zuazo/whisper-large-v3-turbo-eu-cv22.0

openai/whisper-large-v3-turbo

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the common_voice_22_0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.4219
• Wer: 8.2359

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: 100000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0243	10.8234	5000	0.2572	11.3209
0.012	21.6457	10000	0.2926	11.1214
0.007	32.4680	15000	0.3036	11.0386
0.0042	43.2904	20000	0.3139	10.8341
0.004	54.1127	25000	0.3344	11.1519
0.0043	64.9361	30000	0.3439	11.2584
0.0031	75.7584	35000	0.3333	10.4943
0.0023	86.5807	40000	0.3454	10.5746
0.0018	97.4030	45000	0.3443	9.9508
0.0012	108.2254	50000	0.3494	10.5188
0.0008	119.0477	55000	0.3521	10.1148
0.0005	129.8711	60000	0.3651	9.8663
0.0013	140.6934	65000	0.3651	10.3743
0.0002	151.5157	70000	0.3531	9.6043
0.0	162.3380	75000	0.3638	9.3287
0.0	173.1603	80000	0.3724	9.0532
0.0	183.9837	85000	0.3875	8.6906
0.0	194.8061	90000	0.4055	8.4083
0.0	205.6284	95000	0.4180	8.2730
0.0	216.4507	100000	0.4219	8.2359

Framework versions

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