video_coeff_path
else:
ref_pose_coeff_path = ref_eyeblink_coeff_path = None
# Generate coefficients from audio or reference video
if use_ref_video and ref_info == 'all':
coeff_path = ref_video_coeff_path
else:
batch = get_data(first_coeff_path, audio_path, 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)
coeff_path = audio_to_coeff.generate(
batch, save_dir, pose_style, ref_pose_coeff_path)
# Generate video from coefficients
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 = 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}')
return return_path
# Gradio UI
with gr.Blocks(analytics_enabled=False) as demo:
with gr.Row():
with gr.Column(variant='panel'):
with gr.Tabs(elem_id="sadtalker_source_image"):
with gr.TabItem('Source image'):
with gr.Row():
source_image = gr.Image(
label="Source image", sources="upload", type="filepath", elem_id="img2img_image")
with gr.Tabs(elem_id="sadtalker_driven_audio"):
with gr.TabItem('Driving Methods'):
gr.Markdown(
"Possible driving combinations: <br> 1. Audio only 2. Audio/IDLE Mode + Ref Video(pose, blink, pose+blink) 3. IDLE Mode only 4. Ref Video only (all) ")
with gr.Row():
driven_audio = gr.Audio(
label="Input audio", sources="upload", type="filepath")
driven_audio_no = gr.Audio(
label="Use IDLE mode, no audio is required", sources="upload", type="filepath", visible=False)
with gr.Column():
use_idle_mode = gr.Checkbox(
label="Use Idle Animation")
length_of_audio = gr.Number(
value=5, label="The length(seconds) of the generated video.")
use_idle_mode.change(lambda choice: (gr.update(visible=not choice), gr.update(visible=choice)),
inputs=use_idle_mode, outputs=[driven_audio, driven_audio_no])
with gr.Row():
ref_video = gr.Video(
label="Reference Video", sources="upload", elem_id="vidref")
with gr.Column():
use_ref_video = gr.Checkbox(
label="Use Reference Video")
ref_info = gr.Radio(['pose', 'blink', 'pose+blink', 'all'], value='pose', label='Reference Video',
info="How to borrow from reference Video?((fully transfer, aka, video driving mode))")
ref_video.change(lambda path: gr.update(
value=path is not None), inputs=ref_video, outputs=use_ref_video)
with gr.Column(variant='panel'):
with gr.Tabs(elem_id="sadtalker_checkbox"):
with gr.TabItem('Settings'):
with gr.Column(variant='panel'):
with gr.Row():
pose_style = gr.Slider(
minimum=0, maximum=45, step=1, label="Pose style", value=0)
exp_weight = gr.Slider(
minimum=0, maximum=3, step=0.1, label="expression scale", value=1)
blink_every = gr.Checkbox(
label="use eye blink", value=True)
with gr.Row():
size_of_image = gr.Radio(
[256, 512], value=256, label='face model resolution', info="use 256/512 model?")
preprocess_type = gr.Radio(
['crop', 'resize', 'full', 'extcrop', 'extfull'], value='crop', label='preprocess', info="How to handle input image?")
with gr.Row():
is_still_mode = gr.Checkbox(
label="Still Mode (fewer head motion, works with preprocess `full`)")
facerender = gr.Radio(
['facevid2vid', 'pirender'], value='facevid2vid', label='facerender', info="which face render?")
with gr.Row():
batch_size = gr.Slider(
label="batch size in generation", step=1, maximum=10, value=1)
enhancer = gr.Checkbox(
label="GFPGAN as Face enhancer")
submit = gr.Button(
'Generate', elem_id="sadtalker_generate", variant='primary')
with gr.Tabs(elem_id="sadtalker_generated"):
gen_video = gr.Video(label="Generated video")
submit.click(
fn=generate_video,
inputs=[source_image, driven_audio, preprocess_type, is_still_mode, enhancer, batch_size, size_of_image,
pose_style, facerender, exp_weight, use_ref_video, ref_video, ref_info, use_idle_mode, length_of_audio, blink_every],
outputs=[gen_video],
)
with gr.Row():
gr.Examples(examples=get_examples(), inputs=[source_image, driven_audio, preprocess_type, is_still_mode, enhancer],
outputs=[gen_video], fn=generate_video)
demo.launch(debug=True)