zuazo/whisper-medium-eu-cv17_0

Whisper Medium Basque

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

• Loss: 0.3147
• Wer: 7.7586

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: 64
• eval_batch_size: 32
• seed: 42
• 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
0.0757	2.3474	1000	0.1944	11.3425
0.0341	4.6948	2000	0.2069	10.3402
0.0167	7.0423	3000	0.2218	9.6513
0.0109	9.3897	4000	0.2235	9.3508
0.0104	11.7371	5000	0.2353	9.7750
0.0077	14.0845	6000	0.2396	9.7118
0.0052	16.4319	7000	0.2522	9.3985
0.0059	18.7793	8000	0.2482	9.4883
0.0075	21.1268	9000	0.2527	9.8034
0.0048	23.4742	10000	0.2603	9.2785
0.0033	25.8216	11000	0.2584	9.0778
0.0039	28.1690	12000	0.2630	9.0962
0.0036	30.5164	13000	0.2666	9.3646
0.0031	32.8638	14000	0.2695	9.4342
0.0019	35.2113	15000	0.2687	9.0384
0.0025	37.5587	16000	0.2737	9.7768
0.0016	39.9061	17000	0.2789	8.8690
0.0011	42.2535	18000	0.2796	8.6280
0.0006	44.6009	19000	0.2774	8.9422
0.0025	46.9484	20000	0.2801	9.0458
0.0006	49.2958	21000	0.2815	8.6015
0.0016	51.6432	22000	0.2869	9.2134
0.0009	53.9906	23000	0.2873	8.9716
0.0007	56.3380	24000	0.2848	9.0183
0.0006	58.6854	25000	0.2849	8.7489
0.0004	61.0329	26000	0.2960	8.9514
0.0007	63.3803	27000	0.2874	8.7709
0.0005	65.7277	28000	0.2929	8.9193
0.0	68.0751	29000	0.2901	8.4988
0.0	70.4225	30000	0.2943	8.1296
0.0	72.7700	31000	0.2997	8.0307
0.0	75.1174	32000	0.3037	7.9812
0.0	77.4648	33000	0.3072	7.9483
0.0	79.8122	34000	0.3092	7.8667
0.0	82.1596	35000	0.3121	7.8264
0.0	84.5070	36000	0.3147	7.7586
0.0001	86.8545	37000	0.3071	8.1003
0.0	89.2019	38000	0.3069	8.0426
0.0	91.5493	39000	0.3081	8.0316
0.0	93.8967	40000	0.3088	8.0124

Framework versions

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