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 a conversion of Finnish-NLP/whisper-large-finnish-v3 into faster-whisper format.
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