Download

A console application which takes an Anime name or URL and downloads the specified range of episodes

super-anime-downloader A console application written in Python3.x (GUI will be added soon) which takes a Anime Name/URL as input and downloads the range of episodes you specify. Supported Platforms: Instructions for Android: pkg update termux-setup-storage pkg install python git aria2 pip install requests bs4 cd /sdcard ls git clone https://www.github.com/ali-sajjad-rizavi/super-anime-downloader Everytime you need to download Anime episodes, you must open Termux and run these commands: cd /sdcard/super-anime-downloader python cli_downloader.py Enter anime name or URL and set the episode range. Anime […]

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Python

How to (not) use Docker to share your password with hackers

Do you use Docker images to run your software? Does running or building your image involve a password or other credential that you really (don’t) want to share with hackers? Well, you’re in luck, because Docker makes it really easy to share your passwords, cloud credentials, and SSH private keys with the world. Whether it’s runtime secrets, build secrets, or just some random unrelated credentials you had lying around in the wrong place, Docker’s got you covered when it comes […]

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Natural Language Processing

Identify the emotion of multiple speakers in an Audio Segment

MevonAI-Speech-Emotion-Recognition Identify the emotion of multiple speakers in a Audio Segment Getting Started Follow the Below Instructions for setting the project up on your local Machine. Installation Create a python virtual environment sudo apt install python3-venv mkdir mevonAI cd mevonAI python3 -m venv mevon-env source mevon-env/bin/activate Clone the repo git clone https://github.com/SuyashMore/MevonAI-Speech-Emotion-Recognition.git Install Dependencies cd MevonAI-Speech-Emotion-Recognition/ cd src/ sudo chmod +x setup.sh ./setup.sh Running the Application Add audio files in .wav format for analysis in src/input/ folder Run Speech Emotion […]

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Web Crawling & Web Scraping

Scrape all the media from an OnlyFans account with python

OnlyFans DataScraper (Python 3.9.X) Scrape all the media from an OnlyFans account – Updated regularly. Running the app via docker Build and run the image, mounting the appropriate directories: docker build -t only-fans . && docker run -it –rm –name onlyfans -v ${PWD}/.settings:/usr/src/app/.settings -v ${PWD}/.profiles:/usr/src/app/.profiles -v ${PWD}/.sites:/usr/src/app/.sites only-fans Running on Linux https://github.com/DIGITALCRIMINAL/OnlyFans/discussions/889 Running the app locally From the project folder open CMD/Terminal and run the command below: pip install -r requirements.txt Start: python start_ofd.py or double click start_ofd.py Open and […]

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Machine Learning

A sketch extractor for illustration, anime art, manga

Anime2Sketch Anime2Sketch: A sketch extractor for illustration, anime art, manga By Xiaoyu Xiang Introduction The repository contains the testing codes and pretrained weights for Anime2Sketch. Anime2Sketch is a sketch extractor that works well on illustration, anime art, and manga. It is an application based on the paper “Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis”. Prerequisites Get Started Installation Install the required packages: pip install -r requirements.txt Download Pretrained Weights Please download the weights from GoogleDrive, and put it into the […]

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Machine Learning

A Python library that makes it extremely easy to experiment with state-of-the-art

QuickAI QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models. Motivation When I started to get into more advanced Machine Learning, I started to see how these famous neural networkarchitectures(such as EfficientNet), were doing amazing things. However, when I tried to implement these architecturesto problems that I wanted to solve, I realized that it was not super easy to implement and quickly experiment with thesearchitectures. That is where QuickAI came in. It […]

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Machine Learning

Minimal deep learning library written from scratch in Python

SmallPebble Minimal deep learning library written from scratch in Python, using NumPy/CuPy. SmallPebble is a minimal automatic differentiation and deep learning library written from scratch in Python, using NumPy/CuPy. The implementation is relatively small, and mainly in the file: smallpebble.py. To help understand it, check out this introduction to autodiff, which presents an autodiff framework that works in the same way as SmallPebble (except using scalars instead of NumPy arrays). SmallPebble’s raison d’etre is to be a simplified deep learning […]

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RESTful API

Backlog API v2 Client Library for Python

BacklogPy BacklogPy is Backlog API v2 Client Library for Python 2/3 Install You can install the client library with pip: $ pip install BacklogPy Example The client Library has API Call methods for All Backlog v2 API: >>> from BacklogPy import Backlog >>> backlog = Backlog(‘space_name’,’api-key’) >>> response = backlog.get_project_list(all=True, archived=True) >>> print(response.json()[0]) {‘archived’: False, ‘chartEnabled’: True, ‘displayOrder’: 1234563786, ‘id’: 12345, ‘name’: ‘Coffee Project’, ‘projectKey’: ‘COFFEE_PROJECT’, ‘projectLeaderCanEditProjectLeader’: True, ‘subtaskingEnabled’: False, ‘textFormattingRule’: ‘markdown’, ‘useWikiTreeView’: True} Also you can use dict parameters […]

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Imitation learning, NLP

Issue #129 – Simultaneous MT Using Imitation Learning

06 May21 Issue #129 – Simultaneous MT Using Imitation Learning Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction For the second time in our blog series we look at Simultaneous Machine Translation (SiMT). In SiMT, translation begins before the full source input has necessarily been processed, reducing the delay as much as possible. By necessity this results in a trade off between delay and MT quality. This subject was discussed in a previous blog post. The full pipe […]

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Machine Learning

Pytorch implementation for the Dynamic Concept Learner

DCL-PyTorch Pytorch implementation for the Dynamic Concept Learner (DCL). Prerequisites Python 3 PyTorch 1.0 or higher, with NVIDIA CUDA Support Other required python packages specified by requirements.txt. See the Installation. Installation Install Jacinle: Clone the package, and add the bin path to your global PATH environment variable: git clone https://github.com/vacancy/Jacinle –recursive export PATH=/bin:$PATH Clone this repository: git clone https://github.com/zfchenUnique/DCL-Release.git –recursive Create a conda environment for NS-CL, and install the requirements. This includes the required python packagesfrom both Jacinle NS-CL. Most […]

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