{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/9"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/19"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/9"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"}],"enrichment":{"capability":"Converts text to normalized form and back (inverse normalization) for speech recognition and synthesis pipelines, using weighted finite-state transducers and language models.","skillfed_tags":["speech-processing","text-normalization","nlp"],"use_cases":["Prepare text for TTS systems by normalizing written forms into spoken equivalents","Clean ASR output by converting spoken-form text back to written conventions","Build custom text normalization rules using WFST grammar files for domain-specific language","Preprocess multilingual text for NLP pipelines that require consistent text representation","Integrate text normalization into speech processing workflows alongside transformers models"],"what_it_does":"nemo-text-processing is a Python library for bidirectional text normalization\u2014converting written text to spoken form and back. It uses weighted finite-state transducers (WFST) and language models to handle complex linguistic rules. The package is designed for speech pipelines: text-to-speech systems need normalized input, and automatic speech recognition systems produce text that often needs denormalization for readability.\n\nThe library depends on pynini (OpenFst bindings), transformers, pandas, and several NLP utilities. It's maintained by NVIDIA as part of the NeMo ecosystem. Installation via pip works on Linux x86_64; macOS and Windows users should use conda-forge to avoid compilation issues with pynini. The package is actively maintained and production-stable.","worth_installing":"Yes, if you work with speech systems (ASR or TTS) and need robust text normalization. The package is actively maintained, permissively licensed, and has low install friction on Linux. On macOS or Windows, install pynini via conda-forge first. No known vulnerabilities. Not necessary if you only need basic string replacements."},"id":"nemo-text-processing","links":{"html":"https://skillfed.io/packages/nemo-text-processing","md":"https://skillfed.io/packages/nemo-text-processing.md","pypi":"https://pypi.org/project/nemo-text-processing/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-05","license_spdx":null,"license_treatment":"permissive","name":"nemo-text-processing","python_support":"unspecified","summary":"NeMo text processing for ASR and TTS"},"popularity":{"monthly_downloads":126296,"position":11780,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.0"}