zuazo/whisper-large-eu-cv17_0

Whisper Large Basque

This model is a fine-tuned version of openai/whisper-large on the mozilla-foundation/common_voice_17_0 eu dataset. It achieves the following results on the evaluation set:

• Loss: 0.3149
• Wer: 7.8191

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: 3.75e-05
• train_batch_size: 32
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 40000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0767	2.3474	1000	0.1933	11.7382
0.0404	4.6948	2000	0.2059	10.3485
0.0188	7.0423	3000	0.2262	9.8987
0.0121	9.3897	4000	0.2340	9.7997
0.0123	11.7371	5000	0.2298	9.4910
0.0061	14.0845	6000	0.2398	9.3847
0.0094	16.4319	7000	0.2416	9.7750
0.0068	18.7793	8000	0.2533	9.6055
0.0061	21.1268	9000	0.2569	9.2995
0.0054	23.4742	10000	0.2646	9.5386
0.0063	25.8216	11000	0.2684	9.7686
0.0052	28.1690	12000	0.2600	9.2666
0.0042	30.5164	13000	0.2763	9.4654
0.0039	32.8638	14000	0.2705	8.9981
0.0021	35.2113	15000	0.2640	9.0513
0.0026	37.5587	16000	0.2699	9.3435
0.0029	39.9061	17000	0.2838	9.2033
0.0016	42.2535	18000	0.2784	9.2794
0.0033	44.6009	19000	0.2756	9.0558
0.0023	46.9484	20000	0.2804	9.6110
0.0024	49.2958	21000	0.2809	9.0439
0.001	51.6432	22000	0.2824	8.6940
0.0028	53.9906	23000	0.2890	9.3829
0.0007	56.3380	24000	0.2807	8.7398
0.0004	58.6854	25000	0.2881	8.6738
0.0007	61.0329	26000	0.2888	8.9175
0.0008	63.3803	27000	0.2937	8.9578
0.0003	65.7277	28000	0.2858	8.6299
0.0008	68.0751	29000	0.2877	8.8598
0.0006	70.4225	30000	0.2930	8.6308
0.0002	72.7700	31000	0.2852	8.4338
0.0	75.1174	32000	0.2936	8.4530
0.0	77.4648	33000	0.2968	8.2002
0.0	79.8122	34000	0.3008	8.1086
0.0	82.1596	35000	0.3043	8.0243
0.0	84.5070	36000	0.3074	7.9730
0.0	86.8545	37000	0.3101	7.9116
0.0	89.2019	38000	0.3124	7.8502
0.0	91.5493	39000	0.3141	7.8282
0.0	93.8967	40000	0.3149	7.8191

Framework versions

• Transformers 4.52.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1