zuazo/whisper-large-v2-eu

Whisper Large-V2 Basque

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_13_0 eu dataset. It achieves the following results on the evaluation set:

• Loss: 0.3943
• Wer: 11.3391

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: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 20000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0355	4.01	1000	0.2616	14.8224
0.0079	9.01	2000	0.2777	13.5202
0.0041	14.01	3000	0.2764	12.7364
0.0047	19.0	4000	0.2932	12.6939
0.004	24.0	5000	0.2969	12.7992
0.0019	29.0	6000	0.3066	12.6008
0.004	33.01	7000	0.2973	12.6696
0.0007	38.01	8000	0.3253	12.2686
0.0006	43.01	9000	0.3391	12.5319
0.0009	48.01	10000	0.3303	12.2767
0.0004	53.0	11000	0.3383	12.0195
0.0003	58.0	12000	0.3398	11.7441
0.0005	63.0	13000	0.3396	11.8778
0.0001	67.01	14000	0.3544	11.6469
0.0	72.01	15000	0.3752	11.4160
0.0	77.01	16000	0.3860	11.3411
0.0	82.01	17000	0.3943	11.3391
0.0	87.0	18000	0.4013	11.3532
0.0	92.0	19000	0.4063	11.3613
0.0	97.0	20000	0.4086	11.3512

Framework versions

• Transformers 4.33.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3