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
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth [Paper]

Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth [Paper] Downloads [Downloads] Trained ckpt files for NYU Depth V2 and

98 Jan 01, 2023
the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet]

BGNet This repository contains the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet] Environment Python 3.6.* C

3DCV developer 87 Nov 29, 2022
Air Pollution Prediction System using Linear Regression and ANN

AirPollution Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living Publication Link:

Dr Sharnil Pandya, Associate Professor, Symbiosis International University 19 Feb 07, 2022
Natural Intelligence is still a pretty good idea.

Human Learn Machine Learning models should play by the rules, literally. Project Goal Back in the old days, it was common to write rule-based systems.

vincent d warmerdam 641 Dec 26, 2022
Source code for CVPR2022 paper "Abandoning the Bayer-Filter to See in the Dark"

Abandoning the Bayer-Filter to See in the Dark (CVPR 2022) Paper: https://arxiv.org/abs/2203.04042 (Arxiv version) This code includes the training and

74 Dec 15, 2022
Repository for the NeurIPS 2021 paper: "Exploiting Domain-Specific Features to Enhance Domain Generalization".

meta-Domain Specific-Domain Invariant (mDSDI) Source code implementation for the paper: Manh-Ha Bui, Toan Tran, Anh Tuan Tran, Dinh Phung. "Exploiting

VinAI Research 12 Nov 25, 2022
In this project I played with mlflow, streamlit and fastapi to create a training and prediction app on digits

Fastapi + MLflow + streamlit Setup env. I hope I covered all. pip install -r requirements.txt Start app Go in the root dir and run these Streamlit str

76 Nov 23, 2022
Replication attempt for the Protein Folding Model

RGN2-Replica (WIP) To eventually become an unofficial working Pytorch implementation of RGN2, an state of the art model for MSA-less Protein Folding f

Eric Alcaide 36 Nov 29, 2022
Pytorch Performace Tuning, WandB, AMP, Multi-GPU, TensorRT, Triton

Plant Pathology 2020 FGVC7 Introduction A deep learning model pipeline for training, experimentaiton and deployment for the Kaggle Competition, Plant

Bharat Giddwani 0 Feb 25, 2022
A tiny, pedagogical neural network library with a pytorch-like API.

candl A tiny, pedagogical implementation of a neural network library with a pytorch-like API. The primary use of this library is for education. Use th

Sri Pranav 3 May 23, 2022
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022
Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

ColossalAI An integrated large-scale model training system with efficient parallelization techniques. arXiv: Colossal-AI: A Unified Deep Learning Syst

HPC-AI Tech 7.9k Jan 08, 2023
DLFlow is a deep learning framework.

DLFlow是一套深度学习pipeline,它结合了Spark的大规模特征处理能力和Tensorflow模型构建能力。利用DLFlow可以快速处理原始特征、训练模型并进行大规模分布式预测,十分适合离线环境下的生产任务。利用DLFlow,用户只需专注于模型开发,而无需关心原始特征处理、pipeline构建、生产部署等工作。

DiDi 152 Oct 27, 2022
PyTorch wrapper for Taichi data-oriented class

Stannum PyTorch wrapper for Taichi data-oriented class PRs are welcomed, please see TODOs. Usage from stannum import Tin import torch data_oriented =

86 Dec 23, 2022
Easily benchmark PyTorch model FLOPs, latency, throughput, max allocated memory and energy consumption

⏱ pytorch-benchmark Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption Install pip install pytor

Lukas Hedegaard 21 Dec 22, 2022
Gym environment for FLIPIT: The Game of "Stealthy Takeover"

gym-flipit Gym environment for FLIPIT: The Game of "Stealthy Takeover" invented by Marten van Dijk, Ari Juels, Alina Oprea, and Ronald L. Rivest. Desi

Lisa Oakley 2 Dec 15, 2021
Official implementation of NPMs: Neural Parametric Models for 3D Deformable Shapes - ICCV 2021

NPMs: Neural Parametric Models Project Page | Paper | ArXiv | Video NPMs: Neural Parametric Models for 3D Deformable Shapes Pablo Palafox, Aljaz Bozic

PabloPalafox 109 Nov 22, 2022
What can linearized neural networks actually say about generalization?

What can linearized neural networks actually say about generalization? This is the source code to reproduce the experiments of the NeurIPS 2021 paper

gortizji 11 Dec 09, 2022
Tensorflow implementation of MIRNet for Low-light image enhancement

MIRNet Tensorflow implementation of the MIRNet architecture as proposed by Learning Enriched Features for Real Image Restoration and Enhancement. Lanu

Soumik Rakshit 91 Jan 06, 2023
Emotion Recognition from Facial Images

Reconhecimento de Emoções a partir de imagens faciais Este projeto implementa um classificador simples que utiliza técncias de deep learning e transfe

Gabriel 2 Feb 09, 2022