zuazo/whisper-large-v2-eu-cv16_1

Whisper Large-V2 Basque

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_16_1 eu dataset. It achieves the following results on the evaluation set:

• Loss: 0.4206
• Wer: 7.7204

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: 32
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• 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
0.0112	10.04	1000	0.2182	10.1571
0.0052	20.08	2000	0.2372	9.6276
0.0017	30.11	3000	0.2417	9.0150
0.0022	40.15	4000	0.2341	8.8938
0.0023	50.19	5000	0.2451	8.9388
0.0006	60.23	6000	0.2517	8.4161
0.0006	70.26	7000	0.2499	8.0985
0.0008	80.3	8000	0.2548	8.3467
0.0004	90.34	9000	0.2498	7.9559
0.0003	100.38	10000	0.2489	7.6940
0.0	110.41	11000	0.2906	7.5455
0.0	120.45	12000	0.3027	7.4596
0.0	130.49	13000	0.3137	7.4517
0.0	140.53	14000	0.3243	7.4644
0.0	150.56	15000	0.3351	7.4762
0.0	160.6	16000	0.3459	7.4556
0.0	170.64	17000	0.3565	7.4605
0.0	180.68	18000	0.3689	7.4996
0.0	190.72	19000	0.3806	7.5934
0.0	200.75	20000	0.3912	7.6344
0.0	210.79	21000	0.4005	7.5485
0.0	220.83	22000	0.4102	7.6266
0.0079	230.87	23000	0.2467	9.1654
0.0	240.9	24000	0.3097	7.7615
0.0	250.94	25000	0.3311	7.7243
0.0	260.98	26000	0.3446	7.7028
0.0	271.02	27000	0.3551	7.7546
0.0	281.05	28000	0.3646	7.7986
0.0	291.09	29000	0.3729	7.7781
0.0	301.13	30000	0.3811	7.7634
0.0	311.17	31000	0.3878	7.7702
0.0	321.2	32000	0.3948	7.7722
0.0	331.24	33000	0.4003	7.7302
0.0	341.28	34000	0.4058	7.7312
0.0	351.32	35000	0.4108	7.7292
0.0	361.36	36000	0.4142	7.7321
0.0	371.39	37000	0.4170	7.7204
0.0	381.43	38000	0.4189	7.7253
0.0	391.47	39000	0.4202	7.7263
0.0	401.51	40000	0.4206	7.7204

Framework versions

• Transformers 4.37.2
• Pytorch 2.2.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1