使用深度学习框架提取视频硬字幕；docker容器免安装深度学习库，使用本地api接口使得界面和后端识别分离；

Related tags

Deep Learningdockersubtittles
Overview

extract-video-subtittle

使用深度学习框架提取视频硬字幕；

本地识别无需联网；

CPU识别速度可观；

容器提供API接口；

运行环境

本项目运行环境非常好搭建，我做好了docker容器免安装各种深度学习包；

提供windows界面操作；

容器为CPU版本；

视频演示

https://www.bilibili.com/video/BV18Q4y1f774/

程序说明

1、先启动后端容器实例

docker run -d -p 6666:6666 m986883511/extract_subtitles

image-20210801214757813

2、启动程序

简单介绍页面

1：点击左边按钮连接第一步启动的容器；

2：视频提取字幕的总进度

3：当前视频帧显示的位置，就是视频进度条

4：识别出来的文字会在这里显示一下

image-20210801215010179

image-20210801215258761

3、点击选择视频确认字幕位置

点击选择视频按钮，这时你可以拖动进度条到有字幕的位置；然后点击选择字幕区域；在视频中画一个矩形；

image-20210801215258761

4、点击测试连接API

image-20210801220206554

后端没问题的话，会显示已连通；此时所有步骤准备就绪

5、开始识别

点击请先完成前几步按钮，内部分为这几个步骤

  1. 本地通过ffmpeg提取视频声音保存到temp目录（0%-10%）
  2. api通信将声音文件发送到容器内，容器内spleeter库提取声音中人声，结果保存在容器内temp目录，很耗时间，吃CPU和内存（10%-30）
  3. api通信，将人声根据停顿分片，返回分片结果，耗较短的时间（30%-40%）
  4. 根据说话分片时间开始识别字幕（40-%100%）

当100%的时候查看temp目录就生成了和视频同名的srt字幕文件

运行后台

后端接口容器地址Docker Hub

此过程可能时间较长，您需要预先安装好好docker，并配置好docker加速器，你可能需要先docker login

docker run -d -p 6666:6666 m986883511/extract_subtitles

本项目缺少文件

因网速墙的问题，大文件推送不上去，可以参考.gitignore中写的

其他

视频提取

# 视频片段提取
ffmpeg -ss 00:15:45 -t 00:02:15 -i test/three_body_3_7.mp4 -vcodec copy -acodec copy test/3body.mp4
# 打包界面程序
C:/Python/Python38-32/Scripts/pyinstaller.exe main.spec

参考资料

本项目中深度学习源代码为/docker/backend

原作者为：https://github.com/YaoFANGUK/video-subtitle-extractor

You might also like...
Comments
  • 提取人声一直没结果

    提取人声一直没结果

    image 视频是40多分钟的连续剧。CPU版本。之前用YaoFANGUK/video-subtitle-extractor提取字幕很成功也准确，但时间比较长。看到作者用音频分析减少了识别的帧数，所以试了一下。但在提取人声时，已经等待了近50分钟没有结果。而且CPU的占用只有1%左右，这明显不正常。用YaoFANGUK/video-subtitle-extractor整个的耗时可能都没有这么久。另外autosub也是提取音频来语音识别字幕，识别人声也很快，同样的视频几分钟就完了。麻烦作者看看是出了什么问题呢。

    opened by royzengyi 2
  • 项目咨询

    项目咨询

    Hello，我尝试了一下这个软件，感觉还是不错的，不过在实际使用中还是会有不少问题。

    我是一个独立开发者，这边愿意付费或者合作来完善一下，让这个项目更具实用性，不知道你有没有兴趣呢?

