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Slavik2009
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Model Details
Model Provider
Slavik2009
Model Tree
openai/whisper-large-v3-turbo
Input Modalities
Output Modalities
Supported Functionality
| Model | Task |
|---|---|
| TurkmenChat-3B | 💬 Conversational LLM |
| whisper-large-v3-turbo-tk | 🎙️ Speech-to-text |
every session — all numbers below are measured on the SAME data)
| Model | WER, % | CER, % |
|---|---|---|
| Whisper large-v3-turbo, zero-shot | n/a | n/a |
| This model (fine-tuned) | n/a | n/a |
| Improvement | n/a |
| Session | WER, % | CER, % |
|---|---|---|
| - | - | - |
Best WER so far: n/a | Sessions: 1 | Corpus position: 9750 files | This session: 8000 train clips
python
from transformers import pipelineasr = pipeline("automatic-speech-recognition", model="Slavik2009/whisper-large-v3-turbo-tk")print(asr("audio.mp3"))
Transcribes Latin-script spoken Turkmen (broadcast/podcast-style audio, 1-30s clips at 16 kHz). Quality degrades on heavy dialects, far-field or noisy audio, and code-switched speech. WER/CER above are measured on a held-out slice of the training corpus, not an independent test set — treat them as an upper bound of in-domain quality.
bibtex
@misc{whisperturbotk,title = {whisper-large-v3-turbo-tk: Whisper large-v3-turbo fine-tuned for Turkmen ASR},author = {Slavik2009},year = {2026},url = {https://huggingface.co/Slavik2009/whisper-large-v3-turbo-tk}}