This model is a fine-tuned version of openai/whisper-small on the kw-spotting-fsc-sl-agv dataset. It achieves the following results on the evaluation set:

Loss: 0.0183
Accuracy: 0.9998

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 0
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0268	1.0	318	0.0720	0.9685
0.0195	2.0	637	0.0183	0.9998
0.0111	3.0	956	0.2009	0.9168
0.0065	4.0	1275	0.2847	0.8544
0.0086	4.99	1590	0.1895	0.9168

Framework versions

Transformers 4.29.0.dev0
Pytorch 2.0.0
Datasets 2.10.1
Tokenizers 0.13.2

FlandersMakeAGV/whisper-small-keyword-spotting

whisper-small-keyword-spotting

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

FlandersMakeAGV/
whisper-small-keyword-spotting