whisper-medium_mucs_68256_15_100_1e-4_None_hinglish_mixed_scripts_FT API & Inference Endpoint

More information needed

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More information needed

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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: constant_with_warmup
lr_scheduler_warmup_steps: 500
num_epochs: 100

Table with columns: Training Loss, Epoch, Step, Validation Loss, Global Wer
Training Loss	Epoch	Step	Validation Loss	Global Wer
0.8348	1.0	432	0.4487	43.1898
0.5112	2.0	864	0.4393	46.1451
0.3586	3.0	1296	0.4408	54.0143
0.2652	4.0	1728	0.4417	37.4872
0.2037	5.0	2160	0.4607	39.6111
0.1637	6.0	2592	0.5060	36.7156
0.1314	7.0	3024	0.5392	37.4559
0.1076	8.0	3456	0.5432	37.8943
0.0949	9.0	3888	0.5591	40.0951

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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: constant_with_warmup
lr_scheduler_warmup_steps: 500
num_epochs: 100

Table with columns: Training Loss, Epoch, Step, Validation Loss, Global Wer
Training Loss	Epoch	Step	Validation Loss	Global Wer
0.8348	1.0	432	0.4487	43.1898
0.5112	2.0	864	0.4393	46.1451
0.3586	3.0	1296	0.4408	54.0143
0.2652	4.0	1728	0.4417	37.4872
0.2037	5.0	2160	0.4607	39.6111
0.1637	6.0	2592	0.5060	36.7156
0.1314	7.0	3024	0.5392	37.4559
0.1076	8.0	3456	0.5432	37.8943
0.0949	9.0	3888	0.5591	40.0951

whisper-medium_mucs_68256_15_100_1e-4_None_hinglish_mixed_scripts_FT