app.py

import os
import time
import pdb

import gradio as gr
import spaces
import numpy as np
import sys
import subprocess

from huggingface_hub import snapshot_download

import argparse
import os
from omegaconf import OmegaConf
import numpy as np
import cv2
import torch
import glob
import pickle
from tqdm import tqdm
import copy
from argparse import Namespace

from musetalk.utils.utils import get_file_type,get_video_fps,datagen
from musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder
from musetalk.utils.blending import get_image
from musetalk.utils.utils import load_all_model
import shutil



ProjectDir = os.path.abspath(os.path.dirname(__file__))
CheckpointsDir = os.path.join(ProjectDir, "checkpoints")

def download_model():
    if not os.path.exists(CheckpointsDir):
        os.makedirs(CheckpointsDir)
        print("Checkpoint Not Downloaded, start downloading...")
        tic = time.time()
        snapshot_download(
            repo_id="TMElyralab/MuseTalk",
            local_dir=CheckpointsDir,
            max_workers=8,
            local_dir_use_symlinks=True,
        )
        toc = time.time()
        print(f"download cost {toc-tic} seconds")
    else:
        print("Already download the model.")

@spaces.GPU(duration=600)
@torch.no_grad()
def inference(audio_path,video_path,bbox_shift,progress=gr.Progress(track_tqdm=True)):
    args_dict={"result_dir":'./results', "fps":25, "batch_size":8, "output_vid_name":'', "use_saved_coord":False}#same with inferenece script
    args = Namespace(**args_dict)

    input_basename = os.path.basename(video_path).split('.')[0]
    audio_basename  = os.path.basename(audio_path).split('.')[0]
    output_basename = f"{input_basename}_{audio_basename}"
    result_img_save_path = os.path.join(args.result_dir, output_basename) # related to video & audio inputs
    crop_coord_save_path = os.path.join(result_img_save_path, input_basename+".pkl") # only related to video input
    os.makedirs(result_img_save_path,exist_ok =True)

    if args.output_vid_name=="":
        output_vid_name = os.path.join(args.result_dir, output_basename+".mp4")
    else:
        output_vid_name = os.path.join(args.result_dir, args.output_vid_name)
    ############################################## extract frames from source video ##############################################
    if get_file_type(video_path)=="video":
        save_dir_full = os.path.join(args.result_dir, input_basename)
        os.makedirs(save_dir_full,exist_ok = True)
        cmd = f"ffmpeg -v fatal -i {video_path} -start_number 0 {save_dir_full}/%08d.png"
        os.system(cmd)
        input_img_list = sorted(glob.glob(os.path.join(save_dir_full, '*.[jpJP][pnPN]*[gG]')))
        fps = get_video_fps(video_path)
    else: # input img folder
        input_img_list = glob.glob(os.path.join(video_path, '*.[jpJP][pnPN]*[gG]'))
        input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
        fps = args.fps
    #print(input_img_list)
    ############################################## extract audio feature ##############################################
    whisper_feature = audio_processor.audio2feat(audio_path)
    whisper_chunks = audio_processor.feature2chunks(feature_array=whisper_feature,fps=fps)
    ############################################## preprocess input image  ##############################################
    if os.path.exists(crop_coord_save_path) and args.use_saved_coord:
        print("using extracted coordinates")
        with open(crop_coord_save_path,'rb') as f:
            coord_list = pickle.load(f)
        frame_list = read_imgs(input_img_list)
    else:
        print("extracting landmarks...time consuming")
        coord_list, frame_list = get_landmark_and_bbox(input_img_list, bbox_shift)
        with open(crop_coord_save_path, 'wb') as f:
            pickle.dump(coord_list, f)
            
    i = 0
    input_latent_list = []
    for bbox, frame in zip(coord_list, frame_list):
        if bbox == coord_placeholder:
            continue
        x1, y1, x2, y2 = bbox
        crop_frame = frame[y1:y2, x1:x2]
        crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4)
        latents = vae.get_latents_for_unet(crop_frame)
        input_latent_list.append(latents)

