Code I use to automatically update my videos' metadata on YouTube

Overview

mCodingYouTube

This repository contains the code I use to automatically update my videos' metadata on YouTube, including: titles, descriptions, tags, etc.

mCoding YouTube channel

The code in this repository is MIT licensed, see the file named LICENSE.

Disclaimer

The code is for educational purposes, not production use. Do not run any code that you do not understand. I am not responsible if you end up deleting or otherwise irreparably damaging your YouTube account, or getting banned from YouTube/Google services by using/misusing this code.

If you do decide to play with the code, I recommend using a dummy YouTube account so that you don't put your real account in danger. Pay close attention to the amount of quota that you use in order to avoid YouTube/Google thinking you are abusing their API.

I do not condone using or modifying the code in this API to do anything that violates YouTube/Google terms of service or any applicable laws.

Official code and docs from Google/YouTube

If you would like an official set of samples for how to use the YouTube Data API in Python, see https://github.com/youtube/api-samples/tree/master/python.

The official YouTube Data API documentation (not language specific) can be found at: https://developers.google.com/youtube/v3/docs.

Trying to follow my YouTube video?

Video: I Used the YouTube API to Update My Video Descriptions

Install dependencies (execute this from the directory containing requirements.txt):

pip install -r requirements.txt

Here are the important files:

  • app_config.py: In order to avoid publishing my secret client data, I use this config to read a non-uploaded file containing the location of my secret file. If you want to modify the code to work for yourself, you can hard-code the location of your client secret file here, or use dotenv like I did.

  • youtube.py: Contains the code to make an authenticated YouTube service object. You shouldn't need to change anything in this file.

  • download_single_video_data.py: Script to download the snippet metadata to a file for a video with known video id. I recommend making a data directory and putting all your downloaded data there to avoid clutter.

  • download_my_uploads.py: Script to download the playlist item snippets for all your uploads and save each page of results to a file.

  • update_description_on_youtube.py: Functions for updating a single video description.

  • simple_prepend_to_descriptions.py: Script to load data saved using download_my_uploads.py and prepend text read from a file to all your uploaded videos by using functions from update_description_on_youtube.py in a loop.

Owner
James Murphy
I'm James Murphy, founder of mCoding. I'm interested in helping as many people learn about programming and math as possible.
James Murphy
Making a music video with Wav2CLIP and VQGAN-CLIP

music2video Overview A repo for making a music video with Wav2CLIP and VQGAN-CLIP. The base code was derived from VQGAN-CLIP The CLIP embedding for au

Joel Jang | 장요엘 163 Dec 26, 2022
[CVPR2021] The source code for our paper 《Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning》.

TBE The source code for our paper "Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Le

Jinpeng Wang 150 Dec 28, 2022
Structural Constraints on Information Content in Human Brain States

Structural Constraints on Information Content in Human Brain States Code accompanying the paper "The information content of brain states is explained

Leon Weninger 3 Sep 07, 2022
交互式标注软件,暂定名 iann

iann 交互式标注软件,暂定名iann。 安装 按照官网介绍安装paddle。 安装其他依赖 pip install -r requirements.txt 运行 git clone https://github.com/PaddleCV-SIG/iann/ cd iann python iann

294 Dec 30, 2022
Activity tragle - Google is tracking everything, we just look at it

activity_tragle Google is tracking everything, we just look at it here. You need

BERNARD Guillaume 1 Feb 15, 2022
Demonstration of the Model Training as a CI/CD System in Vertex AI

Model Training as a CI/CD System This project demonstrates the machine model training as a CI/CD system in GCP platform. You will see more detailed wo

Chansung Park 19 Dec 28, 2022
A Pytorch implementation of MoveNet from Google. Include training code and pre-train model.

Movenet.Pytorch Intro MoveNet is an ultra fast and accurate model that detects 17 keypoints of a body. This is A Pytorch implementation of MoveNet fro

Mr.Fire 241 Dec 26, 2022
[AAAI 2022] Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

[AAAI 2022] Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding Official Pytorch implementation of Negative Sample Matter

Multimedia Computing Group, Nanjing University 69 Dec 26, 2022
YolactEdge: Real-time Instance Segmentation on the Edge

YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7

Haotian Liu 1.1k Jan 06, 2023
Plenoxels: Radiance Fields without Neural Networks

Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be

Sara Fridovich-Keil 81 Dec 25, 2022
Twins: Revisiting the Design of Spatial Attention in Vision Transformers

Twins: Revisiting the Design of Spatial Attention in Vision Transformers Very recently, a variety of vision transformer architectures for dense predic

482 Dec 18, 2022
Official Repo for ICCV2021 Paper: Learning to Regress Bodies from Images using Differentiable Semantic Rendering

[ICCV2021] Learning to Regress Bodies from Images using Differentiable Semantic Rendering Getting Started DSR has been implemented and tested on Ubunt

Sai Kumar Dwivedi 83 Nov 27, 2022
An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu

Meta Research 141 Jan 07, 2023
This repository is a basic Machine Learning train & validation Template (Using PyTorch)

pytorch_ml_template This repository is a basic Machine Learning train & validation Template (Using PyTorch) TODO Markdown 사용법 Build Docker 사용법 Anacond

1 Sep 15, 2022
Unrolled Generative Adversarial Networks

Unrolled Generative Adversarial Networks Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein arxiv:1611.02163 This repo contains an example notebo

Ben Poole 292 Dec 06, 2022
OpenDILab Multi-Agent Environment

Go-Bigger: Multi-Agent Decision Intelligence Environment GoBigger Doc (中文版) Ongoing 2021.11.13 We are holding a competition —— Go-Bigger: Multi-Agent

OpenDILab 441 Jan 05, 2023
Official PyTorch Implementation of Unsupervised Learning of Scene Flow Estimation Fusing with Local Rigidity

UnRigidFlow This is the official PyTorch implementation of UnRigidFlow (IJCAI2019). Here are two sample results (~10MB gif for each) of our unsupervis

Liang Liu 28 Nov 16, 2022
PyTorch implementation of "A Two-Stage End-to-End System for Speech-in-Noise Hearing Aid Processing"

Implementation of the Sheffield entry for the first Clarity enhancement challenge (CEC1) This repository contains the PyTorch implementation of "A Two

10 Aug 19, 2022
K-FACE Analysis Project on Pytorch

Installation Setup with Conda # create a new environment conda create --name insightKface python=3.7 # or over conda activate insightKface #install t

Jung Jun Uk 7 Nov 10, 2022
Estimation of human density in a closed space using deep learning.

Siemens HOLLZOF challenge - Human Density Estimation Add project description here. Installing Dependencies: Install Python3 either system-wide, user-w

3 Aug 08, 2021