---
language:
- nn
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- norwegian-parliament
metrics:
- wer
model-index:
- name: whisper-medium-nn-v3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Stortingskorpuset
      type: norwegian-parliament
      config: default
      split: validation
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 11.337582785573966
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# whisper-medium-nn-v3

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Stortingskorpuset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2116
- Wer: 11.3376

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 8000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4413        | 0.25  | 2000 | 0.4447          | 26.7707 |
| 0.1945        | 1.1   | 4000 | 0.3042          | 17.8344 |
| 0.1013        | 1.35  | 6000 | 0.2421          | 14.2138 |
| 0.0308        | 2.2   | 8000 | 0.2116          | 11.3376 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2