stf_utils_ori.py

import torch
import os
from concurrent.futures import ThreadPoolExecutor
from pydub import AudioSegment
import cv2
from pathlib import Path
import subprocess
from pathlib import Path
import av
import imageio
import numpy as np
from rich.progress import track
from tqdm import tqdm

import stf_alternative



def exec_cmd(cmd):
    subprocess.run(
        cmd, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT
    )


def images2video(images, wfp, **kwargs):
    fps = kwargs.get("fps", 24)
    video_format = kwargs.get("format", "mp4")  # default is mp4 format
    codec = kwargs.get("codec", "libx264")  # default is libx264 encoding
    quality = kwargs.get("quality")  # video quality
    pixelformat = kwargs.get("pixelformat", "yuv420p")  # video pixel format
    image_mode = kwargs.get("image_mode", "rgb")
    macro_block_size = kwargs.get("macro_block_size", 2)
    ffmpeg_params = ["-crf", str(kwargs.get("crf", 18))]

    writer = imageio.get_writer(
        wfp,
        fps=fps,
        format=video_format,
        codec=codec,
        quality=quality,
        ffmpeg_params=ffmpeg_params,
        pixelformat=pixelformat,
        macro_block_size=macro_block_size,
    )

    n = len(images)
    for i in track(range(n), description="writing", transient=True):
        if image_mode.lower() == "bgr":
            writer.append_data(images[i][..., ::-1])
        else:
            writer.append_data(images[i])

    writer.close()

    # print(f':smiley: Dump to {wfp}\n', style="bold green")
    print(f"Dump to {wfp}\n")


def merge_audio_video(video_fp, audio_fp, wfp):
    if osp.exists(video_fp) and osp.exists(audio_fp):
        cmd = f"ffmpeg -i {video_fp} -i {audio_fp} -c:v copy -c:a aac {wfp} -y"
        exec_cmd(cmd)
        print(f"merge {video_fp} and {audio_fp} to {wfp}")
    else:
        print(f"video_fp: {video_fp} or audio_fp: {audio_fp} not exists!")




class STFPipeline:
    def __init__(self,
                 stf_path: str = "/home/user/app/stf/",
                 device: str = "cuda:0",
                 template_video_path: str = "templates/front_one_piece_dress_nodded_cut.webm",
                 config_path: str = "front_config.json",
                 checkpoint_path: str = "089.pth",
                 root_path: str = "works"
                 
    ):
        
        config_path = os.path.join(stf_path, config_path)
        checkpoint_path = os.path.join(stf_path, checkpoint_path)
        work_root_path = os.path.join(stf_path, root_path)
        
        model = stf_alternative.create_model(
        config_path=config_path,
        checkpoint_path=checkpoint_path,
        work_root_path=work_root_path,
        device=device,
        wavlm_path="microsoft/wavlm-large",
        )
        self.template = stf_alternative.Template(
        model=model,
        config_path=config_path,
        template_video_path=template_video_path,
        )
    

    def execute(self, audio: str):
        Path("dubbing").mkdir(exist_ok=True)
        save_path = os.path.join("dubbing", Path(audio).stem+"--lip.mp4")
        reader = iter(self.template._get_reader(num_skip_frames=0))
        audio_segment = AudioSegment.from_file(audio)
        pivot = 0
        results = []
        with ThreadPoolExecutor(4) as p:
            try:

                gen_infer = self.template.gen_infer_concurrent(
                    p,
                    audio_segment,
                    pivot,
                )
                for idx, (it, chunk) in enumerate(gen_infer, pivot):
                    frame = next(reader)
                    composed = self.template.compose(idx, frame, it)
                    frame_name = f"{idx}".zfill(5)+".jpg"
                    results.append(it['pred'])
                pivot = idx + 1
            except StopIteration as e:
                pass
            
        images2video(results, save_path)
                                
        return save_path