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

Whisper Small N - Final Augmented

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: 0.5052
• Wer: 48.7617
• Cer: 16.8914

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: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
2.3469	0.4986	88	0.9121	91.0842	36.4790
1.4826	0.9972	176	0.6352	79.1414	29.2724
1.2327	1.4929	264	0.5404	77.6555	29.0543
1.1068	1.9915	352	0.4834	67.3638	23.6420
0.9248	2.4873	440	0.4396	66.4832	24.7026
0.8094	2.9858	528	0.4209	60.2091	20.4203
0.6628	3.4816	616	0.4094	57.8976	19.9048
0.6277	3.9802	704	0.3889	55.6412	19.2803
0.4893	4.4759	792	0.3984	55.9163	18.8442
0.4862	4.9745	880	0.3801	53.4948	18.9036
0.3425	5.4703	968	0.4037	55.8063	19.2010
0.3797	5.9688	1056	0.3896	51.1282	18.9334
0.2309	6.4646	1144	0.4003	52.3390	18.4179
0.2416	6.9632	1232	0.4048	57.7876	20.8763
0.1828	7.4589	1320	0.4177	51.4034	18.1701
0.1982	7.9575	1408	0.4211	51.2383	17.9718
0.1416	8.4533	1496	0.4379	51.4034	17.8628
0.1390	8.9518	1584	0.4391	50.4128	17.4861
0.1169	9.4476	1672	0.4437	49.7523	17.4762
0.1106	9.9462	1760	0.4507	50.7980	17.6745
0.0935	10.4419	1848	0.4551	49.9725	17.8628
0.0882	10.9405	1936	0.4590	49.3671	17.6348
0.0660	11.4363	2024	0.4662	49.7523	17.3473
0.0803	11.9348	2112	0.4685	49.7523	17.5952
0.0719	12.4306	2200	0.4869	50.0275	17.4960
0.0612	12.9292	2288	0.4838	48.7067	17.1491
0.0456	13.4249	2376	0.4830	48.2113	17.1392
0.0407	13.9235	2464	0.4867	49.1469	17.1491
0.0399	14.4193	2552	0.4914	49.9174	17.7042
0.0439	14.9178	2640	0.4974	48.9268	16.9112
0.0432	15.4136	2728	0.4989	49.4772	17.3969
0.0292	15.9122	2816	0.5004	51.1282	18.3089
0.0316	16.4079	2904	0.5029	48.9268	17.1590
0.0332	16.9065	2992	0.5020	48.6516	16.8715
0.0203	17.4023	3080	0.5011	48.5416	17.0500
0.0355	17.9008	3168	0.5060	48.6516	17.0599
0.0289	18.3966	3256	0.5048	48.5966	17.0599
0.0313	18.8952	3344	0.5051	48.5416	16.8616
0.0319	19.3909	3432	0.5052	48.7617	16.8914

Framework versions

• Transformers 5.1.0
• Pytorch 2.9.0+cu126
• Datasets 4.5.0
• Tokenizers 0.22.2