nlpaug

This python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand about Data Augmentation in NLP

Feature

Provide both character and word level augmentations which include:
- Character Augmentation: OCR, QWERTY(Keyboard Distance), Random Behavior
- Word Augmentation:
  - Random Behavior: RandomWord
  - Synonym: WordNet
  - Word Embeddings: word2vec, GloVe, fasttext,
  - Language Models: BERT
- Speech Recognition Augmentation:
  - Spectrogram: Frequency Masking, Time Masking
  - Audio: Noise, Pitch, Shift, Speed
Flow orchestration is supported. Flow includes:
- Sequential: Apply data augmentations one by one
- Sometimes: Apply some augmentations randomly

Example

How to use character and word augmentation
How to create custom augmentation
How to use spectrogram augmentation for speech recognition
How to use audio augmentation for speech recognition

Frequency Masking

Time Masking Frequency Masking

Installation

The library supports python 3.5+ in linux and window platform.

To install the library:

pip install nlpaug

Download word2vec or GloVe files if you use Word2VecAug or GloVeAug:

word2vec(GoogleNews-vectors-negative300)
GloVe(glove.6B.50d)
fasttext(wiki-news-300d-1M.vec.zip)

Recent Changes

0.0.3 May 23, 2019: Added Speed, Noise, Shift and Pitch augmenters for Audio

0.0.2 Apr 30, 2019: Added Frequency Masking and Time Masking for Speech Recognition (Spectrogram). Added librosa library dependency for converting wav to spectrogram.

0.0.1 Mar 20, 2019: Project initialization

Test

Word2vec and GloVe models are used in word insertion and substitution. Those model files are necessary in order to run test case. You have to add ".env" file in root directory and the content should be
	- MODEL_DIR={MODEL FILE PATH}

Folder structure of model should be
	-- root directory
		- glove.6B.50d.txt
		- GoogleNews-vectors-negative300.bin
		- wiki-news-300d-1M.vec

Metadata

Release files for nlpaug 0.0.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for nlpaug 0.0.3
File	Size	Uploaded
nlpaug-0.0.3.tar.gz	15.3 kB	May 23, 2019	Details

Built distribution (wheel)

Table of built distributions (wheels) for nlpaug 0.0.3
File	Interpreter	ABI	Platform	Reset
nlpaug-0.0.3-py3-none-any.whl 35.9 kB May 23, 2019	Python 3	none	any	Details

Total release size: 51.2 kB

Release files / nlpaug-0.0.3.tar.gz

Download URL	nlpaug-0.0.3.tar.gz
Size	15.3 kB
Tags	Source
SHA-256 checksum How to use checksums	`4a114a44e56f95cc27ce047aa495fe20880f90f831d87311cf6e3d0505df0ecc`
BLAKE2b-256 checksum How to use checksums	`417f9c76537b093df942327854116d4353c1026d61e24b8c7d83acf838aec7e9`
Upload date	May 23, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.4`

Release files / nlpaug-0.0.3-py3-none-any.whl

Download URL	nlpaug-0.0.3-py3-none-any.whl
Size	35.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`376dea027ee782d593bbbcc27ae771826548517a02959c35680cef094b3e6348`
BLAKE2b-256 checksum How to use checksums	`e48c5671bd785652c92839619cb2d2e3b2da26bd9bda90877b1083b8e1055f4b`
Upload date	May 23, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.4`