--- language: - fi license: apache-2.0 tags: - whisper-event - finnish - speech-recognition - whisper datasets: - mozilla-foundation/common_voice_11_0 - google/fleurs metrics: - wer - cer model-index: - name: Whisper Large V3 Finnish results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: fi split: test args: fi metrics: - name: Wer type: wer value: 8.23 - name: Cer type: cer value: 1.43 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: FLEURS type: google/fleurs config: fi_fi split: test args: fi_fi metrics: - name: Wer type: wer value: 8.21 - name: Cer type: cer value: 3.23 library_name: transformers pipeline_tag: automatic-speech-recognition ---

This is our improved Whisper v3 model that is now finetuned from OpenAI Whisper Large V3

We improve from our previously finetuned Whisper V2 model in the following mannerhttps://huggingface.co/Finnish-NLP/whisper-large-v2-finnish

CV11 (Common Voice 11 test set) WER (Word error rate) 10.42 --> 8.23

Fleurs (A speech recognition test set by Google) WER (Word error rate) 10.20 --> 8.21

Model was trained on Nvidia RTX4080 for 32k steps with batch size 8, gradient accumulation 2


Original OpenAI Whisper Large V3

- CV11 - WER: 14.81 - WER NORMALIZED: 10.82 - CER: 2.7 - CER NORMALIZED: 2.07 - Fleurs - WER: 12.04 - WER NORMALIZED: 9.63 - CER: 2.48 - CER NORMALIZED: 3.64

After Finetuning with Finnish data our V3 got these scores on the test set:

- @14000 finetuning steps - CV11 - WER: 11.36 - WER NORMALIZED: 8.31 - CER: 1.93 - CER NORMALIZED: 1.48 - Fleurs - WER: 10.2 - WER NORMALIZED: 8.56 - CER: 2.26 - CER NORMALIZED: 3.54 - @32000 finetuning steps - CV11 - WER: 11.47 - WER NORMALIZED: 8.23 - CER: 1.91 - CER NORMALIZED: 1.43 - Fleurs - WER: 10.1 - WER NORMALIZED: 8.21 - CER: 2.2 - CER NORMALIZED: 3.23