#Machine Translation

Speech Note is a versatile application available for Linux desktop and Sailfish OS that allows offline note-taking, reading, and translation in multiple languages using technologies like Speech to Text, Text to Speech, and Machine Translation. The application emphasizes privacy by processing all data locally, avoiding internet connections and external data transmission. It harnesses powerful engines, including Coqui STT, Vosk, and Bergamot Translator, and offers customization of language models to suit user needs. Speech Note can be installed via Flatpak or AUR for Arch Linux, facilitating multilingual communication while safeguarding privacy.

This repository provides a wide array of toolkits, datasets, and research papers focused on Simultaneous Translation, updated regularly to meet academic and professional demands. Notable tools such as Fairseq and SimulEval support sophisticated translation and evaluation processes. The repository includes datasets for various translation tasks, like text-to-text, speech-to-text, and speech-to-speech, featuring collections such as IWSLT, WMT, and MuST-C. It also offers a detailed paper list arranged by year and topic to facilitate understanding of field advancements. This repository is an essential resource for researchers and professionals working on Simultaneous Translation projects.

This repository provides step-by-step tutorials on implementing sequence-to-sequence models with PyTorch for translating German to English text. It covers Python 3.9 dependency installation, spaCy tokenization, and seq2seq model workflows. The tutorials enhance translation outcomes by exploring encoder-decoder models from LSTMs to attention mechanisms, ideal for developers and researchers interested in neural network-based statistical machine translation.

This comprehensive project provides resources and experiences in NLP, encompassing text classification, information extraction, knowledge graphs, machine translation, QA systems, text generation, and more, making it an ideal starting point for newcomers aiming to understand and implement NLP effectively.

Explore HAT's ability to leverage Hardware-Aware Transformers to boost natural language processing efficiency. The project offers PyTorch code and includes 50 pre-trained models that aid in locating optimized solutions for distinct hardware, cutting search costs by over 10000 times. HAT provides up to triple the speed and a 3.7-fold reduction in model size with no performance detriment. Featuring latency feedback for hardware like Raspberry Pi and Intel Xeon, HAT presents a cutting-edge method for optimizing machine translation tasks, delivering superior performance across various devices.

This directory provides a well-organized collection of important natural language processing (NLP) research papers, including significant topics like Transformer frameworks, BERT variations, transfer learning, text summarization, sentiment analysis, question answering, and machine translation. It features notable works such as 'Attention Is All You Need' and detailed investigations into BERT's functions. Covering downstream tasks like QA and dialogue systems, interpretable machine learning, and specialized applications, this collection is a valuable resource for researchers and developers exploring advancements and techniques influencing current NLP practices, with a focus on practical implications in machine learning.