OpenSeq2Seq VS nematus

Compare OpenSeq2Seq vs nematus and see what are their differences.

OpenSeq2Seq

Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP (by NVIDIA)
neural-machine-translation multi-gpu Deep Learning sequence-to-sequence Seq2seq multi-node speech-recognition speech-to-text mixed-precision float16 speech-synthesis text-to-speech language-model Tensorflow

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nematus

Open-Source Neural Machine Translation in Tensorflow (by EdinburghNLP)
neural-machine-translation sequence-to-sequence machine-translation nmt mt
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OpenSeq2Seq nematus
1 1
1,420 796
- 0.6%
0.0 0.0
almost 3 years ago over 1 year ago
Python Python
Apache License 2.0 BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

OpenSeq2Seq

Posts with mentions or reviews of OpenSeq2Seq. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-14.

nematus

Posts with mentions or reviews of nematus. We have used some of these posts to build our list of alternatives and similar projects.
  • Can I connect docker to Google Cloud's GPU?
    1 project | /r/docker | 21 Mar 2021
    Sorry if the title is not so clear, I am still very new to Docker and linux/bash scripts. For a project, I want to run a data science model that I have found on github (specifically this one: https://github.com/EdinburghNLP/nematus). However, given the huge amount of data that I want to train, I will need to run this model using GPU (and the only GPU I have access to at the moment is the ones provided by Google Cloud).

What are some alternatives?

When comparing OpenSeq2Seq and nematus you can also consider the following projects:

kenlm - KenLM: Faster and Smaller Language Model Queries

bitextor - Bitextor generates translation memories from multilingual websites

athena - an open-source implementation of sequence-to-sequence based speech processing engine

NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)

awesome-speech-recognition-speech-synthesis-papers - Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)

OpenNMT-py - Open Source Neural Machine Translation and (Large) Language Models in PyTorch

ukrainian-onnx-model - An ONNX model for speech recognition of the Ukrainian language

stopes - A library for preparing data for machine translation research (monolingual preprocessing, bitext mining, etc.) built by the FAIR NLLB team.

spokestack-python - Spokestack is a library that allows a user to easily incorporate a voice interface into any Python application with a focus on embedded systems.

mltu - Machine Learning Training Utilities (for TensorFlow and PyTorch)

OpenSeq2Seq vs kenlm nematus vs bitextor OpenSeq2Seq vs athena nematus vs NeMo OpenSeq2Seq vs awesome-speech-recognition-speech-synthesis-papers nematus vs OpenNMT-py OpenSeq2Seq vs ukrainian-onnx-model nematus vs stopes OpenSeq2Seq vs spokestack-python OpenSeq2Seq vs mltu