This model is a fine-tuned version of openai/whisper-small on the Spoken Bible Corpus: Malasar dataset. It achieves the following results on the evaluation set:

Loss: 0.4331
Wer: 45.4128

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: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0688	11.3636	250	0.2644	46.5023
0.0009	22.7273	500	0.3608	45.5275
0.0003	34.0909	750	0.3923	45.5849
0.0002	45.4545	1000	0.4089	45.8142
0.0001	56.8182	1250	0.4198	45.4702
0.0001	68.1818	1500	0.4273	45.5849
0.0001	79.5455	1750	0.4316	45.6422
0.0001	90.9091	2000	0.4331	45.4128

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.16.0
Tokenizers 0.19.1

leenag/Norm_vasista_Luke

leenag/Malasar_Luke

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

leenag/
Norm_vasista_Luke