whisper-base-sna-cleaned-duration-sortagrad API & Inference Endpoint

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use 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: 200
training_steps: 2000
mixed_precision_training: Native AMP

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer
Training Loss	Epoch	Step	Validation Loss	Wer
0.8798	0.2398	200	0.9193	63.5934
0.6167	0.4796	400	0.6583	50.7509
0.5866	0.7194	600	0.5679	49.3659
0.5302	0.9592	800	0.5203	45.3494
0.4440	1.1990	1000	0.4967	43.8557
0.4213	1.4388	1200	0.4814	42.0742
0.4077	1.6787	1400	0.4709	42.3322
0.4016	1.9185	1600	0.4619	41.7158
0.3498	2.1583	1800	0.4586	40.7164
0.3882	2.3981	2000	0.4571	40.7137

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use 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: 200
training_steps: 2000
mixed_precision_training: Native AMP

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer
Training Loss	Epoch	Step	Validation Loss	Wer
0.8798	0.2398	200	0.9193	63.5934
0.6167	0.4796	400	0.6583	50.7509
0.5866	0.7194	600	0.5679	49.3659
0.5302	0.9592	800	0.5203	45.3494
0.4440	1.1990	1000	0.4967	43.8557
0.4213	1.4388	1200	0.4814	42.0742
0.4077	1.6787	1400	0.4709	42.3322
0.4016	1.9185	1600	0.4619	41.7158
0.3498	2.1583	1800	0.4586	40.7164
0.3882	2.3981	2000	0.4571	40.7137

whisper-base-sna-cleaned-duration-sortagrad