https://github.com/lium-lst/nmtpytorch

Sequence-to-Sequence Framework in PyTorch
https://github.com/lium-lst/nmtpytorch

asr cnn deep-learning multimodality neural-machine-translation nmt pytorch seq2seq speech-recognition

Last synced: 3 months ago
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Sequence-to-Sequence Framework in PyTorch

• Host: GitHub
• URL: https://github.com/lium-lst/nmtpytorch
• Owner: lium-lst
• License: other
• Archived: true
• Created: 2017-12-18T10:07:19.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2023-01-05T07:30:44.000Z (about 2 years ago)
• Last Synced: 2024-10-29T00:59:17.041Z (4 months ago)
• Topics: asr, cnn, deep-learning, multimodality, neural-machine-translation, nmt, pytorch, seq2seq, speech-recognition
• Language: Jupyter Notebook
• Homepage:
• Size: 7.49 MB
• Stars: 392
• Watchers: 17
• Forks: 51
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

• Awesome-pytorch-list-CNVersion - nmtpytorch - to-Sequence框架。 (Pytorch & related libraries｜Pytorch & 相关库 / NLP & Speech Processing｜自然语言处理 & 语音处理:)
• Awesome-pytorch-list - nmtpytorch

README

        
![nmtpytorch](https://github.com/lium-lst/nmtpytorch/blob/master/doc/_static/img/logo.png?raw=true "nmtpytorch")

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)

# Note

This project is not actively maintained so issues created are unlikely to be addressed in a timely way. If you are interested, there's a recent fork of this repository called [pysimt](https://github.com/ImperialNLP/pysimt) which includes Transformer-based architectures as well.

# Overview

`nmtpytorch` allows training of various end-to-end neural architectures including

but not limited to neural machine translation, image captioning and automatic

speech recognition systems. The initial codebase was in `Theano` and was

inspired from the famous [dl4mt-tutorial](https://github.com/nyu-dl/dl4mt-tutorial)

codebase.

`nmtpytorch` received valuable contributions from the [Grounded Sequence-to-sequence Transduction Team](https://github.com/srvk/jsalt-2018-grounded-s2s)

of *Frederick Jelinek Memorial Summer Workshop 2018*:

Loic Barrault, Ozan Caglayan, Amanda Duarte, Desmond Elliott, Spandana Gella, Nils Holzenberger,

Chirag Lala, Jasmine (Sun Jae) Lee, Jindřich Libovický, Pranava Madhyastha,

Florian Metze, Karl Mulligan, Alissa Ostapenko, Shruti Palaskar, Ramon Sanabria, Lucia Specia and Josiah Wang.

If you use **nmtpytorch**, you may want to cite the following [paper](https://ufal.mff.cuni.cz/pbml/109/art-caglayan-et-al.pdf):

```

@article{nmtpy2017,

  author    = {Ozan Caglayan and

               Mercedes Garc\'{i}a-Mart\'{i}nez and

               Adrien Bardet and

               Walid Aransa and

               Fethi Bougares and

               Lo\"{i}c Barrault},

  title     = {NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems},

  journal   = {Prague Bull. Math. Linguistics},

  volume    = {109},

  pages     = {15--28},

  year      = {2017},

  url       = {https://ufal.mff.cuni.cz/pbml/109/art-caglayan-et-al.pdf},

  doi       = {10.1515/pralin-2017-0035},

  timestamp = {Tue, 12 Sep 2017 10:01:08 +0100}

}

```

## Installation

You may want to install NVIDIA's [Apex](https://github.com/NVIDIA/apex)

extensions. As of February 2020, we only monkey-patched `nn.LayerNorm`

with Apex' one if the library is installed and found.

### pip

You can install `nmtpytorch` from `PyPI` using `pip` (or `pip3` depending on your

operating system and environment):

```

$ pip install nmtpytorch

```

### conda

We provide an `environment.yml` file in the repository that you can use to create

a ready-to-use anaconda environment for `nmtpytorch`:

```

$ conda update --all

$ git clone https://github.com/lium-lst/nmtpytorch.git

$ conda env create -f nmtpytorch/environment.yml

```

**IMPORTANT:** After installing `nmtpytorch`, you **need** to run `nmtpy-install-extra`

to download METEOR related files into your `${HOME}/.nmtpy` folder.

This step is only required once.

### Development Mode

For continuous development and testing, it is sufficient to run `python setup.py develop`

in the root folder of your GIT checkout. From now on, all modifications to the source

tree are directly taken into account without requiring reinstallation.

## Documentation

We currently only provide some preliminary documentation in our [wiki](https://github.com/lium-lst/nmtpytorch/wiki).

## Release Notes

See [NEWS.md](NEWS.md).