afrivoices-whisper-turbo 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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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: 200
num_epochs: 3
mixed_precision_training: Native AMP

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer
Training Loss	Epoch	Step	Validation Loss	Wer
2.1024	0.1210	1000	1.0739	0.4950
1.7731	0.2419	2000	0.9169	0.4287
1.6709	0.3629	3000	0.8682	0.3925
1.5405	0.4838	4000	0.8181	0.3641
1.4395	0.6048	5000	0.7728	0.3398
1.4374	0.7257	6000	0.7291	0.3474
1.3565	0.8467	7000	0.7040	0.3477
1.3255	0.9676	8000	0.6845	0.3080
1.1642	1.0885	9000	0.6755	0.2991
1.1368	1.2095	10000	0.6628	0.2991
1.1517	1.3305	11000	0.6355	0.3511
1.1455	1.4514	12000	0.6197	0.2790
1.1246	1.5724	13000	0.6074	0.3015
1.1126	1.6933	14000	0.5968	0.2889
1.1092	1.8143	15000	0.5874	0.3118
1.0218	1.9352	16000	0.5850	0.2715
0.9080	2.0561	17000	0.5868	0.2981
0.8189	2.1771	18000	0.5804	0.2786
0.7982	2.2980	19000	0.5637	0.2824
0.8133	2.4190	20000	0.5823	0.2711
0.8397	2.5399	21000	0.5702	0.2773
0.8672	2.6609	22000	0.5611	0.2721
0.8279	2.7819	23000	0.5568	0.2632
0.8361	2.9028	24000	0.5495	0.2851
0.7827	3.0	24804	0.5509	0.2632

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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: 200
num_epochs: 3
mixed_precision_training: Native AMP

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer
Training Loss	Epoch	Step	Validation Loss	Wer
2.1024	0.1210	1000	1.0739	0.4950
1.7731	0.2419	2000	0.9169	0.4287
1.6709	0.3629	3000	0.8682	0.3925
1.5405	0.4838	4000	0.8181	0.3641
1.4395	0.6048	5000	0.7728	0.3398
1.4374	0.7257	6000	0.7291	0.3474
1.3565	0.8467	7000	0.7040	0.3477
1.3255	0.9676	8000	0.6845	0.3080
1.1642	1.0885	9000	0.6755	0.2991
1.1368	1.2095	10000	0.6628	0.2991
1.1517	1.3305	11000	0.6355	0.3511
1.1455	1.4514	12000	0.6197	0.2790
1.1246	1.5724	13000	0.6074	0.3015
1.1126	1.6933	14000	0.5968	0.2889
1.1092	1.8143	15000	0.5874	0.3118
1.0218	1.9352	16000	0.5850	0.2715
0.9080	2.0561	17000	0.5868	0.2981
0.8189	2.1771	18000	0.5804	0.2786
0.7982	2.2980	19000	0.5637	0.2824
0.8133	2.4190	20000	0.5823	0.2711
0.8397	2.5399	21000	0.5702	0.2773
0.8672	2.6609	22000	0.5611	0.2721
0.8279	2.7819	23000	0.5568	0.2632
0.8361	2.9028	24000	0.5495	0.2851
0.7827	3.0	24804	0.5509	0.2632

afrivoices-whisper-turbo