marinone94/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 (svse, dadk, 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
- trainbatchsize: 32
- evalbatchsize: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lrschedulertype: linear
- lrschedulerwarmup_ratio: 0.1
- training_steps: 10000
- mixedprecisiontraining: 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.