Research · Krio datasets & data pipeline
How we build Krio speech and text datasets.
High-quality language technology depends on a disciplined data workflow. This pipeline turns raw Krio recordings and text into reliable datasets for ASR, TTS, and translation systems — the training data that low-resource African languages have never had.
Why it matters
Data quality shapes model quality.
In low-resource settings, careful dataset design is often the difference between a model that looks promising in a demo and one that performs well in the real world.
- Designed for low-resource language settings where data quality matters more than volume alone.
- Built to support ASR, TTS, and translation workflows through the same underlying data discipline.
- Focused on reliability, reuse, and continuous improvement rather than one-off experiments.
Workflow
Collection
Gather spoken and written language data from native speakers, public sources, and curated community conversations to build a strong starting point.
Cleaning
Normalize audio, remove noise, trim silence, and standardize text so the data is ready for training instead of being riddled with artifacts.
Annotation
Align transcripts, translations, and metadata carefully so the dataset is accurate, consistent, and useful for downstream models.
Training
Use the curated data to fine-tune speech and language models, compare versions, and improve quality across different conditions.
Evaluation
Measure model performance with both automatic metrics and human review to ensure outputs are reliable in real use cases.
Deployment
Package the best-performing systems for applications in transcription, translation, voice interfaces, and other practical tools.
Outcome
A reusable foundation for multilingual voice and language AI.
The aim is not only to build a single model, but to establish a dependable process for creating and improving language technology over time as new communities, use cases, and data sources come online.