Automation

A Simplified Automated CLI tool for GIT

GitFun A Simplified Automated CLI tool for GIT, It’s for Lazy Developers and Newbies Installation It’s simple step to install Pre-requisites: Python > = 3.7 Python Package Installer(PyPI) pip install gitfun For initial push to the remote Repository fun pushbranch -url -m commit message -b branch name For checking the git status fun status For checking the remote fun remote For getting a pull from the branch fun pullbranch -url -b branch name for development setup clone the repository git […]

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Hardware

Upgrade MIB2 STD ZR Firmware without Navigation

mib2std-zr-firmware-upgrade This repository contains some scripts and documentation how to upgrade the MIB2 firmware to a different HW train (e.g.: 02xx -> 03xx, 04xx). The documentation is for the Technisat MIB2 STD unit without navigation. It describes how to patch the swdownload binary, that the unit accepts updates for a higher HW train.In addition to this repository it’s required to have access to the MIB Solutions folder on MEGA.nz (Link can be found in various forums and is changing from […]

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Scripts

A python script for 1-D array data classification

Keras-1D-NN-Classifier This code is based on the reference codes linked below. reference 1, reference 2 This code is for 1-D array data classification. The given data in ‘data’ directory is simple data for training and testing this code. About this code This code is iterated by changing the’learn rate’ variable to find the optimal learning rate. The related part is the code below. var = [ 4e-5,8e-5, 12e-5] for i in range(len(var)): var_str = ‘lr replay %d th’ % i […]

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Transformer

Unofficial TensorFlow implementation of the Keyword Spotting Transformer model

Keyword Spotting Transformer This is the unofficial TensorFlow implementation of the Keyword Spotting Transformer model. This model is used to train on the 35 words speech command dataset Paper : Keyword Transformer: A Self-Attention Model for Keyword Spotting Model architecture Download the dataset To download the dataset use the following command wget https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.02.tar.gz mkdir data mv ./speech_commands_v0.02.tar.gz ./data cd ./data tar -xf ./speech_commands_v0.02.tar.gz cd ../ Setup virtual environment virtualenv -p python3 venv source ./venv/bin/activate Install dependencies pip install -r requirements.txt […]

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Workflow

AI and Machine Learning workflows on Anthos Bare Metal

AI and Machine Learning workflows using TensorFlow on Anthos Bare Metal. TensorFlow is one of the most popular ML frameworks (10M+ downloads per month) in use today, but at the same time presents a lot of challenges when it comes to setup (GPUs, CUDA Drivers, TF Serving etc), performance tuning, cluster provisioning, maintenance, and model serving. This work will showcase the easy to use guides for ML model serving, training, infrastructure, ML Notebooks, and more on Anthos Bare Metal. Terraform […]

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Wrapper

A simple request wrapper for the Pancake-Swap API

Python Pancakes A simple request wrapper for the Pancake-Swap API. Installation Install package # Using pip $ pip install pythonpancakes # Or from source $ git clone https://github.com/scottburlovich/pythonpancakes.git pythonpancakes $ cd pythonpancakes $ python3 setup.py install Import module into your project and initialize API class from pythonpancakes import PancakeSwapAPI ps = PancakeSwapAPI() Usage Please note, the API functionality currently exposed by PancakeSwap is quite basic. This package will be updated as they add new functionality. summary() Returns a dictionary containing […]

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Speech To Text

Chimera: Learning Shared Semantic Space for Speech-to-Text Translation

This is a Pytorch implementation for the “Chimera” paper Learning Shared Semantic Space for Speech-to-Text Translation https://arxiv.org/abs/2105.03095 (accepted by ACL Findings 2021), which aims to bridge the modality gap by unifying the task of MT (textual Machine Translation) and ST (Speech-to-Text Translation). It has achieved new SOTA performance on all 8 language pairs in MuST-C benchmark, by utilizing an external MT corpus. ![](https://github.com/Glaciohound/Chimera-ST/raw/main/chimera/resources/figs/method.png =100%x) This repository is up to now a nightly version, and is bug-prone because of code refactoring. […]

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Visualization

Propuestas electorales de los candidatos a constituyentes, Chile 2021

textos-constituyentes propuestas electorales de los candidatos a constituyentes, Chile 2021 Programas descargados desde https://elecciones2021.servel.cl/informacion-general/ Metodología/Programación Inicialmente, bastaba con utilizar beautifulsoup + fitz en Python para descargar los archivos en formato PDF y extraer sus textos. Luego notamos que venían muchos vacíos, por corresponder a PDF escaneados (como el notable caso de una candidata del distrito 15 que subió su diploma). Para resolver este problema, recurrimos al servicio Transcribe de Amazon Cloud (con la librería boto3). GitHub https://github.com/sergiolucero/textos-constituyentes    

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Deep Learning

Guide & Examples to create deep learning gstreamer plugins and use them in your pipeline

Guide & Examples to create deeplearning gstreamer plugins and use them in your pipeline Thanks to the work done by @jackersson. In this repository we have taken inspirations from: gstreamer-python gst-plugins-tf We have currently tested our code on Ubuntu 18.04. You can also refer to the official installation document for your linux flavor. Packages Required sudo apt install cmake m4 git build-essential sudo apt install libssl-dev libcurl4-openssl-dev liblog4cplus-dev Gstreamer Installation sudo apt-get install libgstreamer1.0-0 gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-doc […]

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Matplotlib

A Python library for plotting hockey rinks with Matplotlib

Hockey Rink A Python library for plotting hockey rinks with Matplotlib. Installation pip install hockey_rink Current Rinks The following shows the custom rinks currently available for plotting. from hockey_rink import NHLRink, IIHFRink, NWHLRink import matplotlib.pyplot as plt fig, axs = plt.subplots(1, 3, sharey=True, figsize=(12, 6), gridspec_kw={“width_ratios”: [1, 98.4/85, 1]}) nhl_rink = NHLRink(rotation=90) iihf_rink = IIHFRink(rotation=90) nwhl_rink = NWHLRink(rotation=90) axs[0] = nhl_rink.draw(ax=axs[0]) axs[1] = iihf_rink.draw(ax=axs[1]) axs[2] = nwhl_rink.draw(ax=axs[2]) The NWHL logo comes from the NWHL site. Customization There is also room […]

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