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 […]

May 6, 2021 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 […]

May 6, 2021 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 […]

May 6, 2021 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 […]

May 6, 2021 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 […]

May 6, 2021 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 […]

May 6, 2021 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 […]

May 5, 2021 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 […]

May 5, 2021 Tool

Python and OpenCV-based scene cut/transition detection program & library

PySceneDetect Video Scene Cut Detection and Analysis Tool. Quick Install: To install PySceneDetect via pip with all dependencies: pip install scenedetect[opencv] For servers, you can use the headless (non-GUI) version of OpenCV by installing scenedetect[opencv-headless]. To enable video splitting support, you will also need to have mkvmerge or ffmpeg installed – see the documentation on Video Splitting Support for details. Requires Python modules click, numpy, OpenCV cv2, and (optional) tqdm for displaying progress. For details, see the dependencies on the […]

May 5, 2021 NLP

WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

Abstract We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects or low-resource languages. We systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences for 1620 different language pairs, out of which only 34M are aligned with English. This corpus is freely available. To get an indication on the quality of the extracted bitexts, we train neural […]

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