This model is a fine-tuned version of openai/whisper-small on the N Demo Final (Augmented) dataset. It achieves the following results on the evaluation set:

Loss: 0.5186
Wer: 52.4803
Cer: 23.2548

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.4115	0.9965	285	0.6066	75.3937	27.9735
1.0703	1.9930	570	0.4821	64.8031	22.3183
0.8049	2.9895	855	0.4383	60.8268	23.3196
0.6565	3.9860	1140	0.4189	55.0787	19.8473
0.5014	4.9825	1425	0.4191	52.5591	18.8171
0.3444	5.9790	1710	0.4216	52.0472	19.9769
0.2355	6.9755	1995	0.4462	49.3307	18.1975
0.1876	7.9720	2280	0.4599	51.3780	19.2061
0.1180	8.9685	2565	0.4719	49.0157	17.7941
0.0884	9.9650	2850	0.4888	49.4094	18.0895
0.0712	10.9615	3135	0.5017	48.6220	17.7509
0.0525	11.9580	3420	0.5097	48.4252	17.5420
0.0477	12.9545	3705	0.5152	49.1732	18.4353
0.0469	13.9510	3990	0.5191	51.9291	23.0891
0.0473	14.9476	4275	0.5186	52.4803	23.2548

Framework versions

Transformers 5.2.0
Pytorch 2.9.0+cu128
Datasets 4.5.0
Tokenizers 0.22.2

wandererupak/whisper-small-n-demo-final-augmented-new-data-X

Whisper Small N- Augmented

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

wandererupak/
whisper-small-n-demo-final-augmented-new-data-X