    # to smooth the first and the last frame
    frame_list_cycle = frame_list + frame_list[::-1]
    coord_list_cycle = coord_list + coord_list[::-1]
    input_latent_list_cycle = input_latent_list + input_latent_list[::-1]
    ############################################## inference batch by batch ##############################################
    print("start inference")
    video_num = len(whisper_chunks)
    batch_size = args.batch_size
    gen = datagen(whisper_chunks,input_latent_list_cycle,batch_size)
    res_frame_list = []
    for i, (whisper_batch,latent_batch) in enumerate(tqdm(gen,total=int(np.ceil(float(video_num)/batch_size)))):
        
        tensor_list = [torch.FloatTensor(arr) for arr in whisper_batch]
        audio_feature_batch = torch.stack(tensor_list).to(unet.device) # torch, B, 5*N,384
        audio_feature_batch = pe(audio_feature_batch)
        
        pred_latents = unet.model(latent_batch, timesteps, encoder_hidden_states=audio_feature_batch).sample
        recon = vae.decode_latents(pred_latents)
        for res_frame in recon:
            res_frame_list.append(res_frame)
            
    ############################################## pad to full image ##############################################
    print("pad talking image to original video")
    for i, res_frame in enumerate(tqdm(res_frame_list)):
        bbox = coord_list_cycle[i%(len(coord_list_cycle))]
        ori_frame = copy.deepcopy(frame_list_cycle[i%(len(frame_list_cycle))])
        x1, y1, x2, y2 = bbox
        try:
            res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1))
        except:
    #                 print(bbox)
            continue
        
        combine_frame = get_image(ori_frame,res_frame,bbox)
        cv2.imwrite(f"{result_img_save_path}/{str(i).zfill(8)}.png",combine_frame)
        
    cmd_img2video = f"ffmpeg -y -v fatal -r {fps} -f image2 -i {result_img_save_path}/%08d.png -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p -crf 18 temp.mp4"
    print(cmd_img2video)
    os.system(cmd_img2video)

    cmd_combine_audio = f"ffmpeg -y -v fatal -i {audio_path} -i temp.mp4 {output_vid_name}"
    print(cmd_combine_audio)
    os.system(cmd_combine_audio)

    os.remove("temp.mp4")
    shutil.rmtree(result_img_save_path)
    print(f"result is save to {output_vid_name}")
    return output_vid_name

download_model()  # for huggingface deployment.


# load model weights
audio_processor,vae,unet,pe  = load_all_model()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
timesteps = torch.tensor([0], device=device)




def check_video(video):
    # Define the output video file name
    dir_path, file_name = os.path.split(video)
    if file_name.startswith("outputxxx_"):
        return video
    # Add the output prefix to the file name
    output_file_name = "outputxxx_" + file_name

    # Combine the directory path and the new file name
    output_video = os.path.join(dir_path, output_file_name)


    # Run the ffmpeg command to change the frame rate to 25fps
    command = f"ffmpeg -i {video} -r 25 {output_video}  -y"
    subprocess.run(command, shell=True, check=True)
    return output_video




css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height: 576px}"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(
        "<div align='center'> <h1>MuseTalk: Real-Time High Quality Lip Synchronization with Latent Space Inpainting </span> </h1> \
                    <h2 style='font-weight: 450; font-size: 1rem; margin: 0rem'>\
                    </br>\
                    Yue Zhang <sup>\*</sup>,\
                    Minhao Liu<sup>\*</sup>,\
                    Zhaokang Chen,\
                    Bin Wu<sup>†</sup>,\
                    Yingjie He,\
                    Chao Zhan,\
                    Wenjiang Zhou\
                    (<sup>*</sup>Equal Contribution, <sup>†</sup>Corresponding Author, benbinwu@tencent.com)\
                    Lyra Lab, Tencent Music Entertainment\
                </h2> \
                <a style='font-size:18px;color: #000000' href='https://github.com/TMElyralab/MuseTalk'>[Github Repo]</a>\
                <a style='font-size:18px;color: #000000' href='https://github.com/TMElyralab/MuseTalk'>[Huggingface]</a>\
                <a style='font-size:18px;color: #000000' href=''> [Technical report(Coming Soon)] </a>\
                <a style='font-size:18px;color: #000000' href=''> [Project Page(Coming Soon)] </a>  </div>"
    )

    with gr.Row():
        with gr.Column():
            audio = gr.Audio(label="Driven Audio",type="filepath")
            video = gr.Video(label="Reference Video")
            bbox_shift = gr.Number(label="BBox_shift,[-9,9]", value=-1)
            btn = gr.Button("Generate")
        out1 = gr.Video()
    
    video.change(
        fn=check_video, inputs=[video], outputs=[video]
    )
    btn.click(
        fn=inference,
        inputs=[
            audio,
            video,
            bbox_shift,
        ],
        outputs=out1,
    )

# Set the IP and port
ip_address = "0.0.0.0"  # Replace with your desired IP address
port_number = 7860  # Replace with your desired port number


demo.queue().launch(
    share=False , debug=True, server_name=ip_address, server_port=port_number
)