---
language:
- sv
- 'no'
- da
- multilingual
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_11_0
- babelbox/babelbox_voice
- NbAiLab/NST
- NbAiLab/NPSC
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium Nordic
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0
      type: mozilla-foundation/common_voice_11_0
      config: sv-SE
      split: test
    metrics:
    - type: wer
      value: 11.31
      name: Wer
    - type: wer
      value: 14.86
      name: Wer
    - type: wer
      value: 37.02
      name: Wer
---

# Whisper Medium Nordic

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (sv-SE, da, nn-NO), the [babelbox/babelbox_voice](https://huggingface.co/datasets/babelbox/babelbox_voice) (Swedish radio), the [NbAiLab/NST](https://huggingface.co/datasets/NbAiLab/NST) (Norwegian radio), the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) (Norwegian parliament) and the [google/fleurs](https://huggingface.co/datasets/google/fleurs) (sv_se, da_dk, nb_no) datasets. The goal is to leverage transfer learning across Nordic languages, which have strong similarities.

It achieves the following results on the common voice Swedish test set:
- Loss: 0.2129
- Wer: 11.3079

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

Please note that a bug during training prevented us from evaluating WER correctly.
Validation loss suggests we started overfitting after 5000/6000 steps.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step     | Validation Loss | Wer         |
|:-------------:|:-------:|:--------:|:---------------:|:-----------:|
| 0.3056        | 0.1     | 1000     | 0.2670          | ~~99.9221~~ |
| 0.16          | 0.2     | 2000     | 0.2322          | ~~99.6640~~ |
| 0.1309        | 0.3     | 3000     | 0.2152          | ~~98.9759~~ |
| 0.097         | 0.4     | 4000     | 0.2112          | ~~100.0~~   |
| **0.091**     | **0.5** | **5000** | **0.2094**      | ~~99.7312~~ |
| 0.1098        | 0.6     | 6000     | 0.2098          | ~~98.6077~~ |
| 0.0637        | 0.7     | 7000     | 0.2148          | ~~98.4625~~ |
| 0.0718        | 0.8     | 8000     | 0.2151          | ~~99.8710~~ |
| 0.0517        | 0.9     | 9000     | 0.2175          | ~~97.2342~~ |
| 0.0465        | 1.0     | 10000    | 0.2129          | ~~96.3552~~ |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2

### WandB run
https://wandb.ai/pn-aa/whisper/runs/xc70fbwv?workspace=user-emilio_marinone

### Baseline model
This model finetuned whisper-medium, and here we can observe imrpovements when evaluated on CommonVoice 11 Swedish(sv-SE), Danish(da), and Norwegian (nn-NO) test splits.

| Language | Whisper Medium (WER) | Whisper Medium Nordic (WER) |
|:--------:|:--------------------:|:---------------------------:|
| sv-SE    | 14.93                | 11.31                       |
| da       | 20.85                | 14.86                       |
| nn-NO    | 50.82                | 37.02