allaninfo-tech/lingualdub

An open, modular framework for building and evaluating low-resource speech-AI pipelines # LingualDub **A composable, registry-based framework for building, adapting, composing, and evaluating speech-AI systems for low-resource languages.** --- ## Overview LingualDub is an open, modular framework designed as reusable infrastructure for speech-AI research and development in low-resource language contexts. It standardises how language metadata, data resources, processing components, pipelines, and evaluation results interoperate — so that existing models and tools can be wired together and extended rather than rewritten for each new language or research task. --- ## The Problem Low-resource languages rarely share the same combination of speech data, text corpora, parallel translations, pronunciation resources, pretrained models, and evaluation sets. Developers and researchers repeatedly glue together incompatible ASR, translation, TTS, alignment, data, and evaluation components by hand. Critical research challenges — code-switching, language transfer, voice preservation, timing alignment, and evaluation — are typically scattered across separate projects rather than available in one composable environment. When a new language or research method is introduced, the surrounding infrastructure often has to be rebuilt rather than simply extended. LingualDub addresses this by making the repeated engineering and research work around low-resource speech **reusable, composable, and replaceable**. --- ## Core Abstractions LingualDub is built around five interoperable objects that form a closed, provenance-tracked loop. ### Language Represents a language together with its metadata, supported processing tasks, available resources, related languages, and compatible components. Resource profile is a first-class property — the framework does not assume every language has the same data or model coverage. ### Resource Represents a data asset — speech recordings, text corpora, parallel translations, lexicons, pronunciation resources, model checkpoints, or eval …