Spaces:
Running
Running
File size: 4,274 Bytes
8d11d43 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from latentsync.utils.util import write_video
from latentsync.utils.image_processor import VideoProcessor
import torch
import os
import subprocess
from multiprocessing import Process
import shutil
paths = []
def gather_video_paths(input_dir, output_dir):
for video in sorted(os.listdir(input_dir)):
if video.endswith(".mp4"):
video_input = os.path.join(input_dir, video)
video_output = os.path.join(output_dir, video)
if os.path.isfile(video_output):
continue
paths.append((video_input, video_output))
elif os.path.isdir(os.path.join(input_dir, video)):
gather_video_paths(os.path.join(input_dir, video), os.path.join(output_dir, video))
def combine_video_audio(video_frames, video_input_path, video_output_path, process_temp_dir):
video_name = os.path.basename(video_input_path)[:-4]
audio_temp = os.path.join(process_temp_dir, f"{video_name}_temp.wav")
video_temp = os.path.join(process_temp_dir, f"{video_name}_temp.mp4")
write_video(video_temp, video_frames, fps=25)
command = f"ffmpeg -y -loglevel error -i {video_input_path} -q:a 0 -map a {audio_temp}"
subprocess.run(command, shell=True)
os.makedirs(os.path.dirname(video_output_path), exist_ok=True)
command = f"ffmpeg -y -loglevel error -i {video_temp} -i {audio_temp} -c:v libx264 -c:a aac -map 0:v -map 1:a -q:v 0 -q:a 0 {video_output_path}"
subprocess.run(command, shell=True)
os.remove(audio_temp)
os.remove(video_temp)
def func(paths, process_temp_dir, device_id, resolution):
os.makedirs(process_temp_dir, exist_ok=True)
video_processor = VideoProcessor(resolution, f"cuda:{device_id}")
for video_input, video_output in paths:
if os.path.isfile(video_output):
continue
try:
video_frames = video_processor.affine_transform_video(video_input)
except Exception as e: # Handle the exception of face not detcted
print(f"Exception: {e} - {video_input}")
continue
os.makedirs(os.path.dirname(video_output), exist_ok=True)
combine_video_audio(video_frames, video_input, video_output, process_temp_dir)
print(f"Saved: {video_output}")
def split(a, n):
k, m = divmod(len(a), n)
return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))
def affine_transform_multi_gpus(input_dir, output_dir, temp_dir, resolution, num_workers):
print(f"Recursively gathering video paths of {input_dir} ...")
gather_video_paths(input_dir, output_dir)
num_devices = torch.cuda.device_count()
if num_devices == 0:
raise RuntimeError("No GPUs found")
if os.path.exists(temp_dir):
shutil.rmtree(temp_dir)
os.makedirs(temp_dir, exist_ok=True)
split_paths = list(split(paths, num_workers * num_devices))
processes = []
for i in range(num_devices):
for j in range(num_workers):
process_index = i * num_workers + j
process = Process(
target=func, args=(split_paths[process_index], os.path.join(temp_dir, f"process_{i}"), i, resolution)
)
process.start()
processes.append(process)
for process in processes:
process.join()
if __name__ == "__main__":
input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/VoxCeleb2/segmented"
output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/VoxCeleb2/affine_transformed"
temp_dir = "temp"
resolution = 256
num_workers = 10 # How many processes per device
affine_transform_multi_gpus(input_dir, output_dir, temp_dir, resolution, num_workers)
|