    没有找到联系方式，只好通过issue来试一下，你可以在看到之后删除，谢谢。

    我的邮箱是yedaxia#foxmail.com

    opened by YeDaxia 1
Releases(0.2.0)
Owner
歌者
失去人性，失去很多；失去兽性，失去一切；活着才能燃烧自己。
歌者
GitHub Repository
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods

ADGC: Awesome Deep Graph Clustering ADGC is a collection of state-of-the-art (SOTA), novel deep graph clustering methods (papers, codes and datasets).

yueliu1999 297 Dec 27, 2022
Benchmarks for Model-Based Optimization

Design-Bench Design-Bench is a benchmarking framework for solving automatic design problems that involve choosing an input that maximizes a black-box

Brandon Trabucco 43 Dec 20, 2022
Target Propagation via Regularized Inversion

Target Propagation via Regularized Inversion The present code implements an ideal formulation of target propagation using regularized inverses compute

Vincent Roulet 0 Dec 02, 2021
Source Code For Template-Based Named Entity Recognition Using BART

Template-Based NER Source Code For Template-Based Named Entity Recognition Using BART Training Training train.py Inference inference.py Corpus ATIS (h

174 Dec 19, 2022
Pytorch domain adaptation package

DomainAdaptation This package is created to tackle the problem of domain shifts when dealing with two domains of different feature distributions. In d

Institute of Computational Perception 7 Oct 22, 2022
Data-driven reduced order modeling for nonlinear dynamical systems

SSMLearn Data-driven Reduced Order Models for Nonlinear Dynamical Systems This package perform data-driven identification of reduced order model based

Haller Group, Nonlinear Dynamics 27 Dec 13, 2022
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning

TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning Authors: Yixuan Su, Fangyu Liu, Zaiqiao Meng, Lei Shu, Ehsan Shareghi, and Nig

Yixuan Su 79 Nov 04, 2022
Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted)

NLOS-OT Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted) Description In this reposit

Ruixu Geng(耿瑞旭) 16 Dec 16, 2022
PyTorch Autoencoders - Implementing a Variational Autoencoder (VAE) Series in Pytorch.

PyTorch Autoencoders Implementing a Variational Autoencoder (VAE) Series in Pytorch. Inspired by this repository Model List check model paper conferen

Subin An 8 Nov 21, 2022
auto-tuning momentum SGD optimizer

YellowFin YellowFin is an auto-tuning optimizer based on momentum SGD which requires no manual specification of learning rate and momentum. It measure

Jian Zhang 288 Nov 19, 2022
Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples

Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples (WACV 2022) and Beyond Simple Meta-Learning: Multi-Purpose Model

PLAI Group at UBC 42 Dec 06, 2022
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

This is the Vowpal Wabbit fast online learning code. Why Vowpal Wabbit? Vowpal Wabbit is a machine learning system which pushes the frontier of machin

Vowpal Wabbit 8.1k Jan 06, 2023
Implementation of Monocular Direct Sparse Localization in a Prior 3D Surfel Map (DSL)

DSL Project page: https://sites.google.com/view/dsl-ram-lab/ Monocular Direct Sparse Localization in a Prior 3D Surfel Map Authors: Haoyang Ye, Huaiya

Haoyang Ye 93 Nov 30, 2022
Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

SETR - Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official

zhaohu xing 112 Dec 16, 2022
A hifiasm fork for metagenome assembly using Hifi reads.

hifiasm_meta - de novo metagenome assembler, based on hifiasm, a haplotype-resolved de novo assembler for PacBio Hifi reads.

44 Jul 10, 2022
Unofficial PyTorch Implementation of AHDRNet (CVPR 2019)

AHDRNet-PyTorch This is the PyTorch implementation of Attention-guided Network for Ghost-free High Dynamic Range Imaging (CVPR 2019). The official cod

Yutong Zhang 4 Sep 08, 2022
An implementation of MobileFormer

MobileFormer An implementation of MobileFormer proposed by Yinpeng Chen, Xiyang Dai et al. Including [1] Mobile-Former proposed in:

slwang9353 62 Dec 28, 2022
ML-based medical imaging using Azure

Disclaimer This code is provided for research and development use only. This code is not intended for use in clinical decision-making or for any other

Microsoft Azure 68 Dec 23, 2022
Implementation of accepted AAAI 2021 paper: Deep Unsupervised Image Hashing by Maximizing Bit Entropy

Deep Unsupervised Image Hashing by Maximizing Bit Entropy This is the PyTorch implementation of accepted AAAI 2021 paper: Deep Unsupervised Image Hash

62 Dec 30, 2022
Let Python optimize the best stop loss and take profits for your TradingView strategy.

TradingView Machine Learning TradeView is a free and open source Trading View bot written in Python. It is designed to support all major exchanges. It

Robert Roman 473 Jan 09, 2023