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Rekody
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Model Details
Model Provider
Rekody
Model Tree
openai/whisper-large-v3-turbo
Input Modalities
Output Modalities
Supported Functionality
Scored on a frozen 198-clip held-out set with the canonical decoding recipe: 30 s chunks, stride (5, 5), beam size 5, temperature fallback.
| Model | CER | CER (orthography-normalized) |
|---|---|---|
| This model (v1) | 0.1959 | 0.1629 |
| Previous best Kalenjin Whisper fine-tune | 0.2170 | 0.1848 |
Both rows use the same evaluation harness. The improvement is significant under a paired bootstrap test (p = 0.005).
Trained on the scripted split of AfriVoices-KE (the Anv-ke/Kalenjin dataset, CC-BY-4.0; Wanzare et al., 2026), with online speed, noise, and gain augmentation plus SpecAugment. Fine-tuning used LoRA (rank 16, learning rate 2e-4); the adapter was merged into the base model for this release.
python
import torchfrom transformers import pipelineasr = pipeline("automatic-speech-recognition",model="Rekody/whisper-large-v3-turbo-kalenjin",torch_dtype=torch.float16,device="cuda", # or "mps" on Apple silicon, or "cpu")result = asr("audio.wav",chunk_length_s=30,stride_length_s=(5, 5),generate_kwargs={"language": "sw", "task": "transcribe", "num_beams": 5},)print(result["text"])
language="sw"?Whisper's tokenizer has no Kalenjin language token. This model was trained with the Swahili token (sw) standing in for Kalenjin, so pass language="sw" at inference. The output is Kalenjin text.
MIT, matching the base model. The training dataset is separately licensed (CC-BY-4.0) under its own access and consent terms.
If you use this model, please cite:
Kipkemboi, T. (2026). Open Kalenjin Automatic Speech Recognition: Adapting Parakeet-TDT to a Low-Resource Nilotic Language. Zenodo. https://doi.org/10.5281/zenodo.21764656
For the dataset, cite Wanzare et al. (2026), AfriVoices-KE.
Training and evaluation code: https://github.com/rekody/kalenjin-asr