Vekol-STT (Sorani, edge) — whisper-base
Central Kurdish (Sorani) speech-to-text that runs offline on CPU. A small Whisper model fine-tuned for Sorani, transcribing audio faster than real time on a laptop CPU. Part of the Vekol hub by Revge.
- Model:
vekol-stt-ckb-base(fine-tuned fromopenai/whisper-base, 74M) - Language: Central Kurdish / Sorani (
ckb), Arabic script - Task: speech-to-text (transcription)
- Accuracy: 29.0% WER, 7.95% CER (spacing-free) on the speaker-disjoint Common Voice 25 test
- Size: 72 / 36 MB (int8 / int4)
- Runtime: ONNX Runtime (torch-free, used by the helper) or transformers / PyTorch — both formats included
License
CC-BY-NC 4.0 (non-commercial). Fine-tuned from OpenAI Whisper (MIT). The weights here are
released non-commercial to keep the hosted service (vekol.krd)
sustainable. See NOTICE. Commercial use needs a license — use the hosted API or get in touch.
Usage
The simplest path is the vekol_stt.py helper from the GitHub repo, which downloads this
model and handles Sorani normalization (ONNX Runtime + numpy, no PyTorch):
pip install transformers librosa torch
python3 vekol_stt.py audio.wav --model base
Or directly with transformers. Decode with language="fa" — Whisper has no Sorani token, so
this model uses the Persian token as a script anchor:
import librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration
proc = WhisperProcessor.from_pretrained("RevgeAI/vekol-stt-ckb-base")
model = WhisperForConditionalGeneration.from_pretrained("RevgeAI/vekol-stt-ckb-base").eval()
audio, _ = librosa.load("audio.wav", sr=16000)
feats = proc.feature_extractor(audio, sampling_rate=16000, return_tensors="pt").input_features
ids = model.generate(feats, task="transcribe", language="fa", max_new_tokens=225)
print(proc.tokenizer.decode(ids[0], skip_special_tokens=True))
Notes
- Trained on Common Voice 25.0 (ckb) and FLEURS (ckb_iq), normalized to Sorani (Arabic to Kurdish letter/digit folding; diacritics, ZWNJ and tatweel stripped).
- Accuracy is on the official speaker-disjoint test split (no speaker leakage). CER is spacing-free because Kurdish has no standard word-spacing.
- For the large models (down to ~1.9% CER) and real-time streaming, use vekol.krd.
Links
- Higher-accuracy hosted version: https://vekol.krd
- Code & all sizes: https://github.com/Revge/vekol-stt-ckb-edge
- Base model: https://huggingface.co/openai/whisper-base
Citation
@software{vekol_stt_ckb_edge,
title = {Vekol-STT: Sorani (Central Kurdish) on-device STT},
author = {Shvan, Darvan},
organization = {Revge},
year = {2026},
url = {https://github.com/Revge/vekol-stt-ckb-edge}
}
Built by Darvan Shvan at Revge, part of the Vekol hub.