This model is a fine-tuned version of openai/whisper-medium on the Common Voice 16.1 + Romanian speech synthesis dataset. It achieves the following results on the evaluation set:

Loss: 0.0743
Wer: 6.5649

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: 10
eval_batch_size: 10
seed: 42
distributed_type: multi-GPU
num_devices: 3
total_train_batch_size: 30
total_eval_batch_size: 30
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 450
training_steps: 4500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2982	0.22	250	0.1790	15.9224
0.1365	0.43	500	0.1313	12.7038
0.1381	0.65	750	0.1126	11.4201
0.1207	0.86	1000	0.1037	11.1432
0.0579	1.08	1250	0.0931	9.6404
0.0665	1.3	1500	0.0929	9.4822
0.0572	1.51	1750	0.0875	9.4457
0.0556	1.73	2000	0.0825	8.6122
0.0458	1.94	2250	0.0778	8.2836
0.0243	2.16	2500	0.0786	7.9095
0.0197	2.38	2750	0.0795	7.8578
0.0229	2.59	3000	0.0758	7.4714
0.0175	2.81	3250	0.0755	7.3497
0.0109	3.03	3500	0.0751	7.0759
0.0098	3.24	3750	0.0773	7.1094
0.0081	3.46	4000	0.0748	6.7778
0.0087	3.67	4250	0.0754	6.6774
0.0086	3.89	4500	0.0743	6.5649

Framework versions

Transformers 4.37.2
Pytorch 2.2.0
Datasets 2.17.0
Tokenizers 0.15.1

VladS159/Whisper_medium_ro_VladS_4500_steps_multi_gpu_28_02_2024

Whisper Medium Ro - Sarbu Vlad - multi gpu - 3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

VladS159/
Whisper_medium_ro_VladS_4500_steps_multi_gpu_28_02_2024