This model is a fine-tuned version of openai/whisper-small on the Whisper Smith & Brock dataset. It achieves the following results on the evaluation set:

Loss: 0.0000
Wer: 98.0392

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: 1e-05
train_batch_size: 16
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: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Epoch	Step	Wer
1000.0	1000	22.5490
2000.0	2000	98.0392
3000.0	3000	98.0392
4000.0	4000	98.0392

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1

chrisd108/whisper-small-smith-and-brock

Whisper Small Smith & Brock

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

chrisd108/
whisper-small-smith-and-brock