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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...

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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)

Langthis control
en8.3 WER
de8.7 WER
es6.4 WER
fr13.4 WER
ru8.0 WER
tr13.1 WER
cy41.5 WER
ar23.9 WER
ka80.0 WER
th6.2 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
Hugging FacePrimaryVersion b500d72446b87a664c95c48a0d4bed2ebc3dd7d9
huggingface.co/burakaydinofficial/whisper-medium-cv11/tree/b500d72446b87a664c95c48a0d4bed2ebc3dd7d9

Verified Aug 30

Model artifacts

1 artifact

model.safetensors

safetensors · 1.42 GB · SHA-256 302703747d55…dfc0 · Hugging Face

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Source excerpts

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--- 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