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---
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
|