🇹🇲 TurkmenAI stack
Benchmark (fixed eval set: first 300 corpus clips, identical
every session — all numbers below are measured on the SAME data)
Table with columns: Model, WER, %, CER, %| Model | WER, % | CER, % |
|---|
| Whisper large-v3-turbo, zero-shot | 117.93 | 72.45 |
| This model (fine-tuned) | 34.74 | 7.30 |
| Improvement | -83.2 pts | |

Table with columns: Session, WER, %, CER, %| Session | WER, % | CER, % |
|---|
| 2 | 39.75 | 8.60 |
| 3 | 34.74 | 7.30 |
Best WER so far: 34.74% |
Sessions: 3 | Corpus position: 29250 files |
This session: 8000 train clips

Real transcription examples (from the fixed eval set)
Table with columns: Reference (human), Base Whisper (zero-shot), This model| Reference (human) | Base Whisper (zero-shot) | This model |
|---|
| esalawmalikim hürmeti adamlar garaşsyz baky bitarap ýurdumyzdaky | Assalamualaikum warimata adammar garas shada bachibi tarabi yorna madhaqe | sulumalikim hurmatly adamlar garaşşada baky bitarap ýurdumyzdaky |
| dürdäne sygyrlaryny içgin öwrenýärler | Durdane-sağırlar, nechkine oran yarlarlar, | dürdäne sygyrlaryny içgine öwrenýärler |
| öz dillerine terjime edýärler | ... | öz dillerine terjime etýärler |
| çünki arkadagly gahrymançy ýerdarymyzyň | Tшоң кей, арқыда алға қалманшердармыдың | çünki arkadagly gahlyman serdarymyzyň |
| bu ajaýyp kitabyndade bellenişi ýaly | Bu acayip kıtahmla de bellensi ali, |
Full comparison data: benchmark_samples.json in this repo.
Usage
from transformers import pipeline
asr = pipeline("automatic-speech-recognition", model="Slavik2009/whisper-large-v3-turbo-tk")
print(asr("audio.mp3"))
- Method: LoRA r=32, fp16 AMP, gradient checkpointing, SpecAugment
- Hardware: 2x Tesla T4 (free tier)
Intended use & limitations
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.
Citation
@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}
}