This model is a fine-tuned version of openai/whisper-small on the Inbrowser Procotor Dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.3099
Wer: 17.0755

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 25
training_steps: 250
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3461	0.4545	25	0.4545	26.0433
0.1902	0.9091	50	0.3309	17.4419
0.1184	1.3636	75	0.3120	14.6543
0.0944	1.8182	100	0.3066	16.7251
0.0632	2.2727	125	0.3046	14.8455
0.0688	2.7273	150	0.3060	14.8933
0.0479	3.1818	175	0.3063	17.1074
0.0515	3.6364	200	0.3081	15.4986
0.0296	4.0909	225	0.3096	17.2507
0.0348	4.5455	250	0.3099	17.0755

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1

lord-reso/whisper-small-inbrowser-proctor

Whisper-Small-Inbrowser-Proctor

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

lord-reso/
whisper-small-inbrowser-proctor