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---
language:
- no
license: apache-2.0
tags:
- whisper-event
- norwegian
datasets:
- NbAiLab/NCC_S
- NbAiLab/NPSC
- NbAiLab/NST
metrics:
- wer
model-index:
- name: Whisper Tiny Norwegian Bokmål
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: FLEURS
type: google/fleurs
config: nb_no
split: validation
args: nb_no
metrics:
- name: Wer
type: wer
value: 47.08
---
# Whisper Tiny Norwegian Bokmål
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) trained on several datasets.
It is currently in the middle of a large training. Currently it achieves the following results on the evaluation set:
- Loss: 1.464
- Wer: 47.08
## Model description
The model is trained on a large corpus of roughly 5.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK, transcribed speeches from the Norwegian parliament and voice recordings from Norsk Språkteknologi.
## Intended uses & limitations
The model will be free for everyone to use when it is finished.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 100.000 (currently @4.000)
- mixed_precision_training: fp16
### Live Training results
See [Tensorboad Metrics](https://huggingface.co/NbAiLab/whisper-tiny-nob/tensorboard)
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