metadata
language:
- fi
license: apache-2.0
tags:
- whisper-event
- finnish
- speech-recognition
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