multilingual-whisper-v3-turbo

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

• Loss: 0.2427
• Wer: 0.1501
• Cer: 0.0510

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.4108	0.1	1000	0.5753	0.3702	0.1237
0.2326	0.2	2000	0.4653	0.2888	0.0881
0.4429	0.3	3000	0.3750	0.2354	0.0782
0.3309	0.4	4000	0.3388	0.2075	0.0674
0.3298	0.5	5000	0.3135	0.1952	0.0635
0.3238	0.6	6000	0.2929	0.1782	0.0592
0.3926	0.7	7000	0.2766	0.1688	0.0545
0.2261	0.8	8000	0.2627	0.1593	0.0519
0.2197	0.9	9000	0.2514	0.1573	0.0506
0.2276	1.0001	10000	0.2427	0.1501	0.0510

Framework versions

• Transformers 4.57.3
• Pytorch 2.9.1+cu128
• Datasets 4.4.1
• Tokenizers 0.22.1