Whisper Base Cv11

--- license: apache-2.0 base_model: openai/whisper-base 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-Base-CV11 — 11 languages fine-tune, standard architecture (scientific control) A **plain `openai/whisper-base`** (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-base-mla-cv11`](https://huggingface.co/burakaydinofficial/whisper-base-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-base-cv11") # no trust_remote_code processor = AutoProcessor.from_pretrained("burakaydinofficial/whisper-base-cv11") ``` ## Reproduce the conversion cost Evaluate this control **and** `whisper-base-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 | 20.1 WER | | de | 29.3 WER | | es | 18.1 WER | | fr | 31.7 WER | | ru | 27.4 WER | | tr | 37.8 WER | | cy | 77.8 WER | | ar | 56.2 WER | | ka | 99.1 WER | | th | 22.1 CER | **Checkpoint note:** this is the FINAL-step checkpoint; the dev-selected checkpoint that produced the reference numbers above was not retained, so an independent re-evaluation may differ by a few tenths of a WER point. The MLA card's reported cost used the dev-selected control. Encoder frozen during fine-tuning; 15,000 steps, warmup+cosine, fp16. Read-speech domain (CommonVoice + FLEURS-validated). "Compression cost" does not apply...

Source context: 13 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo burakaydinofficial/whisper-base-cv11