Whisper Medium Cv11
A plain openai/whisper-medium (unmodified architecture) fine-tuned on the 11 languages of the CC0 Whispered corpus. This is the matched scientific control for burakaydinofficial/whisper-medium-mla-cv11 — trained...
Model source
- Kind
- Text model
- File size
- 1.42 GB
- Base model
- openai/whisper-medium
- Version
- vmodel
- Updated
- Sep 27, 2026
- Downloads
- 11
- Source
- Hugging Face
Source description
Whisper-Medium-CV11 — 11 languages fine-tune, standard architecture (scientific control)
A plain openai/whisper-medium (unmodified architecture) fine-tuned on the 11 languages of the CC0
Whispered corpus. This is the
matched scientific control for burakaydinofficial/whisper-medium-mla-cv11 —
trained identically, minus the MHA→MLA conversion — published so the MLA conversion cost is
independently reproducible. No custom code: loads directly in transformers, and — being a plain unmodified Whisper — is convertible for faster-whisper / CTranslate2 / whisper.cpp via their standard converters.
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
model = AutoModelForSpeechSeq2Seq.from_pretrained("burakaydinofficial/whisper-medium-cv11") # no trust_remote_code
processor = AutoProcessor.from_pretrained("burakaydinofficial/whisper-medium-cv11")
Reproduce the conversion cost
Evaluate this control and whisper-medium-mla-cv11 on CommonVoice-17 (scripts/validate.py in the
code repo); the per-language difference is the
conversion cost reported on the MLA card and in docs/results/.
Results (CommonVoice-17 test, greedy, Whisper normalization + Arabic folding; CER for th/zh/ja)
| Lang | this control |
|---|---|
| en | 8.3 WER |
| de | 8.7 WER |
| es | 6.4 WER |
| fr | 13.4 WER |
| ru | 8.0 WER |
| tr | 13.1 WER |
| cy | 41.5 WER |
| ar | 23.9 WER |
| ka | 80.0 WER |
| th | 6.2 CER |
Encoder frozen during fine-tuning; 15,000 steps, warmup+cosine, fp16. Read-speech domain (CommonVoice + FLEURS-validated). "Compression cost" does not apply to this unconverted control.
Sources
1 source
Sources
1 sourceVerified Aug 30
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: apache-2.0 base_model: openai/whisper-medium datasets: - burakaydinofficial/Whispered language: [en, de, es, fr, ru, tr, cy, ar, ka, th, zh] pipeline_tag: automatic-speech-recognition library_name: transformers tags: [whisper, multilingual, speech-recognition, fine-tuned, control] --- # Whisper-Medium-CV11 — 11 languages fine-tune, standard architecture (scientific control) A **plain `openai/whisper-medium`** (unmodified architecture) fine-tuned on the 11 languages of the CC0 [Whispered](https://huggingface.co/datasets/burakaydinofficial/Whispered) corpus. This is the **matched scientific control** for [`burakaydinofficial/whisper-medium-mla-cv11`](https://huggingface.co/burakaydinofficial/whisper-medium-mla-cv11) — trained identically, minus the MHA→MLA conversion — published so the MLA **conversion cost is independently reproducible**. No custom code: loads directly in transformers, and — being a plain unmodified Whisper — is convertible for faster-whisper / CTranslate2 / whisper.cpp via their standard converters. ```python from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor model = AutoModelForSpeechSeq2Seq.from_pretrained("burakaydinofficial/whisper-medium-cv11") # no trust_remote_code processor = AutoProcessor.from_pretrained("burakaydinofficial/whisper-medium-cv11") ``` ## Reproduce the conversion cost Evaluate this control **and** `whisper-medium-mla-cv11` on CommonVoice-17 (`scripts/validate.py` in the [code repo](https://github.com/burakaydinofficial/Whispered)); the per-language difference is the conversion cost reported on the MLA card and in `docs/results/`. ## Results (CommonVoice-17 test, greedy, Whisper normalization + Arabic folding; CER for th/zh/ja) | Lang | this control | |---|---| | en | 8.3 WER | | de | 8.7 WER | | es | 6.4 WER | | fr | 13.4 WER | | ru | 8.0 WER | | tr | 13.1 WER | | cy | 41.5 WER | | ar | 23.9 WER | | ka | 80.0 WER | | th | 6.2 CER | Encoder frozen during fine-tuning; 15,000 steps, warmup+cosine, fp16. Read-speech domain (CommonVoice + FLEURS-validated). "Compression cost" does not apply to this unconverted control.
Source context: 11 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo burakaydinofficial/whisper-medium-cv11
