| --- |
| language: |
| - ckb |
| license: cc-by-nc-4.0 |
| library_name: transformers |
| pipeline_tag: automatic-speech-recognition |
| base_model: openai/whisper-tiny |
| tags: |
| - automatic-speech-recognition |
| - speech-to-text |
| - asr |
| - kurdish |
| - sorani |
| - central-kurdish |
| - ckb |
| - whisper |
| - edge |
| - on-device |
| --- |
| |
| <p align="center"> |
| <img src="https://huggingface.co/RevgeAI/vekol-stt-ckb-tiny/resolve/main/vekol-white.png" alt="Vekol" width="280"/> |
| </p> |
|
|
| # Vekol-STT (Sorani, edge) — whisper-tiny |
|
|
| 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-tiny` (fine-tuned from `openai/whisper-tiny`, 39M) |
| - Language: Central Kurdish / Sorani (`ckb`), Arabic script |
| - Task: speech-to-text (transcription) |
| - Accuracy: 35.0% WER, 9.85% CER (spacing-free) on the speaker-disjoint Common Voice 25 test |
| - Size: 37 / 18 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](https://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): |
|
|
| ```bash |
| pip install transformers librosa torch |
| python3 vekol_stt.py audio.wav --model tiny |
| ``` |
|
|
| Or directly with transformers. Decode with `language="fa"` — Whisper has no Sorani token, so |
| this model uses the Persian token as a script anchor: |
|
|
| ```python |
| import librosa |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration |
| |
| proc = WhisperProcessor.from_pretrained("RevgeAI/vekol-stt-ckb-tiny") |
| model = WhisperForConditionalGeneration.from_pretrained("RevgeAI/vekol-stt-ckb-tiny").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](https://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-tiny |
| |
| ## Citation |
| |
| ```bibtex |
| @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. |
| |
