nlpaug is a Python library for data augmentation across text (character, word, sentence), audio, and spectrogram modalities. It provides a wide array of augmentation techniques including random insertion/substitution/deletion, synonym replacement via WordNet/PPDB, contextual word embeddings (BERT, GPT-2, RoBERTa), back-translation, TF-IDF-based augmentation, keyboard/OCR error simulation, and audio effects (noise, pitch shift, speed, VTLP). The library follows a consistent pattern: each augmenter inherits from a base class, accepts data via a unified `augment()` method, and returns augmented samples. Composition is supported through `Sequential` and `Sometimes` pipeline classes. It relies heavily on external deep learning models (HuggingFace Transformers, word2vec, GloVe, fastText) and audio processing (librosa). The codebase is well-structured with clear separation of concerns (augmenters, models, utilities, flows), extensive unit tests, and documentation. However, it exhibits significant technical drift due to aggressively pinned dependencies (e.g., `transformers>=5.9.0`, `numpy>=2.4.6`) that may not exist or conflict with current stable releases, and some integration code (conda setup for Python 3.5–3.9) is outdated. Despite this, the core augmentation logic and utility modules are sturdy and reusable.