whisper-medium-combined-lin-sna-lug-olusola API & Inference Endpoint

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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: linear
lr_scheduler_warmup_steps: 500
num_epochs: 30

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer, Cer
Training Loss	Epoch	Step	Validation Loss	Wer	Cer
2.5540	0.5021	500	0.6311	0.4609	0.1696
2.0345	1.0040	1000	0.5528	0.3869	0.1481
1.7164	1.5062	1500	0.5335	0.4307	0.1922
1.3803	2.0080	2000	0.5164	0.4613	0.1879
1.2623	2.5102	2500	0.5028	0.3283	0.1112
0.9971	3.0121	3000	0.5225	0.3535	0.1288
0.8955	3.5142	3500	0.5332	0.3375	0.1205
0.7047	4.0161	4000	0.5651	0.3436	0.1221

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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: linear
lr_scheduler_warmup_steps: 500
num_epochs: 30

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer, Cer
Training Loss	Epoch	Step	Validation Loss	Wer	Cer
2.5540	0.5021	500	0.6311	0.4609	0.1696
2.0345	1.0040	1000	0.5528	0.3869	0.1481
1.7164	1.5062	1500	0.5335	0.4307	0.1922
1.3803	2.0080	2000	0.5164	0.4613	0.1879
1.2623	2.5102	2500	0.5028	0.3283	0.1112
0.9971	3.0121	3000	0.5225	0.3535	0.1288
0.8955	3.5142	3500	0.5332	0.3375	0.1205
0.7047	4.0161	4000	0.5651	0.3436	0.1221

whisper-medium-combined-lin-sna-lug-olusola