#8686th of 206 in ML frameworks and libraries
nlpaug
Python library for augmenting text, audio and spectrogram training data
- Stars
- 4.7k
- License
- MIT
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Jupyter Notebook
4,637 on 22 Apr 20264,665 on 11 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
nlpaug is a Python library that generates synthetic training data by applying augmenters to text, audio waveforms and spectrograms. Text augmenters work at character, word and sentence level, using keyboard and OCR error simulation, WordNet/PPDB synonyms, word embeddings, BERT-family and GPT2 models, and back-translation. Augmenters can be chained with Sequential or Sometimes flows. Version 2.0.x requires Python 3.12+.
Who it is for: ML engineers expanding NLP or audio training datasets
Strengths
- Covers character, word and sentence text augmentation plus audio and spectrogram in one API
- Sequential and Sometimes flows chain augmenters; change-log records what was modified
- Optional extras (transformers, nltk, word-embs, audio, lambada) keep core install light
- Default pytest suite runs offline with mocked transformer models
Weaknesses
- Requires Python 3.12+ in 2.x, excluding older environments
- WordEmbsAug needs manual download of word2vec, GloVe or fasttext files
- Transformer-based augmenters pull heavy dependencies such as torch
- Does not generate data with modern instruction-tuned LLMs; relies on classic models like BERT and GPT2
What it needs
- GPU optional
- Needs nltk, transformers, torch, gensim, librosa, PPDB language pack (optional)
- Models: BERT, DistilBERT, RoBERTa, XLNet, GPT2
What changed
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