DesktopTranslator

Local cross-platform machine translation GUI, based on CTranslate2

Download Windows Installer

You can either download a ready-made Windows executable installer for DesktopTranslator, or build an installer yourself.

Translation Models

Currently, DesktopTranslator supports CTranslate2 models, and SentencePiece subwording models (you need both). If you have a model for OpenNMT-py, OpenNMT-tf, or FairSeq, you can convert it to a CTranslate2 format.

If you would like to try out the app and you do not have a model, you can download my French-to-English generic model here.

Unzip the fren.zip archive of the French-to-English generic model you just downloaded. It has two folders, ct2_model for the CTranslate2 model and sp_model for the SentencePiece subwording models of French (source) and English (target).
In DesktopTranslator, click the CTranslate2 Model button, and select the ct2_model folder.
Click the SentencePiece Model button, navigate to the sp_model folder, and select fr.model.
In the left input text-area, type some text in French or use the File menu > Open... to open a *.txt file.
Click the Translate button.

Build Windows Installer

If you want to adjust the code and then build an installer yourself, you can follow these steps:

Install PyInstaller:

pip3 install pyinstaller

To use PyInstaller, specify the Python file name and the argument -w to hide the console window:

pyinstaller -y -w "translator.py"

Try the *.exe file under "dist\translator" to make sure it works. It might complain about the Pmw library. The solution is either remove the Balloon lines, or add this file to the same folder as the translate.py and run the aforementioned PyInstaller command again.
Compress the contents of the “dist” directory created by PyInstaller into a *.zip archive.
Download and install NSIS.
Launch NSIS, click Installer based on a .ZIP file, and then click Open to locate the *.zip archive you have just created.
If you want to make the files installed (extracted) to the “Program Files” of the target user, in the Default Folder enter $PROGRAMFILES
If you want to add a shortcut to the internal *.exe file on the Desktop after installation, you can add something like this to the file “Modern.nsh” located at: "C:\Program Files\NSIS\Contrib\zip2exe". Depending on your OS, the path could be at “Program Files (x86)”. Note that the exe path should be consistent with the path you selected under NSIS’s “Default Folder” drop-down menu, the folder name, and the exe file name.

Section "Desktop Shortcut" SectionX
    SetShellVarContext current
    CreateShortCut "$DESKTOP\DesktopTranslator.lnk" "$PROGRAMFILES\DesktopTranslator\translator.exe"
SectionEnd

Finally, click the NSIS Generate button, which will create the *.exe installer that can be shipped to other Windows machines, without the need to install any extra requirements.
After installation, if you applied step #8, you should find an icon on the Desktop. To uninstall, you can simple remove the app forlder from "Program Files". For more NSIS options, check this example.

Open Source Neural Machine Translation in PyTorch

OpenNMT-py: Open-Source Neural Machine Translation OpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine trans

4.8k Feb 18, 2021

Yet Another Neural Machine Translation Toolkit

YANMTT YANMTT is short for Yet Another Neural Machine Translation Toolkit. For a backstory how I ended up creating this toolkit scroll to the bottom o

121 Jan 5, 2023

PyTorch Implementation of "Non-Autoregressive Neural Machine Translation"

Non-Autoregressive Transformer Code release for Non-Autoregressive Neural Machine Translation by Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K.

261 Nov 12, 2022

This python module is an easy-to-use port of the text normalization used in the paper "Not low-resource anymore: Aligner ensembling, batch filtering, and new datasets for Bengali-English machine translation". It is intended to be used for normalizing / cleaning Bengali and English text.

normalizer This python module is an easy-to-use port of the text normalization used in the paper "Not low-resource anymore: Aligner ensembling, batch

23 Nov 30, 2022

Free and Open Source Machine Translation API. 100% self-hosted, offline capable and easy to setup.

Seq2seq attn - Use the Seq2Seq method to implement machine translation and introduce Attention mechanism to improve the results

Seq2seq_attn Use the Seq2Seq method to implement machine translation and use the

1 Jun 28, 2022

Implementaion of our ACL 2022 paper Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation

Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation This is the implementaion of our paper: Bridging the

20 Dec 12, 2022

Local cross-platform machine translation GUI, based on CTranslate2

Related tags

Overview

DesktopTranslator

Download Windows Installer

Translation Models

Build Windows Installer

You might also like...

Open Source Neural Machine Translation in PyTorch

Yet Another Neural Machine Translation Toolkit

PyTorch Implementation of "Non-Autoregressive Neural Machine Translation"

This python module is an easy-to-use port of the text normalization used in the paper "Not low-resource anymore: Aligner ensembling, batch filtering, and new datasets for Bengali-English machine translation". It is intended to be used for normalizing / cleaning Bengali and English text.

Free and Open Source Machine Translation API. 100% self-hosted, offline capable and easy to setup.

Training open neural machine translation models

Learning to Rewrite for Non-Autoregressive Neural Machine Translation

Seq2seq attn - Use the Seq2Seq method to implement machine translation and introduce Attention mechanism to improve the results

Implementaion of our ACL 2022 paper Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation

Releases(v0.2.1)

v0.2.1(Feb 19, 2022)

Added

Fixed

v0.1.0(Feb 19, 2022)

First Release of DesktopTranslator

Owner

Yasmin Moslem

XLNet: Generalized Autoregressive Pretraining for Language Understanding

天池中药说明书实体识别挑战冠军方案；中文命名实体识别；NER; BERT-CRF & BERT-SPAN & BERT-MRC；Pytorch

apple's universal binaries BUT MUCH WORSE (PRACTICAL SHITPOST) (NOT PRODUCTION READY)

Unofficial Python library for using the Polish Wordnet (plWordNet / Słowosieć)

NAACL 2022: MCSE: Multimodal Contrastive Learning of Sentence Embeddings

TextFlint is a multilingual robustness evaluation platform for natural language processing tasks,

DeLighT: Very Deep and Light-Weight Transformers

Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper "Statistically Signifigant Stopping of Neural Network Training"

Rootski - Full codebase for rootski.io (without the data)

Implementation of the Hybrid Perception Block and Dual-Pruned Self-Attention block from the ITTR paper for Image to Image Translation using Transformers

Rich Prosody Diversity Modelling with Phone-level Mixture Density Network

Implementation of Fast Transformer in Pytorch

SurvTRACE: Transformers for Survival Analysis with Competing Events

NLP Overview

English loanwords in the world's languages

BERN2: an advanced neural biomedical namedentity recognition and normalization tool

This repo contains simple to use, pretrained/training-less models for speaker diarization.

End-to-end MLOps pipeline of a BERT model for emotion classification.

Unsupervised Language Modeling at scale for robust sentiment classification

nlp基础任务