whisper-medium-nordic

Whisper Medium Nordic

This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_11_0 (sv-SE, da, nn-NO), the babelbox/babelbox_voice (Swedish radio), the NbAiLab/NST (Norwegian radio), the NbAiLab/NPSC (Norwegian parliament) and the 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

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.