Local cross-platform machine translation GUI, based on CTranslate2

Overview

DesktopTranslator

Local cross-platform machine translation GUI, based on CTranslate2

DesktopTranslator

Download Windows Installer

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

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.

  1. 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).
  2. In DesktopTranslator, click the CTranslate2 Model button, and select the ct2_model folder.
  3. Click the SentencePiece Model button, navigate to the sp_model folder, and select fr.model.
  4. In the left input text-area, type some text in French or use the File menu > Open... to open a *.txt file.
  5. 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:

  1. Install PyInstaller:
pip3 install pyinstaller
  1. To use PyInstaller, specify the Python file name and the argument -w to hide the console window:
pyinstaller -y -w "translator.py"
  1. 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.
  2. Compress the contents of the “dist” directory created by PyInstaller into a *.zip archive.
  3. Download and install NSIS.
  4. Launch NSIS, click Installer based on a .ZIP file, and then click Open to locate the *.zip archive you have just created.
  5. If you want to make the files installed (extracted) to the “Program Files” of the target user, in the Default Folder enter $PROGRAMFILES
  6. If you want to add a shortcut to the internal *.exe file on the Desktop after installation, you can add something like this to the file “Modern.nsh” located at: "C:\Program Files\NSIS\Contrib\zip2exe". Depending on your OS, the path could be at “Program Files (x86)”. Note that the exe path should be consistent with the path you selected under NSIS’s “Default Folder” drop-down menu, the folder name, and the exe file name.
Section "Desktop Shortcut" SectionX
    SetShellVarContext current
    CreateShortCut "$DESKTOP\DesktopTranslator.lnk" "$PROGRAMFILES\DesktopTranslator\translator.exe"
SectionEnd
  1. 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.
  2. 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.
You might also like...
Open Source Neural Machine Translation in PyTorch
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

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

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.

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

LibreTranslate Try it online! | API Docs | Community Forum Free and Open Source Machine Translation API, entirely self-hosted. Unlike other APIs, it d

Training open neural machine translation models

Train Opus-MT models This package includes scripts for training NMT models using MarianNMT and OPUS data for OPUS-MT. More details are given in the Ma

Learning to Rewrite for Non-Autoregressive Neural Machine Translation
Learning to Rewrite for Non-Autoregressive Neural Machine Translation

RewriteNAT This repo provides the code for reproducing our proposed RewriteNAT in EMNLP 2021 paper entitled "Learning to Rewrite for Non-Autoregressiv

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

Releases(v0.2.1)
Owner
Yasmin Moslem
Machine Translation Researcher
Yasmin Moslem
A Fast Sequence Transducer Implementation with PyTorch Bindings

transducer A Fast Sequence Transducer Implementation with PyTorch Bindings. The corresponding publication is Sequence Transduction with Recurrent Neur

Awni Hannun 184 Dec 18, 2022
Binary LSTM model for text classification

Text Classification The purpose of this repository is to create a neural network model of NLP with deep learning for binary classification of texts re

Nikita Elenberger 1 Mar 11, 2022
Official implementations for various pre-training models of ERNIE-family, covering topics of Language Understanding & Generation, Multimodal Understanding & Generation, and beyond.

English|简体中文 ERNIE是百度开创性提出的基于知识增强的持续学习语义理解框架,该框架将大数据预训练与多源丰富知识相结合,通过持续学习技术,不断吸收海量文本数据中词汇、结构、语义等方面的知识,实现模型效果不断进化。ERNIE在累积 40 余个典型 NLP 任务取得 SOTA 效果,并在 G

5.4k Jan 03, 2023
Auto_code_complete is a auto word-completetion program which allows you to customize it on your needs

auto_code_complete is a auto word-completetion program which allows you to customize it on your needs. the model for this program is one of the deep-learning NLP(Natural Language Process) model struc

RUO 2 Feb 22, 2022
STonKGs is a Sophisticated Transformer that can be jointly trained on biomedical text and knowledge graphs

STonKGs STonKGs is a Sophisticated Transformer that can be jointly trained on biomedical text and knowledge graphs. This multimodal Transformer combin

STonKGs 27 Aug 11, 2022
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production

Provides an implementation of today's most used tokenizers, with a focus on performance and versatility. Main features: Train new vocabularies and tok

Hugging Face 6.2k Dec 31, 2022
QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries

Moment-DETR QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries Jie Lei, Tamara L. Berg, Mohit Bansal For dataset de

Jie Lei 雷杰 133 Dec 22, 2022
A Structured Self-attentive Sentence Embedding

Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR

Kaushal Shetty 488 Nov 28, 2022
A collection of Classical Chinese natural language processing models, including Classical Chinese related models and resources on the Internet.

GuwenModels: 古文自然语言处理模型合集, 收录互联网上的古文相关模型及资源. A collection of Classical Chinese natural language processing models, including Classical Chinese related models and resources on the Internet.

Ethan 66 Dec 26, 2022
NLP: SLU tagging

NLP: SLU tagging

北海若 3 Jan 14, 2022
Long text token classification using LongFormer

Long text token classification using LongFormer

abhishek thakur 161 Aug 07, 2022
Example code for "Real-World Natural Language Processing"

Real-World Natural Language Processing This repository contains example code for the book "Real-World Natural Language Processing." AllenNLP (2.5.0 or

Masato Hagiwara 303 Dec 17, 2022
NLP - Machine learning

Flipkart-product-reviews NLP - Machine learning About Product reviews is an essential part of an online store like Flipkart’s branding and marketing.

Harshith VH 1 Oct 29, 2021
Implementation of Fast Transformer in Pytorch

Fast Transformer - Pytorch Implementation of Fast Transformer in Pytorch. This only work as an encoder. Yannic video AI Epiphany Install $ pip install

Phil Wang 167 Dec 27, 2022
मराठी भाषा वाचविण्याचा एक प्रयास. इंग्रजी ते मराठीचा शब्दकोश. An attempt to preserve the Marathi language. A lightweight and ad free English to Marathi thesaurus.

For English, scroll down मराठी शब्द मराठी भाषा वाचवण्यासाठी मी हा ओपन सोर्स प्रोजेक्ट सुरू केला आहे. माझ्या मते, आपली भाषा हळूहळू आणि कोणाचाही लक्षात

मुक्त स्त्रोत 20 Oct 11, 2022
Yuqing Xie 2 Feb 17, 2022
BERN2: an advanced neural biomedical namedentity recognition and normalization tool

BERN2 We present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by

DMIS Laboratory - Korea University 99 Jan 06, 2023
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0 🤗 Transformers provides thousands of pretrained models to perform tasks o

Hugging Face 77.3k Jan 03, 2023
RoNER is a Named Entity Recognition model based on a pre-trained BERT transformer model trained on RONECv2

RoNER RoNER is a Named Entity Recognition model based on a pre-trained BERT transformer model trained on RONECv2. It is meant to be an easy to use, hi

Stefan Dumitrescu 9 Nov 07, 2022
Implementation of TTS with combination of Tacotron2 and HiFi-GAN

Tacotron2-HiFiGAN-master Implementation of TTS with combination of Tacotron2 and HiFi-GAN for Mandarin TTS. Inference In order to inference, we need t

SunLu Z 7 Nov 11, 2022