todeschini/medical-whisper-pt

Medical Whisper - Portuguese

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

• Loss: 0.3011
• Wer: 30.6945

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: 9e-06
• train_batch_size: 4
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• total_eval_batch_size: 64
• optimizer: Use 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: 6000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3654	0.3680	500	0.3516	35.6868
0.3355	0.7360	1000	0.3341	35.0002
0.2826	1.1041	1500	0.3248	34.6635
0.2763	1.4721	2000	0.3171	33.6200
0.2715	1.8401	2500	0.3101	33.1267
0.2203	2.2081	3000	0.3071	31.3256
0.2202	2.5761	3500	0.3019	30.5031
0.2169	2.9442	4000	0.2975	30.7246
0.1765	3.3122	4500	0.3002	31.2968
0.1768	3.6802	5000	0.2985	30.5046
0.1594	4.0482	5500	0.3003	30.5781
0.1603	4.4162	6000	0.3011	30.6945

Framework versions

• Transformers 4.46.0.dev0
• Pytorch 2.1.0+cu118
• Datasets 3.0.2.dev0
• Tokenizers 0.20.0