app.py

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)