nlpaug

This python library helps you with augmenting NLP, audio, and spectrogram data for machine learning projects. Visit this introduction to understand about Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestrate multiple augmenters together.

Features

• Generate synthetic data for improving model performance without manual effort
• Simple, easy-to-use and lightweight library. Augment data in a few lines of code
• Plug and play with common machine learning and neural network frameworks
• Support textual, audio, and spectrogram inputs
• Python 3.12-ready V2 baseline with offline-first tests and GitHub Actions coverage

Textual Data Augmentation Example

Acoustic Data Augmentation Example

Quick Demo

• Quick Example
• Example of Augmentation for Textual Inputs
• Example of Augmentation for Multilingual Textual Inputs
• Example of Augmentation for Spectrogram Inputs
• Example of Augmentation for Audio Inputs
• Example of Orchestra Multiple Augmenters
• Example of Showing Augmentation History
• How to train TF-IDF model
• How to train LAMBADA model
• How to create custom augmentation
• API Documentation

Augmenter

Flow

Installation

The library targets Python 3.12+.

Install the core package:

pip install nlpaug

Install feature extras as needed:

pip install "nlpaug[transformers]"
pip install "nlpaug[nltk]"
pip install "nlpaug[word-embs]"
pip install "nlpaug[audio]"
pip install "nlpaug[lambada]"

Install the latest GitHub version:

pip install "git+https://github.com/makcedward/nlpaug.git"

If you use WordEmbsAug (word2vec, glove or fasttext), download the pretrained assets first:

from nlpaug.util.file.download import DownloadUtil
DownloadUtil.download_word2vec(dest_dir='.')
DownloadUtil.download_glove(model_name='glove.6B', dest_dir='.')
DownloadUtil.download_fasttext(model_name='wiki-news-300d-1M', dest_dir='.')

If you use SynonymAug with PPDB, download the language pack from:

http://paraphrase.org/#/download

Testing

Run the default offline suite:

python -m pytest

Run the optional integration suite:

python -m pytest -m integration

uv workflow

Create a Python 3.12 environment and run the default suite:

make test

Install the common optional extras and run the fuller local suite:

make test-full

Install the heaviest optional extras and run only integration tests:

make test-integration

If you prefer direct uv commands:

uv venv --python 3.12
uv pip install -p .venv/bin/python -e ".[dev]"
uv run --python .venv/bin/python pytest

Or use the repo scripts directly:

./scripts/setup_uv.sh core
./scripts/test_uv.sh core
./scripts/setup_uv.sh full
./scripts/test_uv.sh full
./scripts/setup_uv.sh integration
./scripts/test_uv.sh integration

Recent Changes

2.0.0 Jun 2026

• Upgrade runtime baseline to Python 3.12+
• Refresh major optional dependencies, including transformers 5.9, gensim 4.4, librosa 0.11, and NumPy 2.x
• Add uv-based setup and test scripts for core, full, and integration workflows
• Modernize offline-first tests so the default suite runs without downloading real models
• Mock transformer-backed augmenters in tests and add broader regression coverage
• Add GitHub Actions coverage reporting and local coverage scripts
• Refactor shared augmenter hot paths and sentence generation internals for better readability and performance

1.1.11 Jul 6, 2022

• Return list of output
• Fix download util
• Fix lambda label misalignment
• Add language pack reference link for SynonymAug

See changelog for more details.

Extension Reading

• Data Augmentation library for Text
• Does your NLP model able to prevent adversarial attack?
• How does Data Noising Help to Improve your NLP Model?
• Data Augmentation library for Speech Recognition
• Data Augmentation library for Audio
• Unsupervied Data Augmentation
• A Visual Survey of Data Augmentation in NLP

Reference

This library uses data (e.g. capturing from internet), research (e.g. following augmenter idea), model (e.g. using pre-trained model) See data source for more details.

Citation

@misc{ma2019nlpaug,
  title={NLP Augmentation},
  author={Edward Ma},
  howpublished={https://github.com/makcedward/nlpaug},
  year={2019}
}

This package is cited by many books, workshop and academic research papers (70+). Here are some of examples and you may visit here to get the full list.

Workshops cited nlpaug

• S. Vajjala. NLP without a readymade labeled dataset at Toronto Machine Learning Summit, 2021. 2021

Book cited nlpaug

• S. Vajjala, B. Majumder, A. Gupta and H. Surana. Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems. 2020
• A. Bartoli and A. Fusiello. Computer Vision–ECCV 2020 Workshops. 2020
• L. Werra, L. Tunstall, and T. Wolf Natural Language Processing with Transformers. 2022

Research paper cited nlpaug

• Google: M. Raghu and E. Schmidt. A Survey of Deep Learning for Scientific Discovery. 2020
• Sirius XM: E. Jing, K. Schneck, D. Egan and S. A. Waterman. Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts. 2021
• Salesforce Research: B. Newman, P. K. Choubey and N. Rajani. P-adapters: Robustly Extracting Factual Information from Language Modesl with Diverse Prompts. 2021
• Salesforce Research: L. Xue, M. Gao, Z. Chen, C. Xiong and R. Xu. Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks. 2021

Contributions