BLI ASR 0. Lingala speech, transcribed.
Our first automatic speech-recognition model for Lingala. Built on OpenAI Whisper large-v3 and adapted with LoRA, BLI ASR 0 turns Lingala audio into text — an early, community-oriented model for an under-resourced Bantu language.
- Task 01
- Speech recognition
- Language 02
- Lingala
- Base model 03
- Whisper large-v3
- CER · norm. 04
- 0.1703
What it does.
BLI ASR 0 transcribes Lingala speech into Lingala text. It is not a translation model — it listens in Lingala and writes in Lingala.
It is intended as a first-pass transcription model: useful for research on low-resource ASR, for bootstrapping new datasets, and as assisted transcription before review by human annotators.
This is an early release. We publish it openly, with its limitations stated plainly, because shipping and documenting beats waiting for perfect.
Trained on Waxal Lingala.
The model was trained on the Waxal Lingala ASR dataset, split into training, validation and held-out test sets.
Text post-processing
We applied a light normalization pipeline — the goal was to reduce noise, not to impose a strict orthography. We deliberately avoided aggressive spelling correction, because Lingala has substantial orthographic variation across speakers, regions and sources.
- Unicode normalization
- Lowercasing
- Whitespace normalization
- Punctuation & symbol cleanup
- Original raw transcription preserved when available
- Normalized transcription field used for training / evaluation
How it was built.
Fine-tuned from openai/whisper-large-v3 with LoRA, using the transcribe task and the Lingala language token.
- Base model
- openai/whisper-large-v3
- Fine-tuning method
- LoRA / PEFT
- Task token
- transcribe
- Language token
- Lingala
- Precision
- bf16
- Optimizer
- AdamW
- Evaluation
- Random validation subsets during training; full split at the end
- Dataset
- Waxal Lingala ASR
Why we report CER, not WER
We report CER rather than WER for this first release. Lingala does not yet have a single widely enforced normalized orthography in our data, and WER strongly penalizes spelling variants, segmentation differences, and silence-related insertions and deletions. We plan to release a corrected WER metric that better accounts for linguistic and contextual variation.
Honest about both.
- +Lingala speech transcription
- +Research on low-resource ASR
- +Dataset bootstrapping
- +Assisted transcription before human correction
- +Evaluation of ASR pipelines for Bantu languages
- !Silence handling still needs work — may hallucinate text in long silent regions
- !Degrades with music, jingles, intros / outros and strong background noise
- !Limited on overlapping speech in real-world media
- !Training data does not yet cover all Lingala varieties
- !May struggle with recent slang, urban expressions and code-switching
- !Not yet robust across all domains (news, sermons, street interviews, music-heavy content)
See it transcribe.
A subtitled demonstration of BLI ASR 0 on real Lingala audio — the subtitles are produced from the model's own transcription.
Open weights, open to feedback.
BLI ASR 0 lives on Hugging Face. Try it, break it, and tell us where it fails — every reported failure becomes the next dataset and the next model.