whisper-large-v3-basque

This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2181
• Wer: 14.7372

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: 256
• eval_batch_size: 32
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 512
• total_eval_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: 5000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1741	0.42	500	0.2910	23.2171
0.1388	0.84	1000	0.2418	18.3800
0.1084	1.26	1500	0.2245	16.7258
0.1041	1.68	2000	0.2155	15.6607
0.0793	2.1	2500	0.2108	15.0738
0.0794	2.52	3000	0.2102	15.1867
0.0774	2.94	3500	0.2072	14.7105
0.0607	3.36	4000	0.2142	14.7454
0.0608	3.78	4500	0.2117	14.6900
0.0506	4.2	5000	0.2181	14.7372

Framework versions

• Transformers 4.38.0
• Pytorch 2.1.1+cu121
• Datasets 2.8.0
• Tokenizers 0.15.2