library_name, license, base_model, tags, datasets, metrics, model-index

library_name	license	base_model	tags	datasets	metrics	model-index
transformers	apache-2.0	openai/whisper-medium	generated_from_trainer	generator	wer	name results whisper-medium_ro-80mel task dataset metrics name type Automatic Speech Recognition automatic-speech-recognition name type config split args generator generator default None default name type value Wer wer 0.6935

whisper-medium_ro-80mel

This model is a fine-tuned version of openai/whisper-medium on the generator dataset. It achieves the following results on the evaluation set:

• Loss: 0.0839
• Wer: 0.6935
• Cer: 1.202

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 64
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 500
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
0.1665	0.7723	2500	0.9148	0.0839	0.7855
0.108	1.5443	5000	2.035	0.0722	2.1169
0.0805	2.3163	7500	2.5247	0.0718	2.4453
0.0589	3.0883	10000	2.8635	0.0583	2.8751
0.0516	3.8606	12500	3.1281	0.0511	3.2984
0.0415	4.6326	15000	3.2897	0.0540	3.365
0.0368	5.4047	17500	0.0543	3.4251	3.298
0.0315	6.1767	20000	0.0559	3.2634	3.2452
0.0316	6.9490	22500	0.0541	3.6173	3.4674
0.0267	7.7210	25000	0.0547	3.3599	3.2115

"eval_runtime": 113429.374, "eval_samples": 27174, "eval_samples_per_second": 0.24, "eval_steps_per_second": 0.24, "test_samples": 12987, "train_samples": 207181

Framework versions

• Transformers 4.57.0
• Pytorch 2.9.1+cu128
• Datasets 4.4.1
• Tokenizers 0.22.1