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Roadmap

Where Olaverse is heading. Shipped items are marked ✓; everything else is planned and subject to change. Follow progress on GitHub.

v0.1.5 — shipped ✓

  • ✓ 25-language identification (LIDLite25, LIDNeural25, LIDNeural5_1)
  • ✓ diacnet-1.0 — multilingual diacritization across 10 languages
  • ✓ OTK-BPE multilingual tokenizers (Swahili, Kinyarwanda, merged)
  • ✓ Retrieval toolkit (Reranker, Embedder)
  • ✓ Vision module (PrismUpscaler, PrismDenoiser, PrismSteganography)
  • ✓ Datasets API (load_dataset, DiacBench)

v0.2 — planned

  • DiacNet 1.1 — improved Yoruba restoration, better Igbo accuracy, lower CER, faster inference
  • CLI — command-line access to the core models: 
    olaverse diacritize "se eranko naa"
olaverse detect text.txt
olaverse tokenize yo text.txt
  • Pipeline API — chain detection → normalization → diacritization in one call: 
    from olaverse import Pipeline

pipeline = Pipeline(language_detection=True, normalization=True, diacritization=True)
pipeline.process(text)
  • REST inference API — olaverse serve diacnet → POST /diacritize
  • More African languages across LID and DiacNet

v0.3 — planned

  • Custom fine-tuning API
  • Streaming inference
  • More speech tools — progress toward trained acoustic models for the TTS pipeline

v1.0 — planned

  • Production deployment toolkit — Docker images, cloud inference recipes, edge deployment guides

Not on the roadmap (deliberately)

More model families. The current focus is depth over breadth: making the existing models — DiacNet, LID, OTK-BPE, retrieval, MIST, Prism — easier to adopt, deploy, and trust in production.