Whisper Base 24lang
A plain openai/whisper-base (unmodified architecture) fine-tuned on the 24 languages of the CC0 Whispered corpus. This is the matched scientific control for burakaydinofficial/whisper-base-mla-24lang — trained...
Fonte do modelo
- Tipo
- Text model
- Tamanho do arquivo
- 138 MB
- Modelo base
- openai/whisper-base
- Versão
- vmodel
- Atualizado
- 27 de set. de 2026
- Downloads
- 7
- Fonte
- Hugging Face
Descrição da fonte
Whisper-Base-24Lang — 24 languages fine-tune, standard architecture (scientific control)
A plain openai/whisper-base (unmodified architecture) fine-tuned on the 24 languages of the CC0
Whispered corpus. This is the
matched scientific control for burakaydinofficial/whisper-base-mla-24lang —
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-base-24lang") # no trust_remote_code
processor = AutoProcessor.from_pretrained("burakaydinofficial/whisper-base-24lang")
Reproduce the conversion cost
Evaluate this control and whisper-base-mla-24lang 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)
Reconstructed numbers. This control's per-language WER/CER in the table are reconstructed as (the MLA model's absolute score − the measured paired conversion cost), not from a separate evaluation of this checkpoint; an independent re-eval may differ by a few tenths. The paired conversion cost is the directly measured quantity.
| Lang | this control |
|---|---|
| en | 20.5 WER |
| de | 29.7 WER |
| es | 18.8 WER |
| fr | 32.9 WER |
| it | 30.5 WER |
| pt | 28.4 WER |
| ru | 28.0 WER |
| nl | 26.9 WER |
| pl | 34.4 WER |
| id | 38.6 WER |
| tr | 38.8 WER |
| hi | 36.3 WER |
| ms | 35.0 WER |
| sv-SE | 43.2 WER |
| th | 23.9 CER |
| zh-CN | 25.2 CER |
| cs | 53.2 WER |
| vi | 43.1 WER |
| fi | 51.0 WER |
| el | 51.6 WER |
| da | 56.0 WER |
| ja | 34.3 CER |
| nn-NO | 67.3 WER |
| ko | 62.2 WER |
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.
Fontes
1 fonte
Fontes
1 fonteVerificado 30 de ago.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- license: apache-2.0 base_model: openai/whisper-base datasets: - burakaydinofficial/Whispered language: [en, de, es, fr, it, pt, ru, nl, pl, id, tr, hi, ms, sv, th, zh, cs, vi, fi, el, da, ja, "no", ko] pipeline_tag: automatic-speech-recognition library_name: transformers tags: [whisper, multilingual, speech-recognition, fine-tuned, control] --- # Whisper-Base-24Lang — 24 languages fine-tune, standard architecture (scientific control) A **plain `openai/whisper-base`** (unmodified architecture) fine-tuned on the 24 languages of the CC0 [Whispered](https://huggingface.co/datasets/burakaydinofficial/Whispered) corpus. This is the **matched scientific control** for [`burakaydinofficial/whisper-base-mla-24lang`](https://huggingface.co/burakaydinofficial/whisper-base-mla-24lang) — 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-24lang") # no trust_remote_code processor = AutoProcessor.from_pretrained("burakaydinofficial/whisper-base-24lang") ``` ## Reproduce the conversion cost Evaluate this control **and** `whisper-base-mla-24lang` 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) > **Reconstructed numbers.** This control's per-language WER/CER in the table are reconstructed as (the MLA model's absolute score − the measured paired conversion cost), not from a separate evaluation of this checkpoint; an independent re-eval may differ by a few tenths. The paired conversion cost is the directly measured quantity. | Lang | this control | |---|---| | en | 20.5 WER | | de | 29.7 WER | | es | 18.8 WER | | fr | 32.9 WER | | it | 30.5 WER | | pt | 28.4 WER | | ru | 28.0 WER | | nl | 26.9 WER | | pl | 34.4 WER | | id | 38.6 WER | | tr | 38.8 WER | | hi | 36.3 WER |...
Source context: 7 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo burakaydinofficial/whisper-base-24lang
