The model was trained without enough noise added as a form of data augmentation. Do not use this production. I recommend using a larger version of whisper with more hyperparameter tuning especially the learning rate, momentum, weight decay and adjusting the batch size.

Training and evaluation data

I followed the tutorial here. Very minimum edits to the code were done following this tutorial.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
training_steps: 500

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu121
Datasets 2.14.5
Tokenizers 0.14.1

Skier8402/whisper-small-tiny

Whisper Tiny Sw - Skier8402

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Framework versions

Skier8402/
whisper-small-tiny