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 (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