readme: ---
  language:
  - sv
  - 'no'
  - da
  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:
      - name: Wer
        type: wer
        value: 11.31
    - 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: da
        split: test
      metrics:
      - name: Wer
        type: wer
        value: 14.86
    - 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: nn-NO
        split: test
      metrics:
      - name: Wer
        type: wer
        value: 37.02      
  ---
  
  # 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
  